Urban Informatics and Smart Cities: Prospects and ... · Lifelogging A custom 136° eye view lens, an ultra small GPS unit, Bluetooth, and 5 in-built sensors - ambient light / accelerometer

Urban Informatics and Smart Cities:

Prospects and Challenges with New Forms of Data

Piyushimita (Vonu) ThakuriahDean, Bloustein School of Planning and

Public PolicyDistinguished Professor of Transportation

and Urban Informatics

NTTS 2019Please do not distribute without permission

Urban Big Data CentreBloustein School/Rutgers University

Trends

Courtesy ETSI

An explosion of ICT solutions and data

Connected Infrastructure

Smart Buildings

Smart Transportation

Integrated Systems

Personal and Wearable Tech

Smart, collaborative, self-organizing systems

Bloustein School/Rutgers University

Generations of

“Smart Cities” Critical Ingredients:

ICT infrastructure; Effective resource management; Cost reduction and accountability; Performance monitoring.

Business-led development; Strengthened civic leadership; ICT-based urban innovations.

Well-informed and engaged citizens; Addressing problem causes in addition to

service delivery; Social innovations – innovative solutions for

urban problems; Social learning, education and social capital; Citizen choices and wellbeing.

Version 1: Smart Infrastructure

Version 2: Smart Innovations

Version 3: Smart Citizenry

Smar

t C

ity

Inn

ova

tive

C

ity

Futu

re C

ity


Intelligent Transportation Systems Structural Health Monitoring for

asset management Connected systems V2X:

Vehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) Vehicle-to-Grid (V2G)

One example -Connected, Cooperative and Anticipatory Transport Systems

Existing Information

Environment

Elements of New

Information Environment


Physical – low to high-tech (multi-modal transport, connected vehicles, smart buildings, V2G)

ICT –communications systems, sensor networks

Data

Infrastructure

Emerging Forms of “Big Data” for Urban Applications

A wide spectrum of naturally-occurring data:

Generated through transactional, operational, planning and social activities not all of which were specifically designed for research or the linkage of such data to purposefully designed data

Complexities associated with which (e.g. voluminous, heterogeneous, unstructured, hard-to-access) require special considerations: Technological Methodological Theoretical/epistemological Political economy

Urban Big Data Centre Bloustein School/Rutgers University

Data-intensive approaches to analyzing, visualizing, simulating, understanding, interpreting structured and unstructured data on

cities and urban areas to address complex urban challenges.

Urban Informatics

Edited volume of NSF workshop: “Big Data and Urban Informatics”


Urban infrastructure development and monitoring – building and monitoring transport, energy, ICT, water and other infrastructure systems

Urban operations management – transport operations and traffic flow management, energy management and optimisation, crime detection and prevention

Citizen engagement/civic participation – involvement in plan-making, design and idea-generation; crowdsourcing travel and other information

Urban design - create and maintain well-designed, good quality places and sites

Urban planning – large-scale: urban land-use planning, mega-infrastructure planning; small-scale: site design, brownfield planning and regeneration projects

Urban knowledge discovery – understanding emerging issues, behaviours, public mood, critical concerns

Urban policy analysis and evaluation – impact of proposed high-speed rail construction, crime prevention strategies

Big Data and Better Urban Living

Detection Understanding links,

causality and supporting processes

Forecasting and understanding the future

Evaluation of actions or potential actions

Engagement

Timeliness Fit for purpose Value-for-money Understanding biases,

uncertainty, robustness of findings

Keeping up with the rapidly changing data landscape – including privacy, citizen awareness and

Smart City Actions and Analytics


How to operate cities effectively and efficiently

How to build and manage robust and resilient infrastructure

How to evaluate potential consequences of complex social policy change on urban areas

What makes the economy resilient and strong – how to develop shock-proof cities

How to drive economic growth and revenue

How to support business innovation and economic competitiveness

How cities can recover from man-made or natural disasters

What interventions are needed for healthy behavior

What strategies are needed for lifelong learning, civic engagement and community participation

How does one address challenges of social exclusion

Grand Challenges for Urban Management


Social Hazards and Trust in Data- A need to balance the Good, the Bad

and the Ugly

New technology and data has many benefits in the urban space but also has the potential to lead to unfair practices

and unintended consequences


About 1.25 million people died in 2013 in road crashes worldwide (World Health Organization, 2013) – many in urban areas

Many types of traffic deviance leading to crashes are not random, but has a root cause in the same social conditions that result in concentrations of crime.

