Building a Web Application and Land Navigation

Course to Help Develop Military-Relevant

Informal GIS Education

Alex Ruff

MS-GIST 2017

ruff.alex@gmail.com

Introduction

Methods

Results

Conclusions

AGENDA

Importance of Spatial Awareness in Military • 5th Century BC – Military strategist Sun Tzu dedicated an entire

chapter of “The Art of War” to reading terrain.

• US Army Doctrine 5-0 “The Operations Process” suggests commanders use OAKOC: • Observation and fields of fire

• Avenues of Approach

• Key and decisive terrain

• Obstacles

• Cover

• Concealment

https://commons.wikimedia.org/wiki/File:124th_ASOS_at_Orchard_Combat_Training_Center2.JPG Photo by Master Sergeant Becky Vanshur

Avenue of Approach Map • Army Field Manual 5-33 –

Terrain Analysis • Provides instruction on best route

to get to an objective.

• Required topographic map, aerial photography, soils, reference tables.

• Final product is a series of overlays.

• Time intensive.

From Department of the Army, FM 5-33: Terrain Analysis

Using GIS in the Military • Geographic information systems

allow a commander to quickly create and disseminate terrain analysis.

• Commanders can use GIS to perform tasks like: • Observe real time troop movements • Produce map products • Leverage remote sensing imagery for

intelligence • Use elevation models, bathymetry to

identify important terrain. • Many other applications

Introducing GIS to Officer Training

• Reserve officer training corps: • College-based officer training program.

• Serve all branches of the military

• Many ROTC students do not have much information on GIS.

• Could I build GIS applications into informal experiences ROTC students would want to participate in?

https://media.defense.gov/2017/May/23/2001751801/-1/-1/0/170422-A-CV236-002.JPG Photo by Season Osterfeld, POST

Objectives for this Project

• Provide a future military officer in ROTC with useful geospatial information (movement speed).

• Provide an opportunity for those officers to use that geospatial information in an informal setting.

Photo by NSA Crane PAO John Collins

Achieving Objectives

1. Creating a tool and data using Python and ArcGIS. Model movement speed

2. Host the Python tool on a server to be used in a interactive web application.

3. Create a land navigation competition (Navigate Challenge). Provide an opportunity to use the tool to plan for real movement.

Introduction

Results

Conclusions

AGENDA

Methods

1. Prepare Python tool

2. Prepare data

3. Develop web application

4. Develop land navigation competition

Prepare Python Tool • Based on Mounted and Dismounted Cross Country mobility tool –

hosted on ESRI Mature Support GitHub and built on ArcGIS 10.2

• Edited to use data layers prepared for Madera Canyon, Arizona and run in ArcGIS PRO.

• Uses 5 factors based on FM 5-33: 1. Foot march speed

2. Slope

3. Land Cover

4. Soils

5. Surface Roughness

Prepare data – Foot March Speed and Slope • Foot march speed

• Foot march speed was determined using a reference table provided with the tool.

• Slope • A digital elevation model (DEM) of Madera Canyon was

downloaded from USGS EarthExplorer.

Prepare data – Land Cover • Land cover and vegetation type was

used in the Cross-Country mobility tool.

• Land cover had to be identified and joined to a reference table. • Based on land cover types in FM 5-33.

• Unsupervised classification of NAIP imagery to identify: • Barren ground

• Grass

• Trees

• Brush.

Prepare data - Soils • Soil layer for Madera Canyon

was developed from a USDA Soil Map.

• Soil unit symbols were given US Army “Soils Category” code based on texture.

• Example: Barkerville-Gaddes Complex soil found in Madera • USDA = “gravelly sandy loam

and sandy clay loam” by USDA. • US Army code 02 = “gravel or

sandy gravel”.

Photo by pan xiaozhen on Unsplash

Prepare data – Surface Roughness

• Variability of terrain was taken into account. The more varied the terrain, the harder to traverse.

• Using Focal Statistics tool created three DEM rasters. • Mean DEM • Min DEM • Max DEM

• Final Raster derived from the following equation.

