Understanding Megacities with the Reconnaissance ... · PDF fileWhite Volume April 2014 Understanding Megacities with the Reconnaissance, Surveillance, and Intelligence Paradigm Topical

White Volume April 2014

Understanding Megacities with the Reconnaissance, Surveillance, and Intelligence Paradigm Topical Strategic Multi-Layer Assessment (SMA) and U.S. Army Engineer Research Devel-opment Center (ERDC) Multi-Agency/Multi-Disciplinary White Papers in Support of Na-tional Security Challenges

Editor:

Dr. Charles Ehlschlaeger (ERDC)

Contributing Authors:

LTG Michael T. Flynn (DIA), Mr. Douglas E. Batson (NGA), Dr. Eliza Bradley (NGA), Ms. Jill Brandenberger

(PNNL), Mr. Bruce Bullock (CRA), Dr. Katherine Calvin (PNNL), Dr. Leon Clarke (PNNL), Dr. James Edmonds

(PNNL), Dr. Charles R. Ehlschlaeger (ERDC), Dr. David Ellis (GBH), Ms. Ida Eslami (NGA), Mr. Michael Farry

(CRA), Mr. Sean Griffin (NGA), Dr. Mohamad Hejazi (PNNL), Dr. Kathy Hibbard (PNNL), Dr. Randall Hill (USC),

Dr. Marc Imhoff (PNNL), Dr. James Knotwell (KG), Mr. Kalev H. Leetaru (GU), Mr. Ryan McAlinden (USC), Dr.

Karen Owen (NGA), Mr. Randy Paul Pearson (USNORTHCOM), Dr. Jonathan Pfautz (CRA), Dr. David

Pynadath (USC), Mr. James Sisco (GBH), Ms. Allison Thomson (PNNL)

CRA: Charles River Analytics, Inc.; DIA: Defense Intelligence Agency; ERDC: Army Corp of Engineers Engi-neer Research and Development Center; GBH: GoldBelt Hawk, LLC; GU: Georgetown University; KG: Knotwell GeoAnalytics, NGA: National Geospatial Intelligence Agency; PNNL: Pacific Northwest National Laboratory; SMA: Strategic Multi-Layer Assessment; USC: University of Southern California

The views expressed in this document are those of the authors and do not reflect the official policy or position of the organizations with which they are associated.

Approved for public release; distribution is unlimited.

Compiled and edited by: Dr. Charles R. Ehlschlaeger (ERDC), [email protected]

Understanding Megacities with the RSI Paradigm Approved for Public Release ii

Abstract:

Traditional DOD information collection techniques are not robust enough to understand the rapidly changing urban environment in very large urban areas. Developing world megacities are especially difficult to monitor due to large migration in- and out-flows causing quickly changing population as well as the urban dynamics that increase as population grows. Given the challenges of declining DOD budgets while improving our ability to moni-tor megacity populations, the chapters in this white volume describe many of the issues facing the DOD for phase zero operations and collecting the information necessary should conflicts escalate. Most of the chapters’ dis-cussions are heavily influenced by LTG Flynn et al.’s “Left of bang: The value of socio-cultural analysis in today’s environment” PRISM article.1 “Left of bang…” was also revised and included in “National Security Chal-lenges: Insights from Social, Neurobiological, and Complexity Sciences,” available at http://www.nsiteam.com/publications.html.

This White Volume builds on top these research efforts with a particular focus on monitoring and surveillance research in complex urban environ-ments. The target audiences are planners, operators, and policy makers. With them in mind, the articles are intentionally kept short and written to stand alone. All the contributors have done their best to make their articles easily accessible.

1 Flynn, M., Sisco, J., Ellis, D. (2012). “Left of Bang: The Value of Sociocultural Analysis in Today’s Envi-ronment,”PRISM, Vol 3(4), pgs 12-21.

DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR.

Understanding Megacities with the RSI Paradigm Approved for Public Release iii

Table of Contents List of Figures and Tables ..................................................................................................................... vi

Preface .................................................................................................................................................. viii

Lieutenant General Michael T. Flynn, Director, Defense Intelligence Agency ........................ viii Global Population Growth Fueling Megacities ...................................................................... viii Economic and Environmental Challenges .............................................................................. ix

Dismal Economic Prospects ........................................................................................................ ix

A Rapidly-Shifting Demographic Environment ............................................................................ ix Implications for U.S. National Defense ................................................................................... ix

Introduction and Executive Summary ............................................................................................... xii

Dr. Charles R. Ehlschlaeger, Engineer Research and Development Center ............................ xii

Chapter One, Megacity Place-Centric Population Analysis .............................................................. 1

Dr.JamesKnotwell ....................................................................................................................... 1 Abstract ..................................................................................................................................... 1

Keywords ...................................................................................................................................... 1

Introduction to RSI ................................................................................................................... 1 The Geography of Culture ........................................................................................................ 1 How Place Forms Culture ......................................................................................................... 2 Analytical Utility and Relevance ............................................................................................... 3 Conclusion ................................................................................................................................ 4 References ................................................................................................................................ 5

Chapter Two, Evaluating Long-Term Threats to Environmental Security via Integrated Assessment Modeling of Changes in Climate, Population, Land Use, Energy, and Policy ................................................................................................................................................ 6

MarcImhoff,JillBrandenberger,KatherineCalvin,JamesEdmonds,KathyHibbard,MohamadHejazi,AllisonThomsonandLeonClarke .................................................................. 6

Abstract ..................................................................................................................................... 6 Keywords: ..................................................................................................................................... 6

Megacities in Context − Global Threats to Environmental Stability ....................................... 6 Global Change Assessment Model (GCAM) ............................................................................ 8 Integrated Assessment of Forces Influencing Megacities of the Future ............................... 9

Building Energy Demand out to 2095 ........................................................................................ 9 Food Security: Interdependence of Global Food Production and the Energy System .................................................................................................................................... 13 Future Water Demand: Agriculture versus Energy ................................................................ 15 Downscaling Integrated Assessment Models – Regional Expressions of Global Forces...................................................................................................................................... 18 Conclusions ............................................................................................................................ 20 References .............................................................................................................................. 22

Understanding Megacities with the RSI Paradigm Approved for Public Release iv

Chapter Three, Understanding Megacities-RSI – Dhaka’s Design as an Expression of Culture & Politics ......................................................................................................................... 24

Dr.DavidC.Ellis,JamesSisco ..................................................................................................... 24 Abstract ................................................................................................................................... 24

Keywords .................................................................................................................................... 24

Introduction ............................................................................................................................ 24 Neighborhoods and Community Space in the Bangladeshi Ontology ................................. 25 Bangladeshi Politics and the Influence of Islam .................................................................. 26 Dhaka as an Expression of Community, Urban Sprawl, and Hartal: The Politics of Unplanned Growth .................................................................................................................. 29 Conclusion .............................................................................................................................. 31 References .............................................................................................................................. 32

Chapter Four, Black Spots Are No Treasure Island: Land Tenure and Property Rights in Megacities .................................................................................................................................... 34

DouglasE.Batson ....................................................................................................................... 34 Abstract: .................................................................................................................................. 34

Keywords .................................................................................................................................... 34 Introduction ............................................................................................................................ 34 Black Spots ............................................................................................................................. 35 A Plurality of Urban Power Brokers ........................................................................................ 36 Megaproblem: 4.5 Billion Unregistered Properties ............................................................. 38 Tying People to Land Interests: The Human Geography of Megacities ............................... 40

An International Standard for Land Administration ................................................................. 40

Volunteered Geographic Information (VGI) ............................................................................... 41

Dynamic Map Data Supporting Humanitarian Assistance & Disaster Response .................. 42

Conclusion .............................................................................................................................. 43 References .............................................................................................................................. 44

Chapter Five, Urban Socio-Cultural Monitoring with Passive Sensing .......................................... 48

MichaelFarry,BruceBullock,andJonathanPfautz .................................................................. 48 Abstract ................................................................................................................................... 48

Keywords .................................................................................................................................... 48

The Urban Socio-Cultural Monitoring Challenge ................................................................... 48 A Sensor-Based Approach to Social-Cultural Data Collection .............................................. 49 In-Situ Sensors and Sensor Processing ................................................................................ 52 Model Implementation and Sensor Data Conditioning ........................................................ 53 Research Gap Summary ........................................................................................................ 57 References .............................................................................................................................. 57

Chapter Six, Building a Network-based Approach to National Security ........................................ 59

RandyPaulPearson .................................................................................................................... 59 Abstract ................................................................................................................................... 59

Keywords .................................................................................................................................... 59 An Exponentially Expanding Problem Set ............................................................................. 59 A Paradigm Shift for Strategy ................................................................................................ 60 Countering Threat Finance Strategy as a Model .................................................................. 61

Understanding Megacities with the RSI Paradigm Approved for Public Release v

A Way Forward: Information Sharing and Interagency Coordination ................................... 62 References .............................................................................................................................. 65

Chapter Seven, Evaluating Slum Severity from Remote Sensing Imagery ................................... 67

KarenOwen,Ph.D. ...................................................................................................................... 67 Abstract ................................................................................................................................... 67

Keywords .................................................................................................................................... 67 Why remote sensing for megacities? .................................................................................... 67 Accuracy considerations ........................................................................................................ 68 The unit of analysis – differential datatypes ........................................................................ 68 City models ............................................................................................................................. 70 Remote sensing-derived products and applications ............................................................ 70 Features visible in Imagery .................................................................................................... 71 What can remote sensing tell us about stability? ................................................................ 73 Merging social media analysis with remote sensing for megacity neighborhood study........................................................................................................................................ 73 Expanded use of remote sensing data for surveys and polling ........................................... 74 Conclusion .............................................................................................................................. 74

Chapter Eight, Modeling the Discourse of Megacities: Assessing Remote Populations in the Non-Western Worlds .......................................................................................................... 76

Mr.KalevH.LeetaruandDr.CharlesR.Ehlschlaeger ............................................................... 76 Abstract ................................................................................................................................... 76

Keywords .................................................................................................................................... 76

How Are Megacities Different? .............................................................................................. 76 Analytic Complexity in Megacities ......................................................................................... 77 Information Environment Differences ................................................................................... 78

Social Media ............................................................................................................................... 79

Mainstream Media ..................................................................................................................... 80

Field Surveys .............................................................................................................................. 80

Government Records ................................................................................................................. 80

Remote Imaging ......................................................................................................................... 81

Cellular Data ............................................................................................................................... 81

Putting It All Together ............................................................................................................. 82 Conclusion .............................................................................................................................. 83

Chapter Nine, How Semi-Automated Analysis of Satellite Imagery Can Provide Quick Turn-Around Answers to Large Scale Economic and Environmental Questions ................... 84

IdaEslami,SeanGriffin,ElizaBradley ....................................................................................... 84 Abstract ................................................................................................................................... 84

Keywords .................................................................................................................................... 84

Background and Introduction ................................................................................................ 84 MODIS Imagery ....................................................................................................................... 84 Methodology ........................................................................................................................... 85 Agricultural Application .......................................................................................................... 86 Water Reservoir Application ................................................................................................... 87 Conclusion .............................................................................................................................. 89

Understanding Megacities with the RSI Paradigm Approved for Public Release vi

Chapter Ten, UrbanSim: Using Social Simulation to Train for Stability Operations ..................... 90

RyanMcAlinden,DavidPynadath,RandallW.Hill,Jr. .............................................................. 90 Abstract ................................................................................................................................... 90

Keywords .................................................................................................................................... 90

Motivation ............................................................................................................................... 90 Mission Command & UrbanSim ............................................................................................ 91

The Primer .................................................................................................................................. 93

Practice Environment ................................................................................................................. 93 UrbanSim’s Social Model ....................................................................................................... 94

PsychSim .................................................................................................................................... 94

Story Engine ............................................................................................................................... 96 Way Ahead .............................................................................................................................. 97 Acknowledgments .................................................................................................................. 98 References .............................................................................................................................. 98

ListofFiguresandTables

Figures

Figure 1. The Global Change Assessment Model (GCAM) utilizes geographic boundaries for a) geopolitical regions that represent 14 major energy and economic regions of the world and b) 151 agro-ecological zones that represent primary climate-based boundaries for crop systems. .......................................... 9

Figure 2. Potential U.S. and China urban clusters by 2095 derived by statistical data projections from baseline nighttime lights imagery from the Defense Meteorological Satellite Program’s Operational Linescan System; (Zhou et al., 2014). These data help make building energy demand data from other sources spatially explicit................................................................................................................................ 11

Figure 3. Absolute and per capita building energy demand out to 2095 by fuel in the U.S. and China (Source Zhou, 2013) highlighting a need for more cooling in the building sector resulting in an added demand for electricity in megacities. Changing climate is represented by the SRES B1 Scenario, or and constrains 550 ppmv CO2 in the atmosphere to 550 ppmv by 2100. Note that for both simulations, coal is not utilized for the US, but remains a significant fuel for China. Also note, that for constant climate, by 2035, the trend for energy demand per capita in the US begins to level out, whereas, China’s demand continues to increase. On the right, changes in fuel demand under changing climate are calculated as the difference between the simulated year minus the demand in 2005. In both cases, megacities in the future will be evermore reliant on the electric infrastructure. ............................................................................. 12

Figure 4: Figure 4. Cumulative building energy use in the 21st century with consideration of climate feedback in the IPCC A2 Marker Scenario relative to that use without climate feedback (fixed) (Zhou et al., 2014). Some States gain by climate change with reduce ............................................................................ 13

Figure 5: Figure 5. (Left) Global wheat price projected in GCAM for a suite of scenarios representing no major climate policies and no climate impacts (solid line) or the range of climate change impacts projected in the IPCC Fourth Assessment Report (grey area) ............................................................................ 15

Figure 6: Figure 6. Representation of all components of the water-demand sectors in GCAM. ............................ 16

Understanding Megacities with the RSI Paradigm Approved for Public Release vii

Figure 7: Figure 7. Water scarcity index (WSI) in years 2005, 2050, and 2095 in the 14 GCAM regions due to changing water demands. Total water supply (renewable water + desalinated water) is assumed to be fixed to 2005 levels to capture the effect of demand pro ............................................................................. 17

Figure 8: Figure 8. Simulated near-surface winds (arrows) and storm surge (color bar) induced by a Katrina-like hurricane in the Gulf of Mexico for current conditions (top panel) and with one meter of sea-level rise and regionally-varying land subsidence of 0-2. .................................................................................. 20

Figure 9: Figure 9: Location of Slums in Dhaka, Bangladesh ............................................................................ 29

Figure 10: Land Use/Cover Changes in Dhaka Metropolitan from 1975 to 2005 ............................................... 30

Figure 11: Continuum of Land Rights. ............................................................................................................. 39

Figure 12: The ROGUE Approach Courtesy of LMN Solutions ............................................................................ 43

Figure 13: In-situ sensing, tracking pedestrians .............................................................................................. 53

Figure 14: Using video to track vehicles and pedestrians on the same backdrop ................................................ 53

Figure 15: Bayesian Network Example for PMESII Social Variable ..................................................................... 54

Figure 16: PMESII Model Spider Graph and Well-Being Gauge with Bayesian Drill-Down ................................... 55

Figure 17: Pattern of Life chart examples ........................................................................................................ 56

Figure 18: Sociocultural vs. Remote Sensing Modeling .................................................................................... 69

Figure 19: ROC Curve applicability to slum severity measures .......................................................................... 72

Figure 20: Example of smoothing noisy MODIS NDVI data values using the Savitsky-Golay Filter......................... 86

Figure 21: Example of monitoring reservoir relative surface area and comparing this to prior years. In this example the 2013 relative surface area is amongst the lowest seasonally since 2007. Relative surface area (as count of 250-m water pixels for reservoir) for 2013 is plotted as a function of time of year and compared to the minimum, quartiles (25%, 50%, 75%), and maximum ........................................................... 89

Figure 22: PsychSim Example ........................................................................................................................ 96

Figure 23: Top Ten Fastest Growing Megacities ............................................................................................... 98

Tables

Table 1: Mapping from PMESII to Social Science to Sensor Variables ............................................................... 50

Table 2: Social signal inventory ...................................................................................................................... 52

Table 3: Available Imagery Data ..................................................................................................................... 71

Understanding Megacities with the RSI Paradigm Approved for Public Release viii

Preface

Lieutenant General Michael T. Flynn, Director, Defense Intelligence Agency

The nature of global conflict is ever‐changing, and a clear understanding of thethreatenvironmentiscriticaltothemissionsuccessofourpolicymakers,diplomats,andwarfighters.Afewcleartrendsareemergingtotransformtheoperationalenvi‐ronment. Formally declaredwarfare among nation‐states is becoming less likelywhilethepotentialforconflictignitedbyviolenttransnationalandsub‐stateactorsgrows. Arapidlygrowingandfree‐movingglobalpopulation, thegrowthofurbanareas in underdeveloped countries, and expanding access to transformative tech‐nologyposenewandunforeseenchallengesforbothouralliesandouradversaries.Meanwhile,thefragilityofcertainstatesintheMiddleEast,Africa,andAsiarequireustobetterdetecttheprecursorstopolitical,social,andeconomicunrestacrosstheglobe.Andimportantlyforthiscollectionofwhitepapers,inthefuture,ourforcesaremore likely tooperate inanurbanbattlespace than in themountains, jungles,anddesertsofpastconflicts.Thischangingworldrequiresastudiedunderstandingoftheglobalpopulationshiftanditsimpactonconflictsofthefuture.

Global Population Growth Fueling Megacities

The world’s population is growing at an unprecedented rate: in 2010 the globalpopulationhit6.7billion,anditisforecastedtoreach10billionby2050.Toputthisgrowth in historical perspective, global population just surpassed the one billionthresholdin1804.Themostconcentratedareasofthisgrowtharelocatedinwhatsocialscientiststerm“megacities,”orcitieswithmorethan10‐millionpeople. Ac‐cording to thePopulationReferenceBureau,weareexperiencinganurbandemo‐graphicrevolution:in1980,therewerethreemegacitiesinexistence–today,thereare24.1Bythemiddleofthe21stcentury,theurbanpopulationwillalmostdouble,increasingfromapproximately3.4billionin2009to6.4billionin2050.2

The potential for new sources of instability is enormous; the sheer populationgrowthwithinmegacitiesovertaxesalready‐stressedgovernancecapacity,resourceavailability, and infrastructure, making these urban areas the center of mass formanyof theworld's conflict‐prone regions. U.N. projections to2025 suggest thatfuturemegacitypopulationgrowthwillbebypredominantlylower‐incomepeople,primarilyinAfricaandSouthAsia.3Inthesenewandfast‐growingmegacities,thegovernment’s ability to provide basic services often cannot keep pace with thegrowingneedsanddemandsof thepopulace,resulting inanexpectationsgapandpotentiallyafeelingofhopelessnessthatcanfurtherstokeviolentconflict.

Ofcourse,notallmegacitiesarehotbedsofunrest:cities likeNewYork,Shanghai,and Tokyo continue to serve as engines of economic growth for their respective

1http://www.prb.org/Publications/Articles/2000/TheUrbanDemographicRevolution.aspx 2 World Health Organization- Global Health Observatory:

http://www.who.int/gho/urban_health/situation_trends/urban_population_growth_text/en/ 3 http://www.forbes.com/sites/joelkotkin/2013/04/08/the-worlds-fastest-growing-megacities/

Understanding Megacities with the RSI Paradigm Approved for Public Release ix

countries,providingopportunitiesformigrantsofvaryingskilllevelstofindjobsinmanufacturingandtheserviceindustry.1

Economic and Environmental Challenges

Inordertobegintounderstandthecomplexityandthesustainabilityof individualmegacities,wemustassesstwocritical factors: theeconomyandthedemographicenvironment.

Dismal Economic Prospects

Measured growth and sustained economic opportunity is critical to the life of amegacity. AccordingtotheWorldHealthOrganization(WHO),morethanonemil‐lionpeoplemigratetocitieseachweek.Today,themostrapiddemographicgrowthistakingplaceincountrieswithrelativelyyoungpopulations,wherepooremploy‐mentprospectsforagrowingyouthbubbleleavemanywithoutoptionsanddesper‐ate for anymeans to support themselves. Asmore people flood these urban ag‐glomerations, energy demands overwhelm infrastructure andwater requirementsexceedsupply,creatinganoverstressedlife‐supportinfrastructureand,eventually,conflict over basic human needs. Displaced by abject poverty, natural disasters,and/or civil unrest in their ruralhomelands, themost vulnerable segmentsof thepopulation concentrate in the peri‐urban slums surrounding megacities. In thesepockets of destitution, often devoid of the institutional frameworks necessary foreffectivegovernanceandruleoflaw,theyareripeforrecruitmentandradicalizationbyinternaldissidentsandviolentnon‐stateactors.

A Rapidly‐Shifting Demographic Environment

Theglobalgrowthofmegacitiesalsopresentsnewchallengesinmappingthesocio‐culturalcompositionofurbanpopulations–acriticalcomponenttounderstandingthe uniquemindsets of rural‐to‐urbanmigrants – and gaining the cooperation ofdiversemegacitypopulations.Challenges,suchasforcedmigrationduetosprawlingurban development and a lack of decentwater and housing, quickly shift the de‐mographics and dynamics of a region. Sudden changes can exacerbate socio‐economic inequalitiesandfuelconflict,asnewpopulationsenteralready‐occupiedareasandcompeteforexistingresources.

Implications for U.S. National Defense

Theconvergenceofthesefactorshasthepotentialtothreatenstabilityonaregionalscale.Inthisenvironment,theIntelligenceCommunity(IC)willneedtotakeonanincreasinglycriticalrolebyhelpingpolicymakersanticipateinstabilityand,insitua‐tionswherewecannotovercometheseshortfalls,determineandsetconditionsap‐propriatefordefenseplanning.TraditionalDepartmentofDefenseinformationcol‐lectiontechniques,however,arenolongerrobustenoughtorespondtosuchrapidlychangingurbanenvironments.Increasingdemandcoupledwithadecliningdefensebudget requires a renewed look at the structure and direction of our intelligencecollectiontechniquesandprocesses.

1 http://www.prb.org/Publications/Articles/2000/TheUrbanDemographicRevolution.aspx

Understanding Megacities with the RSI Paradigm Approved for Public Release x

FourfactorswillbecrucialtotheIC’ssuccessinthisshiftingenvironment:

• EnterpriseAgility:Wemustbestructuredandreadytorapidlyrespondtonew threats at a moment’s notice. This agility will include breaking down thestovepipesandinformationsilostypicaltolarge‐scalebureaucraciesandcreatingamore focused, integratedenterprise.Drivingourselves to increasing levelsof inte‐gration, fusion, and intelligence sharingboth internally andwithour closest part‐nersmustbeourwayahead.

• UnderstandingtheEnvironment: Ifwe learnedanythingover theprevi‐ousdecadeofwar,itisthatafailuretorecognizeandaccuratelydefinetheopera‐tional environment can lead to amismatch between forces, capabilities,missions,andgoals.1Thedynamicnatureofournewoperationalenvironmentwillrequireafingertipfeelandclear,in‐depthknowledgeofthepolitical,social,cultural,andeco‐nomicatmosphericsrelatedtoeachconflict.

• BuildingPartnershipsandIncreasedBurdenSharing:Aswefacesteadi‐lyincreasingrequirementsinthefaceofcontinuallydiminishingresources,wemustlooktonewavenuesofengagement,evenbeyondtraditional interagencycoopera‐tion, tomaximizeourreach.Regularengagementandcollaborationwithourpart‐ners and allies abroadmust inform our strategic planning and influence howwethinkaboutnewtypesofinternationalconflictthatrequireabroadrangeofmissioncapabilitiesnoteasilyhandledbyonecountryonitsown.

• TechnologicalAdaptation: Emerging technology has the potential to en‐hanceoursecurity,butwillalsochallengeourdefensecapacityinavarietyofnewways.Theexponentialincreaseinthevolumeofdataworldwideisstaggering,andposesmajor challenges to the analysis and synthesis of intelligence. Informationtechnology and social media now elevate local issues to an international stage.Greaterconnectivityfacilitatesworldwideconnectionsbetweengovernments,non‐stateactors,andtransnational threatnetworksbothforbetterandforworse. Wemust continue to adapt and develop innovativemethods appropriate for this dy‐namicenvironment.

The Way Ahead

DIA is transforming the structureandoperationalprocessesof intelligence collec‐tion,analysis,andproductiontoprovidestrategicadvantageforournation.

• An “IntegratedCenter”model for intelligence:DIA’sdecadeofwartimelessons learned demonstrate that the most successful teams integrate personnelfrommultipleintelligencedisciplinestofuseintelligenceandoperations.2Withthislessonas ablueprint, theagencyhas transformed itsoperatingmodel to focuson

1 Joint and Coalition Operational Analysis, “Decade of War, Volume 1: Enduring Lessons from the Past

Decade of Operations”, June 15, 2012. 2 Ibid. Also, the author was directly involved in establishing fusion centers in Iraq and Afghanistan (and

elsewhere). These centers were highly integrated operations and intelligence interagency action arms for maneuver commanders and they proved their value on multiple occasions and the concept is now being applied in other regions of the world.

Understanding Megacities with the RSI Paradigm Approved for Public Release xi

Integrated Intelligence Centers (IICs) that proactively network capabilities fromacrosstheCombatantCommandJointIntelligenceOperationsCentersandtheentireDefense IntelligenceEnterprise.Thisconstruct closely integratesouranalysis, col‐lection,andscienceandtechnologycapabilities–togetherwithembedsfromourICpartnersandarobustmission‐supportnetwork–toprovidesupporttodefensecus‐tomersfromthetacticaltostrategiclevel.

• Makingthe“edgethecenter”:Weareenhancingthelevelofsupportandexpertiseweprovide towarfighters andpolicymakersby increasinglyplacingourmostvaluableassets–ourpeople–overseasandindirectsupportofthoseclosesttotheoperatingenvironment.Wemustcontinuetodriveanexpeditionarymindsetthroughoutourworkforce–aworkforcethathasextensivecombatexperience, in‐cludingourcivilians.

• Leveragingnewtechnology:Improvementsintechnologyallowforfaster,flatter,increasingly‐matrixedintel‐opsteamstorespondtorequestsandanticipaterequirements from across the globe. These new tools, supported by appropriatepoliciesandprocesses,makepossibleunprecedented levelsof interagencyand in‐ternationalcollaborationonourtoughestintelligenceproblemsets.

Wedon’tneedtoreinventthewheel.Inthisbelt‐tighteningfiscalenvironment,wemustidentifyadditionalwaystotailorcurrenttechniquestothedynamicenviron‐mentsofmegacitiesandsurroundingurban fringe. The followingchapters in thispaperidentifymanyoftheissuesfacingtheDepartmentofDefenseandre‐examinethereconnaissance,surveillance,andintelligenceparadigmas itrelatestomegaci‐ties,including:

• How the physical environment impacts operations and contingency plan‐ning;

• UsinganinvertedRSImodelasindicatorsandwarningsofinstability;and

• Tailoringmodelingtoolsforpredictiveanalysis.

Thisresearchisanexampleofhowweneedtoworkoutsidethetraditionalintelli‐gence way of doing business to establish partnerships and knowledge‐sharingacross and outside the defense intelligence community. Only through a fully inte‐gratedprocessthatincludescollaborationamongallofthosewithaninterestinournation’ssecuritycanwemeettomorrow’schallenges.

Understanding Megacities with the RSI Paradigm Approved for Public Release xii

IntroductionandExecutiveSummary

Dr. Charles R. Ehlschlaeger, Engineer Research and Development Center

Theoverridinggoalof thisWhiteVolume ishow tobestunderstand the changingpopulation,infrastructure,andenvironmentincomplexurbanenvironmentssothatwecanrespondtobothshort‐andlong‐termnationalsecuritychallenges.TheSMAresearch project “Megacity Reconnaissance, Surveillance, and Intelligence Project:DhakaPilot”usestheterm`Megacity’ in its title toreflect thechallengesofunder‐standing rapidly changingurban environments. SMA choseDhaka,Bangladesh forthecasestudybecauseDhakaisonethequintessentialurbanspacesthattraditionalDoDsocio‐culturaltechniqueswouldfindmostchallengingtounderstand.Dhakaisoneofthefastestgrowingcitiesintheworld,amegacitywithnoindicationthatitsgrowthratewilldecline.

ThisWhite Volume explores issues relevant to understanding urban populations,bothtodayandyearsfromnow,andthestabilityindicatorsthatwillhelptomonitorinsecurityandpreventpotentialcrisesaroundtheworld.WhiletheU.S.Governmenthaslongmonitoredthestabilityofotherregimes,itdoesnothavealonghistoryofextensively monitoring other populations except when U.S. troops are deployedoverseas.Forexample,theSouthVietnamesemilitaryandtheU.S.Armyworksside‐by‐sideextensivelymeasuringandmonitoringpopulations in ruralareasprone toVietcongactivities.Thisverylaborintensivesurveywasstoredinadatabaseman‐agementsystemandquantitativelyanalyzed.Itwasstatisticallyshownthatthecol‐lectionanduseofthisdatabasereducedviolenceinareaswithdatacollectedcom‐paredtosimilarareasnotmonitored.MonitoringpopulationsusinghumanterrainteamsorU.S.andPartnerNationmilitaryunits,however,isbothriskyandlaborin‐tensive.Thus,existingpopulationmonitoringinAfghanistanandIraqcannotbeper‐formedacrosstheentireglobeduetotheircosts.Itisnecessarytochangethewaywecollect,integrate,anddisseminatepopulationinformationtoachieveshort‐andlong‐termnationalsecuritygoals.

TheWhiteVolume’sauthorsrepresentpractitionersandresearcherswithextensiveexperienceintheirrespectivefields.Whilewewereunabletoincludealltheexpertsdesiredforthiseffort,theWhiteVolumecontainsabroadspectrumofperspectivesfor better understand urban environments. The chapters are organized from themore theoretical and philosophical to themore technical. The following brief de‐scriptionofthesepapersprovidesanoverviewofeachchapter,butdoesn’texpressallthecontentcontainedwithin.

Dr.JamesKnotwell’s“MegacityPlace‐CentricPopulationAnalysis”beginstheWhiteVolumewithadiscussionon theneed for culturalunderstanding inRSIusing the“Fundamental Four” cultural descriptors. He points out the Fundamental Fourdoesn’t account for the “analysis of place”, or how geography affects culture. Dr.Knotwelldiscussesindetailhowsocio‐culturalanalysiscouldbeimprovedbybetterunderstandingofgeographicanalysisofplace.

Next,MarcImhoffetal.’s“EvaluatingLong‐TermThreatstoEnvironmentalSecurityvia Integrated AssessmentModeling of Changes in Climate, Population, LandUse,

Understanding Megacities with the RSI Paradigm Approved for Public Release xiii

Energy, and Policy” chapter discusses regional environmental security forecastingenergy,waterscarcity,andclimatechangesfarintothefuture.

Inthethirdchapter“UnderstandingMegacities‐RSI–Dhaka’sDesignasanExpres‐sionofCulture&Politics,”Dr.DavidEllisandJamesSiscoexplorethesocio‐culturaldynamics of Dhaka, Bangladesh from the community level, mahallas, upwards tobetterunderstandinstabilityindicators.Thechapterdiscussesthetensionbetweenpolitical,religious,andsocialaspectsoftheunplannedmegacity.

DouglasBatson’schapteronlandtenureandpropertyrightsinmegacitiesdiscussesrural‐to‐urbanmigration and the piratical power grabs associatedwith slum for‐mations. Itexaminesungovernedurbanspacesandtheprocessofbuildinghumansecurity by improving poor peoples’ hold on land. Finally, the chapter discussesthreetoolsthatwillaidinidentifyingandunderstandingwhooccupiesandcontrolsmegacityspaces.

Inthefifthchapter,“UrbanSocio‐CulturalMonitoringwithPassiveSensing,”MichaelFarry et al. propose that passive sensors located in difficult to assess urban envi‐ronmentswillprovideactionableintelligenceinlocalortacticalsituations.Theau‐thorsanalyzehowvariousmeasurablesocialsignalscanbeappliedtoPMESIIvari‐ables.Theuseofsmallsensors,whilepotentiallyproblematicifusedinphasezeroenvironments, could provide valuable information in ungoverned urban spaceswhensupportedbypartnernationgovernments.

Whileother chapters in thisWhiteVolumediscussways to representpopulationsgeographically or culturally, Randy Pearson’s “A Network‐based Approach to Na‐tional Security” raises the challenges of understanding illicit financial networks.Pearsondiscusses the trans‐dimensionalityof illicitnetworks,and thedifficultyofrepresenting this information forDODoperational planning. The complexity of fi‐nancialnetworksisnotanuncommononeforsocio‐culturalanalysis.Socio‐culturalsystemscannotbeeasilyrepresentedinanyofthetraditionalquantitativevisualiza‐tionmethodologies (I.e., thematicmaps, topographicmaps,networks)because thenumber of dimensions of socio‐cultural factors is greater thanwhat can easily bereadilyseen.

In Chapter Seven, “Evaluating Slum Severity fromRemote Sensing Imagery,” Dr.KarenOwendiscussesoriginalresearchdemonstratingremotesensingtechniquesthatwillhelpidentifyneighborhood‐scalesocio‐culturalinformationinurbanareas,especially economic variability.While the techniques require calibration for eachmegacity,theresearchprovidesexcellentdirectionforexperiencedremotesensingprofessionals.

ChapterEight,“ModelingtheDiscourseofMegacities:AssessingRemotePopulationsintheNon‐WesternWorlds,”Dr.KalevH.Leetaru&Dr.CharlesEhlschlaegerdiscusstheramificationsofcollectinginformationfromtheinternetinordertounderstandmegacities. They argue that while megacities have unique geographic and socialcharacteristics, the toolsandtechniques forunderstandingcommunicationremainthe same but that additional foundational information will ease the difficulty ofmonitoringurbanareasintheRSIparadigm.

Understanding Megacities with the RSI Paradigm Approved for Public Release xiv

Chapter Nine, “How Semi‐Automated Analysis of Satellite Imagery Can ProvideQuick Turn‐Around Economic and Environmentally Relevant Answers to LargeScale Questions”, by Ms. Ida Eslami et al., provides a technical discussion on re‐searchthatcangreatlybenefittheRSIparadigm.Thegoaloftheirresearchistode‐velop techniques for takingsatellite imageryandprocessing it toactionable infor‐mation. Their technique mainly uses MODIS satellite products updated every 16daysinordertomonitorchangesandtrendsovertime.Possiblesocio‐culturalap‐plicationsincludeurbanization,cropabundance&predictedyields,andtheavaila‐bilityofnaturalresources.Giventhechallengesofmonitoringmegacitiesaroundtheworldinphasezero,automatedtechniqueswillbenecessarytoderiveinformationfromthecopiousamountsofdatacollectedfromsatellitesandtheinternet.

The final chapter, “UrbanSim:UsingSocialSimulation toTrain forStabilityOpera‐tions,” Ryan McAlinden et al. discuss modeling urban environments for training.Socialandculturalmodelingplaysacriticalroleinadvancingoursituationalunder‐standingofthesecomplex,interwovenenvirons.Academia,governmentandindus‐tryallcontinuetomakeadvancementsindevelopingaccuratemodelsofhumanbe‐havior,thoughsignificantchallengesstillremain.Thesechallengesarepresentedinthe context of an existing training application for teaching Battalion commandershowtosuccessfullynavigatecomplex,multifacetedmissionswherethepopulaceisthecornerstonetosuccess.