Crime and traffic crashes often spatio-temporally overlap in cities and are responsible for decreased accessibility and quality of life in cities.

Determine a more unifying approach and integrate operational and policy strategies.

BUT variable levels of reporting – incidents in some areas, especially poor, deprived areas tend to be underreported in official records

Joining up crime detection and safe transport

Urban Big Data Centre

System to help identify social and functional concerns and issues potentially for planning or operational action, eg, where people are not happy with services

The Sensing City: Real-time Monitoring of CitiesContext-Awareness and Semantic Enrichment Using Twitter to Understand Local Concerns and EventsCan we use language patterns detected in different parts of the city to understand underlying uses, activities, and concerns?

Detecting Road Incidents from Twitter data

Known incident from transportation sensor data from highways agencyNegative tweets –tweets posted when there is no incidentPositive tweets –tweets posted when there is an incident


Complexity of the problem

Significant concentrations of crime and crashes in micro-places, but also spread throughout city

Deep distrust of authority and contestedrelationships

Limited English speaking capacity in some areas and limited knowledge of social, medical and legal options

Problem with underreporting of crashes and crimes in some areas

Crime – a huge societal issue Study Area

City of Chicago 758 homicides in 2016 98 people killed, 2028 seriously injured

in 2014 (latest figures)


Predictive Analytics of Traffic Crashes and Crimes

Generally, crimes increase with crashes. Relationship is more evident at points less than the 90th percentile

Combined crashes and crimes is long-tailed to the right; calls for evaluating models at different points in the distribution

What factors predict crashes and crime (“events”)? – final goal:

Interested in quantiles: = .25, .50, .75, .95

Significant spatial dependence – Spatial Autoregressive version of quantile regression

( )i iEvents f X

Model-based Underreporting Correction for Traffic Crashes

In the OLS model, crashes tended to be overpredicted in suburban locations and underpredicted in the Chicago downtown business district (the “Loop”) and in southern areas of the City of Chicago

Crashes modeled with Poisson count data model with heterogeneity which accounts for exogenous underreporting –acknowledging that only a subset of the actual number of crashes that occurred are reported

Model I Model I

Poisson with Heterogeneity Poisson with Exogenous Underreporting

Variable Marginal Effect Marginal Effect

Intercept -4.21*** -2.13***

EJ_TRACT (1=”Yes”) 0.65*** 0.33***

TAI2 2.01*** 1.01***

PED_LOW 1.61*** 0.59***

SUM_AADT2 0.48e-06*** 0.24e-06***

SUM_LENGTH2 -0.28e-03*** -0.14e-03***

NO_SCHOOLS 0.19** 0.09**

POP_SQMILE 9.10E-06 4.60E-04

PERCRIME 0.24** 0.12**

PED 0.09*** 0.05***

WLKTOWRK 0.0008*** 0.0009

MEDHHINC99 -2.20E-07 -1.10E-06

PERNOCAR 2.60** 1.31**

PER_COMM 1.37 0.69

PERCHILDREN -2.09 -1.05

PERLOWENGLISH 0.21 -0.1

Probit Reporting Equation

Intercept 5.40E-08

COUNTY (1=”Cook”) 0.018**

R2

0.58#

0.61#

Log-Likelihood -1763.25 -1511.36

/df 136.8 93.76

Vuong Statistic - -60.75

s 0.13 (p< 0.0001) 0.18 (p< 0.517)

r - 0

Environmental Factors

Behavioral Factors

2

* Significant at 0.10 ** Significant at 0.05 *** Significant at 0.01

Cottrill, C., and Thakuriah, P. (2010) Evaluating pedestrian crashes in areas with high low-income or minority populations. Accident Analysis and Prevention, 42(6), pp. 1718-1728.