(𝑀𝐸𝐴𝑁𝐷𝐸𝑀 −𝑀𝐼𝑁𝐷𝐸𝑀)

(𝑀𝐴𝑋𝐷𝐸𝑀 −𝑀𝐼𝑁𝐷𝐸𝑀)

Develop Web Application • Necessary feature layers, and reference tables were hosted on a

server - ArcGIS Portal.

• Some parameters were left as user defined to give interactive feel to the application. They were: • Area of interest

• Wet or Dry soil

• User’s weight

• Web Application and geoprocessing widget built using ESRI WebApp Builder.

Cross Country Mobility Web Application

Develop Land Navigation Competition • Worked with ROTC Cadre to create

a land navigation competition in Madera Canyon.

• 35 points were placed across 1 square km area of Madera Canyon.

• Locations were noted in 10 digit MGRS Coordinates using a Garmin GPS receiver.

• Each location had a unique AZ peak name.

Develop Land Navigation Competition

Develop Land Navigation Competition

Maps Needed to be on 8” x 11” paper Needed to have scales that fit Military protractors. Had to look like terrain maps used in other trainings.

Web Application Dissemination

•Prior to the competition a link to the web application were sent out via email to all that had signed up.

• The mobility web application link was also shared on social media.

•Used 121 times prior to the event.

Introduction

Methods

Results

Conclusions

AGENDA

Results

• 51 people participated in the NAVIGATE Challenge.

Results

Total Used the Mobility

Application

Did not Use the Mobility Application

ROTC Participants 36 22 14

Non-ROTC Participants

15 12 3

Total 51 34 17

ROTC Participants

71%

Non-ROTC Participants

29%

Percentage of Participants Affiliated with ROTC

[PERCENTAGE]

[PERCENTAGE]

Percentage of Participants that Used the Cross-Country Mobility Web Application

Used the Web Application Did Not Use the Web Application

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Did Not Use the Cross-CountryMobility Application

Used the Cross-Country MobilityApplication

[VALUE]

1.94

Average Number of Points Collected

Introduction

Methods

Results

Conclusions

AGENDA

Conclusions - Quantitative Number of points were identified as the more important success criteria. • Points were analyzed in an unpaired t-test to determine if web application was a

statistically significant variable.

Two tailed P-Value = .0516 Not quite statistically significant

USED THE WEB APPLICATION

DID NOT USE THE WEB APPLICATION

Standard Deviation 1.25 1.10

Mean 1.94 1

N 17 11

Further Directions: • Sample Size

Cards checked for legibility and names.

• Analyze how users interacted with the online mobility tool.

• Identify if impact on “spatial thinking”. (National Academy of Sciences) • Understanding space • Representation • Reasoning

References Cooley, Skye. 2016. "Terrain Roughness - 13 Ways." GIS 4 Geomorphology. Accessed October 20, 2017.

http://gis4geomorphology.com/roughness-topographic-position/.

Committee on Support for Thinking Spatially. 2006. Learning to Think Spatially. Washington, D.C.: The National Academies Press.

Department of the Army, 2005. Field Manual No. 3-25.26, Map Reading and Land Navigation. Washington, DC.

Department of the Army, 2010. Field Manual No. 5-0, The Operations Process. Washington, DC.

Department of the Army, 1990. Field Manual No. 5-33, Terrain Analysis. Washington, DC.

ESRI. 2016. "Cross-Country Mobility." Cross-Country Mobility, ArcGIS for Defense. Accessed November 4, 2017.

http://solutions.arcgis.com/defense/help/cross-country-mobility/.

Grogan, Andrew. 2009. “Creating a Spatial Analysis Model for Generating Composite Cost Surfaces to Depict Cross Country Mobility in

Natural Terrain.” Paper presented at the ASPRS/MAPPS 2009 Fall Conference, San Antonio, Texas.

Jenness, Jeff. 2009. “Calculating Landscape Surface Area from Digital Elevation Models.” Wildlife Society Bulletin. 32. 829-839.

Norman, Laura, Craig Wissler, D. Phillip Guertin, and Floyd Gray. 2002. “Digital Soils Survey Map of the Patagonia Mountains, Arizona.”

U.S. Geological Survey Open-File Report 02-324, 24 pp., https://pubs.usgs.gov/of/2002/0324/