Understanding Megacities with the RSI Paradigm Approved for Public Release 1

ChapterOne,MegacityPlace‐CentricPopulationAnalysis

Dr.JamesKnotwellGeospatialScholar,KnotwellGeoAnalytics

[email protected]

Abstract

The elements of place‐centric population analysis are explained in the context ofhow they informand improve theReconnaissance, Surveillance, Intelligence (RSI)methodology.TheauthoragreesthatRSI’s“FundamentalFour”describecultureinthe most useful way for enhancing Wide Area Security mission accomplishmentthroughpertinentSocio‐CulturalAnalysis (SCA),butexplainshowplaceattributesofthehomesofmegacityimmigrantsinfusestheirculturalformationandthusmustbe considered inunderstanding the cultural transformationongoing inmegacitiesasurbanizationintensifiesculturaltensionattributabletothattransformation.Itissuggested that place‐centric population analysis contains the additional quality ofcontributingtoanSCA“baseline”forstreamliningculturalanalysisfrommegacitytomegacity,throughknowledgeofimmigrantoriginformationalattributesassociatedwiththosetypesofhomeplacesprovideageneralizablestartingpointforanalyticalexpectationsfromwhichinitialinferencescanbemadethatparticularobservationsandexperienceovertimewilladjust.

Keywords

Needs, satisfiers, Great Modern Cultural Transformation, traditions in tow, tradi‐tionsinplace,traditionsinflux,culturalbaseline

Introduction to RSI

ReverseISR,orReconnaissance,Surveillance,Intelligence,a.k.a.theRSIApproachtoSocio‐CulturalAnalysis (SCA),purposely reverses thewell‐established ISR formof“intel”‐collectionandanalysis.ThereversalisintendedtodistinguishSCA’saimasdifferent from thatof gathering “intel”: where theobjectof intelligence ismostlytargeting,theobjectofSCAiscross‐culturalcomprehension(CCC)for interventioneffect,explanation,andexpectation.Theincorporationand/orexpansionofSCAintothemilitarydecisionmakingprocess(MDMP)hasbecomemorecriticalfortheintel‐ligence community in general becausemilitaryoperations are gradually changing.SinceSCA,with theonsetof IrregularWarfare(IW), isneededmorenowthanbe‐fore,theRSIapproachintendstosupportandadvancetheincreasinglymorepreva‐lent Wide Area Security (WAS) mission over Combined Arms Maneuvers (CAM),where ISR reigns supreme. An RSI‐derived understanding of culture in both abroaderanddeepersenseisWASmissioncritical.

The Geography of Culture

The formulatorsof theRSI‐SCAapproach (Puls&Ellis,2011) recognized that cul‐turalunderstandingrequiressomeformofpopulation‐centricanalysisso thatcul‐


turecanbereducedtoitsmorefundamentalcomponents.Theanalyticalgoalistoanswer “why”peopledowhat theydo to satisfy theirneeds—the veryessenceofculture—thus,addingoperationalvaluetoWASinterventionstrategiesandmissiongoals.Tothatend,theaptly‐termedRSI“FundamentalFour”culturaldescriptors—Ontology, Identity, Logics of Appropriateness, andNarrative—emerge to organizeculture‐specific objectives, methods, and relevant interpretations (Puls & Ellis,2011). Itisrelevanttoourpurposetopointoutthatnoneofthesefourcategoriesexplicitlyaddresses thecontributionofplace to cultural formation,yetplace isanessentiallyimportantformationalfactor.Place‐Centricpopulationanalysis,thesub‐jectofthistreatise,“grounds”theRSIFundamentalFourbyforcingconsiderationofthosephysical,material,andgeospatiallysituationalplaceattributesthatfundamen‐tallyaffectculturalevolutionovertimeinthatparticularplace:inotherwords,whatis, 1)materially available for those people in that place to find, identify, use, andadopttosatisfyneeds,or2)thephysicallimitsormaterialparameterswithinwhichsuccessfulculturaladaptationstrategiesandoutcomesmustoccurthere.

How Place Forms Culture

Populationsexhibitculturalexpressionandbehavior inpartbecauseof thecollec‐tiveeffectoftheirvarious,multiple,andsimultaneouspermutationsofpersonalandsocial attitudes, aptitudes, inclinations, anddispositions. Theseare importantbuttheyarenottheonlyformationalforcesthatmatter,theycontributenomoretoul‐timatecultural formationthandothosecharacteristicsof theplacesthosepopula‐tionsinhabit:particularplacesthatprovideallthatismateriallyrequiredtosatisfytheneedsALLpopulationsmustaddress. For thepurposeofclarifyinghowplacefiguresintoanunderstandingofapeople’sculture—again,definedaswhattheyDOtosatisfytheirneeds—adistinctionmustbemadebetweenNEEDSandSATISFIERSof needs. All people, everywhere and always, have the same set of needs. PaulEkins, in an earlierwork (Ekins, 1986), uses only nine categories to delimitwhatthoseare,andalwayshavebeen, foralluswho’veever livedandeverwill liveonEarth:permanence,protection,affection,understanding,participation,leisure,crea‐tivity,identity,andfreedom.That’sit,finiteand,both,spatiallyandtemporallycon‐stant. But satisfiersofneeds, thosesubstances, technologies,andartifacts thatareused to form culture—food, water, housing, building material, routes, social sys‐tems,contracts,religions,etc.—varywidelyoverthefaceoftheearthintype,quan‐tity,andqualityasdictatedbypreviouslyenergizedphysical forcesandgeospatialarrangementsofmaterialsubstances,inadditiontopopulationaptitudesandideals.

Groupsofhumanswhochoose,byproximity,heritage,andexperience,toconstitutethemselvesasapeoplewiththeirownwaysofthinkingandacting,haveonlythosethings to use for doing it that they encounter in the place they inhabit, or thosethingsthatflowthroughfromotherplacesinaccordancewithhowtheirplaceissit‐uatedamongothers. Geospatially inherentpatternsofmobility therematter, too,which are also heavily influenced by physical forces andmaterial landscapes. Inshort,landscapesareradicallydifferentfromplacetoplace,yieldingdifferentitemsforuse in satisfyingneeds, andbuilding traditionsandcultures. Sometimes, evenoverasmalldistanceorshorttime,thephysicalandmaterialattributeswillchangemarkedly:humidtodry,steeptoflat,soiltonosoil,mineralstonominerals,coldtowarm,generally light to generallydark, andsoon. Mobilitywillbe influencedby“pathsofresistance”representedin“deserttrails,”rivers,hills,seas,etc.Peoplein


semi‐aridregionswillnecessarilyvalue, forexample,what littlewaterandpatchesofsoiltheyhaveattheirdisposal,morethanpeopleinhabitingahumid,flatinteriorplain.Differentmaterialsrequiringdifferentutilizationtechnologieswillnecessarilybepreferred.Peopleonoceancoastlinesorsituatedalongnavigableriverswillnec‐essarilyandmorefrequentlyencounterothersfromotherplacesandlikelybelesssuspiciousofsuchencountersthanrelativelyremotesocieties.

Inshort,hillpeople,forexample,arenoticeablydifferentfromflatlanders;aridpeo‐plearenoticeablydifferentfromplainsagrarians;tundraandseasonallytemperatefolkarenoticeablydifferentfromtropicalislandersandrainforestdenizens,andsoon.Wherepeopleareisatleastasimportantaswhothosepeopleare,especiallyindetermininghowtheylive,andhow“tolerant”theyarewitheveryoneelsenotlikethem;which iswhatwe, the SCA community, fundamentallywant to understand,andwhichtheRSIFundamentalFourapproachanalyticallyaddresses.

For our purposes, then, analyzing populations to discern how their culture influ‐encestheirbehaviorinsomewhatpredictableways,demandsthatweunderstandasmuch aswe can aboutwhere theywerewhen theybecameapeople,andhow theyusedthatplacetosatisfytheirneeds;especiallynowthattheyaremovingfromthosetypically rural places to more massively‐concentrated urban places—Megacities.Thatpeople‐shiftconstitutestheGreatModernCulturalTransformationthat isoc‐curringnowandwill continue in thenear future, consistingof urbanization ratestranscendingallrecentexperience.Aswe’veseenoverthelastfewdecades,andatanacceleratingpaceoflate,increasinglymobilizedgroupsofpeopleare,inindivid‐ualunits,testingtheirowncapacitiesforadaptationinlargelyunknown,increasing‐ly congested, and politically intensemegacities. They bringwith them their ownculturalwaysofthinkinganddoingasformedinthoseplacestheyoriginated,bytheattributes of those places—maybe riverside, maybe mountainous, maybe coastal,maybehot,maybesprawling,maybesomethingyetdifferentinmorewaysthanone.Thesepeoplesexerttheirunique,unfamiliarinfluenceonculturaladaptationtotheinstitutionsandbehaviors in thoseneighborhooddestinations theyseek, resultingfrom an inevitable, close proximity clash among the traditions those immigrantsbringwiththem,thetraditionsalreadydevelopedinthatcitybeforetheygotthere,and those traditions evolving from the constantly changing rural/urban ethno‐culturaladmixtures: inotherwords, the inevitableandpotentiallyvolatileclashoftraditionsintow(rural)v.traditionsinplace(urban)v.traditionsinflux. Urbaniza‐tioncomplicatesculturalevolutionandexpression,andmegacitycultureisanythingbutsimple;toanintensedegreeitbeliesprediction,place‐centricpopulationanaly‐sisrepresentsaviableattempttoidentifyexplanationsandgeneralitiesthatallevi‐atesomeofthisanalyticalandpredictiveuncertainty.

Analytical Utility and Relevance

Place‐centric population analysis of migrant’s cultural origins provides a startingplace forunderstandinghowthosewerebefore theshift,once locatedanew inanunfamiliar place, thus also providing somewhat of a cultural “baseline” generallyapplicable to certain peoples of common geospatial “upbringings”: mountain cul‐tures, for example, demonstrate certain traditional generalities that apply acrossspecificmountainranges,desertpeople,plainscultures,tropicaltribes,andtundrapeople,likewise.Thereare,ofcourse,manyspecifictraitsthatdistinguishthesecul‐


tures fromeachother, thatmustultimatelybeexaminedand interpreted,butcul‐tural place qualities often provide an analytical starting point fromwhich frame‐workscanbeformulatedandinferencesputforthinadvanceofthemeticulousob‐servation and actual experience required for more sophisticated, intricate, andnuancedcomprehensiongraduallyformingwithtime.

Buthowdoyoudo it?Place‐centricpopulationanalysissimplyconsistsofasetofmaterial/physical/situationalexplanationsfortheformationand/ortransformationofobservedculturaltraditionswithintheRSImethodologicalspectrum:Ontologies,Identities,LogicsofAppropriateness,andNarratives.UrbanPoliticalEcologistsandUrbanGeographers,amongothers,areadeptat featuringthesekindsofprevalent,inherentlygeo‐spatial,explanationsforevolvingculturalbehaviorsanddisruptionsinboththeculturaloriginsofmegacityinhabitants,andthe“contestedlandscapes”withinwhichthemodernurbanculturaltransformationisoccurring.Place‐centricpopulation analysis examines the formational impact of relative quantity (abun‐dancev.paucity),type, andquality,ofsuchphenomenaasstocksandflowsof lifeessentials, likewater, soil, nutrients, and energy, plus other pertinent value chainrelations and economic throughput streams associatedwith transactions in thesematerialsandtheirwastes.

The RSI‐SCA methodological intent is to “discern, track, and influence culturalchanges[by]deconstruct[ing][them]…inordertoanalyzehowdifferentsocialforc‐esanimateorareconstrainedbyapopulation’sontology,howtheforcesactivateorseektocreate identities,howtheymakeuseof logicsofappropriatenesstoactivatethenormsandvaluesthatprivilegethe‘rational’behaviortheydesire,andhownar‐rativescreateorreinforcetheideationalspacethesocialforcesdesire”(emphasisinoriginal)(Puls&Ellis,2011).Theseare,inmyopinion,preciselythecorrectculturalitems to discern for population cultural analysis,place‐centric population analysisonlydemandsanadditionalcaveatattachtotheendofthatstatementtotheeffectof: “and how each forms and are formed by the physical, material, and geo‐situationalattributesoftheplacestheyoccupy,use,andcallhome.”

Cultural ontologies consist of that set of ideas and paradigms that those people“‘know’tobereal,andunderstanding,forthem,ofhowtheworld‘works’“(Puls&Ellis,2011). Theyknowthese,insubstantialpart,becauseofwhatthey’velearnedtodointheirhomelandscapestoflourish. Culturalidentitiesarehowaparticularconstitutedgroupofpeopledistinguishthemselves fromothers in termsof “inter‐ests,socialpriorities,norms,andvalues”(Puls&Ellis,2011),eachofwhicharede‐rivedfromtheirtraditionalhomeexperience:it’swhypeopleareasked,uponmeet‐ing other people,where they are from . . . because itmatters. In a similar sense,culturalnarratives,howpeopledefine,refer,andattachmeaningtothings,andtheestablishmentofappropriateparameterstogovernbehavior,derivealsofromtrialsanderrorsoflivinginaparticularplacewithitsparticularsetofartifactsthatresultintraditionsthatworkforthemthere.

Conclusion

Learninghoweachofthesederivesfromhomeexperienceandcomparingthesetotraditions of others from places that are similarly dry/wet, steep/flat, cen‐tral/remote, etc., allows analytical generalities to be identified for each “class” of


culturalhomeorigin.Thesegeneralitiescontributetoa“culturalbaseline”forfacili‐tating the advancement of cross‐cultural comprehension: humid, steep, remoteplaces,forexample,tendtoformculturaltraditionswithinacertainsetofparame‐ters.

The cultural clash of rural folk from all sorts of home territories and the culturalbaggagetheycarrywiththemastheymovetonew,denselysettledmegacityneigh‐borhoods, and the inevitable tension that derives from being forced to operate,mostlyunfamiliarly, inthesenewplaces,withnewrulesandnewtraditions, isthereasonweareinterestedinwhatishappeningasthemodernurbanculturaltrans‐formationpresseson.FollowingtheRSI‐SCAprotocol,withspecialattentionpaidtoexplaining its categorical variables—ontology, identity, logic of appropriateness,andnarrative—fromtheperspectiveofhowthecharacteristicsofhomecontributedto their ultimate formation and expression, is the essence and relevanceof place‐centricpopulationanalysis.

References

Puls,MatthewJ.,Ellis,DavidC.,“Socio‐CulturalISRforCounterinsurgencyandSta‐bilityOperations,”USSOCOMJICSOC—IrregularThreatAnalysisBranch,21July2011.

Ekins,Paul,TheLivingEconomy,NewEd.,Routledge,1986.


ChapterTwo,EvaluatingLong‐TermThreatstoEnvi‐ronmentalSecurityviaIntegratedAssessmentModel‐ingofChangesinClimate,Population,LandUse,En‐ergy,andPolicy

MarcImhoff,JillBrandenberger,KatherineCalvin,JamesEdmonds,KathyHibbard,MohamadHejazi,AllisonThomsonandLeonClarke

[email protected]

Abstract

Environmentalsecuritywillrequiretheconsiderationof thephysicalenvironmentwhensettingforthoperationsandcontingencyplanningtomaintainPhase0condi‐tions inthefuture.Modelingtoolsofvariousscales,suchastheGlobalChangeAs‐sessmentModel (GCAM) andRegional IntegratedModeling andAnalysis (PRIMA)platforms,providemethodsforbuildingscenariostoevaluatefutureconditionsandpotentialunintendedconsequences thatmayarise fromthecomplex interdepend‐enciesofhumanandearthsystems.Thesemodelsallowsimulationsthatelucidatetheinterdependenciesbetweensociopolitical,energy,andclimatestressesonvary‐ing spatial and temporal scales.Theyprovidemethods toproject future scenariosandthesensitivityofresourcescriticaltoregionalstability,suchasfoodprices,en‐ergy availability, and water resources. Metrics and indicators may be derivedthroughmodeling scenarios that support intelligence, surveillance, and reconnais‐sance (ISR) planning scenarios that must consider future operating conditions inmegacities,suchaschangingenergydemandsthatstresselectricalgrids,increasingfoodprices,andtradeoffdecisionsforcompetingwaterusage.

Keywords:

Integrated Assessment Modeling, Environmental Security, Policy, Climate Change,BuildingEnergyDemand,Impacts,PopulationDynamics,FoodSecurity,WaterScar‐city

Megacities in Context − Global Threats to Environmental Stability

SeveraldirectivesfromFlynn(2012)werecapturedtoreorganizetheapproachandmethodsfordealingwithfuturethreatscenarios.Hestressedtheneedtoconsiderthe physical environment when evaluating the fragility of peace. This concept iscoinedenvironmentalsecurity.LTGFlynnstressedtheneedtothinkbeyondthehis‐toricalthreatstoU.S.security,suchasemergingnuclearpowers,andconsiderbothhumanandearthsystemswhenplanningforsustainmentofPhase0conditionsun‐derchangingclimateconditions.Theinterdependentnatureofmegacitieshighlightsthecriticalityoffocusingcrisismitigationactionsoneconomic,diplomatic,andeco‐logicalcomponents,notjustmilitarysolutions.Intelligence,surveillance,andrecon‐naissance (ISR) should consider climate change impacts on these components as


potentialprecursorsforfutureconflictasenvironmentalsecurityinmegacitiesbe‐comes increasingly difficult because large populations and resource demands areconcentrated in a small footprint. LTG Flynn concluded that understanding thesecomponentswillenablemoreeffectivediplomacyandbetter‐focusedmilitaryactivi‐tytokeepmanybuddingconflictsleftofbang.

Morethanhalfoftheworld’spopulationlivesincitiestodayandby2030thispro‐portionwilllikelyreachtwo‐thirds.Twenty‐sevencitieshavereachedmegacitysta‐tus, and this number will continue to grow.While megacities such as New York,London,andTokyohavethemeanstomanagethemselves,thosethathaveemergedin the last half‐century have passed the “tipping point,” becoming overwhelmed,dangerous,andoftenungovernable(LiottaandMiskel,2012).Anunderstandingofthefutureofmegacitiesandthestressorsthatmaybeappliedtothemrequiresanunderstanding of the interactions between human and earth system stresses. Ur‐banizationistakingplacewithinbroader‐scaleprocessesthatincludesocioeconom‐ic, demographic, and technological factors. These stressors are global in scale, ex‐pressthemselvesregionally,andareinfluenceddeeplybypoliticalstability,policy,andtrade.Newmegacitieswillbeformedandoldermegacitiestransformedwithinthismilieuandthecommunitieswillbeshapedbytheseoverarchingfactorsastheglobalcompetitionfortheresourcesandcapitalneededtocopewiththesechangesaccelerateinfluencedinpartbyclimatechange.

The factors contributing to theoverall societal riskofdisruptionsassociatedwithchanging climate conditions include event severity, exposure and vulnerability ofpeopleorcriticalsystems,andtheadaptivecapacityorinherentresiliencyofthesesystems.Exposureandvulnerabilityarefunctionsofboththedirecteffectsofacli‐mateeventandthesecond‐orthird‐ordereffects,whicharemediatedbyglobalizedsystems.Therefore,thesecurityriskscannotbepredictedbylookingonlyatclimatetrendsandprojections.Changingsentimentandpolicydecisionsrequiredformiti‐gationoradaptationoccuratmegacityscales,butreverberatethroughouttheglobalsystem.Unintendedconsequencessuchasapro‐biofuelpolicyintheU.S. thatmaydrive up the cost of food globallymust be evaluated using integrated assessment(IA)models.Thesemodelsallowconsiderationof thesynergistic impactsofpolicyandclimatethatmaydestabilizemegacitiesasseenbyrecentbrownouts,blackouts,water shortages, rising food costs, higher temperatures in theurbanheat islands,increasedairandwaterpollution,andimpactsonhumanhealth.

The U.S. Intelligence Community (IC) has already recognized climate change as athreatmultiplier inalready‐volatile regionsand in those thatare currently stable.AnIC‐commissionedreportbytheNationalResearchCouncil(NRC2012)concludedthat,“Weknowbeyondreasonabledoubtthattheconsequenceswillbeextensive.”It states that “Given the available scientific knowledge of the climate system, it isprudent forsecurityanalyststoexpectclimatesurprises inthecomingdecade, in‐cludingunexpectedandpotentiallydisruptivesingleeventsaswellasconjunctionsofeventsoccurringsimultaneouslyorinsequence,andforthemtobecomeprogres‐sively more serious and more frequent thereafter, most likely at an acceleratingrate.Theclimatesurprisesmayaffectparticularregionsorgloballyintegratedsys‐tems,suchasgrainmarkets,thatprovideforhumanwell‐being.”


ThischapterillustrateshowIAmodelingandresearchareusedtosimulatehowtheinterconnections between sociopolitical, energy, and climate stresseswill expressthemselvesinvarioussectorsimportanttothefunctioningofmegacities.ThegoalofIAmodelingistobringtogetherthekeyfactorsthatinfluenceglobalchangeinasin‐glemodelingframework,allowingaself‐consistentexplorationoftheirinteractions.Done properly, IA modeling can retain the influence of global interactions whilesimultaneously simulating and exploring regional or sectoral outcomes. The re‐mainderof thischapterprovidesexampleoutputs fromtheGlobalChangeAssess‐mentModel(GCAM)thatsupportISRplanningscenariosthatconsiderfutureoper‐ating conditions inmegacities, such as changing energy demands, increasing foodpricesandsecurity,andtradeoffdecisionsforcompetingwaterusage.

Global Change Assessment Model (GCAM)

IAmodels,suchasGCAM,aretheworld’spreeminenttools for integratingcompo‐nentsofeconomic,energy, terrestrial,andclimatesystems inorder tounderstanddriversofgreenhousegasemissions(GHG)andconsequencesofclimatechangemit‐igationpolicyoptions.GCAMisdynamicallycapableofintegratingtheseinterdisci‐plinaryforcesthatactuponmegacitiesatspatialscalesrangingfromlocaltoglobaland over temporal scales operating from decadal to the immediate. The PacificNorthwest National Laboratory has developed andmaintained GCAM for over 30yearsandsupportedbothprivateindustryandgovernmentscenariodevelopment.Thescenariossupportstrategicresearchanddecisionscienceoninterdependenciesbetweenenergy,technology,policy,andclimatechange.GCAMisdesignedtoallowanalyststoexplorehowregionalandnationaleconomiesmightrespondtoclimatechange mitigation policies including carbon taxes, carbon trading, land‐useregulation,andaccelerateddeploymentofenergytechnology.Withasimulationpe‐riodextendingfromthepresenttotheendofthecenturyat5‐yearintervals(1‐yeartimestepindevelopment),GCAMcanexploretheseinteractionsfromanoperation‐alscaletoamorestrategic,long‐rangeforwardoperatingscale.

Aparticular strengthofGCAM is that itwasdesigned tobedifferentially scalable.Thatis,itwasdesignedtolinklarge‐scalephenomenasuchasglobalagricultureandenergy markets with phenomena taking place at finer spatial and technologicalscales,suchascropplantingatthescaleofagro‐ecologicalzones(

Figure 1; 14 geopolitical regions and 151 agro‐ecological zones). It was also de‐signed to integrate modeling of higher sectoral or spatial resolution (e.g., crops)withcoarserresolutionmodeling(e.g.,globalclimate).Forexample,itcanlinka50‐staterepresentationoftheU.S.withtherestoftheworlddisaggregatedinto14geo‐political regions. It can linkaglobal‐scale climatemodelwith thebuilding sectors


thatwill formthebasisofmegacities,and itcanbescaledtoprovidedetailedsce‐nariosforasinglegeopoliticalregionoraggregatedtoabroaderscale.

GCAMisaRepresentativeConcentrationPathwayclassmodel.Thismeansitcanbeusedtosimulatescenarios,policies,andemissiontargets fromvarioussources in‐cluding the IntergovernmentalPanelonClimateChange (IPCC).Themodeloutputincludes projections of future energy supply and demand and the resulting GHGemissions,radiativeforcingandclimateeffectsof16GHGs,aerosols,andshort‐livedspeciesat0.5x0.5‐degreeresolution—allcontingentuponassumptionsaboutfuturepopulation,economy,technology,andclimatepolicy.ThefollowingsectionsprovideexampleoutputsofGCAMforscenarios focusedonbuildingenergydemands, foodsecuritythroughcropproductionsandmarketanalysis,tradeoffscenariosbetweenfood andwater sectors, and the potential for down‐scaling scenarios to a specificmegacity region.Data for energy system (including technologies and fuels),watersupplyanddemand,economics,populationdynamics,andemissionsoperatewithinandbetweenthesefields.

Figure 1. The Global Change Assessment Model (GCAM) utilizes geographic boundaries for a) geopolitical regions that represent 14 major energy and economic regions of the world and b) 151 agro-ecological zones that represent primary climate-based boundaries for crop systems.

Integrated Assessment of Forces Influencing Megacities of the Future

Building Energy Demand out to 2095

GCAM isa technology‐richmodel. It representsenergy sectordetailby threeend‐use sectors (Building, Industry, and Transporation), energy supply, and energytransformationsectors includingfossil fuels(oil,naturalgas,coal),biomass(tradi‐


tionalandmodern),electricity,hydrogen,andsyntheticfuels.Scenarioscanbede‐velopedthatholdonesetofvariablesconstantinordertotesttheinterdependen‐ciesof others. For example, if policy assumptionsare applieduniformly across allcountries,GCAMcanexaminehowpopulation,migration,andclimatechangeinter‐acttocreatevariabledemandonbuildingheatingandcoolingenergyincommercial,industrial, andresidentialbuildingsout to2095.Becauseallof thedriving factorsvaryspatially,theimplicationsvaryregionally.

GCAMwasrecentlyusedtocomparetheeffectofglobalclimatechange,populationdistribution, and mitigation policy on building energy demand in the U.S. versusChina(Zhouetal.,2013).Theuncertaintyof the impactsofclimatechangeon theheatingandcoolingrequirementwasestimatedusingthenumberoffutureheatingandcoolingdegreedays(HDD/CDDs)thatmightoccurundertwopolicyscenarios:1)no‐climatepolicyand2)acarbonemissionpolicyscenario.Theanalysisassumedanemissionpolicywasimplementedbysufficientmembercountriestostabilizetheatmosphericcarbondioxide(CO2)concentrationat550partspermillionbyvolume(ppmv)and included three scenariosof griddedpopulationdistribution.The inte‐grated energy technology and earth systemsmodels required three keymodelingactions:1)definethepotentialfutureglobaltechnologicalandeconomicconditionsthatdrivetheamountofgreenhousegasinputtotheatmosphere(e.g.,CO2concen‐trationpathways);2)runtheglobalclimatemodelstoestimatetheHDD/CDDsre‐sulting from this technology and policy mix; and 3) define the demographic andpopulation scenarios that best describe likely shifts in populations including ageprofiles, urban vs. rural, andmigration between climate zones. All three of thesestepsusedinputfromcarefullyvettedscenariosemergingfromtheIPCCandotherbodies of work designed to capture known interactions, data, and trends(Nakicenovicetal.,2000;IPCC2001;Collinsetal.,2006;Russelletal.,2000;Gordonetal.,2000;Grübleretal.,2007).

The implications thatchangingclimateandpopulationdistributionmighthave forbuildingenergyconsumptionintheU.S.andChinawerethenexploredbyusingtheresultsofHDD/CDDsasinputstoadetailed,buildingenergymodel,nestedinGCAM.Theresultsacrossthemodeledchangesinclimateandpopulationdistributionsin‐dicatethatunabatedclimatechangewouldcausethebuildingsector’s finalenergyconsumptiontodecreasemodestly(6%decreaseorlessdependingonclimatemod‐els)inboththeU.S.andChinabytheendofthecenturyasdecreasedheatingcon‐sumptionmorethanoffsetsincreasedcoolingusingprimarilyelectricity(Figures2&3). At first thismayseem likeapositivedevelopmentdue todecreasedoverallenergyrequirementsasa resultof climatechange.However,nationalaveragesdonot tell the entire tale relative tomegacities. For very large urban areas, climatechange combinedwith urban heat islandswill result in an even higher electricitydemand forcooling thanshownhere. Insomepartsof theworld, this isalreadyacriticalhumanhealthissueinthehotsummermonths.


Figure 2. Potential U.S. and China urban clusters by 2095 derived by statistical data projections from baseline nighttime lights imagery from the Defense Meteorological Satellite Program’s Operational Linescan System; (Zhou et al., 2014). These data help make building energy demand data from other sources spatially explicit.

Inthisscenario,climatechangegenerallyresultsinalargerincreaseofcoolingen‐ergyuseinChinathanintheU.S.,whilethedecreaseinheatingenergyuseisaboutthesameinbothcountries.Dependingontheclimatemodels,theU.S.hasa20–35%increase incoolingenergyuseand9–20%savings inheatingenergyuse,whereasChinahasa37–41%increaseincoolingenergyuseand12–19%savingsinheatingenergyuse.Although theHDDsandCDDschangeroughlyat thesamerates in thetwo countries, the asymmetric energy response results from far less ubiquitouscooling service inChina than in theU.S.When increasedCDDs inducedby the cli‐mate changeare combinedwithChina’s rapid incomegrowth, cooling serviceandassociatedcoolingenergyusewillgrowfaster inChinathan in theU.S.duringthe21st century. With abated emissions (i.e., the 550‐ppmv scenario), however, thetrendsinheatingandcoolingenergyusesarenotnoticeablydifferentbetweenthetwocountriesasthemagnitudeoftheimpactsbecomerelativelysmallinbothcoun‐tries.

Whentheimpactsaredownscaledtoaregion,megacitiesareprojectedtobewarm‐er in the future.Thiswoulddriveanacross‐the‐board increase incoolingrequire‐mentsthatdirectlytranslatestoincreasesindemandforelectricity.Formegacitiesthiswillputconsiderablymorepressureontheelectricalgridandelectricalpower‐generation infrastructure tomaintainhealthyconditions.Thisneed formoreelec‐tricalpowergenerationalsohasramificationsforotherpartsoftheintegratedsys‐tem such as water resources for power plant cooling and material for electricaltransmission.


Figure 3. Absolute and per capita building energy demand out to 2095 by fuel in the U.S. and China (Source Zhou, 2013) highlighting a need for more cooling in the building sector

resulting in an added demand for electricity in megacities. Changing climate is represented by the SRES B1 Scenario, or and constrains 550 ppmv CO2 in the atmosphere to 550 ppmv by

2100. Note that for both simulations, coal is not utilized for the US, but remains a significant fuel for China. Also note, that for constant climate, by 2035, the trend for energy demand per capita in the US begins to level out, whereas, China’s demand continues to increase. On the

right, changes in fuel demand under changing climate are calculated as the difference between the simulated year minus the demand in 2005. In both cases, megacities in the

future will be evermore reliant on the electric infrastructure.

In aU.S. centric scenario, theGCAManalysiswasdownscaled to the state level tofurtherresolveimpactsbysectorandidentifypotentialurbanclustersormegacitiesshowinghigher rates of energyuse. This approachprovideshigher‐resolutionde‐mographic and energy‐use datawithin theU.S. computed in the context of globalclimate, energy, and socioeconomic forcing factors. The state‐level assessment re‐vealsasituationby2095,wheredespiteloweroverallenergydemandwithchang‐ingclimate,demandforelectricitywentup(Figure4).


Figure 4: Figure 4. Cumulative building energy use in the 21st century with consideration of climate feedback in the IPCC A2 Marker Scenario relative to that use without climate feedback (fixed) (Zhou et al., 2014). Some States gain by climate change with reduce

Food Security: Interdependence of Global Food Production and the Ener‐gy System

Formostofhumanhistoryamajorityofpopulationslivedinruralsettingsengagedinagriculturalpursuits.Butduringthe20thand21stcenturies,theshareofpopula‐tionslivinginurbanenvironmentshasclimbedsteadilysuchthatthepresentshareofruralpopulationshasdeclinedtoapproximately47%in2012(e.g..66%in1960).Concomitantly,theshareofglobalgrossdomesticproducts(GDP)originatingfromagriculturalactivitieshasdeclinedprecipitously.

ResearchusingGCAMcanexploretheinteractionsoftheagriculturalsystemandtheenergy systemwithin the contextofdifferent futurepolicyoptions. Such researchhaspointedtotheimportanceoffuturelandavailabilityforagricultureindetermin‐ingfuturefoodprices,whichhasimplicationsforfoodsecurity.InthecurrentGCAM


calculation,thedemandforfoodisinelastic;thatis,enoughfoodmustbeproducedtofeedeveryoneontheplanet.However,ascompetitionfor landforurbanization,bioenergycropproduction,orcarbonsequestrationthroughafforestationincreases,thepriceof foodrisesand thebalanceof foodconsumedshiftsaway fromanimalproducts,whicharemorelandintensivetoproduce.

A recent study, Calvin et al. (2013a) conducted scenarios in GCAM to explore theimplicationsofclimatechangemitigationdecisionsonfoodproductiontoevaluatepotential unintended consequences from policy decisions. They found that globalcrop prices are sensitive to climatemitigation policies; for example,wheat pricesarelikelytoincreasewithclimatechange,evenintheabsenceofanyconcertedpoli‐cyeffortstoreduceGHGemissionsorincreasebioenergyusage(Figure5–Left).

When climate changemitigation policies that place additional economic value onlandthroughacarbontaxthatvaluesbioenergycropsorcarbonstorageinforestsareassumed,thepricesofagriculturalcommoditiesarealsoaffected.UsingGCAM,Calvinetal.(2013binpress)foundthatthetypeofpolicyorregulationimposedonterrestrialcarbonandforestresourcescanhavealargeeffectonfoodprices.Com‐prehensive policies, such as putting an economic value on all terrestrial carbonemissions, aremost effective at reducing the costof climate changemitigation ef‐forts,butalso result ina threefold increase in thepriceofwheat (Figure5 right ‐universalcarbontax(UCT)and99%landscenario).Alternatepoliciesthatpreserveforestareaorspecificallyregulatebioenergycankeepfoodpriceslow,butresultinhighermitigationcosts.

Increasesinglobalfoodpricesfrombothdirectclimatechangeimpactsandindirectpolicydecisionsrelatedtoclimatechangemitigationandadaptationcouldexacer‐bateregionaland local foodsecurity issuesandconflicts.This isespecially trueasurbandwellersareprimarilyconsumersofagriculturalproductsandnotproducers.Futureworkunderwayonthistopicisexplicitlyexploringregionalself‐sufficiencyinagriculturalproductsand the implicationsofpotential futurewaterscarcity foragriculturalproductionandcosts.