Social media (Twitter) data is useful in detecting events but very sparse

Geolocalized TweetsGeotagged Tweets

Twitter users are not representative of the population; locations of those who choose to geotag are further not representative of the locations of all Twitter users – but we get a much larger sample allowing us to detect more events, and see activities in more places


Using our methods, we have discovered traffic-related tweets that are not in incident databases – in disadvantaged areas as well as in outlying areas;

This has significant potential for filling in underreporting and for more accurate understanding of risky areas and hazard spaces in cities

Davide-Paule, J. G., Y. Sun and P. Thakuriah. Beyond Geo-Tagged Tweets: Exploring the Geo-Localization of Tweets for

Transportation Applications. Forthcoming in Big Data and Transportation, edited volume to be published by Springer.

Paule, J. D. G., Y. Moshfeghi, J. Jose and P. Thakuriah (2017). On Fine-Grained Geo-Localization of Tweets. Proc ACM SIGIR

conference, Amsterdam, Netherlands, 2017 (ICTIR’17).


The Reality – unintended consequences – or algorithmic bias?

Developing location-based micro-place operational strategies helps to reduce crime as well as hazards from traffic crashes.

Yet, huge problems with predictive policing and bias - “The City of Chicago has its own secretive [predictive policing] algorithm called the Strategic Subject Lists (SSL)….. 56 percent of black men in the city [between] the ages of 20 and 29 have an SSL score,”

“involves racial profiling, deconcentration of crime, and perpetuating corrupt policing practices”

Gunshot detection technology – eavesdropping on personal conversations?

How do you make trade-offs between technology, hazards and these complex social problems?


High-fidelity understanding of behaviors and how we live, work and play

– Links to health and economic and social wellbeing and externalities

A paradigm shift from theoretical model-based approaches to AI – need an “optimal”

mix of the two

Lifelogging

A custom 136° eye view lens, an ultra small GPS unit, Bluetooth, and 5 in-built sensors - ambient light / accelerometer / magnetometer / PIR / temperature

Autographer - Still pictures every 5 seconds both outdoors and indoors

Lifelogging through wearable sensors – a multimedia personal archive

Image data on citizens’ everyday living

Digital image processing to retrieve data on multiple factors on which it is difficult to survey people

Outdoors Indoors

Research possibilities: Travel behaviour

research Driving styles and

eco-friendly behaviour

Fine-grained data on quality of built environment

Social networks Many others

Data Preparation: Multi-sensor wearable device data Movement analysis to annotate movement data with the contextual information and to discover new insights into

indoor mobility patterns among different people.

Acceleration

Magnetometer

Light sensor

Luminance

Temperature

Exposure

Orientation

Image + sensors = multi-sensor data analysis

GPS

Identifying complete movement profiles and social interactions

Indoor/outdoor classification -identify on the basis of temperature and luminosity values whether person is indoors or outdoors. Results show that we can classify images into outdoor and indoor locations with 93.24 % correctly classified instances.

Activity detection - Differences in acceleration patterns can be used for annotation of various activities, as well indoor as outdoor ones.

Various acceleration values for 1-standing; 2-sitting; 3-walking and 4-driving.

LuminosityTemperature

Indoor

Outdoor

Co-detection problem – find out the extent to which people have interactions with others, how much time they spend with others, how often they are in meetings etc

Indicators possible: Time-varying indicators of waste generation,

energy and water usage Total (indoor + outdoor) activity levels Independence in daily living Degree of uneasiness and disturbance in

mobility Degree of isolation in everyday living


Development of traffic disturbance index Driver inattention is a leading cause of crashes Pedestrian uncertainty at key locations (looking for cars, conflicts etc) affect

quality of travel Can we use lifelogging data to sense areas of conflict – disturbance index By disturbance we mean here looking (turns and reorientation – and extent of

reorientation - of an individual’s body into a direction different to the one the individual is heading)

Individual disturbance can be defined as a difference between GPS /Road network heading and Life-logging data orientation

Images showing heading of a driving/riding individual

Using multiple sources of personal sensor information, we can index the street network with the degree of uncertainty and perceived conflict from image and related data


Indoor and outdoor walking

How much do people walk indoors?

Do people who walk a lot indoors walk less outdoors (eg – people who walk more indoors may live in larger houses, hence have higher incomes and own cars, and hence may walk less outdoors due to car travel)

Estimation of outdoor walking possible due to mode detection from GPS data

Estimation of both outdoor and indoor walking is possible due to mode detection with lifelogging data (input features - acceleration, magnetic field readings and orientation)

Contrary to our expectations -

People who walk more outdoors also walk more indoors

People who walk less outdoors tend to stand or sit more indoors

Could propensity for physical activity be more intrinsic;

How do we ensure indoor design and (outdoor) built environment to offer physical activity possibilities for “low volume” users


Co-detection - Developing a social isolation index – using machine vision algorithms to count people and distance/depth and orientation from images – work in progress

Face Detection dlib library Pretrained model on 3

million faces from various datasets

ResNet network with 27 convolutional layers

Precision 0.996

Recall 0.869

Person Detection Tensorflow Deep Learning

library Pretrained model on

Microsoft COCO (Common Objects in Context) dataset

Faster R-CNN with ResNet

Precision 0.944

Recall 0.851


Social Isolation and Worker Wellbeing and Mental Health

Occupations Managerial and professional

positions are exposed to interactions with others

Greater share of clerical and semi-routine and manual jobs are exposed to social isolation, compared to other occupations

Work Status

Most workers work in “moderately” social environments

Those who are unemployed and seeking work tend to be quite socially isolated


Public Transport Availability and Housing including Rental Housing Price Data

Private Sector Data

Advertisements for property sales

Sentiment mining

of real estate

agent language

(create thesaurus)

Linkage to wider

set of urban

indicators

Link

to

Sales data

– Land

registry

What is the role of

transportation services

and infrastructure in

increasing or falling

prices? – Implications

for economic benefits

analysis

Where are new

developments

occurring or where are

areas losing

population? –

Implications for service

development

Labour Market Accessibility – Access to jobs by public transport

Monitoring transit performance for

every train, bus and ferry stop Transit Availability Index

– London Bus Stops

Good transit availability – 24 hour service & small headways

Poor transit availability – specific service hours and longer

headways between vehicles

Transit GTFS data

Transit Availability Index

– Manchester Bus Stops

Identifying areas at high risk of transport poverty

1.78 million people at risk of transport poverty in England and Wales

Temporal, not just spatial mismatch

New project looks at the spatial distribution of jobs estimated to be lost due to massive automation Will draw links to future infrastructure policy

What is the role of transportation systems on joblessness and employment outcomes?

By tracking UK-wide public transport and roads performance, our results show that UK public transport schedules and operations need to be re-evaluated to match the changing nature and location of jobs and locations of workers.

An increase in traffic congestion is positively associated with rise in unemployment benefits claimants.