Figure 5: Figure 5. (Left) Global wheat price projected in GCAM for a suite of scenarios representing no major climate policies and no climate impacts (solid line) or the range of climate change impacts projected in the IPCC Fourth Assessment Report (grey area)

Future Water Demand: Agriculture versus Energy

Estimationofgrowthintotalwaterdemandreliesonourunderstandingoftheun‐derlying social, economic, and environmental drivers of changes in sectoralwaterdemands. A sufficient, secure water supply is essential for meeting basic humanneeds and for the functioning ofmany sectors of the economy,making an under‐standing of futurewater demands crucial for policymakers to address thewaterscarcitychallengeforthegenerationstocome.Inarecentstudy(Hejazietal.,2013),GCAMwas used to assess future water demands representing six socioeconomicscenariosthroughtheendofthecentury.Withinthesixstorylines,GCAMidentifiedtwobroaddescriptors:eitherprogressdevelopsaheadoforcopeswithrisingpopu‐lations, or theobjectivedescribed in theUnitedNationsMillenniumDevelopment


Goals(MDGs)isnotachievedandtheproportionandnumberofextremelypoorin‐creases.Foreachgroup,weexplored the implicationsof these setsof futuresandtheirassociatedtrends indomesticgovernance, internationalrelations,populationpatterns,andenvironmentalconditions.Thegoalwas toexamine thedemandsonwaterandotherresources,aswellas thehuman, financial,andenvironmentalca‐pacitytomeetthem.

Themodelingframeworkexplicitlytracksfuturewaterdemandsfortheagricultural(irrigationandlivestock),energy(electricitygeneration,primaryenergyproductionandprocessing),industrial(manufacturingandmining),andmunicipalsectors(Fig‐ure6).Theenergy,industrial,andmunicipalsectorsarerepresentedin14geopolit‐icalregions,andtheagriculturalsectorisfurtherdisaggregatedintoasmanyas18agro‐ecologicalzoneswithineachregion.

Figure 6: Figure 6. Representation of all components of the water-demand sectors in GCAM.

The results of these scenarios all showed increases in global water withdrawalswith 3710 km3 per year in 2005, 6195–8690 km3 per year in 2050, and 4869–12,693 km3 per year in 2095. Comparing theprojected total regionalwaterwith‐drawals to thehistorical supplyof renewable freshwater, theMiddleEastexhibitsthehighestlevelsofwaterscarcitythroughoutthecentury,followedbyIndia(Fig‐ure 7) This is a particularly salient scenario for Karachi, Pakistan. Pakistan has aprominentagriculturalsectorthatmakesup23%oftheGDPand44%ofthelaborforce, but faces sharp tradeoffs in water allocation geographically and betweenfarmingandpowergeneration.TheflowoftheIndusRiver,whichirrigatesmuchofPakistan, is likely to decrease as the Karakoram Glacier shrinks. Rising ambient


temperatureswillalsoaffectgrowingcycleswhileincreasedprecipitationvariabil‐itywillleadtodroughtsinsomeregionsandfloodsinothers.Fromasecurityper‐spective, thesevulnerabilitiescould interactwith the internalprotests thatareal‐readyoccurringinresponsetorepeatedpoweroutages.Incontrast,waterscarcityimprovesinsomeregionswithlargebase‐yearelectricsectorwithdrawals,suchasthoseintheU.S.andCanada,duetocapitalstockturnoverandthealmostcompletephase‐outofonce‐throughflowcoolingsystems.

TheGCAMframeworkdemonstratesthat1) freshwateravailabilitymaybeinsuffi‐cienttomeetallfuturewaterdemandsinsomeregionssuchastheMiddleEastandIndia; 2) many regions can be expected to increase reliance on non‐renewablegroundwater, water reuse, and desalinated water, but they also highlight an im‐portantrolefordevelopmentofwaterconservationtechnologiesandpracticespar‐ticularly inmegacities of developing countries; and3) tradeoff scenarios betweenagricultural and energy needs forwatermay drive instability in already sensitivecountries.

Figure 7: Figure 7. Water scarcity index (WSI) in years 2005, 2050, and 2095 in the 14 GCAM regions due to changing water demands. Total water supply (renewable water + desalinated water) is assumed to be fixed to 2005 levels to capture the effect of demand pro


Downscaling Integrated Assessment Models – Regional Expressions of Global Forces

Globalforces,whetherdrivenbyhumansystems(migration,trade,etc.)orthephys‐ical environment, affect regional and local systems. For instance, changes in theglobal climate system, such as the frequency andmagnitude of El Nino events ormonsoonseasons,areexpressedatregionalscalesthroughdroughts,floods,orex‐tremetemperaturesandcanhavedestabilizing impactsonsocieties.Suchchangesarenotwellcapturedbyglobalmodels.Globalmodels,however,provideboundaryconditions,orglobal forcingswhendownscalingtoregionalmodels. Intheclimatesystem, regionalmodels are eitherdownscaled through statistical representationsorbasedonphysicalrepresentationsoftheclimatesystemthatarerelevantforre‐gionalanalyses,suchasaccountingfortopographicfeatures,managementpracticessuchasforestryoragriculture,andhydrologicsystems.Atthisscale,changesincli‐mateextremesmaypresentlargerchallengesforclimatemitigationandadaptationthanchangesinmeanclimatebecausemegacitiesarevulnerabletoextremeevents(e.g.SuperStormSandy).Hence,regionalexpressionsofglobalforcingscanbecon‐sidered through integrated frameworks that account for regional climate‐relateddrivers of energy production and socioeconomic processes (e.g., Hibbard andJanetos,2013).AnexampleofthisframeworkisthePlatformforRegionalIntegrat‐ed Modeling and Analysis (PRIMA) which allows simulations of extreme climaticeventsandtheirimpactsonandinteractionsbetweenenergydemand,energyinfra‐structure,waterresources,andcropproductions.

Accompanyingthechangesintheglobalenergybalancearechangesinatmosphericmoisture, which scales with temperature, and large‐scale atmospheric circulationsuchasshiftsinthestormtracks.Overall,climatemodelshaveprojectedanintensi‐ficationofthewatercyclewithmorefrequentandintenseextremesincludingbothfloodsanddroughtsinthefuture(e.g.,HeldandSoden,2006).Formegacities,thisgenerallymeansmorefrequentflooding,particularlysincemanymegacitiesarelo‐catedon theperimeters of continentalmargins, coastal zones, or rivers.ModelingtoolssuchasPRIMAarerequiredtoevaluatethepotentialfrequencyandmagnitudeoffloodinginmegacities.Forexample,coastalcitiesandmegacitiesmaybevulnera‐ble to synergistic coupling of sea‐level rise, increased storm frequency, and in‐creasedstormintensity.RegionssuchastheU.S.GulfCoastareperfectexamplesofthevulnerabilityofenergysystemsinurbancenterswheretheenvironmentalsys‐temnolongerprovidesadequateprotectionfromstormsurgecausedbysubsidenceofthelandrelatedtoresourceextractionoracompletelossofthecoastalstructure(Figure8).

Inundationofcoastalsystemsmayresultinpotentialmigrationpatterns,whichmaycauseconflictinalreadyvulnerableareasoutsidetheU.S.Forexample,Bangladeshisparticularlyvulnerabletoclimatechangeastheeighthmostpopulouscountryintheworld and its capital,Dhaka, is one of theworld’s fastest growingmegacities.Alongwithgovernancechallenges,muchofBangladesh’slandareaislessthanonemeterabovesealevel,andtheIPCCreportsthatthecountrywilllose17−20%ofitslandmassby2050.Thiswouldlikelydisplacemorethan20millionBengalis.Manywouldtry torelocate internally,but international tensionscouldrise ifmanyseekrefugeinIndia.Bangladesh’slargerneighborhasalreadyerectedafenceandborderguards will use lethal force against those who come too close. Regionally


downscaled models could further evaluate the vulnerability and resiliency of thehuman and earth systems to both currentweather events and long‐range climatechanges.


Figure 8: Figure 8. Simulated near-surface winds (arrows) and storm surge (color bar) induced by a Katrina-like hurricane in the Gulf of Mexico for current conditions (top panel) and with one meter of sea-level rise and regionally-varying land subsidence of 0-2.

Conclusions

Whowillthe“winners”and“losers”beinachangingworld?Climatechangeiscon‐sideredathreatmultiplierandthedefensecommunityrecognizestheneedtocon‐sider the physical environment when evaluating the fragility of peace. The largepopulationsandinterdependentnatureofmegacitieshighlightthecriticalityof fo‐cusingcrisismitigationactionsoneconomic,diplomatic,andecologicalcomponents,notjustmilitarysolutions.Thevulnerabilityofmegacitiestoclimatechangeimpactsisafunctionofthedirecteffectsofaclimateevent,thesecond‐orthird‐ordereffects

EnergyInfrastructureLocations


drivenbythedependenciesonglobalizedsystems,andunintendedconsequencesofpolicies. Therefore, the security risks for megacities cannot be predicted withoutalso bringing inmultiple interacting factors that include energy, policy, economy,anddemographicchangesassignificantdrivers. Byconsideringthesedrivers inasinglemodel,IAapproachesallowfortheinterrogationofhowtheseinteractingfac‐torsmaycreateinequitiesinimpactsanddefine“winners”and“losers”inmoreso‐phisticated ways. Understanding how these inequities will manifest themselves,whichcouldrange fromregionsandcountries toeconomicsectorsorage cohortswithinoracrosscountries,willhelp identify thepotential threats thatmightarisefromthem.

IAmodelssuchasGCAMshowhowbothclimateandpolicychoicescaninteracttochange the energy paradigm in megacities and potentially generate economicstresses due to competingdemands for food andwater resources. Even if projec‐tions50or100yearsintothefutureseemextreme,thesetimescalesareusefulforestablishingthetrendsthatareinplaynowandintheimmediatefuture(seeMaloneandMoss in this issue).Forexample, it is likely thaturbanheat islandscombinedwith(orevenintheabsenceof)climatechangewillresult inoverallwarmertem‐peratures inmegacities inboththenear‐and long‐termfutures.Thiswillputcon‐siderablymorepressureon theelectrical grid andelectricalpower‐generation in‐frastructure tomaintain healthy conditions inmegacities and failureswill be feltevenmorekeenlybyurbanpopulations.These IAmodels showhowmigrationofpopulations to megacities and away from agricultural practices coupled with theneedtoenactclimatechangemitigationstrategies(e.g.,carbontaxes,increasedbio‐energy,etc.)haveadirectimpactonfuturefoodprices.Energypoliciesthatexacer‐batethecompetitionforland(forbioenergycropproduction,orcarbonsequestra‐tionthroughafforestation)cangreatlyincreasethepriceoffood(e.g.,wheatprices)creating inequitiesbetweenproducersand theurbanconsumers. In the future(asnow),theMiddleEastfollowedbyIndiawillexperiencethehighestlevelsofwaterscarcity. These regions can be expected to increase reliance on non‐renewablegroundwater, water reuse, and desalinated water and face tradeoff decisions be‐tweenwaterneeded for agricultural andenergyneeds. In contrast,water scarcityactuallydiminishesintheU.S.andCanadaasaresultofcapitalstockturnoverandthealmostcompletephase‐outofonce‐throughflowcoolingenergyproductionsys‐tems.Havingtheability tounderstandhowthesefactorstradeoramplify inanIAmodelingdomainallowsforprojectionsaboutwho“wins”orwho“loses”inachang‐ingworld.

PreservingPhase0conditionsinmegacitieswillrequirepreemptiveactionsacrossmultiplesectorsinaninterdisciplinaryapproachtopreventreachingcriticaltippingpoints that could rapidly drive a high‐density population into conflict. However,existing modeling paradigms and their inherent uncertainties do not necessarilymapcleanlyontopolicyandhumandecisionmakingprocesses.IAmodels,suchasGCAM depend on well‐conceived and vetted scenarios that explicitly account forclimate, climate change mitigation policies, and likely socio‐economic factors atgreaterspatialresolutionsandshortertimesteps.TheICcancontributetothede‐velopmentofthisnewIAcapability.PlanningforPhase0operationsbeginswithadialogueacrossthesecommunitiestomapoutaclearplantoharnesstherichassetsandpotential to leverage existing capabilities. Developmentof a spatially explicitrepresentation of vulnerability and threats to megacities coupled with an IA ap‐


proachnowandintothenearfuturewillbenefitsociety,enhancenationalsecurityandprovideleadershipopportunitiesforinternationalconsortia.

References

Calvin,K.V.,Clarke,L.E.,Edmonds,J.A.,Kyle,G.P.,Luckow,P.,Thomson,A.M.,&Wise,M.A.(2013a).Implicationsofsimultaneouslymitigatingandadaptingtoclimatechange:InitialexperimentsusingGCAM.ClimaticChange,117(3),545−560DOI10.1007/s10584‐012‐0650‐y.

Calvin,K.V.,Wise,M.A.,Kyle,G.P.,Patel,P.,Clarke,L.E.,&Edmonds,J.A.(2013b).Trade‐offsofdifferentlandandbioenergypoliciesonthepathtoachievingcli‐matetargets.ClimaticChange,(doi10.1007/s10584‐013‐0897‐y,inpress).

CollinsW.D.,Bitz,C.M.,Blackmon,M.L.etal.(2006).TheCommunityClimateSystemModelVersion3(CCSM3).JournalofClimate,19(11):2122–2143.

Flynn,M.2012.Leftofbang:Thevalueofsocio‐culturalanalysisintoday’senviron‐mentinNationalSecurityChallenges:InsightsfromSocial,Neurobiological,andComplexitySciences2012WhiteVolume”,Astorino‐Courois,A.,Cabayan,H.,Casebeer,B.,Chapman,A.,DiEuliis,D.,Ehslchlaeger,C.,Lyle,D.,andC.Riceeds.275pp.

GordonC.,Cooper,C.,Senior,C.A.etal.(2000).ThesimulationofSST,seaiceextentsandoceanheattransportsinaversionoftheHadleyCentrecoupledmodelwithoutfluxadjustments.ClimateDynamics,16(2):147–168.

GrublerA.,O’Neill,B.,Riahi,K.etal.(2007).Regional,national,andspatiallyexplicitscenariosofdemographicandeconomicchangebasedonSRES.TechnologicalForecasting&SocialChange,74(7):980–1029.

Hejazi,M.I.,Edmonds,J.,Clarke,L.,Kyle,P.,Davies,E.,Chaturvedi,C.,Kim,S.(2013).Long‐TermGlobalWaterUseProjectionsUsingSixSocioeconomicScenariosinanIntegratedAssessmentModelingFramework.TechnologicalForecastingandSocialChange(inpress)http://dx.doi.org/10.1016/j.techfore.2013.05.006.

Held,I.M.andSoden,B.J.(2006)Robustresponsesofthehydrologicalcycletoglobalwarming.J.Clim.,19,5686–5699.

Hibbard,K.A.andJanetos,A.C.(2013).TheRegionalNatureofGlobalChallenges:ANeedandStrategyforIntegratedRegionalModeling.ClimaticChange118(3‐4):565‐577.DOI:10.1007/s10584‐012‐0674‐3.

IPCC,(2001).ClimateChange2001:SynthesisReport.AContributionofWorkingGroupsI,II,andIIItotheThirdAssessmentReportoftheIntergovernmentalPanelonClimateChange,Watson,R.T.andtheCoreWritingTeam(Eds.),Cam‐bridge,UKandNewYork,NY:CambridgeUniversityPress.

Liotta,P.H.&Miskel,J.F.(2012).TheRealPopulationBomb.Washington,DC:Poto‐macBooks.


NationalResearchCouncil.(2012).ClimateandSocialStress:ImplicationsforSecuri‐tyAnalysis.CommitteeonAssessingtheImpactsofClimateChangeonSocialandPoliticalStresses,Steinbruner,J.D.,Stern,P.C.,&Husbands,J.L.(Eds.),BoardonEnvironmentalChangeandSociety,DivisiononBehavioralandSocialSciencesandEducation.Washington,DC:TheNationalAcademiesPress.

Nakiçenovic,N.,Alcamo,J.,Davis,G.,deVries,B.,Fenhann,J.,Gaffin,S.,…Z.Dadi(2000).SpecialReportonEmissionsScenarios:ASpecialReportofWorkingGroupIIIoftheIntergovernmentalPanelonClimateChange,Cambridge,UK:CambridgeUniversityPress.

Russell,G.L.,Miller,J.R.,Rind,D.etal.(2000).Comparisonofmodelandobservedregionaltemperaturechangesduringthepast40years.JGeophysRes,105(D11):14891–14898.

Yang,Z.,Wang,T.,Leung,L‐YR,Hibbard,K.A.,Janetos,A.C.,Kraucunas,I.P.,Rice,J.S.,Preston,B.andWilbanks,T.(2013).Amodelingstudyofcoastalinundationin‐ducedbystormsurge,sealevelriseandsubsidenceintheGulfofMexico.Natu‐ralHazards(DOI10.1007/s11069‐013‐0974‐6,inpress).

Zhou,Y.,Eom,J.,Clarke,L.(2013).Theeffectofglobalclimatechange,populationdistribution,andclimatemitigationonbuildingenergyuseintheU.S.andChina.ClimaticChange,119:979–992.

Zhou,Y.,Clark,L.,Eom,J.,Kyle,P.,Patel,P.,Kim,S.H.,Dirks,J.,Jensen,E.,Liu,Y.,Rice,J.,Schmidt,L.,andT.Seiple.(2014).ModelingtheeffectofclimatechangeonU.S.state‐levelbuildingsenergydemandsinanintegratedassessmentframework.AppliedEnergy113:1077‐1088.http://dx.doi.org/10.1016/j.apenergy.2013.08.034.


ChapterThree,UnderstandingMegacities‐RSI–Dha‐ka’sDesignasanExpressionofCulture&Politics

Dr.DavidC.Ellis,JamesSiscoGoldbeltHawk,LLC

[email protected],[email protected]

Abstract

Dhakaisnotasingleentity;ratheritisaconglomerationofpopulationswhosehis‐tories and contemporary interests formaround communities, calledmahallas. ByfocusingonthemahallaasthebuildingblockofDhakasociety,itispossibletomoreaccurately capture IndicatorsandWarnings (I&W)of instability. This chapterex‐plainswhythemahalla isimportantinBangladeshiontologyandhowitappliestotheRSI‐Dhakainitiative.ItthendescribesthebasisofsocialtensioninDhakaform‐ingaroundpoliticalIslaminmodernBangladeshipoliticsdespiteasecularsocialistindependencemovementandSufiMuslimpast.Thechapterconcludesbyassessingthe consequences ofDhaka’s evolution as anunplannedmegacity basedupon thepropensityfornaturaldisasterandsocialtension,whicharenowexacerbatedbytheriseofpoliticalIslam.

Keywords

Dhaka,mahalla,ontology,RSI,politicalIslam,hartal

Introduction

WhileDhaka’slayoutmightappeartodefylogictoforeigners(Mowla,1997,p.270),understandingBangladeshiontologyrevealstheorganizingprinciplesoftheneigh‐borhoods, and, therefore, the substance of politics, values, and the needs of eachward. Indigenous Bangladeshi culture is rooted in community and social spacebasedprincipallyonagriculturalproduction.EvenamegacitylikeDhakacontinuesinitsbasicformtoevincethesevaluesdespiterapid,unplannedgrowth. Thecon‐tentofmahallasocio‐politicalspaceisthuslikelytobecharacterizedbyafairlyco‐hesivesetofnetworksandinterestsandcanserveastheanalyticalbaselinefortheM‐RSIDhakainitiative.

InstabilityinDhakaislikelytoarisefromtwomainsources,naturaldisasterbefall‐ingshantycommunitiesandpoliticalskirmishesbetweenleftistandIslamistpoliti‐calparties.HistoricDhakafollowsthemahalladesignandconsistsoffamilieswithlonghistoriesandsimilarsocio‐politicalinterests.RecentlyconstructedDhakawilllikelyreflectmoderncitydesignwithliberalandIslamistresidents,orbetheresultof rapid, unplanned growth in areas prone to flooding by recent rural migrants.Since themahalla is the ontological foundation of both historic Dhaka and ruralpopulations,itprovidesagoodI&Wbaselinetomeasuresocialandpoliticalinstabil‐ity.


Mappingoutmahallas canbeundertakenby two complementarypractices. First,geospatial analysisofDhakacan identify traditional frommodernstretchesofde‐velopment and the likely boundaries betweenmahallas. Second, ground‐level re‐searchcanverify theboundariesand further investigate thevalues, attitudes, andinterestsofeachmahalla.Thiscombinedeffortshouldrevealinrelativelyshortor‐derandwithminimalexpensetheareasofDhakathatmostlikelythreatenstabilityand thecontentof theontologyandpolitical reality that couldserveas the threatvectors.

This chapter explainswhy themahalla is important in Bangladeshi ontology andhowitappliestotheRSI‐Dhakainitiative. Itthendescribesthebasisofsocialten‐sioninDhakaformingaroundpoliticalIslaminmodernBangladeshipoliticsdespiteasecularsocialistindependencemovementandSufiMuslimpast.Thechaptercon‐cludesbyassessingtheconsequencesofDhaka’sevolutionasanunplannedmegaci‐tybaseduponthepropensityfornaturaldisasterandsocialtension,whicharenowexacerbatedbytheriseofpoliticalIslam.

Neighborhoods and Community Space in the Bangladeshi Ontology

The basic building block of Bangladeshi villages is the mahalla (Mowla, 1997, p.259),andevenDhakaasamegacitystillexhibitsmostoftheontologicalunderpin‐ningsofBangladeshiculture.Eachmahallaisaseriesofcircularhouseholds(ghar)combined into largerunits. A family linegenerally cluster ghars intohomesteads(bari), and the homesteads combine to become family lands (paribar). Multipleparibar form neighborhoods (para), and multiple neighborhoods form villages(gram)(Mowla,1997,p.264).

Beingrootedincommunityrelationships,Bangladeshiontologyleadseverylevelofcommunitydevelopmenttoorientaroundsocialspace.Gharsformacirclearoundcentralsquaresasdobari,paribar,para,andultimatelygrams.Additionally,Bang‐ladeshiontologymaintains thedifferencebetweenmenandwomen’ssocialspace.Eachgharhasapublicspaceimmediatelyadjacenttothepubliccenterandaprivacywallseparatingtheinteriorspaceofthefamilylifefromthecommunity.Theformal,publicspaceofthegharisreservedformen,andwomeninhabittheinteriorcourt‐yard. Bazaars (hat) serveeachgram, though theyare typicallyplacedalongmainstreets.Gharsarefoundbehindthehats(Mowla,1997,p.264).

This basicmahalla formhasbeen considerably compressed in theurban environ‐ment, even in theolderquarters. Urbanghars typically consist of a semi‐circularhomewithacentralcourtyardreservedassocialspace. Narrowalleys(gali)sepa‐rate ghars to form bari. Women and children have traditionally been allowed toview this space as accessible for socialization. Broader streets (mahalla) connectbari together into paribar, with corners (morh) becoming important social spacemainlyformenandadolescentmales.Itisatthislevelthaturbancommunityspaceis establishedand the characterof the socio‐political content formed. Formalizedsocialspace(mosques,theaters,etc.)arecalledchouksandareimportantvenuesforinteractionamongyoungmales. Hats, ormarkets, serve the gramsandmany arenowfoundforminglongstretchesofshopsalongmainroadsandaretheplaceforadultstoconductbusiness(Mowla,1997,p.265).


From the outside looking in, themahalla is, thus, a series of inward facing socialspaceswith a larger community that gets progressivelymore discreet and familyoriented.Fromtheinsidelookingout,themahallaisakinship,mutualsupportnet‐workthatgrowswhilebalancingtheneedforprivacyandeverlargercommunitiesof relationships. Galis, alleyways (uthan),morh, andchouksall relyuponBangla‐deshi logicsofappropriateness thatstressoutdoor,communal interaction. Mowlaexplains,

Onacityscale,thebazarappearstobealongstreetlinedwithshops,butac‐tuallyisasequenceofbazarspassingthroughdifferentmahallas.Theconti‐nuity is emphasized because of being specifically built up in terms of theshop fronts…Thus a single pattern…gives rise to two different built formsand the indigenous city appears to consist of linear bazars and circularmahallas.Itappearsthatotherinformalsettlementshaveasimilarmorpho‐logical pattern that was detected in the pre‐planning indigenous areas.(1997,p.267)

ModernstretchesofDhakaaremore likely toresemblecontemporary,automobilebasedplanning,butevenunplannedshantycommunitiesdemonstratemuchofthetraditionalvillagecharacteristics(Mowla,1997,p.269).Mowlacontinues,

Besides the corepatternofmahalla andbazar, a careful observer canalsoseealogicalarrangementoflandusesinindigenousDhaka.Thenetworkofstreetsandalleywaysorgalisevolvedasatypeofbuilt‐insystemoftrafficcontrol and informal but effective zoning plan (Mowla, 1990, p.30). Thespacesbetweenthebuildingsarevibrantandprovidemanyclosedandshortvistas, whichmake streets and galis comfortable, satisfying and secure towalkthrough.Theoverallsequenceshowsaunityofpatternwithbuildingsand spaces tending to increase in importance as a public area/building isapproached. Thisvisualpatterning,asScargill(1979,p.193)preferstocallit, accounts for the easewithwhich the residents find theirway about inwhat,toastranger,isalabyrinthofgalis.(1997,pp.269‐270)

IndigenousDhakasocietyreliedonlocalcouncilsoffiverespectedmen(punchayet)togoverntheuseofmahallaterritory(Mowla,1997,p.262).InindigenousBangla‐deshi culture, any individual can alter a public space so long as neither thepunchayet nor anothermember of themahalla objects (Mowla, 1997, p. 273). Inthisway, neighborhoods police themselves andmaintain harmony based on localperceptionsandneeds.Itisunclearthedegreetowhichthepunchayetsystemre‐mainseffective,butitcouldserveasanimportantsystemofchecksandbalancesasDhakaexperiencesrapidexpansion.

Bangladeshi Politics and the Influence of Islam

Dhaka’smainpoliticalsourceofinstabilityisnowafunctionoftheentrenchmentofpoliticalIslamingovernmentandpoliticalpartycoalitions.Overthelastthreedec‐ades,there‐introductionofIslamismintothegovernmentandpoliticalspherehasledtotensions inthecity,whichare likelytoberepresentedbyconcentrationsorclusters of like‐minded people in distinct neighborhoods. The fault lines can be


mappedandmonitoredovertimetoidentifydriversofinstabilityusingthemahallaastheunitofanalysis.

AlthoughBangladeshisinpartcomprisedoftheworld’sthirdlargestMuslimpopu‐lation,thepopulationitselfishistoricallysuspiciousofpoliticalIslam.Thepopula‐tion’sconversiontoIslamwasaccomplishedthroughSufiordersthatallowedsyn‐creticpractices and tolerance for religiousdifference (Riyaja, 2003,p. 302&304;Ahmed,2009;Ahmed,2010). InherenttotheontologyofmostBangladeshis,then,isabasicacceptanceofothersregardlessofreligiousaffiliation.

TheBangladeshi independencemovement leveraged the indigenousontology thatexpressed cultural and linguistic respect for and inclusion of all religious groupswhetherMuslim,Christian,Hindu,orother(Anisuzzaman,2008).Thefirstgovern‐ment,representedmostpowerfullythroughtheAwamiLeague(AL),espousedsecu‐larsocialismandtheinstitutionsofstatesoughttocreateasenseofinclusionalongnon‐religiouslines.Thefirstconstitutionevenbannedtheuseofreligioninpolitics(Riyaja,2003,pp.301‐302).SecularismintheBangladeshigovernmentcontextwasdefinedasneutralityofthestatetowardreligion,meaningthatitactsasanarbiterguaranteeingequalaccess,notanon‐religiouspolity(Riyaja,2003,p.303).

Bangladeshis also retain an activememory of themillions killed by the PakistanimilitaryandindigenousIslamistgroupsinthewarforindependencein1971. ThePakistanimilitaryattemptedtouseadherencetopoliticalIslamasabasisofnation‐alunification. Riyajaexplains, “TheexploitationofIslambythePakistanicolonialrulerstolegitimizetheperpetuationofthecolonialruleandtheexcessescommittedbythePakistaniArmyandcollaboratingIslamicparties ‘tosavetheintegrityofIs‐lamicPakistan’createdbitterresentmentamongthepeopleagainsttheuseofreli‐gioninpolitics”(2003,p.309).

AcrisisoflegitimacycameearlyforAwamiLeague,duetoaninherentlimitationinits drive to create a national identity. As a cultural and linguisticmovement, theBangladeshiindependenceleadersestablishedaframeworksettingthestagetode‐ny ethnic and tribal minorities a place since the nation was formed for Bengalis(Riyaja,2003,p.309). Thereare,consequently,minoritygroupswhoseontologiesareonesofexclusionormarginalization,suchasUrduspeakersleftasrefugeesafter1971(Kelley,2010)andthehilltribeslocatedpredominantlyintheeasternpartofthecountry(Riyaja,2003,p.309).Underthesecircumstances,integrationthroughreligious identity isoftenseenasameansofgainingaccess topoliticsandoppor‐tunity.

Socialism, nationalism, and democracy comprised the original platform of AwamiLeague,butradicalleftistmembersandpeasantpartiessplitfromALinpursuitofapurescientificsocialistgovernmentandfreedomfromSovietUnionandIndiangov‐ernmentinfluence(Riyaja,2003,p.307;Rashiduzzaman,1994,pp.976‐979).Withtheassassinationofthesecularpresidentin1975duringamilitarycoupd’étatandaftertwoyearsofcountercoups,amilitarydictatorshipunderGENZiaurRahmanwasformed(Riyaja,2003,p.310).

PoliticalIslamre‐enteredBangladeshisocietyin1977.GENRahmandidnotpartic‐ipateinthe1975coup,buthefacedachallengeoffomentinganewnationalidentity


upontakingcontrol.Hispoliticallegitimacywasquestionable,andheneededauni‐fying identity construct other than thenow‐discredited classdiscourse. He chosenationalismagainstIndia, thereligiousidentityofthemajority,andtheroleofthemilitaryinnationalindependence(Riyaja,2003,p.310).Rahmaneliminatedrefer‐ences tosecularism in theconstitutionand inserted language trusting thegovern‐menttothewillofAllah.NationalismwasfromthispointonintertwinedwithIslam(Riyaja,2003,p.303).

In1978Rahman’sgovernmentestablishedstatesupportformadrassasandthein‐troduction of Islamic curriculum in primary and secondary schools, creating theconditions,especiallywithSaudiassistance, fora longertermontologicaltransfor‐mation of Bangladeshi culture to include a stronger Islamist component (Riyaja,2003, p. 311). Though the state began the process of elevating religious IslamicidentityinBangladeshisociety,theprocessdidnoteliminatethebasictoleranceofmostMuslims.In1979,thetwomajorIslamistpartiesgarneredjust20parliamen‐taryseats(8%ofthepopularvote)inthatyear’selection(Riyaja,2003,p.311).

GENErshad,Rahman’ssuccessor,alsousedpoliticalIslamasthebasisofhislegiti‐mization. HeformedallianceswithMuslimreligiousleadersandin1988madeIs‐lam the state religion. The secular oppositionwasunable to effectively resist hiseffortstopoliticizereligionortopplehismilitaryruleuntil1991(Riyaja,2003,pp.301‐302&311‐312). The identityeffectsofnearly fifteenyearsofrule throughamilitary‐religious alliance caused an increase in the number of self‐identifying Is‐lamistsandasignificantriseintheimportanceofIslamicmoralityinevenhistorical‐lysecularparties’discourse.

Traditional centristparties inearly1990s facedanenvironmentwhere Islamwaspartlyconstitutiveofthenationalconsciousness,somostadoptedapoliticalfaceofsupportinggoodIslamicvirtue(Riyaja,2003,pp.312‐313).From1991on,centristpartiesfeltcompelledtoformparliamentarypoliticalallianceswithIslamistparties,thuselevatingtheirstatus.Jaamat‐i‐Islami’sabilitytoattractvotesincreasedalonefrom750,000in1979to4.1millionormorein1991(Riyaja,2003,pp.314‐315).

IncontemporaryBangladeshiparliamentarypolitics,Islamistpartiesaresmall,buttheyenjoysignificantinfluence. Thetwomajorparties,AwamiLeague(center‐leftcoalition)andBangladeshNationalParty(BNP)(formedbyGENRahmanasacen‐ter‐right coalition) (Hossain, 2000, p. 512), rely on the Islamist parties to fill outtheircoalitions,whichelevates their influence innationalpolitics (Riyaja,2003,p.316;Rashiduzzaman,1994,p.984).TheIslamistpartiescompetingforthereligiousconstituency include Jaamat‐i‐Islami, Islami Oikya Jote, Parliament), the IslamicConstitutionMovement,KhilafatMajlish,NationalMusalliCommittee,Ahl‐e‐Hadith,Ulema Committee, Islamic Chatra Sena, Jamiatul Modarassin, Nezami‐i‐Islam, andtheMuslimLeague(Riyaja,2003,p.301;Rashiduzzaman,1994,p.982).

Insum,BangladeshicultureremainsoverwhelminglySufiMuslimandlargelytoler‐ant of other religions, but awell‐organized,motivated, andpolitically empoweredIslamistcoreofpartieshavetheabilitytoshapepublicdiscourseandpolitics.Cen‐trist AL and BNP each attract approximately 35% of the vote for a total of 70%,while themain Islamist parties generate only 20% of the popular vote (Hossain,2000,p.514).


Political instability inDhaka is inpartaresultof thiscentristpoliticaldistributionbecauseALandBNPengageinhartal1politics,orthepoliticsofhyperboleandabso‐lutism. Alliances are generally made for short‐term power considerations, whilediscourse tends to emphasize differences and incite the passions of the people(Hossain,2000,pp.516‐517&521‐522).

ItisnotuncommonforDhakatobeparalyzedbylargedemonstrationsorforpoliti‐calviolencetooccurbasedonhartalagitation.Monitoringnarrativesanddiscourseisparticularmahallascanprovide indicationsof the intensityof tension inDhaka,whichcantheninformhypothesesaboutthetypesofsparksthatmightleadtoactu‐alviolenceandinstability.MappingoutthesupportforeachpartyandassessingthecontentofcontemporarydiscoursebymahallacouldprovideagoodfoundationfortheRSI‐Dhakaeffort.

Dhaka as an Expression of Com‐munity, Urban Sprawl, and Har‐tal: The Politics of Unplanned Growth

Dhaka’s stability as a megacity is afunction of both natural disaster andpolitics. Duetoitsrapidgrowth, loca‐tion in flood and seismic zones, andhartalpolitics,Dhakahasanumberofnatural tensions that could lead to in‐stability. Identifying the substance oftensions and understanding their his‐torical contextwill lead tobetter I&Wofpotentialsourcesofinstability.

Dhaka’slocationinbetweenfourriversmadeitidealasapointofcommunica‐tionandcropproduction,anditshigh‐estelevations, reachingapproximatelyfourteenmetersweresettled firstdueto persistent flooding (Dewan, Yama‐guchi, & Rahman, 2012, p. 317). His‐toricDhakacanbe found in theupperelevations with newer, especially in‐formal,shantytownsbeingconstructedin the flood plains astride the rivers(Figure 2) (Rashid, Hunt, & Haider,2007, p. 96; Dewan, Yamaguchi, &Rahman,2012,p.327). Figure3illus‐trates the surface usage changes be‐tween 1975 and 2005. Dense urbangrowth (orange) progressively ex‐

1 See http://en.wikipedia.org/wiki/Hartal for a full description of Hartal or Hortal.

Figure 9: Figure 9: Location of Slums in Dhaka, Bangladesh


pands over cultivated land at lowerlevels (yellow), leading by 2005 toencroachment on areas extremelysusceptibletoflooding(aqua).1

Dhakaresidents’ identitiesand inter‐ests depend heavily on their originsand current living locations. Resi‐dents living in the upper elevationshaveaccess tohistoricwellsormod‐ern water and transportation infra‐structure. Limitations on economicopportunity and exposure to devas‐tatingnatural elements aremuch re‐duced as a daily preoccupation.Moreover, residents have a higherlikelihoodofenjoyingalongerhistoryinDhakaandmoreestablishedsocialnetworks due to the desirability ofthelocation.