Results highlights relationships between spatial economy, urban form and changing nature of jobs

Labour Force Survey, 2011 and 2016


Build complex person-level microsimulation models to forecast impacts of urban transport policy

Potential User Work-life Index Forecasts

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

20 25 30 35 40 45 50 55 60 65

Age Cohort during Base Year (yrs)

Es

tim

ate

d N

et

Ben

efi

ts o

ve

r W

ork

life

(200

2 d

oll

ars

)

Cost Scenario 3

Cost Scenario 1

Cost Scenario 2

Average lifecycle economic return on $1 investment in smart mobility for low-wage workers is estimated

to be $15


Start new job

Life choices

Agent-Based Models of Social Exclusion

Juveniles0-15 year old

Make some life decisions

Working age adults16-64 years old

Make life choices

Retirees65+ years old (at least retirement eligible, for simplicity)Removed from workforce, but can be part of the networks of others

Types of agents

Determine wageFrom salaries of available occupations based on• Highest completed level of

education• Age• Ex-convict status

Explore influence of other factors on wages• Gender• Race

Involvement in crime

Family decisions• Begin cohabitation• End cohabitation• Have children

Continue Education

Move/change neighborhood

Leave/lose job

Global variables- Job

stock/Economy- Retirement

Age- Safety-net

- Era: To modify parameters that

capture legal/cultural

changes

High incidence of childhood poverty is a strong predictor of adult poverty as is living in deprived neighbourhood during childhood; Higher likelihood for escape from a life of poverty for those who turned 16 in the period from 1990-1999. Least successful were the ones who turned 16 prior to 1980.


Implications

New forms of data allow previously unobserved behaviours to be analysed

Applications – transport and mobility, energy consumption, public health, assistive living, use in economic studies, time use assessment

Travel behaviour and health research (examples) Driving styles and eco-friendly behaviour Fine-grained data on quality of built and social environment Social networks Good part of our lives indoors – and alone without interaction with

others – implications for mental health and social strategies New ways of being and increasing digitalisation of our daily lives have

implications for use of resources, ways of learning and education, social and political behaviours and other aspects with implications for planning and policy

AI and the Future of Work and Infrastructure


IMPACT

Adoption/ Implementation

Value-Proposition and Actionable

Strategies

Knowledge Discovery

Data Analytics

Urban Data Infrastructure Urb

an In

form

atic

s

Go

vern

ance

, A

dvo

cacy

, Act

ivis

m,

Pu

blic

an

d P

riva

te

Lead

ersh

ip, C

itiz

en

Enga

gem

ent

The Process and Impact

Biggest Challenge of all –How do we go from data and technology to impact and “good” societal and economic outcomes?

What does it take for data-driven public and civic systems to work?

Data infrastructure – the technical, methodological and the “soft” aspects

Domain knowledge and understanding paradoxes and redundancies

Value networks and leadership and champions

Skills – disciplines, techniques and teams

Communications strategies – decision-making on the basis of scientific evidence, public engagement strategies, prepared citizenry


(1) Data Infrastructure - Context Driving the Work

Aspects CharacteristicsTe

chn

olo

gica

l Information management:

1) Information generation and capture

2) Management

3) Processing

4) Archiving, curation and storage

5) Dissemination and discovery

Me

tho

do

logi

cal

Data Preparation

1) Information retrieval and extraction

2) Data linkage/information integration

3) Data cleaning, anonymization and quality assessment

Analysis

1) Develop and apply methods to analyse various domain challenges

2) Ascertain uncertainty, biases and error propagation in the data

Getting and using data is hard Data acquisition

What are the data sources? Making a case for data sharing and resolving:

Incompatibilities with business models Concerns over reputational harm Lack of resource to facilitate data sharing

Governance and ethical issues around data Mix of established and fluid legal framework Data protection and privacy Commercial and other sensitivities Licensing and partnership-building with data owners

Sustainable data sources Responsibilities Business model for the data access to continue Risks to continued accessibility of data – technical, organizational,