Conversely, residents living at lowerelevationsmostlikelylackbasiccivicinfrastructure and services and aresusceptibletoannualfloodingwheth‐er through periodic river overflowsor normal surface runoff during themonsoon season (Rashid, Hunt, &Haider,2007,p.95;Dewan,Yamagu‐chi, & Rahman, 2012, pp. 317‐318).Whereas historic Dhaka mahallas

were built around common water facilities, slum dwellers must share makeshift,unsanitary toiletsandwater fountainsandare regularlyexposed to industrialby‐products from tanneries, hospitals, and other industrial plants (Kamal & Rashid,2004,p.108). Theirsocialnetworksarealso likelytobeweakerandprovidelessopportunitythanintheestablishedareas.Onestudyestimatesapproximatelyone‐thirdofDhakaslumdwellerstosufferfromoneormorediseases(Kamal&Rashid,2004,p.109).

Nevertheless, recent researchon incentives to relocate slumdwellersoutof floodzones indicates material incentives are insufficient in some cases due to the im‐portanceandstrengthofsocialnetworksandrelationship‐basedsafetymechanisms(Rashid,Hunt,&Haider,2007,p.103).Thisfactcouldindicateanimportantcultur‐al predisposition to valuehuman and familynetworksovermaterial rewards andshould informresearchonhow to effectivelyprovideassistance.Geo‐spatiallyde‐pictingtheresultscouldleadtomoreprecisetargetingofdevelopmentassistance.

1 Foramoredetailedanalysis,pleaseseeAshrafM.Dewan,“VulnerabilityofaMegacitytoFlood:ACaseStudyofDhaka.”SpringerGeography(2013),pp.75‐101.http://link.springer.com/chapter/10.1007/978‐94‐007‐5875‐9_3

Figure 10: Land Use/Cover Changes in Dhaka

Metropolitan from 1975 to 2005


Dhaka is divided into90officialwards, each containingmultiplemahallas. Thereare at least 1,200 slumswithover180,000households inDhaka (Rashid,Hunt,&Haider,2007,p.96).Todissuadeunplannedurbangrowth,thegovernmentperiod‐icallybulldozesslumsettlementsandrestrictstheavailabilityofsocialservices.Intheplaceofgovernment,aslummafiahasevolvedcontrollingaccesstoland,busi‐nessrights,andsecurity(Rashid,Hunt,&Haider,2007,p.103). Geo‐rectified linkanalysisandsocialnetworkanalysiscouldalsoenableanalysisonbarrierstoeffec‐tiveaiddistribution.

Civilsocietyandnon‐governmentalorganizations(NGOs)haveattemptedtofillthegovernance void, providing support for income enhancing activities, basic educa‐tion,andhealthservices(Kamal&Rashid,2004,p.109;Habib,2009).ThisactivityhasbroughtwithitapoliticaldimensionsinceNGOsarecharacteristicallybelievedtosupportthecenter‐left,secularendofthepoliticalspectrum.NGOassistancetowomeninparticularhasbroughtviolencefromfundamentalistIslamistgroupsup‐on providers and recipients, including indigenous NGOs like the Grameen Bank(Shehabuddin,1999,pp.1012‐1013;Rashiduzzaman,1994,p.975).

Socially,Dhaka’sgrowthasanopportunityhubandmanufacturingcenteriscausingtensionsalonggenderlinesaswell.Thesecularsocialist,non‐sectariancharacteroftheindependencemovementcreatedtheconditionsinwhichwomenbelievedtheyshouldbeabletoseekopportunity.Combinedwiththeeconomicnecessityofwom‐en seeking industrial employment, Bangladeshis are locked in a cultural strugglewithIslamistpartiesovertheacceptabilityofwomenwalkingtoworkandmovingbeyondtraditionalsocialspace(Rashiduzzaman,1994,p.989).ThefactthatNGOssupportmany of these initiatives also politicizes their ability to aidDhaka’smostvulnerable.

Playinguponthesenaturalandsocialvectorsof instabilityarethepoliticalpartiesrootedinhartalpolitics.Liberalsandcentristsarecomprisedofthetypicaleducat‐edelite inthearts,media,politics,andNGOcommunitywhiletheIslamistscanbefoundamongmadrassastudents,professionals,militarymembers,andcivilservants(Rashiduzzaman,1994,p.976&983). Surprisingly,thereisagrowingcadreofIs‐lamist ormore piouswomen found among the educated and professional classes(HuqS. , 2011;HuqM. ,2008). GivenDhaka’s relianceon industrial, educational,andNGO‐relatedactivities,itsitssquarelyinthemiddleofBangladeshisocialpoli‐ticsandhasahighpropensityforprotestanddisruption.

Conclusion

Understandingthecultureofapopulationrevealsmuchabouttheform,design,andfunctionofcityspace.Inthiscase,Dhaka’sbasicdesignfollowstheformandfunc‐tionofBangladeshiontology,whichremainsrootedinacommunity‐based,familialorganizationunit.Thoughtrappingsofmodernitylieoverit,themahallamatterstoDhaka’spopulationandcanserveasabasicunitofanalysisfortheRSI‐Dhakapro‐ject. Instability inDhakawill result frombothsocio‐politicalandnatural sources.Mapping out the political interests and alignments of the population will revealmuchabouttheintensityoftensionsbetweengroupsovertimeandallowforbetterdatacollectionalongtheway.Similarly,understandingthespecificneedsofpopula‐


tionsbymahallawillpermitbettertargetingofdevelopmentassistancetoovercomethenaturaldriversofinstability.

TheM‐RSIprojectcanbenefitmarkedlybyconductingsocio‐culturalanalysisonapopulationtorevealtheculturallogicofcitydesign.Dhakaisaparticularlyinterest‐ingcasebecauseitfusesatraditionalontologywithmoderncitydesign.Fortunate‐ly,muchoftheresearchforgeneratingbaselineunderstandingsofapopulational‐readyexistsinacademia.FutureM‐RSIeffortscanbenefitsimilarlyandmakegooduseofsubjectmatterexpertsinthedesignphases.

References

Ahmed,S.J.(2009).PerformingandSupplicatingManikPir:InfrapoliticsintheDo‐mainofPopularIslam.TDR:TheDramaReview,51‐76.

Ahmed,S.J.(2010).MimicryandCounter‐DiscourseonthePalimpsestofNagori:ThePlayofSaintAnthonyandHisDouble,SadhuAntoni.AsianTheatreJournal,40‐75.

Anisuzzaman.(2008).ClaimingandDisclaimingaCulturalIcon:TagoreinEastPaki‐stan.UniversityofTorontoQuarterly,1058‐1069.

Dewan,A.M.,Yamaguchi,Y.,&Rahman,M.(2012).Dynamicsoflanduse/coverchangesandtheanalysisoflandscapefragmentationinDhakaMetropolitan,Bangladesh.GeoJournal,315–330.

Habib,E.(2009).TheRoleofGovernmentandNGOsinSlumDevelopment:TheCaseofDhakaCity.DevelopmentinPractice,259‐265.

Hossain,A.(2000).AnatomyofHartalPoliticsinBangladesh.AsianSurvey,508‐529.Huq,M.(2008).ReadingtheQur’aninBangladesh:ThePoliticsof‘Belief’AmongIs‐

lamistWomen.ModernAsianStudies,457–488.Huq,S.(2011).DiscussingtheBangladeshRifles(BDR)rebellion:non‐Islamist

womenandreligiousrevivalinurbanBangladesh.ContemporaryIslam,267‐283.Kamal,N.,&Rashid,R.(2004).EnvironmentalRiskFactorsAssociatedWith

RTI/STDSymptomsAmongWomeninUrbanSlums.TheJournalofDevelopingAreas,107‐121.

Kelley,N.(2010).Ideas,Interests,andInstitutions:ConcedingCitizenshipinBangla‐desh.UniversityofTorontoLawJournal,349‐371.

Mowla,Q.A.(1997).SettlementTexture:StudyofaMahallainDhaka.JournalofUr‐banDesign,259‐275.

Rashid,H.,Hunt,L.,&Haider,W.(2007).UrbanFloodProblemsinDhaka,Bangla‐desh:SlumResidents’ChoicesforRelocationtoFlood‐FreeAreas.EnvironmentalManagement,95‐104.

Rashiduzzaman,M.(1994).TheLiberalsandtheReligiousRightinBangladesh.AsianSurvey,974‐990.

Riyaja,A.(2003)."GodWilling":ThePoliticsandIdeologyofIslamisminBangla‐desh.ComparativeStudiesofSouthAsia,Africa,andtheMiddleEast,301‐320.


Shehabuddin,E.(1999).ContestingtheIllicit:GenderandthePoliticsofFatwasinBangladesh.Signs,1011‐1044.


ChapterFour,BlackSpotsAreNoTreasureIsland:LandTenureandPropertyRightsinMegacities

DouglasE.BatsonNationalGeospatial‐IntelligenceAgency

[email protected]

Abstract:

Thischapteraddressesthegeographicphenomenaofmegacities,urbanareaswithoverapopulationover10million.Itexaminesthepaceofrural‐to‐urbanmigration,itspromisesof abetter livelihood for180,000whodailymigrate to cities,but forbillionsofpeopleinthecomingdecades,theperilsofslumproliferation.Theterm"Black Spots" is borrowed from Bartosz Stanislawski to describe urban areas be‐yond the reach ofmunicipal governance.Megacity slums dwellers lack basic ser‐vices, butnone sodebilitating as a lackof land tenure andproperty rights. Threenewtoolsarediscussedwhichcanhelpdecodethehumangeographyofmegacitiesand thus inform future urbanoperational decisions: an international standard forlandadministration,volunteeredgeographicinformation,anddynamicmapdatatosupporthumanitarianmissions.

Keywords

Urbanization, urban, city, megacity, ungoverned space, demographics, slum, landtenure, property rights, governance,migration, development, land administration,humanitarianassistance,disasterresponse,volunteeredgeographicinformation.

Introduction

ThecityofLondon'spopulationincreasedfromonemilliontoitspeakofeightmil‐lionon theeveofWWII;aprocesswhich tookplaceover thecourseofa century.Karachi,Pakistan,however,addedmorethaneightmillionnewresidentsinjustthelastdecade!Withapopulationofover23million,Karachi isnowthethird largestcityintheworld!CentenariansinBangladesh'scapitalcityhavewitnesseda30‐foldpopulation increase in their lifetimesthathasalso transformedDhaka intoameg‐acity,definedbytheUnitedNationsaspopulationcentersofover10million.Securi‐tyexpertRobertMuggahreferstothisbreakneckpaceofrural‐to‐urbanmigrationas"turbourbanization,"andfurthersuggeststhattheglobalriseofmegacitiesoffersboth promise and peril. Megacities are increasingly becoming the focal point foreconomicgrowth,diplomaticwrangling,anddevelopmentalaid(Muggah2013).In‐deed,oneweekafterMuggahpostedhisarticle,MegacityRising,theU.S.AgencyforInternational Development (USAID), shifted its 50‐year focus on alleviating ruralpoverty to include stark urban realities. Associate AdministratorMark Feierstein,uponreleasingUSAID's"SustainableServiceDelivery inan IncreasinglyUrbanizedWorld"policy,"notedthat

Onebillion people currently live in slumswithout access to basic serviceslikecleanwater,electricityorhealthservices.USAID’sfieldprogramsneedtoplanforthechallengesthatcomealongwithrapidurbanizationsowecanmoreeffectivelyhelpthesecommunitiesreachtheireconomicpotentialand


alleviate poverty...An estimated 180,000 peoplemove into citieseachday(boldfontmine).ThispolicywillhelpUSAIDandthedevelopmentcommu‐nitytorespondtotheneedsofthe1.4billionwhowillhavemovedintour‐banareasbetween2011and2030(USAID2013).

In the 1970s, there were only threemegacities on planet earth; bymany countstherearenow24ofthesemunicipalbehemothswithanotherdozenexpectedtoap‐pearoverthenextdecade.Thispaperexaminesoneaspectofthesecurityandgov‐ernance perils posed by rapid urbanization andmegacity growth. It also suggestsnewassessmenttoolsinordertoanalyzeandunderstandthesocio‐politicaldynam‐ics that can informnewgovernancepolicies andprocesses suited to themegacityphenomenon.

Black Spots

InthepirateloreofRobertLouisStevenson’TreasureIsland,blackspotssymbolizedthe collective, malignant will of miscreants. When rural‐to‐urban migration istrackedbybusiness,voter,vehicle,propertyandothercivilregistrations,migrantsand the gainingmunicipality both stand to benefit. However, when local govern‐ments remain uninformed about new arrivals and their needs for basic services,muchmoreforebodingBlackSpots,butwithnolessgroupmalice,inevitablyform.Dr.BartoszStanisławski,oftheMaxwellSchoolofCitizenshipandPublicAffairsatSyracuseUniversity,definestheseatrocious,geographicalBlackSpotsas

parts of the world that are (1) outside of effective governmental control;(2)controlled,instead,byalternative,mostlyillicit,socialstructures;and(3)capableofthebreedingandexportationofinsecurity(e.g.,illicitdrugs,con‐ventional weapons, terrorist operatives, illicit financial flows, strate‐gic/sensitiveknow‐how)tofarawaylocations.Similartothenotionof“blackholes”inastronomywhicharelocatedmainlybyanalyzinganomalousgravi‐tyfields,blackspotsarealsodifficultto“see,”astheyusually,orforextend‐ed periods of time, operate with a high degree of “international invisibil‐ity”(Stanislawski2010).

ButtheproactiveStanislawskiismorethanamerecoinerofnovelterms.HisMap‐pingGlobalInsecurityprojectisbestviewedasan“intelligencesupportactivitythatthroughresearchandanalysiscanprovidereal‐timecrisissupport,enablenewandunique insights, ‘tip’ other intelligence collection, ‘cue’ further classified researchandanalysis,andleadtothediscoveryofnewrequirementswithintheIntelligenceCommunity(IC)”(Stanislawski,2013).HefurthercontendsthatBlackSpotsarenotthesameasfailed,failing,orweakstates,andassuch,createverydifficultsecuritychallenges. Indeed,BlackSpotsarenothingtobetreasured;theyareunchartedis‐lands in billowing seas ofmassed humanity, imperceptible to the IC’s classic sen‐sors.His project uniquely discoversBlack Spots through the triangulation of dataregardinganomalouseventsandtransfersinparticularregions.“Mappingthemal‐lowsustobeonestepaheadoftheso‐called‘globalbads’…itisourtasktoscanforthem,pinpointthem,andmonitorthem”(Stanislawski2013).

Dr.GeoffreyDemarest,aresearcherattheU.S.ArmyForeignMilitaryStudiesOffice,wouldbepleasedwiththeworkofStanislawski’sMappingGlobalInsecurityproject.


For over a decade, Demarest has “advised industrial‐level GIS (geographic infor‐mation systems) cadastral (land and property registry) projects for countries ofspecialinterest,”(Demarest2004)andpleadedfortheU.S.andUNnottocontinuetoplacepropertyformalizationonasecondaryplaneofgoals.Apolitythatdoesnotformalizeownershiprightsandduties,especiallyrightsanddutiesrelatedto land,will not enjoy peace. Comprehensive, precise and transparent expression of realproperty is a necessary precondition of peace, adjuresDemarest. “Places outsidethe lines of formal property necessarily slump toward possession by force….Theprocessof formalizingproperty,moreover, illuminatespowerandpowerrelation‐ships.Italsoexposestheotherwiseinvisiblelinesofcommunicationandsanctuarythatpoweroverplacesprovides”(Demarest,2008,p.iii).Demarestfurthersketcheshowillegalarmedgroups(IAG)pursueandenjoyeightprincipal,overlappingusesofurbanslum land in relation to their illicitpursuits.Theseeight landuses, innoparticularorder,are:1.taxation;2.freetrade;3.sanctuary;4.clandestinemanufac‐tureorprocessing;5.stagingforviolentoperationsoutsidetheslum;6.safetransitofcontraband;7.recruiting;and8.asaprisonorgraveyardfortheirvictims.

Theeightcategoriescouldbeusedaspartoftaxonomyforgeographicprofil‐ing(predictivegeographicforensics).TheIAGland‐usecategoriesaresuita‐bleasvariables(fielddescriptionsorattributenames–thetitlesofthecol‐umnsatthetopofanSQLspreadsheetperhaps)inaforensicpolice/militaryGISdatatable.WhilesuchuseofGISmaybethemostimmediateordirectlyrelevantapplicationtogovernmentreductionofillegalarmedgroups,otheruses,suchasinformingurbanbuildingandstreetdesign,mayyieldthemoreimportantlonger–termsecuritybenefits(Demarest2011).

Demarest, a noted Colombia analyst,would likewise be pleased by “TheMedellinMiracle” (Colby 2012). The city’s grim past of violence, drug lords, guerillas, andparamilitarydeathsquadshasbeenradicallytransformedbypoliticalwillandsocialinclusion,fromthemostdangerouscityintheworldtoashowcaseofurbanrenewalandevenhostoftheUN’sWorldUrbanForumVIIinApril2014.

A Plurality of Urban Power Brokers

Overthepastdozenyears,theU.S.hasbeenmilitarilyandpoliticallytransfixedonIraqandAfghanistan.Asaresult, stupendoussocio‐demographic trends in thede‐velopingworldhavereceivedscantnotice.Theseincludetheaforementionedrapidurbanization:70millionnewurbanitesannually,theequivalentoftwonewTokyos,appearon theearthscape(Engelke2013); theemergenceofsub‐stateentitiesandprivatizedversusstate‐deliveredservices;and,fromurbancenters,wherepoliticalconsciousness is first formed, the growing demand for participatory governancethatsparkedtheArabSpring.Indeed,futureoperationalenvironmentsarescarcelyrecognizablefromthepre‐9/11era.

Recentrural‐to‐urbanmigrationhas resulted in23urbanagglomerations thatcanbeclassifiedasmegacities.By2025therewilllikelybe37megacities(Jones2012.p.38).Despite a rapidly risingmiddle class, onebillionurbanites ekeout a living ininformal settlements (slums)worldwide (UNHABITAT 2005). Slumdwellers lackinfrastructure; yes, photographs of urban squalor are shocking, but more signifi‐cantly, theylackanydocumentable landtenureandpropertyrights.Terrorists, in‐


surgents,crimebosses,narcoticsandhumantraffickers,andslumlordswellunder‐standthepowerinformalsettlementsofferforthetaking.Harrowingscenesfroma1992 film (Cityof Joy)depicthoweagerlynefariousactors await thenextbillion‐personvulnerablecohortexpectedtojointhecurrentonebillionslumdwellersby2030(TheBostonGlobe2012).Unimaginably,Africa,thelastcontinenttourbanize,by2030maybecomefiftypercentmetropolitanaswell(Jones2012,p.37).Withoutlittletoanyindustryprovidingthenewcomerswithjobsandlivelihoods,theynev‐ertheless organize themselves into place‐based ethnic enclaves, informal shadoweconomies,and intricatesocialnetworks.Thegovernancevacuum, to include landgovernance,isquicklyfilledbyotheractors.

Fundamentally, landgovernance isaboutpowerand thepoliticaleconomyof land.Land tenure is therelationshipamongpeoplewithrespect to landanditsresources.Therulesoftenuredefinehowaccessisgrantedtorightstouse,control,andtransferland….theydevelopinamannerthatentrenchesthepowerrelationsbetweenandamongindividualsandsocialgroups(FAO2009).

Shieldedbyanonymityand impunity,purveyorsof instabilityhide inunregisteredproperties,andusethewealthhiddeninthosepropertiestofundtheirillicitactivi‐ties. Theydecidewho getswhat by imposing their own “property ownership” re‐gimesonthelocalpopulace;theyofferservicestooffendedclaimants,createloyal‐tiesandobligations,andsow fear (Demarest2008,pp.277,286).Thisdynamic isillustrated in Sandra Joireman‘s research on property rights enforcementmecha‐nismsinaNairobislum.Sheidentifiedthreesub‐stateentitiesinKibera―apocketofstatelessnesslocateddirectlyinthegeographiccenterofpowerinKenya―thathaveemergedtofillthevoidleftbyastatethatlacksthepoliticalwilltoresolvelanddisputes.Non‐governmentalorganizations(NGOs),thefirsttypeofnon‐stateentity,conductalternativedisputeresolutionbecausethegovernmentisperceivedasaloof(offeringbureaucratic forms to filloutatunaffordablecosts), ethnicallybiased,orcorrupt (demanding bribes). The second alternative is government officials who,outsidetheirformalauthorities,misusetheirpositionstoresolvedisputesforper‐sonal enrichment. The notorious, third option is ethnic gangswho run protectionrackets and use violence and intimidation on behalf of clients seeking redress(Joireman2011).

ProfessorSethG.Jonescitesanumberofreasonswhyinsurgentshavehistoricallyfavored rural areas as their base of operations. This assumption alsowill have toadapt to21st century changes: insurgentsand their ilkmaywell shift theiropera‐tionstourbanareasinordertoavoidthelethalscrutinyofdroneaircraft.Jonesalsoascribes to rural areas theadvantage that “government security forces oftenhavebetter intelligence penetration in urban settings” (Jones 2012). That rings truewheregovernmentsprizetransparencyofpropertyownershipandlanduse.Sadly,thatisnotoftenthecasefortheslumsoftheworld’smegacities,wheregovernmentofficials(ortheirwell‐placedrelatives)benefitfromthestatusquointermsofsocialpowerandprofitablerentschemes.Dr.PeterEngelke,SeniorFellowattheAtlanticCouncilandco‐chairoftheUrbanWorld2030workinggroupcontendsthatmegaci‐tiesarethemostcomplexprobleminthehistoryofhumankind.“Wehaveseenthefutureand it isurban!” (Engleke,2013)Otherssharehis concern “thatpoorplan‐ningandgovernanceofdevelopingworldmegacities‐‐‐includingthefailuretoposi‐


tively engage slum dwellers‐‐‐will both diminish national economic growth andleavebehindahugeurbanunderclass” (LiottaandMiskle2012) (Commins2011)(Davis2006).

Yet Engelke offers a prescription: “the world’s foreign and security policy estab‐lishmentsmust not only becomemore cognizant ofmass urbanization, but begincreatingtheprocessesthatwillproductivelyintegratecitieswithglobalgovernancestructures”(Engleke,2013).Dr.JamesKnotwell,aRegionalandGeospatialScholarwiththeCulturalKnowledgeConsortium(CKC),recentlysurveyedmegacity litera‐ture in four informativeCKCblogs (Knotwell,2013).Witha toneconcordantwithEngelke’sanalysis,heappreciateswhatliesbeyondthepervasiveimagesofmegaci‐typoverty.

Theseimpoverishedurbangroupingsare,atthesametime,highlyenterpris‐ingandresourcefulincreatingtheirowninformalinfrastructuresandinte‐grating these in themore formal, state‐originated, networks of assistanceand support.This demonstrates a critical change in analytical perspectivethat leadsdecision‐makersofpotential interventions,betheydevelopmentorsecurity‐related,inadifferentdirectionfromthosesubscribingtoformerCDP[colonial/dependency/poverty]explanatorymodels,re‐orientingtheseinstitutions (which will include military/NGO) toward the locali‐ty/neighborhoodanditsinherentpowerstructureandawayfromtherepre‐sentativesofstatepowerbrokers.Italsodemonstratestheutilityofinvest‐ingresourcesinidentifyingthoselocalpowernetworks(Knotwell2013)

AccordingtofuturistMikeDavis,“Slumpopulationscansupportabewilderingvari‐ety of responses to structural neglect and deprivation, ranging from charismaticchurchesandprophetic cults toethnicmilitias, street gangs, neoliberalNGOs, andrevolutionary socialmovements.” How futureU.S. defense, diplomacy, anddevel‐opmenteffortsmightidentifyandpositivelyengagemegacitypowerbrokersisthesubjectofthenextsection.

Megaproblem: 4.5 Billion Unregistered Properties

By2030,sixtypercentoftheworld’spopulationwillliveincities,withthemostex‐plosivegrowthoccurringindevelopingcountries(UN2013).Conflictsinmegacitieswill inevitably occur and they will be increasing horrid. Future U.S. operationsabroad,ofanyscale,willnowmostlikelyencountermultipleactorsindense,urbanenvironmentswhere shaping andwinning the informationdomain invariablywillincludearobustknowledgeofhowpersonsaretiedtoplaces.

Landadministration,adisciplinescarcelyknownintheU.S.,istheprocessofdeter‐mining,registering,anddisseminatinginformationabouttherelationshipbetweenpeople and land (ISO 2013). Until a decade ago the preferredway to confer landrightsuponindividualsandgroupsacrosstheglobehadbeenthroughformal landtitlingadministeredbythestate.Anewparadigmhasrecentlyemerged,recognizingacontinuumofrightsandinterestsinland,oneinclusiveofindividualsandgroupswholiveinmegacityslumsandotherareaswhereformaltitlesarenotthenorm,ornotaccessibleoraffordable.Registeredtitle(ordeed)ownershipof land,commonin only 50 or so developed countries, account for 1.5 billion landparcels. The re‐


maining4.5billionof theworld‘sestimatedsixbillion landparcelsareheld infor‐mallyandarethussusceptibletodisputes, landgrabs,environmentaldegradation,foodinsecurity,andsocialunrest(McLaren2011).

Figure 11: Continuum of Land Rights.

Thus,thecontinuum,fromlefttoright,beginswithinformallandrights,whichareusuallyoralagreements,andthencustomarytenures,bothrelieduponbypoorandmarginalized populations (Deininger et al, 2011). Certificates of occupancy,whilenot enabling the holder to sell or lease, do provide ameasure of tenure securityagainstpredatoryslumlordsandunscrupulouscustomaryorgovernmentauthori‐tiesbentoneviction.Grouptenureismucheasiertosecurethanindividualtenure.Efforts to formalizegroup tenures in landadministrationsystems(LAS)havealsodonemuch to improve the livelihoods of people at risk of losing their communalland rights in land grabs and otherwicked schemes. The authors of a primer ongloballandadministrationconcur:

LASareaboutformalizingtenure,irrespectiveofitslocalformandcontent,whether it’s short‐term occupation rights or full ownership. Simply, landadministrationisaboutformalsystems.Wedon’tapologizeforthis.Weac‐ceptthat informalsystemsareessentialpartsofanysystemofsociety,butwithout organizing a coherent, formal system for administering land...acountryorsocietywillbedoomedtopoverty.Thisdoesnotmeanthatthatthe formal systemneeds tobe complicated,national in scale, or expensive(Williamsonetal2010).

Cadastres provide a degree of systematic clarity in identifying owners, spatial ex‐tents,andrightsandinterestsoverland.Whenofficialorauthoritativedataislack‐ing, an increasingnumber of land agencies are recognizing the potential utility ofcrowd‐sourced information.Volunteeredgeographic informationcanbeharvestedtohelpaddress‐‐‐andredress‐‐‐thebillionsofunregisteredlandrightsandinterests


thatneedbettervisibilityintheimmediateterm–regardlessofwhethertheyrepre‐sent full ownership or not (McDougall et al 2013). Moreover, there are now ad‐vanced Information Communication Technology capabilities that can accomplishsuchaherculeanfeat.Thefinalsectionexaminesthreeofthese.

Tying People to Land Interests: The Human Geography of Megacities

ThehundredsofmillionsofpeopleinformallyresidinginBlackSpotsareoftenin‐visibletotheirgovernmentsandinternationalactorsbecausetheirsecondarylandrights are not documented. Fortunately, three new tools have recently becomeavailabletohelpdecodethehumangeographynarrativeofmegacities.

An International Standard for Land Administration

A decade‐long effort in land administration recently achieved a strategic break‐throughand,onapercapitabasis,informalsettlementpopulacesstandtoreapsub‐stantial benefits. In November 2012, the Land Administration Domain Model(LADM) became the first international standard for the unsung discipline of landadministration.ISO19152providesanabstract,conceptualmodelwiththreepack‐agesrelatedto:

parties(peopleandorganizations);

basic administrativeunits, rights, responsibilities, and restrictions (owner‐shiprights);

spatialunits(parcels,andthelegalspaceofbuildingsandutilitynetworks);with sub‐packages for spatial sources (surveying), and spatial representa‐tions(geometryandtopology)(LemmenandvanOosterom2013)

BasedontheLADM,theSocialTenureDomainModel(STDM)isaninitiativeofUN‐HABITATtoaddresslandtenuregapssuchasthosefoundinBlackSpots.Itidenti‐fiesrelationshipsbetweenpeopleandlandindependentoflevelsofformalizationorthelegalityofthoserelationships.TheSTDMenablescomparisonsofpeoples’holdonlandacrossculturalandpoliticalboundaries. Thelinkageofpeopletoinformalsettlements isdoneby capturingbestavailableevidence: waterandelectrical re‐ceipts,groundphotos,recordedoraltestimony,copiesof leases,etc.BypotentiallyincludingeveryhumanbeinginsomeformofLAS,theSTDMcancontributetopov‐ertyreduction,asthelandrightsandclaimsofthepoorarebroughtintotheformalsystemover time. Itopensnew landmarkets,andaidsdevelopmentbyequippingurbancommunitieswithlandmanagementskills.WhiletheadventofLADM/STDMofferscheerynews forslumdwellers,spatialentrepreneur JillUrban‐Karradmon‐ishesthatturningtheLADM/STDMfromaconceptualintoalogicalmodelthatde‐scribes cadastral data requirements for an actual urban environment is an oftenoverlookednextstepforISO19152’simplementation.Tothisend,sheandhercol‐leagues examined data correlation between LADM‐based logical models and casestudies in Belize and Victoria, Australia. Their evaluation positively compares thecasestudiesagainstthesamemodelinanArcGISgeodatabase(Kalantarietal2013).


Volunteered Geographic Information (VGI)

Black Spot residentsmay, at times, be invisible, but they are no longer silent. In2008, thenotoriousNairobislumofKiberawaswrackedbypost‐electionviolenceanddisplacement.At the time, the slumof at least250,000Kenyanswas “ablank[black] spot on themap until November 2009,when young Kiberans created thefirstfreeandopendigitalmapoftheirowncommunity.Landho!TheexcitementfeltbyStevenson’sseafarersuponsightinglandfollowingalongseavoyagewasrepli‐catedbyaquarter‐millionKiberaslumdwellers,who“saw”theirlanddepictedonachart for the first time. Map Kibera has now grown into a complete interactivecommunityinformationproject(MapKibera2013).MilitarygeographersattheNa‐tionalGeospatial‐IntelligenceAgency(NGA)vettedtheMapKiberadata,toincludespatial extents of the named neighborhoods of this extraordinary participatorymappingproject.Citing it as “best availabledata”overKibera, theydeviated fromtheirdecadesoldpracticeofusingonlyofficialhostnationgovernmentcartographicsources, and coded the Kibera neighborhood names as U.S. Board on GeographicNames (BGN)‐approved toponyms.NGA added theMapKibera place names to itspublicallyavailableGeonetNamesServer(NGA2013)weeksbeforetheMarch2013Kenyanelections(FosterandBatson2013).HadKiberaagainconvulsedinmayhem,first responders and international actors would have been much more informedaboutKibera’shumangeography.Thatpoliticalviolenceinthe2013electioncyclewasminimalwasnoaccident.MapKiberaandotherKenyanNGOsbandedtogethertoformtheKiberaCivicWatchConsortium,anetworktorespondtoandcoordinatethecommunity’sefforts tomaintainpeaceandprovide interventionswherepossi‐ble.AccordingtoEricaHagen,co‐founderofMapKibera,“asea‐changeisunderwayintermsofhowpeopleengagewith information inKenya: theyfeel it’s theirrightandresponsibilitytospeakoutandtoprotectpeacebycounteringrumor;andtheyincreasinglyfeeltheyhavetoolswithwhichtodoso”(Hagen2013).

Oneofthosetoolsisthemobiletelephone;inmegacities,theyaremanytimesmoreprevalent than toilets. Crowd‐sourced datamaven RobinMcLaren notes how cellphonesprogressivelyintegratesatellitepositioning,digitalcamerasandvideocapa‐bilities.Inthehandsofeventhepoorestslumdweller,theyprovidetheopportunitytodirectlyparticipateinthefullrangeoflandadministrationprocessesfromvide‐oingpropertyboundariestosecurepaymentoflandadministrationfeesusing“mo‐bile”banking.Buteventoday’ssimplerphones…wouldallowapartnershiptobeestablishedbetweenlandprofessionalsandcitizensandwouldencourageandsup‐port citizens to involve themselves in directly capturing and maintaining infor‐mation about their land rights. Crowdsourcing initiatives in land administrationmay coalesce into a much wider open data phenomenon similar to the globalOpenStreetMapinitiative.Ifthishappenedthenafreeandopensourcesoftwareso‐lution to store and manage the crowd‐sourced land administration informationwouldbecreatedandpopulatedbyvolunteers(McLaren2013).

MoreguardedintheiroptimismaboutthevalueofVGIforlandadministrationareAndrewFrankandGerhardNavratiloftheTechnicalUniversityofVienna.TheAus‐triangeoscientistsbelievethatVGIcanprovideinformationontopicswheredirectobservation ispossible, forexample,occupationanduseof land.Rightsover land,however, the forte of landadministration, cannotbeobserveddirectlyby citizenswithlocalknowledgebecausetheyarenotobservable.Theysuggestthatpublically‐


sourceddatamightprovideanadvantageousperspective,e.g.,landusedataincon‐trasttolandcoverdataprovidedbyremotesensing,andserveassupportinginfor‐mation to VGI (Navratil and Frank 2013). In any case, the activities surroundingcrowdsourcingandVGInotonlyservetheprimarypurposeofdocumentingthespa‐tialandsocialdimensionsofBlackSpots,theyalsobuildthepolitical,andevenen‐trepreneurial,capacityofwomen,disabled,andyouth‐‐‐thepoorestofthepoor.Ofallthedeprivationsslumdwellersface,politicalexclusionisthemostdestabilizingformegacities.

Dynamic Map Data Supporting Humanitarian Assistance & Disaster Response

Third,while U.S. government agencies, especially those taskedwith humanitarianassistanceanddisasterresponse(HA/DR),wouldagreethatlocalknowledgeshouldinform operational decisions; they nevertheless wrestle with how to treat volun‐teered versus “authoritative” geographic information. The unorthodox answer tothisdilemma isnot“either/or”butratherboth! TheRapidOpenGeospatialUser‐drivenEnterprise(ROGUE)isaninteragencyprojectunderdevelopmentbytheU.S.ArmyCorpsofEngineers.ROGUEoffersthecapabilityfororganizationstocollabo‐ratively develop,manage, and share geographic feature datawith traditional andnon‐traditionalpartners.Thisnew,rogueparadigmallowsauthoritativegeographicinformation andVGI to coexist. An improvedOpenGeo Suite ingests, updates, anddistributes non‐proprietary feature data utilizing open source software and openstandards‐‐‐suchastheLADM/STDM.

Theauthoritativeorganizationcanmakecertified copies (versions)availa‐ble, andmost userswould clone or fork it according to their needs. Theycouldwork on a dataset outside themain repository or push the changesback to the authoritative organization. Either way, those individuals inchargeofthecentralrepositorywouldbeabletopullthenewchangesandincorporatethemintotheirqualityassuranceprocessbeforepublishingthechangesasanupdatedversion(Clarketal2013).

ByintegratingthesecapabilitieswithPacificDisasterCenter’sDisasterAWAREplat‐formandtheDepartmentofStateHumanitarianInformationUnit,theDoD,andmis‐sion partners, are able to plan, analyze, and collaborate using dynamicmap datasupportingHA/DRandothergeospatial collaboration scenarios.ThedevelopmentofGeoGit(asetofdatarepository‐creatingutilities)isthecornerstoneofROGUE.Itallows fordistributedcollaborationandversioningofgeographicdata.GeoGitwillprovide the ability tomaintain a history of the changes to geospatial vector data,trackwhomadethechangesandwhen,andstorecommentsonthereasonsforthechanges.

GeoGit is fundamentally different from previous geospatial versioning ef‐forts in that it is designed to support distributed operations at its core. Itdoes not rely on network connectivity to be fully functional. GeoGit is de‐signed to handle projects that have a very large number of contributors(Clarketal2013).

ROGUEseeksto improvemechanismsfortracking,managing,andcuratingdata. Itpositsthatcollaborationismaximizedbyputtingtheuseratthecenteroftheinfra‐


structureandencouragingdatacontribution.Perhapsmoreimportantly,enhancedsituational awareness is gained from a bevy of internal and external viewpointsaboutthedataandcurrentgroundtruth.

Figure 12: The ROGUE Approach Courtesy of LMN Solutions

Conclusion

Intheworld’sBlackSpots,powerbrokersriseandfallwithlittlenoticebythoseinofficialpositionsofpower.Butasmegacitiesare increasinglyengorgedwithmorepeoplewhoreceiveinadequatemunicipalservices,akeynon‐stateactor's(anindi‐vidualorgroup)demisehasthepotentialtocollapsenotonlyacity,butanalreadyfragilestateconnectedtothemassiveurbanengine.Remotelysensed imagerycandetectanumberofphysicalandhuman“patternsof life”changes.ButBlackSpotsleave little evidencewithwhich to gaugewhat is normal to beginwith, namely, adearthofinformationonwhooccupiesandcontrolsland.

Dhaka,Bangladesh,afast‐growingmegacityofover14millionresidents,issituatedontheworld’slargestriverdelta,theGanges‐Brahmaputra,effectivelysurroundingitbywater.Itisthuspronetomultiplenaturalhazards,namely,monsoonfloodingandocean typhoons. “Ifwaterdoesn’t pose enough risk,Dhaka sits close to threeactiveseismicfaultlines–theMadhupur,Dawki,andHimalayanfaults–forwhichabigtremorisoverdue”(OpenCitiesProject2013).Dhaka’s5.5millionslumdwellersareatgraveriskforamega‐disaster.Fortunately,Dhakaisamegacityinwhichpub‐


lically‐sourceddataisobtainable.TheOpenCitiesProjectgraphicallyportraysdataon Dhaka's building conditions, inhabitants, and associated risks; building plans,use,andotherinfrastructureuse.Atotallyseparteeffort,GlobalHousingIndicators(GHI,)createsdataforhousingpractitionerstouseasastandardwaytocollectin‐formationabouthousingpoliciesacrosscitiesandcountries(GHI,2013).AccordingtoRobertLopez,ahumangeographeratNGA,crowdsourcingcouldwell reconcilediscrepanciesbetweenGHI’srecentlypublishedfullassessmentofDhaka’shousingandOpenCities’metadataonDhaka’sbuildings,andthusalertcitizensandmunici‐palauthoritiestothepeopleatgreatestperil.

Similarly, the LADM/STDM can overcoming previously vexing constraints by ena‐bling municipal authorities to record informal land tenures alongside statutoryones.CrowdsourcingandVGIcanalertthemtowhichmegacityneighborhoodsde‐mandattentionsothattheymightnotfallundertheswayofrogues.Finally,ROGUEcaninformUSGoperationaldecisionswithbothvolunteeredandauthoritativegeo‐graphic information.Acentury‐longprecedent inUSGexists forsuchanapproach.By publishing millions of BGN‐approved foreign place names and their standardspellings,butalsomillionsofvariantandunverifiednamesofgeographic featuresusedby ethno‐linguistic groups beyond a nation’s official language,NGA’sGeoNetNamesServerisatreasuretroveofhumangeographyinformation.Thecomplexityofactorsandfactorssurroundingmegacitylandmattersdemandsthefusionofvol‐unteeredandauthoritativeinformation.Newtoolsareavailabletoharvestthat in‐formation and extend governance to the globe’s Black Spots. The JollyRoger willthen,asitdidcenturiesago,flyonlyoverafewuncharteddesertislesandnotoverpopulousmegacityenclaves.

References

CityofJoy.(1992)StarringPatrickSwayze,PaulineCollins,OmOuri.WrittenbyDominqueLapierre(book)andMarkMedoff(screenplay).DirectedbyRolandJoffe’.TristarPictures.

Clark,S.,Mesdaghi,S.,Palumbo,D.andHolmes,C.(2013,August21).RapidOpenGeospatialUser‐DrivenEnterprise(ROGUE)JointCapabilityTechnologyDemonstration(WhitePaper).Reston,VA:LMNSolutions.

Colby,S.(2012).“TheMedellinMiracle:VotingOutViolenceandInequality”inCitiesLeadtheWayforGlobalChange,SAISPHERE.ThePaulH.NitzeSchoolofAd‐vancedInternationalStudiesoftheJohnsHopkinsUniversity,WashingtonDC,pp.48‐51.

Commins,S.(2011,April).UrbanFragilityandSecurityinAfrica.AfricaSecurityBriefNo.12.TheAfricaCenterforStrategicStudies,pp.1‐7.Retrievedfromhttp://africacenter.org/wp‐content/uploads/2011/04/ASB‐12_Final‐for‐Web.pdf

Davis,M.(2006).PlanetofSlums.London:Verso.p.202.Deininger,K.,Augustinus,C.Enemark,S.,andMunro‐Faure,P.(2011)“LandGovern‐

anceforRapidUrbanization,”Chapter5inInnovationsinLandRightsRecogni‐tion,Administration,andGovernance,TheWorldBank,Washington,DC,p.178.


Demarest,G.(2011,October17)UrbanLandUsebyIllegalArmedGroupsinMedel‐lin.Retrievedfromhttp://smallwarsjournal.com/sites/default/files/885‐demarest.pdf

Demarest,G.(2004).Cadastres(LandRegistries)andGlobalSecurity.ESRIUsersConferenceProceedings.Retrievedfromhttp://proceedings.esri.com/library/userconf/proc04/abstracts/a2177.html

Demarest,G.(2008)Property&Peace:Insurgency,StrategyandtheStatuteofFrauds,ForeignMilitaryStudiesOffice(FMSO)p.iii.

Engelke,P.(2013).“ForeignPolicyforanUrbanWorld:GlobalGovernanceandtheRiseofCities.”AtlanticCouncil,BrentScrowcroftCenteronInternationalSecuri‐ty,WashingtonDC.Retrievedfromhttp://www.atlanticcouncil.org/publications/issue‐briefs/foreign‐policy‐for‐an‐urban‐world‐global‐governance‐and‐the‐rise‐of‐cities.

FoodandAgricultureOrganizationoftheUnitedNations(FAO).(2009,September).“TowardsImprovedLandGovernance,”LandTenureWorkingPaperNo.11,pp.1‐2.Retrievedfromftp://ftp.fao.org/docrep/fao/012/ak999e/ak999e00.pdf

Foster,D.andBatson,D.(2014).“AfricanInstancesofTechnology‐drivenGeograph‐icandSocio‐politicalChange,”JournalofMilitaryGeography,FrancisA.Galgano,Ed.Vol.2.Inpress.

GlobalHousingPolicyIndicators.(2013)DhakaAssessment.Retrievedfromhttp://globalhousingindicators.org/en/content/dhaka-bangladesh.

Hagen,E.(2013,May1).CitizenElectionReportinginKenya:aFailureofTechnolo‐gyDuplication,oraBreakthroughinOnline‐OfflineCollaboration?DisruptiveWitness,Retrievedfromhttp://www.ericahagen.com/citizen‐election‐reporting‐in‐kenya/

InternationalOrganisationforStandardisation(ISO)TC211/SC.(2013)GeographicInformation—LandAdministrationDomainModel(LADM)asaDraftInterna‐tionalStandard.Geneva,Switzerland,retrievedfromwww.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=51206

Joireman,S.(2011).WhereThereisNoGovernment:EnforcingPropertyRightsinCommonLawAfrica,NewYork:OxfordUniversityPress,p.151.

Jones,S.(2012).“UrbanGuerillas”inCitiesLeadtheWayforGlobalChange,SAISPHERE.ThePaulH.NitzeSchoolofAdvancedInternationalStudiesoftheJohnsHopkinsUniversity,WashingtonDC,pp.36‐39.

Kalantari,M.,Rajabarad,A.,Urban‐Karr,J.,andDinsmore,K.(2013,September24).“BridgingtheGapbetweenLADMandCadastres,”unpublishedpaperpresentedattheInternationalFederationofSurveyors(FIG)workshopontheLADM,Kua‐laLumpur,Malaysia.


Knotwell,J.(2013,January23)MegaCitiesTwo‐PagerI‐‐SubalternUrbanism,Pov‐ertyAssets,UrbanSubsidence,andUrban.BlogoftheCulturalKnowledgeCon‐sortium,retrievedfromMininghttps://www.culturalknowledge.org/Blog/ViewCategory.aspx?cat=62&mid=15&pageid=8

LemmenC.andvanOosterom,P.(2013,September24).“TheLandAdministrationDomainModel(LADM)Standard,”unpublishedpaperpresentedattheInterna‐tionalFederationofSurveyors(FIG)workshopontheLADM,KualaLumpur,Ma‐laysia.

LiottaP.andMiskel,J.(2012).TheRealPopulationBomb:Megacities,GlobalSecuri‐tyandtheMapoftheFuture.PotomacBooks,WashingtonDC.

MapKibera.(2013).AboutUs.Retrievedfromhttp://mapkibera.org/Maxwell,D.(2012,March27)StatementbyColonelDavidMaxwell,USA,(Ret.),As‐

sociateDirector,SecurityStudiesProgram,EdmundA.WalshSchoolofForeignService,GeorgetownUniversitybeforetheHouseArmedServicesCommittee,SubcommitteeonEmergingThreatsandCapabilitiesonUnderstandingFutureIrregularWarfareChallenges.Retrievedfromhttp://armedservices.house.gov/index.cfm/files/serve?File_id=be69fdc5‐4233‐4507‐840c‐6c8023f94bf6

McDougall,K.,Bennett,R.andv.d.Molen,P.(2013,July)“TheGlobalCadastre”inGIMinternational,Vol.27,No.7.Retrievedfromhttp://www.gim‐international.com/issues/articles/id2010‐The_Global_Cadastre.html

McLaren,R.(2011,November).“CrowdsourcingSupportofLandAdministration:aNew,CollaborativePartnershipbetweenCitizensandLandProfessionals,”RoyalInstitutionofCharteredSurveyors(RICS)Reportretrievedfromwww.rics.org/site/scripts/download_info.aspx?fileID=10844&categoryID=545

McLaren,R.(2013,February6).TechnologytoPromoteTransparencyaroundLandAcquisitions,KnowEdgeLtd.P.16Retrievedfromhttp://zunia.org/post/evidence‐on‐demand‐helpdesk‐report‐technology‐to‐promote‐transparency‐around‐land‐acquisitions

Muggah,R.(2013,October24)MegacityRising,InternationalRelationsandSecurityNetwork.Retrievedfromhttp://www.isn.ethz.ch/Digital‐Library/Articles/Detail/?lng=en&id=171570

NationalGeospatial‐IntelligenceAgency.(2013).NGAhoststheGEOnetNamesServerathttp://earth‐info.nga.mil/gns/html.

Navratil,G.andFrank,A.(2013,June1)“VGIforLandAdministration–aQualityPerspective,”

InternationalArchivesofthePhotogrammetry,RemoteSensingandSpatialInfor‐mationSciences,VolumeXL‐2/W1,8thInternationalSymposiumonSpatialDataQuality,HongKong,p.162.Retrievedfromhttp://www.int‐arch‐photogramm‐


remote‐sens‐spatial‐inf‐sci.net/XL‐2‐W1/159/2013/isprsarchives‐XL‐2‐W1‐159‐2013.html

OpenCitiesProject(2013).Dhaka,BangladeshSurveys.Retrievedfromhttp://opencitiesproject.com/cities/dhaka/

Stanislawski,B.(2013)GlobalBlackSpots.Retrievedfromhttp://www.maxwell.syr.edu/moynihan/gbs/Definition_and_Background/

Stanislawski,B.(2010,Fall)“MappingGlobalInsecurity”inTheNationalStrategyForumReview,Vol.19,Issue4.Retrievedfromhttp://nationalstrategy.com/Portals/0/documents/Fall%202010%20NSFR/B.%20Stanislawski‐Black%20Spots.pdf

TheBostonGlobe,(2012,February24).OneBillionSlumDwellers.Retrievedfromwww.boston.com/bigpicture/2012/02/slum_life.html.

UnitedStatesAgencyforInternationalDevelopment(USAID).(2013,October31).UrbanPolicyLaunchedToPrepareforPopulationGrowthtoCities.Retrievedfromhttp://www.usaid.gov/news‐information/press‐releases/oct‐31‐2013‐urban‐policy‐launched‐prepare‐population‐growth‐cities

UnitedNations,DepartmentofEconomicandSocialAffairs,PopulationDivision.(2013).WorldPopulationProspects,2012Revision.Retrievedfromhttp://esa.un.org/unpd/wpp/Documentation/pdf/WPP2012_Volume‐I_Comprehensive‐Tables.pdf

Williamson,I.,Enemark,S.,Wallace,J.andRajabifard,A.(2010)LandAdministrationforSustainableDevelopment,ESRIPress:Redlands,California,p.438.


ChapterFive,UrbanSocio‐CulturalMonitoringwithPassiveSensing

MichaelFarry,BruceBullock,andJonathanPfautzCharlesRiverAnalyticsInc.

{mfarry,bbullock,jpfautz}@cra.com

Abstract

Themostchallengingoperationalrequirementin localizedsocial‐culturalsituationassessment(SCSA)istoprovidetimely,localinformationfortacticaloperatorsoftenlocatedinremote,oftenhostile,smalltownsandvillages,andpotentiallydeniedar‐eas.Thischallengehasledustoinvestigatethenon‐traditionalconceptofcollectingSCSA data using in‐situ sensors. Our approach is to use this data to update localmodelsandreasonaboutchangesinthelocalareaofoperation(AO)’satmosphericstateintermsofgroundedsocial‐culturalandPMESIIvariablesandPatternofLifeeventanomalies.Inthispaper,weprovideanoverviewoftheinsightsprovidedbythis system, its theoretical underpinnings, and the sensor processing techniquesemployed.Basedonlessonslearnedfromthiseffort,wealsopresentacollectionofcriticalresearchandoperationalgapsrelatedtotheMegacity‐RSIeffort,providingabasistoapplythoselessonstoreasonaboutchangesonthemegacityscale.

Keywords

Socio‐culturalsituationassessment(SCSA),sensornetworks,PatternsofLife(POL),atmospherics,humansensing

The Urban Socio‐Cultural Monitoring Challenge

TherecentSMAstudyonMegacity‐RSIpointedoutthatremotesensingdataalonewillbeinsufficientandneedstobesupplementedwithground‐levelfieldandsocialsciencedata(M‐RSISMA,2012).Unfortunately, therearesignificantgaps today incollecting timely ground‐level fielddatausing traditional censuses, polls, and sur‐veysatthesameglobalscaleandfrequencyasispossiblewithremotesensing.Thisgapisespeciallytrueinremote,hostile,andpotentiallydeniedtacticalareaswhereunits requireupdates commensuratewith thebattle rhythmsof theiradversaries,whichismeasuredinhoursandminutes(Flynn,2012).Inthischapter,wedescribeseveralrecentandongoingeffortsatCharlesRiverAnalyticsfocusedondevelopingacapabilitytoassesssocial‐culturalsystemsinsupportofmodernmissionrequire‐ments.The key feature of these efforts is a novel focuson in‐situpassive sensorsand social‐cultural models locally calibrated to provide information unavailablefromremotesensing.Wereportourresultsand lessons learnedbelowin thecon‐textofthegapsidentifiedinthepriorUM‐RSISMAstudies.Inparticular,wefeelthatthisworkmayaddresstheproblemofmissingground‐leveldataaboutchangesinatmosphericindicatorsandPatternofLife(PoL)anomaliesthatareinvisibletotwoprimarysourcesofemergingimportance:remotesensorsofanytype/altitude,andsocial media sources.We believe that unprecedented socio‐cultural awareness ofmegacities iswithin reachby extending the approachesdescribed in this chapter.Wewilldescribeexamplesthatincludeimage‐basedatmosphericindicatorsthatare


visible from a horizontal vantage point (e.g., pedestrian gender, ratios of chil‐dren/adults,gestures,gait)andacousticindicators(e.g.,decrease/increaseinambi‐entsoundlevel, friendly/angryyelling, laughing,gunshots). Inthischapter,wead‐vancetheideathatin‐situsensorcollectionandlocalizedmodelswilladdresssomeof the inherent concerns for providing timely ground‐level data to better under‐standsocialatmosphericsandchangingconditions.

A Sensor‐Based Approach to Social‐Cultural Data Collection

ThemostchallengingoperationalrequirementinlocalizedSCSAistoprovidetimelyandlocalsocial‐culturalinformationforwarfightersandoperatorsoftenlocatedinremote,hostile,andpotentiallydeniedareas.Thischallengeledustoinvestigatethenon‐traditional concept of collecting SCSA data using in‐situ sensors. The systemconceptistousethisdatatoupdatelocalmodels(Heimann2013)andreasonaboutchangesinthelocalAO’satmosphericstateintermsofgroundedsocial‐culturalandPMESIIvariablesandPatternofLifeeventanomalies.

We initially investigated the use of in‐situ passive sensors based on successfuldemonstrations of automated recognition of non‐verbal social signals from facialexpressions related to basic hidden emotional states (e.g., anger, fear, happy, sad,surprise,anddisgust)(Ekman1957;Picard1995).Theinitial“affectivecomputing”demonstrationsbasedonfacialcueswereexpandedlatertomultiplesocialsignalsfromhandgestures,posture,andvoicecharacteristics(Picard1995;Pentland2004;Vinciarelli2009).

Earlyon,weidentifiedseveralconflictsbetweensensorresolutionrequiredforfaci‐alcuesandprogramrequirementsforgroup‐levelobservation,aswellaspotentialproblemswithcausalambiguityofaffectivecues.Theseconflicts ledustoconducttwoconcurrentliteraturesearches,oneforvariablesgroundedinsocialsciencethe‐ories,andasecondsearchforapplicablesensorandindicatorfeaturedetectional‐gorithms.WealsoincludedthesensingofvisibleandaudibleindicatorsofSCSAvar‐iablesfromFieldManualsforcounterinsurgency(FM3‐24)andhumanintelligencecollection(FM2‐22.3).

Social and political scientists traditionally collect group‐level data using uncon‐strained survey/poll questions. Unfortunately, this process requires human inter‐pretationabout indicators thatcouldnotbedirectlyobservedorsensed,e.g., foodsupply, grievance level, quality of life, unemployment, legitimacy, level of crime(Goldstone,2010).Thisaspectpresentedasignificantdifficulty,andisprobablythekeyreason that littleworkhasbeendonepreviouslyonsensor‐baseddatacollec‐tion.Toaddressthisdifficulty,weinvestigatedfurtherintothesocialsciencelitera‐ture to identify underlying observable behavior indicators related to the theory‐basedvariablesandthenproxyindicatorsthatcouldbesensed.Forexample,unob‐servable“foodsupply”valuescouldbeapproximatedbytherelative“numberofin‐dividualsinamarketplacemid‐day”,whichcanbemeasureddirectlywithappropri‐ately placed sensors. Social scientists go to extreme lengths to confirm that theirsurvey‐based variable measurements provide pier‐reviewable support that theirmeasurementsreflectanintendedtheoreticalconcept(Glaser&Strauss1967;Car‐mines1979).Asaresult,manysocialscientistsmayreject these“proxy”meansofmeasurement.However,thefocusofourworkistoprovideactionableintelligence,


rather than support the refinement of social science theories. Our results haveshownthatwecaninfactcollectautomatedmeasurementsofchangesinsocialindi‐catormagnitudeanddirectionthatcorrelatewithrealchangesinlocalsocialcondi‐tions.

Inoperations,theDoDusestheholisticsetofsixwellknownandwidelyacceptedPMESIIvariables(Political,Military,Economy,Social,Infrastructure,Information).1Asaresult,becauseouroperationaluserrequirementsforSCSAinformationareinterms of PMESII variables, we had to devise a mapping of the six categories ofPMESII variables to social science variables. The result is a three‐stage mappingfromPMESIIvariables, togroundedsocial sciencevariables, to low‐level indicatormeasurement variables that can be sensed. The complete catalog of thismappingacrossthesixPMESIIvariableshasgrownquitelargeafterseveralyearsofiteration.Forillustration,asimplifiedversionforoneofthesixvariables,thesocialor“S”var‐iable,isshowninTable1.Atthisearlyproofofconceptstage,thisthree‐stagemap‐pinghasonlybeen informallyvalidatedwith limiteddatacollection.However, thegeneralcoverageatthelevelofsocialsciencevariablescorrespondstosimilarcon‐flict related structuralvariablemodels contained inother “large‐scale”modelswehave identified (Astorino2012;Salerno2013;Maybury2010;Chamberlain2013).The significantdifference is that these largemodelsareprimarilyat the strategic,not tactical, level and theydonot relyon sensordata.The listof sensorvariablesthatwedevelopedappearstobenovelandcanlikelybeadaptedtoM‐RSIandothersystems.

Table 1: Mapping from PMESII to Social Science to Sensor Variables

DoD FM 3-0 Variables Social Science Variables Sensor Variables

Demographics

Foreign Populations Quality of Life Terror Events Migration

Social Capital Sense of Community

Feelings of Membership

Feelings of Support Shared History Ethnic Fractionalization

Evening Teahouse/Restaurant Traffic

Mosque/Church Attendance

Pedestrian Couples Public Celebrations

Demographic Pressure

Ethnic Mixing

Youth Bulge Environmental Pressure

Population Density Age and Gender Distribution Tribal Dress Ratios

Life Conditions Neighborhood Audits Physical Disorder Quality of Life Missing Shoes

Air Pollution

Inward Morning Trucking

Morning Market Traffic

Overall Human Activity

Image Entropy

Shoeless Individuals Atmospheric Condition

Sense of Security Graffiti

Unattended Women/Children

Pets/Joggers 1 While PMESII is sometimes criticized based on being originally designed for CMO targeting, it has been

supported and largely adopted as providing a common sharable framework for COIN situation assessment as well as being linkable to the Military Decision Process (MDMP) because it can be easily trained and pro‐vides useful real world data about the area in which a unit operates http://afghanquest.com/?p=457


Civil Violence Explosions, Crowd Noise Trash Accumulation

Trash

Inaddition toconstructing thesocial sciencevariableandsensorvariable catalog,wehavebuiltasecondcatalogthatlinksthesensorvariablestoalistofsensorfea‐ture extraction primitives, associated sensors and feature extraction algorithms.This catalogwasdivided into three sensor range categories: “micro” close‐up sig‐nals,“meso”intermediaterangesignals,and“macro”signalswhichstarttooverlapwithtraditionalremotesensingdata.AsmallsubsetofthiscatalogislistedinTable2.


Table 2: Social signal inventory

Social Signal Feature Primitives Sensor Collection Difficulty

Mic

ro

Non-Contact Remote Physiological Signals

Stress/Agitation Temperature changes IR, Hyper-Spectral High

Stress/Agitation Blood pressure, respiration rate MRI, NIRS, fMRI High

High-Resolution Individual Human Signals

Known Faces Biometric face match Video, BAT/HIIDE High

Auditory Cues Tone, intensity, rate/prosodity Audio Low

Mes

o

Medium Resolution Individual Human Signals Body Pose Skeletal features Video Medium

Body Shape/Gender Anthropometric cues Video Medium

Aggregated Group/Crowd Signals Car & Foot Traffic Statistics & Pat-

terns

Number, time, type, direction, velocity Video, Acoustic, Seismic Low

Local Neighborhood Service Quality

(e.g. trash pickup) Volume, rate Video Low

Air Quality Aerosols, NO2,CO2 Electrochemical/IR Low

Ambient Noise Volume, regularity Audio Low

Local Weather Temperature, baro-metric readings Generic Weather Sensors Low

Mac

ro Village/Town Signals

Foot or Vehicle Paths

New path appear-ance Video Medium

Presence of Electricity

Power monitoring, night-time glow Remote Sensing Low

Migration, Diaspora

Vehicle, pedestrian traffic streams

Remote Sensing, Mobile Device Tracking Low

In‐Situ Sensors and Sensor Processing

Theinitialin‐situsensorsincludedasinglewide‐anglevideocameraatbuildingrooflevelandanUnattendedGroundSensor(UGS)thatincludedaseismicsensor.Inthesecondgeneration,thevideocamerawasreplacedwithasmallAndroid‐basedcam‐era,andtheseismicsensorhasbeenaugmentedwithanacousticsensor.Wehavefoundthattheacousticsensordetectsrelativelyequivalenteventsinanurbanset‐ting(asopposedtoabattlefieldsensing)withsignificantlyfewerinstallationissues.ExamplesofdatacollectionandfeatureextractionareshowninFigure4&Figure5.


Figure 13: In-situ sensing, tracking pedestrians

Figure 14: Using video to track vehicles and pedestrians on the same backdrop

Model Implementation and Sensor Data Conditioning

SixBayesiannetworkswereconstructedforeachofthecategoriesofPMESIIvaria‐bles,asillustratedbythesimplifiedexamplefortheSocial(“S”)variablesshowninFigure6.Theoccurrenceofdetectedsocialsignalindicatorsbythesystemsensorsaretransformedintospatial/temporallycontextualizedmeasurementvariablesbyarelativelycomplexprocessthatembodiesthemappingsidentifiedinTable1.


Figure 15: Bayesian Network Example for PMESII Social Variable

Theoutputs fromthePMESIIBayesiannetworksarestoreddaily inadatabase toenabletheirdisplayonaspidergraphtocomparetopastvalues.AnexampleofthatspidergraphisshowninError!Referencesourcenotfound..Althoughnotillus‐tratedhere,therearealsofunctionsforalarms,tips,andcues.


Figure 16: PMESII Model Spider Graph and Well-Being Gauge with Bayesian Drill-Down

Inaddition tomodelingandreasoningaboutstructuralatmosphericvariables, thesystemdetectslocalPatternofLifepatternsandeventanomalies.Thesystempro‐videsdirectobservabilityofthesepatterns,eventdrill‐down,andcomparingselect‐ed(orparticular)daystoabaselinethathasbeencollectedovertime.AnexamplechartisprovidedasError!Referencesourcenotfound..


Figure 17: Pattern of Life chart examples

Therearetwoprimaryimplementationchallengesinusingin‐situsensorsforSCSA.Firstisthenumberandcostofsensorsto“cover”anareaofinterest.Thesecondistherequirementforsensorplacement.Theoriginaloperationalconceptforusingin‐situsensorswasaimedatacquiringtimely,localtacticalSCSAinremote,oftenhos‐tile, towns and villages, and potentially denied areas. Those caseswould only re‐quirearelativelysmallnumberofsensorsneededtocoverasmallnumberofkeyneighborhood contexts. Cooperative in‐country human resources are also usuallyavailable for sensorplacement. Scaling an in‐situ implementation to amegacity isnotnecessarilyassimpleasalinearscalingbyareawhensparseneighborhoodsaretakenintoaccount.Androidplatformsensorshavealsobecomerelativelyinexpen‐sive. In‐situvideoandaudio sensorsarealreadycommon in somemegacitiesandwillbecomemorecommoninthenearfutureasameanstosolvearangeofman‐agement problems (McLaren, 2009). In those cases, data can be repurposed forSCSAassessment.

The work described here may support warfighters in using ground‐level sensor‐baseddatatoassessasubsetofsocial‐culturalvariablesthatareinvisibletoremotesensorsor socialmedia sources.This typeof information is typicallyobserveddi‐rectly by tactical warfighters, but these observations do not have the ubiquity ofsensors,andaresubjecttohumanconcernsofavailability,bias,interpretation,andreporting. While in‐situ sensors were originally conceived for remote/hostile,sparsescaling, fallingtechnologycosts,andthegrowthof in‐situsensorsforotherpurposesmakesthemofpotentialinterestinmegacities.Ourexistingenablingtech‐nologyrepresentsanimpressivecapability,butthisprogressisjustthebeginningofunlocking the truepotentialof a sensor‐basedapproach for social‐cultural assess‐ment. There aremanymore sensors, processing capabilities, and social variablesthatwarrantinvestigationandanalysis.


Research Gap Summary

Basedonourassessmentoftheurbansocial‐culturalmonitoringchallengeandourown research results, we identified long term research gaps for understandingphasezerostabilityindynamicurbanenvironments:

Gap:Validation.Thecommunityshouldreviewsocialsciencemodelvariablesandtheirintendedmeaning,basedonstructuredinterviewswiththeircreatorsandthewarfighterswhoputthemintopractice.Thecommunityshouldpromotesharingofa catalog that documents indicators relevant to stability and durability from agrounded social science theory perspective. This effort should encourage refine‐mentandclarificationoftheconceptof“atmospherics.”ItshouldalsoclarifythelinktoPMESIIvariablesforsocialscientists,andidentifyasubsetof“observable”indica‐tors.

Gap: Cross‐discipline teams. To avoid stovepiped development, funded effortsshouldbeencouragedorevenrequiredtoincludecross‐disciplineexpertstoavoidthecommonissuessurroundingtheapplicationofsocial‐culturaltheoriestoopera‐tionalenvironments.

Gap:Operationalneedsandusecases. TheGovernment andperformers shoulddocumenta setofcommonusecases thatadequatelyrepresents thechallengesofdatacollectionandprocessinginoperationalsettings.Modelsprovidethecapabilitytodescribeandreasonaboutsocio‐culturaltrendsoccurringinmegacities,buttheinsightprovidedbythemodelmustbealignedwiththeinformationrequirementsoftheusercommunity.

Gap: Sensor variable catalog. Efforts should catalog sensor variables that canserveasproxyvariablesforphenomenaofinterest.

Gap:Commonchallengeproblems.Ratherthanexpectingindividualeffortstoac‐quiredata,commonsetsofdatashouldbeestablishedfordevelopmentandevalua‐tionefforts

References

Astorino‐Courtois,A.,“Pak‐StaMAnalysis:DriversofStability&InstabilityinPaki‐stan,”NSI,2012.

Carmines,EdwardD.andRichardA.Zeller(1979).ReliabilityandValidityAssess‐ment.SageUniversityPaperseriesonQuantitativeApplicationsintheSocialSciences,seriesno.07‐017,NewburyPark,CA:Sage.

Chamberlain,R.andW.Duquette.“Athenain2013andbeyond,”JPLPublication13‐9,2013.

Davis,P.“SpecifyingtheContentofHumbleSocial‐ScienceModels,”SummerSimula‐tionConference,2009;reprintedbyRANDasRP‐1408,2009

Flynn,M.T.,Sisco,JandD.C.Ellis.(2012).“LeftofBang:TheValueofSocioculturalAnalysisinToday’sEnvironment,”PRISM3(4):14


Glaser,B.,&Strauss,A.(1967).Thediscoveryofgroundedtheory.Chicago:Aldine.Goldstone,J.,Bates,R.,Epstein,D.,Gurr,T.,et.al,“AGlobalModelforForecasting

PoliticalInstability,”AmericanJournalofPoliticalScience,Vol.54,No.1,pp.190‐208,January2010.

Heimann,R.“BigSocialData”ImagingNotes,Winter2013.Maybury,M.,“SocialRadar,”1stConferenceonAHFE,2010.MacMillan,J.,Freeman,J.,Zacharis,G.,Bullock,B.,Pfautz,J.,“StrategicRoadmapfor

HumanSocial,Cultural,andBehavioralScienceandTechnology,”2010McLaren,R.,TheRoleofUrbanSensinginManagingMegacities,InternationalWork‐

shoponSpatialInformationforSustainableManagementofUrbanAreas,2009.O’Brien,S.P.2010.CrisisEarlyWarningandDecisionSupport:ContemporaryAp‐

proachesandThoughtsonFutureResearch.InternationalStudiesReview12:87‐104.

Pentland,A.,“Socialdynamics:Signalsandbehavior.”InternationalConferenceonDevelopmentalLearning,2004.

Salerno,J.“TheNOEM:AToolforUnderstanding/ExploringtheComplexitiesofTo‐day’sOperationalEnvironment,”HandbookofComputationalApproachestoCounterterrorism,2013.

Vinciarelli,A.,“SocialSignalProcessing:SurveyofanEmergingDomain,”JournalImageandVisionComputing,Volume27Issue12,2009.

UM‐RSISMA,“WhatCanRemoteSensingTellUsAboutStability?StrategicMulti‐layerAssessment(SMA)forU.S.ArmyEngineerResearchandDevelopmentCen‐ter,”2012.

UM‐RSISMA,“TheUseofRemoteSensingtoSupporttheAssessmentofPoliticalDu‐rability:APilotWorkshop,StrategicMultilayerAssessment(SMA)forU.S.ArmyEngineerResearchandDevelopmentCenter,”2010.


ChapterSix,BuildingaNetwork‐basedApproachtoNationalSecurity

RandyPaulPearsonU.S.NorthernCommand

[email protected]

Abstract

Theexponentialexpansionofinformationisrapidlychangingthenationalsecurityenvironment.Therefore,itisnecessarytoevolveourthinkingandapproachtona‐tionalsecurity,enablingcooperationamongstallelementsofnationalpower–fromstrategytotactics–toinfluenceandshapethatchangingenvironment.Thismeansshifting our strategic view from linear and legacy approaches to network‐basedones. Todothat,therearechangesthatneedtotakeplaceininformationsharingand interagencycoordination. Effective informationsharing requires thedissemi‐nationof information, thegrantingofaccess to information,orboth. Thekeyele‐mentsofinteragencycooperationare:1)aninteragencyconferenceframework;2)abasedesignormapof roles, responsibilities,andprocesses;and3) thedevelop‐ment of educational platforms. This paper uses the DOD counter threat finance(CTF)experienceasamodeltodeveloprecommendations.However,theimplemen‐tationoftheserecommendationsrequirescooperationacrossgovernment,private,andacademicsectorstobegindevelopingthenecessaryrelationshipsinthepresentthatwillenablecivilsocietiestocounterthreatnetworksinthefuture.