legal, political


(2) Need for domain knowledgeAspects Characteristics

The

ore

tica

l an

d

ep

iste

mo

logi

cal 1) Having a theoretical or conceptual framework to guide the

system

2) Understanding metrics, definitions, and changing ideologies and

methods to solving domain problems

3) Determining validity of approaches and limits to knowledge

from data-driven approach

4) Information paradoxes (Jevons paradox), user equilibrium versus

system equilibrium

Po

litic

al e

con

om

y 1) Data entrepreneurship, innovation networks and power

structures

2) Value propositions and economic implications

3) Data acquisitions strategies, access and governance framework

4) Privacy, security and trust management

5) Responsible innovation and emergent ethics

General-purpose ICT Infomediaries

Smart City Companies

Multiple-service ICT Companies

Urban Information Service Provider Infomediaries

City Information Services

Location-Based Services

Location-Based Social Networks

Urban Open and Civic Data Infomediaries

Open Data Organizations

Civic Hacking Organizations

Community-Based Information Service Organizations

Independent and Open Source Developer Infomediaries

Independent App Developers

Open Source Developers

Data Entrepreneurs for Smart Cities and Institutional Transformations- Partnerships with academics, industry and local governments

Traditional Urban Data Users

Planning organizations

Operational agencies

Research organizations and universities

Consulting firms

Thakuriah, P., L. Dirks, and Y. Keita Mallon (2016). Emerging Urban Digital Infomediaries and Civic Hacking in Emerging Urban Data Initiatives. In Seeing Cities through Big Data: Research, Methods and Applications in Urban Informatics, Springer, NY, pp. 189-207.

(3) Value Networks and leaders

(4) Skills – Backgrounds & Disciplines

Substantive knowledge of the field (urban studies, transport planning and engineering, criminology, social work, energy, etc)

Spatial sciences (GIScience, spatial analysis)

Statistics (modelling uncertainty, mixed models and hierarchical data structures)

Computer science (information management, information retrieval, HCI)

Economics

(4) Skills - Techniques Specialist urban modelling and simulations

Data gathering: science of sensors, remote sensing, survey methods, core understanding of new forms of data and how they work

Data analytics: machine learning, advanced statistical analysis, urban and transport modelling and simulations, GIS, spatial analysis, visualisation

Information management: systems, databases, programming skills, machine learning, data structures, algorithms

Information governance: legal and economic aspects of data management, privacy and security

Business management: project management, business case development, monetisation and ROI analysis

(4) Skills - Team Composition

Domain experts

Information management

Analysts

Experts on data acquisition, sharing, standards

Experts in governance, ethics, privacy

Consumer analysts – people who assess and understand users needs and market

Communications and outreach

Experts in commercialisation, business case development

Successful teams learn from each other, listen to needs, are open to new ideas, and are constantly seeking to collaborate.

(5) Scientific evidence – the crisis

“At a time when decision-makers too often ignore, misunderstand, or misuse relevant evidence, we need new ways to communicate policy-relevant scientific evidence to decision-makers and influencers in all areas of government and society,” said Rush Holt, chief executive officer at American Association for Advancement of Science (AAAS).

One of the biggest challenges “is to be as unbiased and neutral as possible” and to avoid any notion that scientists and researchers are “just another special interest group,”

Public Communications & Prepared Citizenry

Civil infrastructure and planning have a long history of public engagement - tends to be somewhat top-down, to inform or to defuse tensions

Ideas behind Future Cities – long-term and sustained engagement with members of the public throughout, not just to discuss plans that have already been made

The other side of the coin – how can we support citizens to be diligent and receptive to new ideas and solutions?

Lifelong learning – and the role of persuasion for investment in lifelong learning due to economic benefits

Perhaps technology can play a bigger role - use of interactive and participatory tools, hackathons, town-hall meetings – but sustaining public interest is difficult

Incentive-based models? Tax policy? Personal learning environments?

Many thanks to the following collaborators:

Yeran SunKatarzyna Sila-NowickaCaitlin CottrillJinhyun HongObinna AnejinouAndrew McHughNebiyou TilahunJorge Davide-Gonzalez PauleChristina BoididouMesut Yucel

Please do not distribute without permission


[email protected]

@vthakuriah

Thank you!

Documents

Urban Informatics and Smart Cities: Prospects and ... · Lifelogging A custom 136° eye view lens, an ultra small GPS unit, Bluetooth, and 5 in-built sensors - ambient light / accelerometer