Keywords

Counter threat finance, information sharing, interagency cooperation, trans‐dimensional,illicit,industry,academia,Bitcoin,network,moneylaundering,frame‐work,conference,education,professionaldevelopment,enterprise,USNORTHCOM,webinar,nationalpower

An Exponentially Expanding Problem Set

Providingscopeinthemoderneraisbecominganincreasinglydifficultthingtodo.Thenameoftheconceptof“BigData”aloneismisgiving,butthatisthesimplenamegiventoacomplextrans‐dimensionaluniverseofdatathatisgeneratedatarapidlyexpandingexponentialrateeverysecondofeveryday. Forinstance,it’sestimatedthat four exabytesofunique informationwerecreated in2008 (Fitch2008). Justfiveyearsearlier in2003, fiveexabyteswere“equivalenttoallwordseverspokenbyhumanssincethedawnoftime”(Klinkenborg2003).Thegrowthofthepopula‐tionsthatgeneratethisdata,asmoreandmorehumanbeingsinteractonamulti‐tudeofplatformsandsystems,requirestheconstantrepair,expansion,orreplace‐ment of the infrastructures that support their daily activities, thus creating evenmoredata.Thevolume,velocity,andvarietyofthatdata,aswellasthemobilityofits producerswill present both challenges and opportunities to the structuring ofconventionalthoughtandthewayweuseit.

Injustafewshortyears“[m]etropolitanregionswillspillovermultiplejurisdictionscreatingmega‐regions.By2030,therewillbeatleast40bi‐nationalandtri‐national


metroregions”(NIC2012). In fact,overthenext15years, themegacitiesof todaywill experience both the expansion and limits of their infrastructures, whichwillplaceincreasingdemandsontheirgovernments. Theywillbe“limitedbyphysicalland constraints and burdened by vehicular congestion and costly infrastructurallegacies,entrenchedcriminalnetworksandpoliticalgridlock,anddeterioratingsan‐itationandhealthconditions”(NIC2012).It’salsoimportanttonotethatamajorityofdatamayliewithininformalsystemsasonlysixoutof28ofthecurrentbiggestcitiesonEarthareinthedevelopedworld(Kotkin&Cox2013). Sohowdoesonecomprehendtheamountorimpactofthedatageneratedbytheseincreasingpopu‐lationsanditsresultinguniverseofincomprehensibleoutcomes?Thefactis,it’snoteasy,andittakesmanyhands–orinthiscaseminds–tobreakitdown,makesenseofit,matchdatasetstootherpiecesinthatvastuniverseofinformation,andgiveitmeaning.Nobodycandoitalone.

A Paradigm Shift for Strategy

Strategiesmustevolvetokeeppacewiththenetworkedenvironment.Thiswillre‐quiretheUSGtochangeat least twothemeswhenitcomestodevelopingandim‐plementingstrategy. First, itmust findways to facilitate interagencycooperation.Second,itmustthinkaboutproblemsfromaninfrastructurepointofviewtobetterembraceanetwork‐basedapproach.

Allrelationshipsarebasedonacertainamountoftrust.Awayofestablishingthattrustcanbemakingagood faitheffortofsome fashion. Forexample, theDepart‐mentofDefense(DOD)caneasily further interagencycooperationbychanging itsapproachtothedevelopmentofitsstrategies,“de‐militarizing”languagewhereap‐propriate,andinvolvinginteragencypartnersearlyonintheprocess.Thiswillas‐sisttoaccuratelydevelopmutuallyagreedupongoalsandmethodsneeded–espe‐ciallyintheareaofcivilsupport. Thesesimpletrustbuildingstepsareimportant,not just because they change away of doing business, but because they begin tobuild the network necessary to navigate the globalized system and counter thethreatsfoundthere,withanetwork‐basedapproach.

OneofthegreatlegacystrategiesfromtheColdWaristhatofContainment.WhiletheimplementationofContainmentStrategyhaditsdetractorsandtookalongtimetoproduceresults,itdidwork.Thesuccessofitwasbasedontheconcertedeffortsofallelementsofnationalpowerintheconfinedspaceofabi‐polar,superpower‐centric world. The strategy was equal to the environment. Today’s universe isstuffedwithinformation,growingpopulations,andtheinfrastructuresthatsupportthem. Makingsenseof itrequiresaparadigmshift fromlegacystrategiestothosemorecapableofaddressingthiscomplexenvironment.Thatiswhere“networkthe‐ory” may contribute. According to critical infrastructure expert Ted G. Lewis(2006),networktheoryisawaytomodelcritical infrastructures. It is“morethantheory;itisactuallyapracticalwaytomodel,analyze,andhardenpotentialtargetsinnearlyeverycriticalinfrastructuresector.”Networktheorycanbeappliedtoin‐frastructures,andthenknowntechniquescanbeusedtoaddressthecommonde‐nominatorsofproblemsassociatedwiththosenetworks.Thus,networktheoryhasa strong potential application as a policy (and resulting strategy) to address theproblemsassociatedwithtoday’sgloballynetworkedenvironment.


Countering Threat Finance Strategy as a Model

A transaction is any combination of actors and technologies that facilitates thetransfer of value from one party to another (Global Paymenst Handbook 2013).Theycanbeformal,suchasphysicallymakingabankdeposit,ortheycanbeinfor‐mal, suchasbartering for itemsonCraigslist. Badactorsexploit these infrastruc‐turestotransfervalueusinganumberofmethodsrangingfromwiretransfersandbulk cash smuggling, to trade‐based money laundering (TBML), informal valuetransfer systems (IVTS), stored value cards, and virtual money such as bitcoin.1“Money laundering is not just a trade in itself but an irreplaceablemechanism ofeveryotherillicittrade. Inasense,moneylaunderingisamirroroftheglobalun‐dergroundeconomy(Naim2005).” Eachoneof thesemethodsadapts toanenvi‐ronment,whetheritbetotheglobalfinanceandbankingsystem,thetransportationsystem,ortheInternet.Themethodsarerealizedeitherphysicallyorvirtually,andmovedwithinoracrosssystemsinmanyconcealedoralternativeforms.

Thecurrentstrategiclookatillicitfinanceisagencyorauthorityspecific.Essential‐ly, authority lines divide up the elements of national power that focus on threatnetworks. The Strategy to Combat Transnational Organized Crime (White House2011), tookasteptoward leveragingthesevariousauthorities,evencarvingoutasupportniche forDODinvolvingtheprioritizationofworktoenhance intelligenceandinformationsharing.Whilethemaineffortofthestrategylieswiththreemainelements,theDepartmentsofState, Justice,andTreasury,DODplaysan importantsupportingrole. It leverages its civil supportmission through intelligenceand in‐formationsharing. However, thissupportrolealsoenhancesDOD’shomelandde‐fensemission helping to shape the global or regional environment, thus reducingstressesassociatedwithitshomelanddefenseandsecuritycooperationmissions.

Tosupportandshapeenvironments,DODhascarefullyconstructed linkingstrate‐giesforcounternarcotics,whichprovidestheauthoritiesthatDODusesforitsCTFactivities.CTFisatermusedbyDODtodescribeitssupporttothelargerUSGandinternational community goals of safeguarding the financial system against illicituseandcombatingothernationalsecuritythreats(Treasury2013).ItisdefinedbyaDODpolicydirectivethatstatesDOD“shallworkwithotherUSGdepartmentsandagencies andwith partner nations to deny, disrupt, or defeat and degrade adver‐saries’abilitytousegloballicitandillicitfinancialnetworkstonegativelyaffectU.S.interests” (Defense 2012). Both DOD and its interagency partners use seeminglyvaguelanguagetodescribetheirroleswithreferencessuchas“othernationalsecu‐ritythreats”and“networks[that]negativelyaffectU.S.interests”.However,thelan‐guageisprecisioninthatitrecognizesthecomplexityoftheenvironment,allowingforanetwork‐basedapproachtotackleaproblemsetthatnosingleentitycansolveonitsown.

1 Bitcoins are virtual in the sense that they exist solely in cyberspace, but they’re expressly designed for the real world—specifically, any form of commerce where anonymity and untraceability are essential. Funds can be sent digitally across borders without physical transfer or anyone looking over your shoulder, and there are no fees or international exchange rates to worry about. For these reasons, bitcoins originally ap‐pealed mainly to anarcho‐utopian types, plus drug dealers, gamblers and thieves. For more on the com‐plexity of bitcoin see Cecil Adam’s article, Cash Flop at: http://www.cityweekly.net/utah/article‐17937‐cash‐flop.html.


Allof these strategies link froma loosedoctrinalpointofview. They tie togetheracrossagenciesandauthorities,facilitatingaviewofthebigpicture,apossiblefoot‐hold intodeciphering those exponentially expanding exabytes of information, andan understanding of the developing massive infrastructures in which they move.Thiscanbedonewithoutneworexpandedauthorities,butitmustinvolveapara‐digmshift fromlinear legacyapproaches to thosenetworkcentric techniques thataddresstheillicitnetworkproblemset.Frameworksthatsupportinformationshar‐ingandinteragencycoordinationarecriticaltothatsuccess.

A Way Forward: Information Sharing and Interagency Coordination

Oneofthefivemajorrecommendationsofthe9/11CommissionReportwas“unify‐ing themanyparticipants in thecounterterrorismeffortand theirknowledge inanetworked‐based information sharing system that transcends traditional govern‐mentboundaries” (9/112004). Informationsharing isaconcertedmantraofgov‐ernment talking points and leader engagement, especially in the post‐9/11 era.However,italmostnevergetsthein‐depthproblemdefinitionandparticipationthatitdeserves.Forexample,betweentheintelligencecommunityandlawenforcementtherearetwokeyconcerns.Fromtheintelligencecommunitytheconcernisintelli‐genceoversightwiththerestrictionsithastoprotectsourcesandmethods. Fromlawenforcementtheconcerniswithdiscovery–theprotectionoftheinvestigation,prosecution,andtitle10prohibitionsoninvolvementwiththoseactivities.Howev‐er,whatreallyneedstobeoffocustoensurethattrueinformationsharingcomestofruition isa communications framework thatoutlines theprocesses forprotectingthe information thatneeds to remain secure,whiledisseminating the informationthat needs to be shared. In its simplest terms, a communications framework re‐quiresoneoftwooptionsfromallinteragencypartners:1)facilitatingthedissemi‐nationofinformationbywritingproductstothelowestclassificationlevelpossible,withareviewordeclassificationprocesstogetcriticalinformationtoausablefor‐mat;orifthatcannotbeachieved,2)theremustbeamechanismtogranttimelyac‐cess toclassified information. This frameworkmustalso facilitatequickresponsetimes, supported by a common understanding of the goal and the means to getthere.

Since9/11, interagencycooperationhasalsobeena frequentpointofneeded im‐provement. However, there are things that support the relationship building andcollaborationrequiredtobuildasuccessfulinteragencyconstruct.Somearepriori‐tieswhile others are not. For instance,many organizations have some form of aconferenceschedulewheretheyinteractwithcounterparts,shareinformation,andsometimesconcludeagreements.There’stheannualG20Summit,whereleadersoftheworld’s topeconomicpowerhousesmeet topromoteglobaleconomicstability(G202013).GettingmoreUSGoriented,theDrugEnforcementAdministrationholdsanannual InternationalDrugEnforcementConference inadifferentcountryeveryyearbringingtogetherthecounterdrugservicesofmorethan120countriestoad‐dresstheworld’smostpressingdrugenforcementchallenges(Filippov2013).EvenU.S. Special Operations Command’s former Global Synchronization Conference(GSC)–thenurseryforCTF–wasaplacewheremilitary,civilians,lawenforcement,industry,andacademiaallcametogethertobetterunderstandillicitfinanceandde‐velopwaystoworkwitheachothertocounterit.


Thereare threekeyelements to improvingCTF interagencycooperationso that itcan shape the illicit finance environment and outpace the threats – serving as amodelforthedevelopmentoffurthernetworked‐basedapproaches.

1. First,developan interagencyconferenceframework(suchastheGSC)thatdevelops decisions for leadership, gives agencies a strategic level event toplantheiractivitiesto,andusesindividualconferences,committeemeetings,working groups, and interagency activities to build agendas in support ofstrategicgoals.Thisframeworkorganizesagencyeventsintoaninteragencynetworkof key conferences that build to a single, culminating interagencyconference.Thekeydifferencebeingthattheultimategoaloftheinteragen‐cyconferenceistodeveloprecommendationsthatarerefinedandthenpre‐sentedtoaninteragencyexecutivecouncilwhereleadershipprovidesevalu‐ation,adjustment,anddecision.Thisinturnhonestheinteragencynetworkandthefocusofitsactivities.

2. Second,mapoutabasedesignofwhodoeswhatand theprocesses togetthingsdone so that there is a ready referenceof information that lends totheimprovementofCTFsupportorganizationandmanagement.SnapshotsofthismapareembodiedintheaforementionedDODDirectiveforCTFPoli‐cyandtheTOCStrategy,aseachassignsrolesandresponsibilities.ButtherearerelativelyfewcomprehensivedocumentsthatmapouttheCTForlargeranti‐illicit finance enterprise; and those thatdooftenprovidemoremisin‐formationthan fact. Althoughspecificallydesigned forU.S.NorthernCom‐mand,anexampleofmappingtheenterprisecouldbethe“CTFStakeholdersandEntryPoints”developedbytheJointAdvancedWarfightingProgramin(USNORTHCOM2011).Thismap’spurposewastoidentifykeynodesintheUSGwhereU.S.NorthernCommandcouldinitiateCTFsupportand/orplaceitsverylimitedresources.Thefactremainsthatitisoneofthefewcompre‐hensivedepictionsof theanti‐illicit financeenterprise. Thisproductcouldwellserveasthebasistofurtheroutlinetheinteragencycommunity’sroles,responsibilities, and processes, thus improving the organization andman‐agementofCTFsupporttocounterthreatnetworks.

3. Third,developeducationalplatformsthatnotonlyfacilitatethisreadyrefer‐encebut also identifyCTF experts todrawupon, andpromote anetwork‐based approach to countering threat finance. This effort must focus onbuilding the core cadre among CTF professionals, vice contractor sourceddevelopmentoftrainingbynon‐specialist formerefamiliarization. Itmustalsoservetheenterpriseasa“one‐stopshop”toeducateleadershipandan‐ti‐illicitfinancepersonnelthroughouttheUSG.

a. ManysuchCTFprofessionaltrainingcoursesalreadyexist.ThereistheDIAThreatFinanceCourse,whichspecificallydevelopstheana‐lyticaltalentforthreatfinanceintelligence(TFI). ThereistheJointSpecial Operations University (JSOU) CTF Course, which takes thestepbeyondintelligenceanalysistoamorespecificoperationalsup‐portskillset.TherearealsocoursesapplicabletocounteringthreatfinanceinotherveinsoftheelementsofnationalpowersuchastheFederal Law Enforcement Training Center’s Criminal Investigator


Training Program, and various training courses in methodology,economics, and financial flows associated with the CIA’s ShermanKentSchool. CodifyingtheDIAandJSOUcoursesintocorerequire‐ments,aswellascoursesfromlawenforcementandtheintelligencecommunity intoelective courses foraprogramofCTFprofessionaldevelopment, could advance interagency cooperation and thus in‐formation sharing, far beyond what it is now, and prepare a net‐work‐basedapproachforitscriticalfuturemissions.

b. A further step forward in the area of professional developmentwould be to leverage interagency relationships to better advertisethese training opportunities, and facilitate the cross‐training – in‐residenceorviamobile training teams– to improve fluencyacrosstheinteragencylexiconandensureunityofeffort.Anadditionalandmore accessible approach would be to participate in and developweb‐based training similar to that of the Naval Post GraduateSchool’s (NPS) Terrorist Financing and State Response Self StudyCourse or theAssociation of CertifiedAnti‐Money Laundering Spe‐cialist“webinar”offerings.TakingagiantleapevenfurthercouldbeCTFeducationalongthelinesofanaccrediteddegreegrantinginsti‐tutionsuchasthatofthedistancelearningofferingsfromtheKing’sCollegeofWarStudiesortheNPSMaster’sDegreePrograms.

c. ProvidingforreadyreferenceofCTFrelatedmaterialsisalsoanec‐essary resource to develop. A model to follow could be the JointElectronicLibrary,whichservesasaplatformtogainaccesstoguid‐ance,training,theofficialDODdictionary,andprofessionaljournals.Thiswouldsupporttheon‐goingprofessionalreadingandreferencefor practitioners as well as support leadership and USG personneleducation. Formsof this currently exist inpockets throughout theCTFand largeranti‐illicit financecommunitiesof interest,butnoneareaccessibleinacomprehensiveorenterprisefocusedmanner.

Finally,U.S.SpecialOperationsCommand(SOCOM)hasbeenthe“synchronizer”forCTF since the program’s inception. In the course of developing and coordinatingunder this role, it has identified subjectmatter experts among the array of thosewho specialize in CTF frompractitioners in government to those in industry, andacademia. Whilenot thesole resource,SOCOMcanreadilymatchdemandsignalswithasupplyofCTFSpecialists tosupportcustomers, facilitatingexpertise,main‐taining network integrity, and ensuring unity of effort in countering threat net‐works.

Networkspresentacomplexitythatisnotfullyunderstoodandisdifficulttoexplainbecauseofitstrans‐dimensionality. Somewillthenconsidertheproblemset“too‐hard”, making resulting strategies unequal to their environments and falling farshortofnationalgoals. ThatwouldbeamistakefortheUSGtomake,significantlyreducingitscapabilitytoshapeandinfluencetheglobalenvironment.Mega‐regionsareanexampleof thecomplexproblemsetsanddemandsthatwewill face in thefuture. Learningfromthatrisingenvironmentandapplyingnetworktheorytoad‐dressthecomplexitiesassociatedwithitisanenormousinvestmentinthepresent


thatcanhaveexponentiallypositive impacts inthefuture. Since9/11,manyhavepromotedtheconceptthatitisbettertoexchangebusinesscardsintheclassroomratherthanonthebattlefieldoratthecrimescene.Thebestwaytofacilitatethatistogetpeopletogethersothattrustnetworksform.Thisisoftendonethroughcon‐ferencesandeducational fora. Establishingconferenceandeducation frameworkswill develop the community trust necessary to facilitate information sharing andinteragencycooperation. Itwillhelp tochange thewaywestructureour thoughtaboutthevolume,velocity,andvarietyofdata.Itwillprovidethebasisforleverag‐ing the strengthsof all elementsofnationalpower, andbuildinganetwork‐basedapproachtonationalsecurity.

References

9/11CommissionReport(2004).FinalReportoftheNationalCommissiononTer‐roristAttacksUpontheUnitedStates,W.W.Norton&Company,Inc.,July22,2004,pg400.

Defense,U.S.Departmentof(2012).DODCounterThreatFinancePolicy,DODDi‐rective5205.14,November16,2012,page1,http://www.dtic.mil/whs/directives/corres/pdf/520514p.pdf.

Filippov,A.(2013).MoscowHostsInternationalDrugEnforcementConference,thinkRussia,June10,2013,asfoundat:http://www.thinkrussia.com/policy‐initiatives/moscow‐hosts‐international‐drug‐enforcement‐conference.

Fisch,K.,McLeod,S.,&Brenman,B.(2008).DidYouKnow3.0,Research&Design,October2008.

G20(2013).G20Summit,St.Petersburg,Russia,September2013,asfoundat:http://www.g20.org/docs/about/about_G20.html.

GlobalPaymentsHandbook(2013).February,page3.Klinkenborg,V.(2003).TryingtoMeasuretheAmountofInformationThatHumans

Create,TheNewYorkTimes,November12,2003,http://www.nytimes.com/2003/11/12/opinion/12WED4.html.

Kotkin,J.,&Cox,W.(2013).TheWorld'sFastest‐GrowingMegacities,Forbes,April8,2013,http://www.forbes.com/sites/joelkotkin/2013/04/08/the‐worlds‐fastest‐growing‐megacities/print/.

Lewis,T.(2006).CriticalInfrastructureProtectioninHomelandSecurity:DefendingaNetworkedNation,JohnWiley&Sons,Inc.,Hoboken,NJ,2006,page77.

Naim,M.(2005).Illicit:HowSmugglers,Traffickers,andCopycatsareHijackingtheGlobalEconomy,AnchorBooks,NewYork,page137.

NIC(2012).GlobalTrends2030:AlternativeWorlds,TheNationalIntelligenceCouncil(NIC),December2012,pg29.

Treasury,U.S.Departmentof(2013).TerrorismandIllicitFinance,OfficeofTerror‐ismandFinancialIntelligence,asaccessedonSeptember27,2013at:http://www.treasury.gov/resource‐center/terrorist‐illicit‐finance/Pages/default.aspx.


USNORTHCOM(2011).MexicanTransnationalCriminalOrganizationsCounterThreatFinanceStudySeries,IdentificationofCounterThreatFinanceEntryPointsandPrioritizationofUSNorthernCommandSupport,JointAdvancedWarfightingProgram,InstituteforDefenseAnalyses,December2011.

WhiteHouse(2011).StrategytoCombatTransnationalOrganizedCrime,Address‐ingConvergingThreatstoNationalSecurity,TheWhiteHouse,July19,2011,page18.


ChapterSeven,EvaluatingSlumSeverityfromRemoteSensingImagery

KarenOwen,Ph.D.NationalGeospatial‐IntelligenceAgency

[email protected]

Abstract

Theapplicationofremotesensingimagerytechniquestoextractindicatorsofmeg‐acityneighborhoodscanbeappliedtogetherwithtraditionalsocioculturalanalysisbyaddingrealdepthtomodelswithquantifiablemetrics.Commercialandcivilgov‐ernmentimagerycanandisbeingusedtoevaluateintra‐urbandifferencesinneigh‐borhoodsofmegacitiestorevealeconomicvariation.Afterperformingimageclassi‐fication, these indicators can be measured at the neighborhood or local level togenerateascaleofseverity.Thispaperarguesthattraditionalsocioculturalmodel‐ingcanbeaugmentedandsupportedby theunderlyingphysicalmetricsavailablefromhighresolutionimagery,andthatscientistsfrombothfieldsshouldworkmoreclosely todevelopmodelsexplainingwherepocketsof theworst livingconditionsexist inmegacitiesworldwide. Missionplanners can then focuson theseareas toanticipate future conflict or instability, andmitigate negative impacts on stressedpopulations.

Keywords

Remotesensing,megacities,neighborhoodanalysis,terrainmodeling

Why remote sensing for megacities?

Megacities are thriving and constantly changing in terms of composition, shape,neighborhood characteristics, building materials, and quality of life. They can beviewed as singular organisms, but this unnaturally blinds us to the regional andneighborhoodvariationfoundwithintheirboundaries.Economiczones,pocketsofdeprivedhousing,accessibilitytotransportation,andpublicserviceavailabilitycanvarymarkedlyindifferentsectionsofamegacity1.Thispaperevaluatessomeofthecapabilitiesofremotesensingimagerytohelpunderstandandsegregatetheseveri‐tyofslumsbyneighborhood.Knowingwheretheareasofhighesteconomicdepri‐vationexist inamegacityhelpsanswerLieutenantGeneralFlynn’s call forunder‐standing the environment ‐ especially how that environment impacts operationsandwhoisimpacted.

Someinherentdifferencesindata,methods,andapproachhavepreventedtheadop‐tionofimagery‐basedtechniquesintothetypeofexplanatorymodelingoftenusedto assesspolitical instability througha sociocultural lens.Therearedifferences inmeasuringaccuracy,vastlydifferentelementaldatatypes,andvariationinsamplingunitsbetweenbothtypesofanalysisthatcanhelpexplainthislackofmethodologi‐calintegration.Thehypothesisisthatcharacteristicsofslumsderivedfromremote‐

1 A megacity is defined as an urban areal extent with ≥ 10 million residents.


lysenseddatacan,infact,allowustomakeinferencesaboutthehumancondition,andassumesthatthesecharacteristicscanbescaledbyseverityasfactorsforinsta‐bilitymodels.

Accuracy considerations

Remotelysensedinformationiscreatedfromdatageneratedwithoutdirectcontact.Thatsimpledefinitionmeansthatnotonlysatellitesandairbornesensors,butalsothevisualobservationsofhumanactivitiesareallconsideredremotesensingplat‐forms. The focus here will be on the traditional sources of remotely sensed dataprocessedfromsatellitesensors,andnotairborneorvisualobservationdata.Sen‐sorphotogrammetryand theneed forprecisepositioningandgeolocationrequireexplicitspatial, spectralandtemporalaccuracy inremotelysensed imagery.Theseaccuracymeasuresareaninherentpartofremotesensingscience.

However, thesamecannotbereported forhumanbehaviorandattitudes.Thede‐sireforhumanindicatorsthatcanbemeasuredfromimageryisthemaindriverbe‐hindquality‐of‐lifeandslumseverityresearch(Stoleretal.2012;Cowen&Jensen,2001;Owen,2012a).Decision‐makerstryingtoanticipatehumanactivityneedac‐curate background on the physical landscape. To a certain extent, geo‐referencedimagerycandeliver. Indicators fromneighborhoods inmegacities canbedirectlymeasured from imagery sources.Those indicators can serve as surrogates forhu‐mandeprivationorwell‐beingintheneighborhood.Itisknownthatvegetation(andconversely, the fractionof impervious landcover), roadsurfacesandaccessibility,image texture (entropy), exposed soil content, and terrain characteristics (slope,curvature)varysignificantlyinwealthyversuspoorsettlementsinpartsofameg‐acityinthedevelopingworld(Owen,2012a).Butestimatesaboutindividualhumanbehaviorcannotbemadefromsuchmeasures.Inotherwords,theresearcheriscau‐tioned toavoid theecological fallacy,whereby thecharacteristicsof theneighbor‐hoodareincorrectlyascribedtoanindividuallivingtherein.Thisdifferenceinfocusbetweenthe individualandthephysical landareashouldbeexploitedtobetter in‐formsocioculturalmodeling.

The unit of analysis – differential datatypes

Theunitofanalysisdiffersbetweensocioculturalmodelingthatisfocusedonindi‐viduals and groups while imagery analysis often focuses on the physical village,townorregion.Ingeospatialandimageryscience,researchhasbeendevotedtothestudyofneighborhoodsas theunitofanalysis. The term ‘neighborhood’, just liketheterms‘urban’and‘rural’,aredefinedinvariousways,andthisimpactsourabil‐ity to scientificallymeasure them (Weeks et al., 2007).Neighborhoods canbede‐fined by their structural parameters such as similar housing or roads, from theirethniccompositionincensusstatistics,fromtownplanningmapsthatdefineprop‐erty subdivisions, or historically from names given when amemorable event oc‐curred and residentswere spatially polarized over an issue such as reacting to ahazard (volcanic eruption, floodwaters,nuclear accident).Neighborhoods canalsobenamedbyvotingprecincts,orproximitytoafeature(cliff‐dwellers,losbasureros1(Owen, 2012b)). Toproperlyutilize imagerydata for researchonneighborhoods

1 In Spanish, refers to those who live adjacent to and make their living from a landfill.


within megacities, the historical and spatial context must be understood beforeboundariesareascribedtoeachunitofanalysis.

Thedecisionofwheretodrawneighborhoodboundariesforanalysiswithinameg‐acity is thereforeavery importantone, since it impactsanymeasures that canbetakenfromremotelysenseddata.Themodifiablearealunitproblem,orMAUP,re‐sults when boundaries are not defined by the theme being analyzed (Openshaw,1984).Almostallpopulationdemographicsandculturalinformationislinkedtore‐gions defined by administrative boundaries that are not tied to specific socio‐culturalthemesdesired.Theresearchershouldthereforequestionwhetherthepo‐liticalboundaryisthebestonefortheiranalysis.

Forsamplingwithinamegacity,itisgenerallysafetoascribeboundariestoareasofsimilarhousingifonelacksancillarysocioeconomicdata,however,otherclusteringtechniquescanalsobeapplied.Onemayarguethatsuchclusteringintroducesbiaswhenspatialorstructuralsimilarityself‐selectsforthesamplingunit.Theresearch‐ermust strive to explain the spatial unit of analysis,whether imagery‐derived, orfromancillarypoliticaladministrativeboundaries.Geospatially‐referencedimagerysamplesdiffermarkedlyfromthesamplingunitoftenusedinsocioculturalanalysis.In sociocultural analysis, surveys and polls are conducted on individualswithin alargerpopulationtomeasureattitudesorsentiment.Thus,thedifferentunitofanal‐ysis and different datatypes explain the challenge of fusing socioculturalwith re‐

motesensing‐basedvariables.Figure8depictsthesedifferences.

Figure 18: Sociocultural vs. Remote Sensing Modeling


City models

Geographicmodelswitheconomic,socialandstructuralemphaseshavebeenstud‐ied and applied to the shape and growth of cities for over a century,well beforespace‐borne and airborne remote sensing platforms existed. They include CentralPlaceTheory1(economic),ConcentricZoneModel2(social),MultipleNucleiModel3(social), and Roberts Settlement Framework4 (structural). Many of these theorieswereappliedtoexplainsectorsofthecity,ortherelationshipofthecitycenterwithoutlying regions. Some theories focused on differences between individual neigh‐borhoods,buthistorically,therewasnoabilitytodigitallymarkboundariesorun‐derstand neighborhoods from the air. The original layout of cities in developingcountries,oftenwithrootsinEuropeanColonialism,maynolongerbeusefulinex‐plainingvariabilitybetweenneighborhoodsbecauseethnicmigration,zoninglaws,and new road construction have caused drastic changes in their spatial arrange‐ment.Fromaremotesensing lensofveryhighresolutionsatellite imagery,neigh‐borhood variabilitymight be explained now through the classification of buildingmaterials,buildingheights,favorableproximitytomarkets,orunfavorableproximi‐tytoman‐madeandnaturalhazards.Theselastitemscanbemappedasproxiesforlowerlandvalues, lowerlivingcosts,andthus,wheretheurbanpoornormallyre‐sideinmegacities.

Remote sensing‐derived products and applications

Table1 isapartial listofbaselinecivilorcommercialremotesensing‐derived im‐agerydata, itspurpose foruse,andapproximategroundsampledistance (GSD,orpixel size). The GSD impacts the minimum size feature that can be extracted.Leachtenaueretal.,(1997)suggestthattofinda15m2dwellingwouldrequirepixelsizestobeaminimumof1/5to1/3ofthestructuresizesothat:

pi=(0.2or0.33xaf)wherep=pixelsizeinimageia=areaoffeatureobject(f)tobedetected

Other image characteristics can impact theminimum size feature that can be ex‐tracted,suchastheelongationofthefeatureanditssmoothnessoftexture. Intheexampleabove,givenasquaredwellingandconsistenttexture,theminimumpixelsizewouldbeontheorderof6.7m,whichiseasilyachievablewithcommercialim‐agery.Thismeansthearrayofcommercialveryhighresolutionremotesensingdatashouldbesufficient,butLandsatdatawouldnot.

1 Bradford & Kent, 1977, Central Place Theory: The Cristaller Model, in Human Geography – Theories

and their application, Oxford University Press, Oxford. 2 Park, Burgess & McKenzie, 1925, The City, University of Chicago Press, Chicago. 3 Harris & Ullman, 1959, The Nature of Cities, in Mater & Kohn (Eds.) Readings in Urban Geography,

University of Chicago Press, Chicago.

4 Roberts (1996), Landscapes of Settlement, Prehistory to the Present, Taylor & Francis, 1996


Table 3: Available Imagery Data

BaselineNeed ExampleSen‐sorType

Purpose GroundSampleDistance(GSD)

LandsurfaceCharacteristics,Landcover

MODIS Weatherpatterns,Impervioussurfaces,Vegetationcoverage

250–500m

Landsat 28.5m

Elevationandterraingeomorphology

ASTERGDEM2

Terrainslope,Elevation,Aspect,Surfacecurvature

15m(VNIR)30m(SWIR)90m(TIR)

SRTM 90m

Roadsandroadsurfacematerials

WorldView‐2 Transportationandaccessibility <0.5m

Quickbird 0.6mor2.4m

Built‐upareas WorldView‐2 Extentofanthropogenicstruc‐tures

<0.5m

Quickbird 0.6or2.4m

IKONOS 1mand4m

RADARSAT 50m

TerraSAR‐X 1m,3m,18m

TanDEM‐X <20m

Surfaceandsubsurfacehydrology

SRTMTRMMMODIS

Floodrisk,soilmoisture,waterquality,precipitation

90m~27km+/‐250‐500m

Vegetationtype Landsat Croptypesandtrends,cropyieldvegetationhealth(phenology)

28.5m

WorldView‐2 <0.5m

AVHRR ~1km

ElectricalPowerUse DMSP‐OLS Nighttimelights 1km

ThoughmanyoftheseproductspresentaGSDthatistoocoarsetodetectcharacter‐isticsofindividualcityblocksorbuildings,regionalscalebaselineneedscanstillbemet.Thefieldsofhydrological,atmospheric,andlandcovermodelingfromremotesensinghavewell‐documentedmethodsofprocessingthisdatawhichprovidesthefoundationtomanyusefulhumangeography‐relatedproducts.Normallythesmall‐est GSD resolution data should be sought, especially for analysis at the neighbor‐hoodscalewithinmegacities.

Features visible in Imagery

Automatedmethods to extract featureboundaries from imageryofmegacitiesarechallenging, but it is precisely such features that are needed to perform furtheranalysis of socioeconomic characteristics of megacity neighborhoods. These fea‐tures include roads, buildings, powerplants, health facilities, schools, recreationalareas,public servicebuildings andmarkets/bazaars. For example, the spectral in‐formationfromveryhighresolutionimagerycanclassifybuildingmaterialsbyloca‐


tion.Certainbuildingmaterialsareindicativeofwealthorpovertyinanurbanarea(e.g.,tilevs.adobe/mud),thiscanandhasbeenusedasanindicatorofslums(Jain,2007;Thomson&Hardin,2000).Similarly,thephysicalcompositionofaroadsur‐facecanalsobederivedfromimagery,andthisleadstoadeeperunderstandingofaccessibilitytosocialservices(pavedroadsprovidegreateraccessibility)(Owen&Wong,2012b).Man‐madehazardssuchaslandfillsandfactoriescanbemarkerstogeolocateslumswithinamegacitybecauselowerresidentiallandvaluesusuallyex‐istnearby,andsomecharacteristicsofthoselocationscanbederivedfromimagery.

Abasicclassificationofneighborhoodslumseverityusingarangeofindicatorscanexplain the spatial variation in human needs by neighborhood thatwould be im‐portant during humanitarian, stability, or reconstruction operations. For example,Figure9displaysacut‐scorefromthereceiveroperatingcharacteristiccurvefortheindicator of roadentropy.Roadentropy is an image texturemeasure that is oftenhigherinpoorareasduetomultiplematerials foundonroads,andthiscanbede‐fined by neighborhood. In such a case, a score lower than the index means theneighborhood would be considered wealthy, and a score higher than the indexmeansitwouldbeclassifiedasmoreimpoverished.Inamegacity,similarmeasurescouldbedevelopedforanumberofdifferentindicatorsbyneighborhood,toassignaslumseverityindextoeach.ThisisaslightlydifferentapproachtotheinhabitabilityindexdevelopedforthesameproblemsetusingimagerydatafromthemegacityofRecife,Brazil (Filho&Sobreira,2007).Tobecertain,a slumseverity indexwouldvarybyclimateorculture.Forexample,an index ina tropical,mountainousLatinAmericanclimatewouldhavedifferentindicatorsthananindexinadesertbiomeintheAfricanSahel.

Figure 19: ROC Curve applicability to slum severity measures


What can remote sensing tell us about stability?

Remotesensingcandescribethecharacteristicsofplaceover time(using imagerytime series), but it cannot explain theattitudes andbeliefs of residents in a givenimage scene. Therefore remote sensing provides context and background uponwhich furthermodeling and analysis canbe conducted.Questions that canbe an‐sweredbyremotesensinginclude:

Whatistheweather(precipitation,landsurfacetemperature,soilmoisture)normallylikeinagivenregionduringaparticulartimeofyear?(prediction,Bayesianestimation)

Whataretheroadsurfaces,meanslope,andcurvaturetounderstandtraveltimesinaregion,ortherelativeaccessibilitytoknownhumanneeds(mar‐kets,healthfacilities,schools)

Whattypesofcropsaregrownandwhatistheircondition(phenology),andaretheychanging,expandingorshrinking?

Howcongestedaretheroads,parkinglots?

Whatistheproximitytohazards,bothman‐madeandnatural(floodplains,steepslopes)towhichresidentsareexposed?

Whatisthebuildingdensity,andthus,estimatedpopulationdensity?

What is thequalityof thesoilandwhereare the impervious surfaces,andhowdoesthisimpactrunoff,flooding,erosion,orstreamhealth?

Howmuchvegetation isnormally foundwithin residential areas,how is itdistributed?

Whatisthedisasterriskforlandslidesandflooding(terrainandhydrologicmodeling)?

Eachofthesequestionscanbeansweredfromimagery,andtheirresultscanbede‐scribed geospatially or temporally given the correct image product. Those resultscanthenunderlay,inform,orbecomeinputvariablesforneighborhoodanalysisinamegacity.

Merging social media analysis with remote sensing for megacity neigh‐borhood study

Giventhathumanopinionsandattitudescannotbemodeleddirectlyfromimagery,a possibility is to fuse imagery‐derived variables with social network analysis toevaluatewhether landscapeandsettlementcharacteristics impactmodeloutcome.Techniqueshavebeendevelopedthatextractviewpointsfromusersofsocialmedia


and place them by location1. Ambient geospatial information about an event orphenomenon(earthquake,forexample)cantemporallydepictthecenterofmassorconcern (re‐tweets) for anevent (Stefanidis, et al., 2011).Physicalhazards, trans‐portationcorridors,weatherpatterns, terraincharacteristics,andvegetativecoverare examples of remote‐sensing derived data that can now be modeled togetherwithsocialmedia‐derivedambientgeospatialinformationforthefirsttimeinhisto‐ry. This fusion of remote sensing‐derived proxy variables with geo‐located socialmedia information could provide new insight for understanding how social con‐cernsvarybylocation.

Expanded use of remote sensing data for surveys and polling

Inadditiontosynthesizingattitudesandviewpoints fromsocialmediawithvaria‐bles input from remotely sensed data, opportunities also exist to bind imagery‐deriveddatawithpollsandsurveys.Thelaborintensiveness,costandrisksassoci‐atedwith field‐based researchmust beweighed by their benefits, but can be en‐hanced in the early stages by setting up sampling strategies that are informedbyplace,terrain,andneighborhood.Researcherscandevisesurveyinstrumentsstruc‐tured by neighborhood type instead of by potentiallymeaningless administrativeboundariesinamegacity,byusingsurrogateinformationaboutrelativewealthandpovertytoinsertcontrolsandremovepotentialbiasinthesurveyinstrument.Hereagain,theboundaryproblemsurfaceswhensurveyresultsareaggregatedtoapolit‐icallydefinedborder.Butthisneednotbethecase.Structuralimagery‐basedanaly‐sisofneighborhoodswithinmegacitiescouldensurethatcomprehensive,systemat‐ic sampling strategies account for different economic zones or different terrainwithinamassiveurbanregion.Fieldsurveyorsorpollsterswhoknowtheyarelike‐lytobedelayedbyinaccessiblestreets,teamingmassesofdensely‐populatedhous‐ing,orconversely,theelitewhohavenotimetorespondtosurveyquestionscouldbenefitfromtheknowledgegainedusingremotesensing‐derivedinformationwhendesigning and implementing surveys. This represents another examplewhere thefusionofimagery‐derivedinformationwithsocioculturalmodelingmayhelpexplainsome of the underlying factors that influence behavior differentially by neighbor‐hoodinmegacityslums.

Conclusion

Althoughremotesensingcannottelluswhetheraregionorneighborhoodisstatis‐tically likely to be politically or ideologically unstable (thereby increasing risk forarmedconflict),theincorporationofvariablespopulatedfromimagerysourcesintosocio‐culturalmodelsisanewdirectionthatcouldenhancetheresultsbyinsertingspatiallymeasurableobservations.Theoutcomecouldincludestatisticalandspatialcorrelationwithphysicallandscapecharacteristics.Itishighlyadvisable,therefore,thatthecapabilitiesofsocio‐culturalmodelersandremotesensingimagescientistsblendwithgeospatialanalysistoprovideanewtypeofproduct–onethatisbetterabletodescribehumanbehaviorbylinkingpeopletothecharacteristicsofthephys‐icalenvironmentinwhichtheylive.

1 See MacEachren et. al (2011). Geo-Twitter Analytics: Applications in Crisis Management, In: 25th Inter-

national Cartographic Conference, Paris. With bibliography at: http://www.geovista.psu.edu/publications/2011/MacEachren_ICC_2011.pdf


References

Cowen,D.,&Jensen,J.(2001).ExtractionandModelingofUrbanAttributesUsingRemoteSensingTechnology.PeopleandPixels:LinkingRemoteSensingandSo‐cialScience.Washington,DC:NationalAcademiesPress.

Filho,B.,&Sobreira,F.(2007).AMultiscaleAnalysisofSocio‐SpatialPatterns.Pre‐sentedatthe10thInternationalConferenceonComputersinUrbanPlanningandUrbanManagement,SaoCarlos,Brazil.

Jain,S.(2007).UseofIKONOSsatellitedatatoidentifyinformalsettlementsinDeh‐radun,India.InternationalJournalofRemoteSensing,28(15),3227–3233.

Leachtenauer,J.C.,Daniel,K.andVogl,T.P.1997.DigitizingCoronaimagery:Qualityvs.cost.InR.A.McDonald(ed.),Corona:BetweentheSun&theEarth,ThefirstNROreconnaissanceeyeinspace,p.189‐203.AmericanSocietyPhotogramme‐tryandRemoteSensing.

Openshaw,(1984).TheModifableArealUnitProblem,Norwich:GeoBooks.Owen,K.(2012a).GeospatialandRemoteSensing‐basedIndicatorsofSettlement

Type–DifferentiatingInformalandFormalSettlementsinGuatemalaCity.PhDDissertation,GeorgeMasonUniversity.

Owen,K.andWong,D.(2012b).Exploringstructuraldifferencesbetweenruralandurbaninformalsettlementsfromimagery:thebasurerosofCobán,GeoCarto,Ac‐ceptedSept2012.

Owen,K.andWong,D.(2012c).Apracticalmultivariateapproachtodifferentiateinformalsettlementsusingimage‐basedandgeospatialmetrics,(forthcoming)AppliedGeography.

Owen,K.,Obregón,E.,andJacobsen,K.(2010).AgeographicanalysisofaccesstohealthservicesinruralGuatemala,InternationalHealth,2(2)143‐149.

Stefanidis,Crooks&Radzikowski(2011).Harvestingambientgeospatialinfor‐mationfromsocialmediafeeds,GeoJournal,3Dec2011.

Stoler,Daniels,Weeksetal.(2012).AssessingtheUtilityofSatelliteImagerywithDifferingSpatialResolutionsforDerivingProxyMeasuresofSlumPresenceinAccra,Ghana,GIScience&RemoteSensing49(1)31‐52.

Thomson&Hardin(2000).Remotesensing/GISintegrationtoidentifypotentiallow‐incomehousingsites,Cities,Vol17(2):97‐109.

Weeks,Hill,Stow,Getis&Fugate,(2007).Canwespotaneighborhoodfromtheair?DefiningneighborhoodstructureinAccra,Ghana,Geojournal69(1‐2):9‐22.


ChapterEight,ModelingtheDiscourseofMegacities:AssessingRemotePopulationsintheNon‐WesternWorlds

Mr.KalevH.LeetaruandDr.CharlesR.EhlschlaegerYahoo!FellowinResidence,GeorgetownUniversity;ERDC‐CERL

[email protected]

Abstract

Megacitieswillplayanoutsizedroleinglobalpoliticsandnationalstabilityoverthecomingdecadeswithsomeestimatessuggestingnearlythreequartersoftheworld’sinhabitantswillbelivinginsuchurbanenvironmentsby2050.Fromwateraccesstosanitation, floodingtohealthcare,communicationstotransportation,megacitiespresentahostofuniquechallenges,includingthevastslumareasthatsurroundorresidewithinthem. Theseurbanslumsarecharacterizedbyaconstant influxandoutflowofmigrantworkers, highun‐ orunder‐employment, and complete lackofauthoritative infrastructure.Developingworldmegacitiesoftenhavehigh levelsoftension between residents, the government, and urban elite as gentrification anddevelopmentconstantlyerodeordemolishhousingandresourcesforthepoor.Cit‐ieslikeDhaka,BangladeshorLagos,Nigeria,havenothistoricallybeenfocalpointsof Western diplomacy and the lack of knowledge of their informational environ‐mentsandculturalnarrativesposesuniquechallengestoanalysis. Whileevenur‐banslumareasarecharacterizedbyhighcellphonepenetration,theaveragenon‐elite citizen is not necessarily walking through the streets blogging, tweeting, orfacebookingtheirdailylives. Themassiveandrapidinfluxofdiversecultures,theitinerantnatureoflargeportionsofthepopulation,andlackofhistoricWesternex‐pertise in these regions complicate the constructionof the culturalnarrativesandsourcecatalogsatthecoreofOSINTanalysis. Inthisstateofconstantfluxandin‐formation scarcity, how does one effectively measure, monitor, and understandmegacities?

Keywords

SocialMedia,MediaAnalysis,Megacities,FieldSurveys

How Are Megacities Different?

Toanswerthequestionofhowoneassessesthepopulationsofmegacities,onemustfirstbeginbyasking thequestion “justhowaremegacitiesdifferent?” How isex‐ploring the population of a megacity like Dhaka or Lagos any different from as‐sessingthatofacity likeMoscoworParis? Whatarethegreatestsimilaritiesanddifferencesbetweenmegacities and themore traditional large cities thatWesternOSINThasbeenmonitoringforthebetterpartofacentury?Atfirstglance,therap‐idgrowthandconstantinfluxofahighlyheterogeneouspopulationintomegacitiesmightsuggestthattheprocessofnarrativeconstruction,socriticaltounderstandingand contextualizingmediamessaging and impact,may bemuchmore complex inmegacities.Mostmegacitiesarealsoinareasoftheworldthatinvolveculturesandlanguagesnotashighlyrepresentedwithintheintelligenceandmilitarycommunity


and thus might present a more “foreign” analytic environment more difficult foranalyststoimmersethemselvesin.

Another keydifferencewould seem to lie in the limitedknowledgebase of the in‐formationsourcesofthesecities. Overthe75yearsofformalizedWesternOSINT,richcatalogshavebeencompileddocumentingeveryknownwhiteandgrey infor‐mationsourceincitiesof interest. TheOSINTcommunityhasfocusedonbuildingandactivelymaintaining complexnetworksof resources tomonitor activities andperceptions throughpublicsourcesandhasdeveloped thedeepculturalexpertisenecessarytointerpretandtranslatethosetrendstoplacethemintotheirrespectivenarrativesandpolicymakingneeds.Yet,mostofthenewemergingmegacitieshavenot historically been locations of substantial interest to Western policymakingneeds. The knowledge of themedia system and cultural narratives of a city likeDhaka,especiallyitsslumareas,palescomparedwiththevastexpertiseonMoscowassembled over many decades of intense focus. One might therefore argue thatmegacitiespresenttwocriticallyuniquechallengestopopulationassessment:ana‐lyticcomplexityandinformationaccess.

Analytic Complexity in Megacities

Megacitiespresentahighlyuniquechallengetoanalystsattemptingtounderstandtheir cultural narratives and rhythms. Most apparent is the lack of depth amongWestern analysts in the geographic locales and cultures ofmany of the emergingmegacities. Overnearlyhalf a centuryof theColdWarAmerican intelligenceandmilitaryanalystsdevotedsubstantialfinancialandhumanresourcestothemonitor‐ingandunderstandingofRussiansociety. TothisdayRussianremainsoneof theprioritylanguagesoftheintelligencecommunityandRussianrepresentsthesinglemostcollectedlanguagebytheOSINTcommunityafterEnglishoverthepastquar‐tercentury. ContrastthiswithBengali, the languageofDhaka, forwhichjustovertwelvehundredarticlesweremonitoredbytheForeignBroadcastInformationSer‐vice(FBIS)overtheentireperiod1974to1996.

Inadditionto linguisticbarriers, thenewwaveofmegacitiesareemerging ingeo‐graphic locations and cultures that are often comparativelymore “foreign” to theWestern‐trained analysts assigned to understand them. While Russian society isdifferentthanAmericansocietywith itsownnarrativesandculturalhistory, therearesufficientsimilaritiesandsharedheritagewithotherEuropeannationsthatananalystcanatleastlargelymakesenseofit. Thedevelopingworldwherethenewwaveofmegacitiesareemergingareoftencharacterizedbycomplextribal,ethnic,linguistic, religious, familial, and societal affiliations and interconnections not ascommonintheWesternworld.

Complicatingthisistherural‐to‐urbanmigrationthatfeedstheenormousgrowthofmegacities, leadingtoahighlydiverseinfluxofculturesandsocietiesthatchangespopulationcomposition faster thananalystscanproperlymakesenseof it. Whileother largecities likeMoscowandParisexperiencetheirownurbanization‐drivengrowth,suchexpansionisstillrelativelyhomogenousandchangestotheirunderly‐ing population demographics occur far more slowly than megacities. Thus, theshearspeedofmegacities’growthandtheculturaldiversityoftheruralpopulation


theybringtogetherpresentanevengreaterchallengeinthatanalystsmustbringtobearexpertiseacrossamuchwiderandconstantlygrowingsetofcultures.

Atthesametime,theincreasingroleofnon‐stateactors,increasedsubnationalcon‐flictsoverthepastquarter‐century,andespeciallytheriseofsocialmediaoverthepastdecade,haseffectedasimilaraccelerationofchange.Thesechangeshavealsoeffectivelyremovedgeographicdistanceasabarrierto interaction. Today,peoplefromacross the globe catalog for the entireworld their daily lives and innermostdreamsandfears.Ideas,beliefs,andperspectivesthatformerlyweregeographicallycircumscribedtoaspecificsocialorculturalgrouparenowavailablefortheentireworldto interactwith. Inessence,whilemegacitiesmaybeuniqueforthewayinwhichtheyrapidlybringsuchdiverseculturestogethergeographically,thisprocesshasactuallyalreadybeenunderway in thevirtualworld through theriseofsocialmedia.Ifiteverwaspossibleinthepasttoexhaustivelymapoutthemediasystemofanationandallofitsculturalnarratives,recordtheminabook,andplaceitonashelfforadecadebeforeitneededupdating,thattimehaslongpast.

From1945until1974theUSOSINTcommunityneededonlyissueitsglobalcatalogofradioandtelevisionbroadcaststations(“BroadcastingStationsoftheWorld”)onanannualbasis:suchwastherelativelyslowrateofchangeofthemediaecosysteminmostnations.Astheriseofcableandsatellitetelevisionrelaxedgeographiccon‐straints on information consumption in parallelwith exponentially growth in thenumberofavailablechannels,maintainingthisglobalcatalogbecamemoredifficult.Suchacatalogtodaywouldbeoutdatednearlytheseconditwasfinished.Withup‐wardsof600newwebsitesbeingcreatedeveryminute, tensof thousandsofnewblogslaunchedeveryhour,andhalfamillionnewdailyusersjoiningFacebookeachday,themediaenvironmentofthedigitalageisinastateofnear‐constantflux.Thisdoesnotmeanthatwecannotmeasuretheonlinemediaworld; itmeanswemustmeasure it farmore rapidly while constantly reassessing its changing narratives.Thisisespeciallytrueashighlydiverseandoften“foreign”culturesintersectinfun‐damentallynewways.

Comparedtotraditionalcities,megacitiesoftengrowfasterandwithanacceleratedthe rate of cultural assimilation and interaction. This requires that the interval atwhichmegacities’communicationecologymustbeassessedbereducedconsidera‐bly. Inotherwords,thechallengespresentedbymegacitiestoremoteassessmentarenotthatdissimilarfromthoseoftherapidlychangingonlineworld.Thehistori‐calapproachoftheintelligencecommunityofhiringindividualswithin‐countryex‐pertiseandthenplacing theminacubicleacross thePotomac for therestof theircareermustgivewaytocloserandmorefluidintegrationtoabsorbthismorerapidrateofchangeoftheinformationlifecycle.

Information Environment Differences

Inadditiontogreateranalyticcomplexitycausedbyfasterandmorediversegrowth,amoreseriousobstacletoassessingthepopulationsofdevelopingworldmegacitiesiscausedbytherelativescarcityandgreatercomplexityoftheirinformationenvi‐ronment.


Assessing the entirety of themedia environment in a nation, from secular to reli‐giousmedia,mainstreamtosocial,broadcast toprint,andunderstandingnotonlytheproduction,butthedemographicconsumptionofthatmedia,requiresasubstan‐tial and sustained investment over time that has not been made in the regionswheremanyofthenewmegacitiesareemerging. Intheareaswheresomeinvest‐menthasbeenmade, it isoftenon themorepopularmainstreammediaoronas‐sessingthefootprintofWesternsocialmedialikeFacebookandTwitter,whileanal‐ysesofdomesticsocialplatformslikeVKontake,andWeibooccurfarlessoften.

In order to better understand the communication ecology of megacities, we willneedtofusemultipletypesofinformationincludingsocialmedia,mainstreamme‐dia,fieldsurveys,governmentrecords,remoteimagery,andcellularrecords,asdis‐cussedinmoredetailbelow.

Social Media

In developing world megacities, social media use in particular is often highlyskewedtoanarrowsliceoftheurbaneliteandnearlynon‐existentintheslumareasweareoftenmosteagertounderstand.ArecentstudyofdomesticTwitteroutputduring theSeptember2013Pakistanearthquakeby theDigitalHumanitarianNet‐workonbehalf of theUnitedNationsOffice for theCoordinationofHumanitarianAffairsfoundthat“nonewereactuallyinformativeoractionable.”1 Thereportfur‐therfoundthatthe“disaster‐affectedregionisaremoteareaofsouth‐westernPaki‐stanwithaverylightsocialmediafootprint”andthus“therewaspracticallynous‐er‐generatedcontentdirectlyrelevanttoneedsanddamagepostedonTwitter…instarkcontrast toourexperiencewhenwecarriedoutaverysimilaroperation fol‐lowingTyphoonPablointhePhilippines.”Oneofthecoreconclusionsofthestudywasthat“ifthere’slittletonosocialmediafootprintinadisaster‐affectedarea,thenmonitoringsocialmediaisofnouseatalltoanyone.”

Whilethismightseemobvious,itisimportanttonotethatmanyoftheurbanslumareashavesimilarlylowsocialmediapenetration.Just5%oftheentirepopulationofBangladeshhasaccesstotheInternetandjust2%useFacebook,comparedwith81%ofAmericanshaving Internet access andmore thanhalf of themusingFace‐book regularly. More importantly, 78% of those Bangladeshi Facebook users aremale,largelybetween18and34yearsofage,meaningthesocialmediaecosystemof Bangladesh is enormously skewed towards a narrow slice of its urban elites.Whilethissuggeststhatsocialmediamaybelessusefulasaproxyofpopulationbe‐liefswithinmegacities inthedevelopingworld,TokyoandJakarta,thetwolargestmegacitiesintheworld,arebothamongthemostactivecitiesintheworldinsocialmediause,suggestingitmaybemoreusefulinthedevelopedworld.Buildingaccu‐rateneighborhood‐scalemapsofwhereland‐based,wifi,and2G,3G,and4Gcellularnetworksaredevelopedthroughouttheworldwillprovidefurtherunderstandingofthefutureofsocialmediapenetration.

1 http://irevolution.net/2013/09/27/results-of-micromappers-pakistan-quake/


Mainstream Media

Despitelowliteracyrates,localcommunityoutletsoftenprovidemediaservicesinslumareas,usuallythroughlocalizedlow‐poweredradiobroadcastsorgreylitera‐tureinpublicspacesthataremoredifficulttoaccessremotely.Mainstreammediain theurban core ismoreeasily accessible andpresents fewcollectiondifficultiesbeyond those of other cities other than thatmedia can bemore fractionalized inmegacitiesdue to thedensityofdifferentcultures. While tieronepress functionsthesameasinothercities,megacitiescanhavealargerprevalenceofmorelocalizedmediasuchasneighborhoodoutletsthatservespecificcommunities,especiallyse‐lectsocialorculturalgroups.Thestrongerroleofreligious,ethnic,andothersocialbondsinmanyoftheareaswheremegacitiesareemergingalsoleadstospecializedmedia,suchasIslamicmediainBangladesh,whichareaprimaryshapinginfluenceamongtheuneducatedpoorandlower‐middleclass. Yet,overall,mediaappeartofunction inmegacities inmuch the sameway theydo everywhere else across theworld.

Field Surveys

Fieldsurveys,especiallyface‐to‐faceinterviews,arethegoldstandardofpopulationassessments.Whether face‐to‐faceorviaphone, fieldsurveysaremoredifficult inmegacities,especiallytheurbanslumareas. Whilecellularphoneownershipisex‐tremelyhighinslumareas,theyareusednearlyexclusivelyforjobandinformationseekingratherthanforcasualconversationorentertainment,withairtimeminutesapreciouscommodity,makingphonepollingmoredifficult. Face‐to‐facefieldsur‐veyresultsbecomequicklyobsoleteasthedemographicsinanareachangeduetothehightransientpopulation,whilehighdistrustingovernmentandauthorityfig‐uresinslumareascangreatlyreducebothresponseratesandaccuracyofresults.

Ontheplusside,developingworldcountriesencouragingNGOparticipation,suchasBangladesh,aremuchmoreopentoNGOandpartnernationfieldsurveysaswellassharinggovernmentrecordsforphasezerooperations.OngoingresearchatERDCismaking iteasiertocombinemultiple fieldsurveys intoasinglesocio‐culturaldataframeprojectedontogeographiccoordinates.1

Government Records

Whileuseful in theurbanelite areasofmegacities, governmental recordsprovidepoor to non‐existent coverage of urban slum areas. Buildings are often erectedwithout government approval or oversight and power, water, and sewer connec‐tionsarefrequentlyillegallytapped.Manyareasdonotevenhaveregulargovern‐mentpresence.InpreparationsfortheWorldCup,Braziliangovernmentforcesaretakingcontrolofsomefavelasthathavenothadstandingpolicepatrolsfordecades.Thus,governmental recordsofslumsand theirpopulationsdonotcaptureacom‐prehensiveimageofsociety.Instead,asutilitycompaniesincreasinglyfightbackonillegal tapping, the detailed records they keep of the connections they locate and

1 A component of the SMA/ERDC project “Megacities – RSI” is prototyping this research effort with im-

plementation in the ASA(ALT) funded ERDC project “Phase Zero Assessment of Urban Security Threats”.


disablecanbeusedtotracethedevelopmentofinformalindustryintheseareas.Inthiswaythelackofregulargovernmentpresenceintheslumareasofsomemegaci‐tiesdoprovidesomeuniquechallenges toassessment,butnotnecessarilygreaterthanthatoftheruralareasofothernations.

Remote Imaging

Theapplicationofremoteimaginginmegacitiesissignificantlycomplicatedbytheirexceptionallyhighpopulationdensity. Thefiveresidential towersofSouthCity inKolkata, India for example togetherhousemore than seventeenhundred families,withasinglebuildinghousinghundredsofhousingunits.Whilenotnecessarilydif‐ferentfromothermajorcities,thegreaterrelianceonhigh‐densityresidentialtowercomplexesmakesassessmentmoredifficult. However,assuchcomplexesbecomeincreasinglypopularthroughouttheworld(7ofthe10tallestbuildingsintheworldareinDubaidespiteitnotyetreachingmegacityproportions),thisdistinctionwillfade.

PleaseseeChapterSevenbyDr.KarenOwenforanindepthdiscussiononhowhighresolution remote imagery can better measure socio‐cultural indicators in slumsandotherurbanareas.

Cellular Data

Cellularphonedatahas increasinglybecomeavailable foropenacademicresearchthrough so‐calledCellDataRecords (CDRs) that record themetadataofwho callswhom,when and for how long. To preserve privacy, these records arewiped ofidentifying informationandaggregated toasufficiently largegeographicregion tomakeidentificationofindividualsdifficult.Theyalsodonotincludeanyinformationaboutthecontentofthecall,onlythemetadatarecordingthatthecalltookplace.Agrowingbodyofacademicstudieshasdemonstratedhowsuchdatacanbeusedtoexplorethedailyrhythmsofasociety,fromestimatinglikelymigrationpatternsinthe aftermath of a natural disaster tomapping informal neighborhood networks.Whileatfirstitmightseemcounterintuitivetoutilizecellphonedatatomaptheur‐banflowofmegacities,especiallytheirslumareas,theyareinfactoneoftherichestdatasourcesonrealtimepopulationmovement.

Phonerecordsareparticularlygoodatmappingtheurbanslumareasforwhichfewotherdatasources,suchasfinancialtransactiondata,exist.Populationsinslumar‐easareoftenimmigrantsfromruralareaswhorarelyhavestableemployment.In‐stead, they use their cellular phone to seek and receive jobs,much as a rickshawdriveruseshiscarttoconstantlysolicitandreceivepassengers.Phonepenetrationin slum areas is therefore extremely high, but few aremodern smart phones andeven fewerhave largemobiledataplansavailable for socialmediause. Yet, eventhoughtheyarenotsignificantsourcesofsocialmediamaterial,phoneuseinslumareascanstillyieldsubstantialinformationonsocietalpatterns.Informaltranspor‐tation networks in particular can easily be captured through the population‐aggregated trail of cellular breadcrumbs, such as popular shortcuts andwhich al‐leys,paths and roadwaysaremost commonly taken. Evenmorepowerfully, suchdatacanbeusedtomapouttheinformalcommunitystructure,especiallyneighbor‐


hood boundaries, within a slum area, showing which communities interact mostcommonlywithinthemselves,wheretheywork,andhowtheygetthere.

Cellularpatternsareperhapstheonlyinformationstreamwhichisequallyavailablein both elite and slum areas andwhich exhibits little difference in availability inmegacitiesandordinarycities.Yet,attheirbest,CDRrecordscanshowusonlyso‐ciety’sdailypatternsandthedeviations fromthosepatternsthatareareactiontoeventsorchangesinbeliefs,butnotthecontextaroundthem.Theycanshowthatthenormally‐bustlingweeklymarketwasemptyorthateveryoneevacuatedfromacertainneighborhoodatacertainhour,butcannottelluswhy.Inthiswaytheyaremuch like satellite imagery,but captured from thegroundand thusunaffectedbytheresidentialdensitythatmaystymieoverheadimagery.

Putting It All Together

Therapidpaceofchange,highculturaland linguisticdiversityandperceived“for‐eignness”ofthenarrativeenvironmentofmegacitiescomplicateanalysis,whiletherelativescarcityoftheinformationenvironmentandlackofdeepexpertiseintheirmediasystemsmakescollectiondifficult.Yet,thesearealsothedefiningcharacter‐isticsoftheonlinesphereinwhichallcitiesnowresideandthusarenotuniquetomegacities.Non‐megacitiesmaybemoregeographicallyhomogenouswithalowerphysical influx of residents, but all cities today simultaneously exist in a rapidlygrowingandgloballyheterogeneousonlinespherethatmirrorsallofthesamechal‐lengesofmegacities. Thediversity of voices and culturesbrought into close geo‐graphicproximitywithsuchrapidspeedinmegacitiesarenodifferentthantherap‐idly‐shortening communicativedistance among theworld’s culturesonline. Fromthis standpoint, the same approaches andmethodologies being developed for un‐derstandingtherapidlychangingandunfamiliarenvironmentofsocialmediahavedirectapplicabilitytomegacities.

Fromthestandpointoftheavailableinformationenvironment,megacitiessimilarlydonotappeartobeasdissimilarfromtraditionalcitiesashasbeenportrayed.So‐cialmediauseisextremelylowinmegacitiesinthedevelopingworld,butitsuseindevelopedworldmegacitiesisamongthehighestintheworld.Ascellphoneinfra‐structure improves in the developingworld, therewill likely be quick transitionsfromlowtohighsocialmediausebysubpopulationwithindevelopingcountrymeg‐acities. Other thangreaterprevalenceof localmedia,mainstreammediaoperatesvery similarly to that of other cities. Field surveys and remote imaging aremoredifficult due to the extreme residential density and highly transitive population,whilegovernmentrecordsareoftennon‐existentinslumareas.However,thesearenot significantlymore dissimilar than in other cities. Cellular data provides evencoverageofbotheliteandslumareas,butattheexpenseofcontextdescribingtheunderlyingpatterns.

Megacities in the developed world have very high densities of social media use,meaningsocialchannelscanbeusedtoassesstheircitizenryinmuchthesamewaytheyareusedinanyothercityintheworld. Tokyo,whichwasoneoftheoriginalfocal cities ofWestern OSINT, has a rich and vibrant culture of social media useacrossawideswathofthepopulation.Atthesametime,thelargeinfluxofmegaci‐tiesinthedevelopingworldoverthecomingdecadedoespresentspecificchalleng‐


esinthatsocialmediamaynotbeaswidespreadorasrepresentativeoftheoverallpopulationinthosecities.

Literacy rates are still relatively low in the developingworld,which translates tohigherrelianceonbroadcastmedia,especially inurbanslumareas. Low‐poweredcommunityradio,broadcastingwithjustenoughsignaltobeheardbyothersintheimmediatesurrounds,addressissues,news,andbeliefsspecifictothelocalcommu‐nityandcanprovidehighlydetailed“neighborhood”mappingsofslumareas.Whileexceptionallyrichsourcesofinformation,theoutputoftheseradiostationsisinsuf‐ficienttocollectremotelyandthusmustbemonitoredlocally,greatlyincreasingthecost.Outsideoftheslumareas,however,broadcastandprintmediafunctionlargelythesameastheydoinanyothercity,albeitoftenwithalargernumberofavailablechannels,requiringgreaterresourcestomonitorthem.

Akeyquestion inallmegacities ishowtobestmapthemediaenvironment,espe‐ciallythedemographicsofitsproducersandconsumers.WhilefordevelopedworldmegacitieslikeTokyo,thisenvironmentisfairlywellunderstood,farlessisknownaboutmegacities likeDhakaorLagos. However,outsideof justahandfulofcoun‐tries,mostnationshaveprivateindustryinwhichcompaniesdevelop,manufacture,andsellproducts to thedomesticpopulation. Inorder topromotetheirproducts,companiesinmostplacesoftheworldbuyadvertisingtoputtheirbrandinfrontofpotentialcustomers. Anywhereadvertisingisboughtandsoldarichecosystemofmarket research, advertising, andmedia advisory firms develop that specialize inmappingoutthelocalmediaenvironment,whatmediaoutletsareusedinwhatare‐asandbywhatpeople,thedemographicseachreach,thetransferrateofmessagesacrossmediums,andingeneralhowtoreachanyspecificsegmentofthepopulation.Evencities likeDhakaandLagoshaveaburgeoningarrayof suchcompanies thatcanreadilyprovidestatisticsonwhichradioshowsaremostpopularinaparticularcommunity,orhowwidespreadTwitterusageiswithacertaindemographic.

Conclusion

Puttingallof this together,megacitiesare foundnot tobeexoticentities thatwillrequireenormousinvestmenttounderstand,butratherfamiliarconstructstothosewealreadyfocuson. Inparticular,socialandmainstreammediaprovidethesamepowerfulopensourcesignals inmegacities that theyhaveprovided inother largecitiesoverthepastthreequartersofacentury.Thelargestdifferenceappearstolieinthegeographiclocationofmostofthenewmegacitiesinthedevelopingworld.InessencethegreatestchallengeofmegacitiesisthepivotingofattentiontoareasoftheworldnotpreviouslyemphasizedbytheWesternOSINTcommunity,ratherthanofcharacteristicsuniquetomegacitiesthemselves.


ChapterNine,HowSemi‐AutomatedAnalysisofSatelliteImageryCanProvideQuickTurn‐AroundAnswerstoLargeScaleEconomicandEnvironmentalQuestions

IdaEslami,SeanGriffin,ElizaBradleyNationalGeospatial‐IntelligenceAgency

[email protected],[email protected],[email protected]

Abstract

Civilsatelliteimagerytime‐series,fromsuchplatformsasNASA’sModerateResolu‐tion ImagingSpectroradiometer (MODIS) andUSGS’sLandsat, canprovidekey in‐sightsintoenvironmentalandeconomicconditions.Researchersusethesedatasetstomonitor large‐scalespatiotemporalLandUse/LandCover(LULC)changes,e.g.the expansion and composition of urban areas, crop abundance and predictedyields, and the availability of natural resources. However, completing time seriesanalysiswiththeseinherentlynoisydatasetscanbealaboriousandintensivepro‐cess,asanalysisiscomplicatedbyvariableimagingconditions,includingsensorar‐tifacts,cloudcover,andshadows.Inthischapter,wedescribethedevelopmentofanENVI/IDL‐basedTime‐SeriesToolkit(TSTK)tomitigatethesechallengesandgreat‐lyexpeditetheprocessoftime‐seriesexploitation.

Keywords

MODIS, NDVI, Agriculture, Time Series Analysis, Environmental Applications, FastFourierTransform,HarmonicAnalysisofTimeSeries(HANTS),Savitzky‐GolayFil‐ter

Background and Introduction

U.S.interestinboththewarsinIraqandAfghanistanrangednotjustinmilitaryac‐tion but also in capacity building, infrastructure repair, and job growth. This re‐quires understanding Iraq and Afghanistan’s economic and environmental statusandevaluatingagriculturalandnaturalresources.Thisinformationcanbeespecial‐lycriticalgivenmanyareaswith localdependenceonagriculturalcapacity tosus‐tainlivelihoods.Thesecountriesalsosufferfromcyclicaldroughtsandfloods,whichcanleadtofurtherinstability.Insupportoftheefforttomonitoragriculturalcondi‐tionsandwaterresources,theU.S.Government(USG)beganusingprocessedNASAcivilimagerytoidentifylargescalechangesinlandcover.Thesedatawereutilizedtodeterminetheoverallvigorofvegetation,waterresourceavailability,andtoat‐temptpredictiveanalysisofimpendingfoodscarcityissues.

MODIS Imagery

Inordertoaccomplishthesetasks,theUSGbeganemployingNASA’sModerateRes‐olution ImagingSpectroradiometer (MODIS) imagery.MODIS sensorsareonboard


NASA’s Terra and Aqua platforms and each have 36 spectral bands. Additionally,MODIS imagerycoversmostof theworldtwicedaily, includingtheUSGregionsofinterest.MODISimageryisusuallyavailablewithinhoursofcollectionandhasvary‐ingresolution,rangingfrom250metersto1000meters.TherawMODISimageryisinitiallyprocessedbytheGlobalAgriculturalMonitoring(GLAM)project,acollabo‐rativeprojectundertakenbytheUSDepartmentofAgriculture’sForeignAgricultureService,NASA,andtheUniversityofMaryland, toprovidetailoredMODISNormal‐izedDifferenceVegetationIndex(NDVI)datain16‐daycomposites.NDVIisanim‐agebandratiothattakesthedifferencebetweenthereflectanceofthenear‐infrared(NIR)andredbandsdividedbythesumoftheNIRandredbands.Ingeneral,abun‐dantvegetationischaracterizedbyhighNDVIvaluesduetovegetationstronglyre‐flecting in theNIRbandandweakly in theredband.Waterreservoirsurfaceareacanalsobe calculated fromthecompositeNDVIproductaswater is assigned toaunique integervalue (50).TheNDVIproduct isprocessed to reducecloudandat‐mosphericcontaminationandisprovidedinlargeareacoverageGeoTIFFtiles.Thetilescover71regionsaroundtheworld.Inordertoutilizethedata,theUSGfurtherprocessestheNDVItobecomeusableanalyticproducts.

Methodology

TheTime‐SeriesToolkit (TSTK,PointofContact–SeanGriffin)wasdeveloped in‐ternally at the National Geospatial‐Intelligence Agency (NGA) as a plug‐in for theENVI/IDLimageprocessingsoftware(ExelisVisualInformationSolutions,Inc.).TheTSTKcanbeutilizedformanydifferentmonitoringapplications, includingagricul‐tureandwaterreservoirs.Foragriculture,itcanbeusedtomonitorhowvegetationchangesover timeand todevelop abaseline for comparison, e.g. inter‐ and intra‐seasonalvegetationgrowth.Thetoolalsosupportsassessingtheagriculturalimpactofnaturaldisasters,suchasdroughtor flooding.Forwaterreservoirs,quantifyingsurfaceareathroughtimeprovidesameasurementofrelativewaterlevels.

TheavailableNDVIdatafromGLAMrangesfrom2000tothepresent.OncethedataisretrievedfromGLAM,itrequiresfurtherfilteringtohelpeliminatebaddata,snowcover,andotherartifactsthatmaycreateanomalies inthedataset.Thetwofiltersused for thismethod include theHarmonic Analysis of Time Series (HANTS) andSavitzky‐Golay (SavGol) filters.HANTS is amodifiedFast FourierTransformalgo‐rithm that screens and removes cloud contaminated observations and allows fortemporalinterpolationoftheremainingobservationstoreconstructgaplessimagesataprescribedtime.TheSavGolfilterusesamethodoflinearleastsquareswithalow‐degree polynomial to fit equally spaced successive sub‐sets of adjacent datapoints,exampleresultsareshowninFigure10.


Figure 20: Example of smoothing noisy MODIS NDVI data values using the Savitsky-Golay Filter

Agricultural Application

TheagriculturalassessmentmethodologyusingTSTKisoutlinedbelow:

1) Create a layer stack of MODIS NDVI subsets based on sequential time periods and region of interest (ROI).

2) Using an ENVI Region of Interest (*.ROI) or ENVI Vector File (*.EVF) file, subset your layer stack to your area of interest.

3) Apply HANTS filter (typical application: interpolated with good data from 51 – 250 (eliminates water which has a value of 50), frequency of 0‐2, fit tolerance of 60, and maximum iteration of 10).

i) Further smooth the data with HANTS (requires a water mask)

ii) Convert 8‐bit integer values to original floating point NDVI values(i.e., 0.0 to 1.0)

4) Apply the SavGol filter for additional signal processing. This step is optional.

5) Identify potential crop of interest (e.g., wheat, barley, rice, cotton, etc.).


a) Determine ideal growing season of crop within the region of interest using a combination of signal interpretation and crop calendars. Best practices would also involve applying an analysis mask that would distinguish agricultural re‐gions from forested, urban, desert and other non‐agricultural regions. If more specific crop masks are available for certain crop types, those should be used in order to reduce the inclusion of non‐crops of interest.

6) Identify peak NDVI.

a) After narrowing down the crop to the harvest season, the TSTK allows the user to determine the peak NDVI value during that time period. The user should at‐tempt to narrow down specific peak NDVI dates for their time periods of inter‐est and apply these to the baseline comparisons.

(i) Determine peak NDVI date during the ideal season (i.e., summer vs. winter crops) Use TSTK to develop difference products that either provide an absolute difference or percent change in NDVI.

7) Output difference products to assess change.

a) TSTK can develop GIS‐ready NDVI analysis products that either compares the current year with the previous or with the 5‐year and 10‐year average for the date of interest.

b) The final product can be displayed in ArcMap and assigned a color legend using standardized layer files.

Examples of the output would include a classification map that shows the percent dif‐ference of the current crop NDVI from the 10‐year average.

Water Reservoir Application

The water reservoir methodology is outlined below:

1) Create a layer stack of MODIS NDVI subsets based on sequential time periods and region of interest.

2) Using an ENVI Region of Interest (*.ROI) or ENVI Vector File (*.EVF) file, subset your layer stack to your reservoir of interest.

3) Visualize and apply user‐defined SavGol filter parameters (typical is 5x5, Degree of Polynomial is 2). The SavGol filter in the TSTK was modified to not only remove noisy data values, but it converts the stretched 8‐bit NDVI integer values to the original NDVI value range from 0 to 1, with water assigned to a value of zero and non‐water assigned to values greater than zero.

4) Open the processed layer stack in the MODIS Water Monitor application (an inte‐grated feature available in the TSTK):

a) Select your area of interest (see #2 above)


5) Modify the date range of interest (the default is to use the entire date range of the time‐series dataset)

a) There is an option to modify the sample dates from 16 days (as in one compo‐site per data point) to 32 (2 composites/data point) or 48 (3 composites/data point). Modifying the sampling dates can be useful in eliminating areas that have consistently bad data throughout several 16 day composites.

6) Apply the contour fix that can often fill in bad data values that were not rectified during the initial smoothing process.

7) Use the MODIS Water Monitor to plot and visualize relative water levels for the res‐ervoir of interest (this feature plots the water pixel counts within a reservoir over the date range of interest).

a) Save out the plotted data to a comma separated value (.csv) file.

i) Open the .csv file in TSTK and you have the option of fine tuning the smoothing process. The final exported .csv file from this process is your end data set (and you can display it as you see fit).

b) Graph the results to compare current levels to the baseline.

i) Data can be presented using a quartile analysis to allow seasonal compari‐son. This involves plotting the relative surface area as a function of time of year in comparison to quartiles from prior years. For example, the 2013 rel‐ative surface area plot would be compared to the minimum, quartiles (25%, 50%, 75%), and maximum for years 2007‐2012. As shown in Figure 11, this can be used to identify when relative surface area is amongst the lowest seasonally over a given range of years.


Figure 21: Example of monitoring reservoir relative surface area and comparing this to prior years. In this example the 2013 relative surface area is amongst the lowest seasonally since 2007. Relative surface area (as count of 250-m water pixels for reservoir) for 2013 is plotted as a function of time of year and compared to the minimum, quartiles (25%, 50%, 75%), and

maximum

Conclusion

The Time‐Series Toolkit can be very useful for expediting civil satellite imagerytime‐series analysis forenvironmental andeconomicapplications.Asdiscussed inthis chapter, it can be utilized to assess vegetation and water reservoir changes,withdirect implications formonitoring food security and the stabilityof a region.Potential improvements includeexpansiontoothercivilsensorsand furtherauto‐mationofdifferentprocesses.


ChapterTen,UrbanSim:UsingSocialSimulationtoTrainforStabilityOperations

RyanMcAlinden,DavidPynadath,RandallW.Hill,Jr.UniversityofSouthernCalifornia–InstituteforCreativeTechnologies

{mcalinden,pynadath,hill}@ict.usc.edu

Abstract

AstheUnitedStatesreorientsitselftowardstoaperiodofreducedmilitarycapacityandawayfromlarge‐footprintmilitaryengagements,thereisanimperativetokeepcommanders and decision‐makersmentally sharp and prepared for the next ‘hotspot.’Onepotentialhotspot,megacities,presentsauniquesetofchallengesduetotheirexpansive,ofteninterwovenethnographiclandscapes,andtheiroveralllackofunderstandingbymanywesternexperts.Socialsimulationusingagent‐basedmod‐els isoneapproach for furtheringourunderstandingofdistantsocietiesand theirsecurity implications,andforpreparingleaderstoengagethesepopulationsifandwhentheneedarises.Overthepasttenyears,thefieldofsocialsimulationhasbe‐come decidedly cross‐discipline, including academics and practitioners from thefieldsofsociology,anthropology,psychology,artificialintelligenceandengineering.Thishas ledtoanunparalleledadvancement insocialsimulationtheoryandprac‐tice,andasnewthreatsevolve tooperatewithindensebutexpansiveurbanenvi‐ronments,socialsimulationhasauniqueopportunitytoshapeourperspectivesanddevelopknowledgethatmayotherwisebedifficulttoobtain.

Thisarticlepresentsasocialsimulation‐basedtrainingapplication(UrbanSim)de‐velopedby theUniversityof SouthernCalifornia’s Institute forCreativeTechnolo‐gies(USC‐ICT)inpartnershipwiththeUSArmy’sSchoolforCommandPreparation(SCP).UrbanSimhasbeenin‐usesince2009tohelpArmycommandersunderstandand train for missions in complex, uncertain environments. The discussion de‐scribeshowthesocialsimulation‐basedtrainingapplicationwasdesignedtodevel‐opandhonecommanders'skillsforconductingmissionsinenvironswithmultifac‐eted social, ethnic and political fabrics. We present a few considerations whenattemptingtorecreatedense,rapidlygrowingpopulationcenters,andhowtheinte‐grationofreal‐worlddataintosocialsimulationframeworkscanaddalevelofreal‐ismandunderstandingnotpossibleevenafewyearsago.

Keywords

Socialsimulation,missioncommand,POMDP,counterinsurgencytraining

Motivation

Backin2006,theUnitedStateswasdecisivelyengagedinmajoroperationsinIraqandAfghanistan.Thoughtraditionaloffensive‐defensiveoperationsremainedprev‐alent,thechallengesoffightingagainstorganizedyetsurreptitiousinsurgenciesandfactionsdrewwidespreadattention. Today, it iswidelyaccepted that futuremili‐taryleaderswillfacesimilarlystressfulanddemandingsituationsthatare,inmanycases,notcoveredbystandardtacticsanddoctrine(Smith,2005).Theseoperations,


whichcombinebothlethalandnon‐lethalaspectsofwarfare,havebeenreferredtoas“armedsocialwork,”inwhichmilitaryforcesattemptto“redressbasicsocialandpoliticalproblemswhilebeingshotat”(Kilcullen,2006).Theoverarchingchallengeis to develop leaders who possess adaptive expertise and function effectively incomplex environments, and to prepare them for novel situations unlike any theymayhaveexperiencedinthepast.

TheSchoolforCommandPreparation(SCP)atFt.LeavenworthistheprimaryArmyinstitutionforpreparingnewly‐selectedBattalionCommandersforalltypesofmis‐sions, including thosecenteredonprotectingandempowering indigenouspopula‐tionswhomaybeexperiencingnationalsecurity threatsof theirown. Theschoolhas an imperative to ensure their curriculum is updatedwith topics andmaterialthatbestpositionscommandersforsuccessoncedownrange.Toaddressthischal‐lenge back in 2009,USC‐ICT, in partnershipwith SCP, ArmyResearch Laboratory(Human Research and Engineering Directorate and the Simulation and TrainingTechnologyCenter), andArmyResearch Institute,developedan instructionalsoft‐waresuiteformilitarycommandersandtheirstaffstopracticedirectingandcoor‐dinatingoperationswith a “stability‐focused” component. TheUrbanSimLearningPackage(orUrbanSimforshort)focusespredominantly,butnotexclusively,onmil‐itaryoperationsinsupportofthelocalcitizenryandgovernmentthattakeplaceaf‐terprimaryoffensiveanddefensiveeffortshaveconcluded.Applyingtheprinciplesfrom Guided Experiential Learning (GEL) (Clark, 2004), UrbanSim was designed,developed,anddeployedwithastrongpedagogicalfocus.Theresultinglearningob‐jectives called for a complex,dynamic, yethighly realistic simulatedenvironment,which brought about the need to employ agent‐based research technologies andtransitionthemtosoftwarethatwouldeventuallybeusedinaclassroomsetting.

Mission Command & UrbanSim

The foundations for commanding in the Army are framed around the precepts ofmissioncommand(MC):Understand,Visualize,Describe,Direct,LeadandAssess.AseminalpreceptofMC requires the commander toblend theartof commandandthe scienceof control, focusingon thehumandimensionofmilitaryoperationsasopposedto technologicalsolutions.A toolkitofcommandercompetencieshelps tofeedcorefundamentals,includingMCdomainknowledge,communication,decision‐making,adaptability,self‐awarenessandself‐assessment.Thesecompetenciescan‐notbelearnedsolelyoutofabookorasasetofrules.Insteadtheyrequirepractical,tacit skills which typically are developed through experience, time, and with thehelpoffeedbackfrommentors,superiors,peersandsubordinates.

The UrbanSim training package specifically targets the need for practicing theseskills and techniques. Theapplication targets trainees’ abilities tomaintain situa‐tional awareness, anticipate second and third order effects of actions and adapttheir strategies in the faceof difficult situations. It allows commanders and theirstaffs to develop skills in executing the “art of mission command” in counter‐insurgency (COIN)or stabilityoperationsenvironments. The application includestwocomponents:(1)aself‐pacedPrimer;and(2)acomputergame‐basedpracticeenvironment;. Thetrainingexercisetypicallytakesonefulldaytoexecute. Itmayeitherbeconductedindividuallyinaclassroomsettingwithaleadinstructor(asis


doneatSCP),orinaStaffExercisewithdifferentplayersassumingtherolesofaBat‐talionStaff(BNCDR,S2,S3).

UrbanSimadheres to theGELmodelbyproviding learnerswithacompletecogni‐tivefoundationrequiredtoconductcomplex,dynamicoperationsrangingfromhighOPTEMPO,highlykineticandsecurity‐focused, to lower‐profile,governanceorde‐velopment‐focusedmissions.Theterminallearningobjectives(TLOs)oftheexperi‐enceinclude:

1. Achieve and maintain situational awareness and understanding in a complex environment

2. Balance offense, defense and stability. Understand the role of intelligence and reconnaissance security / raids

3. Anticipate 2nd/3rd order effects of decisions; tactical effects with strategic con‐sequences

4. Reinforce doctrinal principles of “Shape, Clear, Hold, Build”

TheseTLOsareexercisedthroughvariousstagesintheapplication,discussedinde‐tailbelow. Theenabling learningobjectives(ELOs)arecoreMCtopics that,whenexercisedatvariousphasesofthegame,helpsatisfytheTLOs.ELOsforUrbanSiminclude:

1. Mission Overview – understanding and interpreting higher‐headquarters (HQ) intent; higher‐HQ lines of effort (LOEs); and higher‐HQ information require‐ments (CCIR)

2. Mission Analysis – understanding the landscape in the area of operations (AO) (e.g. political, economic and military networks, key individuals, organizations and groups)

3. Mission Plan – being able to author a tractable, realistic commanders intent, formulating and monitoring LOEs, CCIRs and measures of effectiveness (MOE)

4. Mission Execution – directing action of subordinate units in support of a desired end state

5. Mission Assessment – being able to self‐assess performance along the LOEs and MOEs over time

TheUrbanSimapplicationhasbeenusedtotrainSoldiersinavarietyofinstitutionalsettingstoincludeSCP’sTacticalCommandersDevelopmentProgram(TCDP);vari‐ousCaptains’CareerCourses;theEngineerBasicOfficerLeadershipCourse; and the AMEDD SeniorLeadersCourseat JointBaseSanAntonio. UrbanSim has beenused successfully to stimulatebattalion‐level, battle staff exer‐cisesandtostimulatetrainingforCompany Intel Support Teams(CoIST) for Active and NationalGuardunitsatFt.Hood, the JointManeuver Training Center(JMTC), and the California Na‐


tionalGuard. ThetrainingpackagewastransitionedfromtheR&DcommunitytotheArmyGames forTraining(AGFT)Program inNovember2011and isavailablefor distributionArmy‐wide via theArmy’sMilGamingweb portal. UrbanSim alsotransitioned to theArmyLow‐OverheadTrainingToolkit (ALOTT)Program inDe‐cember2011andwasfieldedatJointBaseLewis/McChordinJune2012aspartoftheALOTTNewEquipmentTraining(NET)program.

The Primer

Thefirstcomponentoftheexperience,theUrbanSimPrimer,providestherequisiteconceptualandtaskknowledgerequiredforthelearnertoleadafull‐scalestabilityoperation, from analyzing background information via target folders and intelli‐gencebriefings,tocoordinatingtheactionsthatarecarriedoutinsupportofachiev‐ing a desired end state. Taking the form of an interactive tutorial, the UrbanSimPrimer is divided into nine lessons, each of which contain a narrative, interviewsegmentsfromformerCommanders,andassortedpracticeexercisesasameansofdemonstratingspecifictaskstothelearner.Takingapproximatelyonetotwohourstocomplete,theself‐pacedPrimerpreparesthelearnerforthesecondapplication,themorecomplexUrbanSimPracticeEnvironment.

Practice Environment

TheUrbanSimPracticeEnvironment is agame‐based tool thatallowsa learner toplan,prepare,execute,andassessafullstabilityoperation.Similartoaturn‐basedstrategygame(suchasCivilizationorAgeofEm‐pires), the learner directs subordinate units in the game to takeactionwithandagainstagents(i.e.non‐playercharacters,NPCs)ina virtual environment, and attempts to successfully complete amission using the products/strategies learned in the UrbanSimPrimerandintheclassroom.Actionsinthegamearetakenagainstkey individuals, groups, and structures in an area of operation(AOR)withtheintentofreachingthedesiredendstate.Eachturn‐cycle in the game represents one day in simulation time, thoughactionscantakemultipleturns(i.e.,days),andcanbeinterruptedifconditionsintheworlddonotallowtheactiontocomplete(e.g.,moneyrunsouttoconstructaschool).Uponcompletionofasce‐nario,thelearnerisbroughttoadebriefphasewhereasummaryof themission is presented for the learner to evaluate their pro‐gress.

Thegameisdrivenbyanunderlyingsocio‐culturalbehaviormod‐el,coupledwithanovelstoryenginethatinjectseventsandsitua‐tions based on real‐world experiences of former commanders. Italso includesan intelligent tutoringsystem,whichprovidesguid‐ance to trainees during execution, aswell as after action reviewcapabilities.

The fundamental scoringmechanism of the game (i.e., howwelltheplayerdoes)isviatheLOEs.TherearesixprimaryLOEs:CivilSecurity,Governance,HostNationSecurityForces(HNSF),Essen‐


tialServices,InformationOperationsandEconomics.Everyactioninthegamehasassociatedeffects,bothfirstandsecond‐order,ononeormorelinesofeffort. ThevalueoftheLOEschangesoverthecourseofthegameandattheendofthescenarioaresummarized for theplayer toseewhere their focusareaswere(security,gov‐erning,developing),andwhethertheywerealignedtheirdesiredendstate. TheseLOEs,aswellasallothersecondaryscoringvaluesinthegame(MOEs,CCIRs,etc),aredeterminedbytheunderlyingbehaviormodel,describedbelow.

UrbanSim’s Social Model

ThetechnicalchallengeofanygameorsimulationAIisthatitmustbebothexpres‐sivebutapplicationfriendly.Havingamulti‐tiered,self‐organizedandself‐steeringbehavior systemmay in theorybe realistic anddesirable, but canquicklybecomeintractableeitherintermsofperformanceorusability.Thereforeonemustcareful‐lydeterminewhattherequirementsareforanybehaviorsystemdrivinganunder‐lying experience. ForUrbanSim, thismeantbalancingexpressivity and realismofthemodelwithauthorabilityofscenarios. Toaccomplishthis,UrbanSimusestwoseparatebutcoupledAItechnologies:PsychSimandtheStoryEngine.Theintegra‐tion of these technologies is via UrbanSim's system architecture, which follows adata‐drivendistributionmodelwhereAI componentswork together ina synchro‐nouscycle.Eachcyclebeginswhenalearnerspecifiesasetofactionstobeexecutedbysubordinateunitsforthegiventurn.Theseactionsarethensenttoanintelligenttutoringsystemforevaluation,whichmay initiateaquestion‐answertutoringdia‐loguewiththelearner.Oncethisdialogueiscomplete,thelearnercommitstheac‐tionsandthesimulationcycleisexecutedandrepeated.

PsychSim

PsychSim is a multi‐agent system developed bythe University of Southern California (USC) thatmodelsbeliefsaboutothers toaffectbehaviorofsimulation entities. It is a framework for socialmodelingandsimulationthathasbeenused inarange of domains from analysis and planning tobasic research on human behavior (Wang et al,2012). InUrbanSim, entities aremodeled usingPsychSimandcanrepresentbothkeyindividualsand aggregate‐level features such as organiza‐tions, tribes, geographic regions or structures.The decision to aggregate was due to perfor‐mance and the objectives of the trainer.UrbanSimisnotamissionrehearsaltool,norwasitneededtoincludeanaccurate,validatedsocialsimulation.Instead,itisintendedtoteachcriticalthinkingskillsincommandersinenvironsthatarerepresentativeofwheretheymighteventuallyde‐ploy.Thoughinsomecasesthismayrequireahighly‐detailed,realisticmodeldowntotheindividuallevel, forthepurposesofthistrainerthatrequirementwasneverspecified.

ThePsychSimarchitectureisrootedintheTheoryofMind(ToM)principle.ToMistheabilitytoattribute[mental]statestooneselfandothers,andtounderstandthat


others have beliefs, desires and intentions that are different from one’s own(Premack, 1978). In PsychSim this refers to agents that have subjective perspec‐tives on others, and are able to potentially predicts others’ actions/reactions, butalso be able choose actions for themselves that will change the beliefs of others.Agentsalsohavetheabilitytocommunicate,distortandhideinformationto influ‐enceothers.InadditiontoToM,decision‐theoreticreasoningplaysapivotalpartinPsychSim. This reasoning states that agents are free to pursue their own goalsbasedontheirvaluesandbeliefs.Oftenagentsarepresentedwithconflictinggoals,or choices that must be made under uncertainty. In both cases, the agents willweighthetradeoffsandmakethebestdecisiongiventhesituation.

Todoso,eachagentgeneratesitsbeliefsandbehaviorbysolvingapartiallyobserv‐ableMarkovdecisionprocess(POMDP).APOMDPconsistsofstate,actions,transi‐tion,observations,andreward.ThestateofaPOMDPinUrbanSimrepresentsvari‐ous featuresof thedifferententities (e.g.,astructure’scapacity,agroup’smilitarypower,aperson’spoliticalsupport),eachareal‐valuednumberfrom‐1to1(e.g.,1means that the structure is functioning at 100% of capacity). Theactions are thechoicesavailable toeachagent (e.g., repairastructurevs.patrolaneighborhood),andthetransitionrepresentstheeffectsofthesedifferentchoicesonthestate.Theobservationscapturetheprobabilitythatcertainstatesandactionsarehiddenfromcertainagents.Therewardfunctionrepresentswhateachagentseekstoachieveintheworld(e.g.,maximizing itsownsecurity,minimizinganenemy’smilitarypow‐er).

GivenasetofsuchPOMDPmodelsfortheentitiesinthescenario,eachcorrespond‐ingPsychSimagentcanusestandardalgorithmstocomputeitsbestcourseofaction(Kaelbling, 1998).These algorithmsoperate byprojecting the effects of candidateactionsintothefuture,aggregatingtherewardresultingfromthoseeffects(aswellas theeffectsof theanticipatedresponsesby theotherentities),andselecting theactionwiththehighestexpectedreward.

InUrbanSim, thePOMDPmodels of theunderlying ‘society’were createdbynon‐technical subject‐matter experts (SMEs) specializing in Iraqi and Afghan cultures.ThebaselineUrbanSimscenario,al‘Hamra,contains92non‐playeragents.Thisde‐composes to over 1400 features and another 4700possible actions. This quicklygrowsexponentiallytoalmost450,000possibleeffectsofactions.Inanygiventurn,thereareapproximately1000differentactionsthattheplayercanchoosefrom,and1100possibleresponsesfortheagents.

OneofthecorefeaturesofPsychSim,andkeytoseveralofthelearningobjectivesofthegame,istheabilitytogeneratecausalitychainsofactorstocapturebothintend‐edandunintendedeffectsofagentactions.ManyAIsingamesandsimulationscov‐erwho,what,whereandwhenquitewell.Howeverthewhy(whichelicitscausality)hasprovenallusiveduetothecomplexityofmodelinghumancognitivefunctioninthemindsofnon‐playercharacters.ForentertainmentgameAIthisrarelyposesaproblem.NPCsareoftentacticalintheirbehavioranddonotrequireelaboratede‐cision‐making capability to execute actions like selecting theirweapon,moving tocontact, and even basic formation control. However for social simulation‐basedtrainingapplications,agentsrepresentindividualsandgroupsinasocietywithmyr‐iadbeliefs,desiresandintentionsthatmustworktogethertoproducecoordinated,


plausibleaction.Moreover, theseactionsmusthavemeaningfuleffectsthatcanbedescribedtotheuserinawaythataddstrainingvalue.InUrbanSim,thisisaccom‐plishedthroughcausalitychains. Causalitychainshelpestablishconcrete linkagesbetween an agent’s actions and their effects, and if the linkages extend multipleturnsandarepartofafullyconnectedsocietalgraph,itallowsustoaddressissuesrelatedtofirst‐order,second‐orderandthird‐ordereffects.

Story Engine

Even with a tight control over authoring in terms of the available actions toPsychSimagentsandtheirgoalstructure,thescaleandscopequicklybecomesdiffi‐culttomanage.Thoughagentsweretakingactionsthatwereplausibleandcontrib‐utedtowardsthepedagogicalexperienceoftheapplication,therewerespecificin‐stancesthatprojectSMEs(formercommanders)wantedtohighlighttostudentsastheyplayedthroughtheexperience.Thiswasdifficulttoforcewithacomplexmul‐ti‐agentsystem,andeventuallyledtothedevelopmentoftheStoryEngine.TheSto‐ry Enginewas specifically designed for instructors and SMEs to incorporate real‐worldeventsandsituationsinthegame.Theseeventscouldbestrungtogethertoformstoriesthatwouldplayoutovermultipleturns.

The Story Engine uses as input variable states fromPsychSim agents. The figurebelowpresentsanexamplewherePsychSimagentstookactiontokillanIraqipoliceofficer.Theeventchecksforwhenthisconditionoccursandthenlaunchesastory‐linethatinvolvesconductinganinvestigationandworkingalongsidethepolicechieftodeterminewhathappened.Theseeventswereauthoredtoalwaysoccur,regard‐lessofwhataction(s)theusersoragentsinthegametake.TheStoryEngineisin‐tendedtoconveykeyteachingpointsrelatedtothelearningobjectivesofthegame.

TheuseofdualAI technolo‐gies to driveUrbanSim hasallowed sce‐nario authorsand instruc‐tors to tailorthe experiencefor certain au‐dience types.IncaseswhereUrbanSim wasused to train

operationalcommanders

during StaffExercises,theheavyrelianceonstoryeventsderivedfromsimilarreal‐worldsitua‐tions theymightencounterwas important inhelping themand theirstaffprepareforconditionsdownrange.Incaseswhereclassroominstructorsweresimplycover‐ingthebasicsofMCwithnospecificoperationorregionin‐mind,thediversityandrichness of the multi‐agent system was sufficient for students to gain an under‐

Figure 22: PsychSim Example


standingofthecomplexitiesoftheenvironmentsinwhichtheymayfindthemselvesinoneday.

Way Ahead

The strategic trajectory of theUnited States in terms ofmilitary engagements re‐mains uncertain. It will obviously be influenced by geopolitical currents thatwemayormaynothaveinfluenceover.TRADOCcontendsthatthestrategicenviron‐mentwillbe“characterizedbymultipleactors,adaptivethreats,chaoticconditions,andadvanced‐technology‐enabledactorsseekingtodominatetheinformationenvi‐ronment. TheArmymust be operationally adaptive to defeat these complex chal‐lenges and adversaries operatingwithin this environment” (Operational Environ‐ments to 2028, 2012). The National Intelligence Council outlines four potentialworldsin2030,influencedby‘megatrends:’individualempowerment;thediffusionofpower;demographicpatternsdividingtheworldintozonesofpopulationgrowthand others with stable or even declining populations; and a food/water/energynexus that will lead to increasing competition for these commodities in places(GlobalTrends2030,2013).

1. “Stalled Engines” (a worst case scenario in which the United States draws in‐ward, globalization stalls, and the risks of interstate conflict increase);

2. “Fusion” (the most plausible best‐case scenario in which the United States and China collaborate on a number of issues leading to broader global cooperation);

3. “Genie‐Out‐of‐the‐Bottle” (inequalities within and between nations explode and the United States no longer manages world order); and,

4. “Nonstate World” (driven by new technologies, nonstate actors surpass states in confronting global challenges).

AsMetzpointsoutintheStrategicLandpowerTaskForceReport(2013),themostlikelyopponentsof theUSmilitaryarehybridcompositionsofmilitariesandnon‐military entities, or ‘evolved irregular threats’ (Flynn, 2011). Theywill be highlycomplex,adaptrapidly,relyonasymmetricmethods,andoftenoperateincongestedurbanareas.

Astrainingandtechnologycontinuetoevolvealongsideemergingthreatsfromthisfuturescape, one important capability is being able to accuratelymodel the socialenvironmentsinwhichwemayfindourselves. Thoughsocialsimulationhasmor‐phedsignificantlysincethedaysoftheVonNeumannmachineandConway’sGameofLife,investmentmustcontinuefromacross‐sectionofdisciplines(sociology,psy‐chology,anthropology,computerscience)tomakesocialsimulationsamainstayinfuturetrainingsolutions.Additionally,withtheinfluxofbigdatafromallcornersoftheglobeviasocialmedia, there isauniqueopportunityto incorporate it intoourmodelingapproaches.Forexample,combiningdataminingwithsocialmediaanal‐ysistechniques,wecouldadjustnon‐playeragentstomakechoicesbasedonspecif‐ic locations:DhakaandCairomighthaveverydifferent responsesprobabilities tothesamesituation.Notonlyhassocialmediabeenshowntoinstrumentchangeintherealworld(Casilli&Tubaro,2012),itprovidesthesocialsimulationcommunitywithavalidandusefultoolfordevelopingandtuningtheirmodels.Researchinthisspace remains scant, thoughas thisdatabecomesmoreavailable and researchers


understanditsutility(andlimitations),wecanexpect it tobeacorefoundationofsocialsimulationsinthefuture.

As highlighted in(Kotkin & Cox, 2013),of the top 10 fastest‐growing megacities intheworld,allareeitherin Asia or Africa. 10‐year growth in theseareas is between 35and 81%, yet our un‐derstanding of theseregions remains lim‐ited. Obvious culturaland social differencesmake them unique tostudy,andcombinethiswith complex andopaque political andeconomic structures,

there is aneed to findalternative approaches todevelopingourunderstandingoftheselocales.Oneapproachinvolvesusingdataminingandscrubbingtechniquestohelp a cross‐disciplinary team of anthropologists, demographers, social scientistsandengineersdevelopmodelsofpopulationsresidingintheseareas.

For UrbanSim, work is underway to develop a new set of models and scenariosbasedonTRADOC’sCommonTrainingScenarios (CTS) framework. TheCTS isanexpansivesetofusecasesthatcoveravarietyofoperationtypesfrommajorcombattostabilitytodisasterrelief.Atitscore,CTSattemptstobebothbroadanddeepinits coverage of potentialities. USC‐ICT isworking alongside social scientists, dataminersandmilitarySMEstodevelopastability‐focusedscenariowithastrongnon‐stateandcoalitionfocus.Onedifferenceinthedesignapproachfromprevioussce‐narioswillbetherelianceonsocialmedia feedsfromtheareaof interest– inthiscaseGeorgia,Armenia,Azerbaijan,Turkey(GAAT)–toseedthemodelingoftheun‐derlyingbehaviormodel.Thescenarioisscheduledforreleaseinmid‐2014.

Acknowledgments

Thisworkwas supportedby theUnited StatesArmyResearch,Development, andEngineering Command. Statements and opinions expressed in this material arethoseoftheauthorsanddonotnecessarilyreflecttheviewsoftheGovernment.Noendorsementshouldbeinferred.

References

Casilli,A.&PaolaT.(2012).SocialMediaCensorshipinTimesofPoliticalUnrest:AsocialsimulationexperimentontheUKriots,BulletinofSociologicalMethodol‐ogy,115(1).

Figure 23: Top Ten Fastest Growing Megacities


Clark,R.(2004)DesignDocumentforaGuidedExperientialLearningCourse.FinalreportoncontractDAAD19‐99‐D‐0046‐0004.

Flynn,K.(2011).TheEvolvedIrregularThreatProject,PaperfortheOfficeoftheSecretaryofDefense.Washington,DC:NoeticCorporation,2011.

Kaelbling,L.P.Littman,M.L.,andCassandra,M.L.(1998).Planningandactinginpartiallyobservablestochasticdomains.ArtificialIntelligence,101:99–134.

Kotkin,J.&Cox,W.(2013).TheWorld’sFastestGrowingMegacities.NewGeogra‐phy,April2013.http://www.newgeography.com/content/003630‐the‐worlds‐fastest‐growing‐megacities.

Kilcullen,D.(2006)Twenty‐EightArticles:FundamentalsofCompany‐levelCoun‐terinsurgency,MilitaryReview,May‐June2006(pp103‐108).

Metz,S.(2013).StrategicLandpowerTaskForceResearchReport.Accessed6Octo‐ber2013.http://www.strategicstudiesinstitute.army.mil/index.cfm/articles/STRATEGIC‐LANDPOWER‐TASK‐FORCE/2013/10/3.

OperationalEnvironmentsto2028:TheStrategicEnvironmentforUnifiedLandOp‐erations.FortEustis,VA:TrainingandDoctrineCommand,2012.

Premack,D.G.;Woodruff,G.(1978).Doesthechimpanzeehaveatheoryofmind?BehavioralandBrainSciences1(4)(pp515–526).

Smith,R.(2005)TheUtilityofForce:TheArtofWarintheModernWorld.London,UK:AllenLane,2005.

Wang,N.,Pynadath,D.V.,&Marsella,S.C.(2012).Towardautomaticverificationofmultiagentsystemsfortrainingsimulations.InIntelligenttutoringsystems(pp.151‐161).SpringerBerlinHeidelberg.

Documents

Understanding Megacities with the Reconnaissance ... · PDF fileWhite Volume April 2014 Understanding Megacities with the Reconnaissance, Surveillance, and Intelligence Paradigm Topical