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Penn State University MGIS Program Capstone Project Proposal Peer Presentation: Creating a Spatial Analysis Model for Generating Composite Cost Surfaces.

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Presentation on theme: "Penn State University MGIS Program Capstone Project Proposal Peer Presentation: Creating a Spatial Analysis Model for Generating Composite Cost Surfaces."— Presentation transcript:

1 Penn State University MGIS Program Capstone Project Proposal Peer Presentation: Creating a Spatial Analysis Model for Generating Composite Cost Surfaces to Depict Cross Country Mobility in Natural Terrain Andrew Grogan (MGIS) Advisor: Dr. Peter Guth USNAApril 20 th 2009

2 2 Introduction Creating a Spatial Analysis Model for Generating Composite Cost Surfaces to Depict Cross Country Mobility in Natural Terrain

3 3 Objective To create a spatial analysis model which generates standardized Cross- Country Mobility (CCM) cost surface data depicting the ease or difficulty of vehicle movement in natural terrain for a designated area of interest.

4 4 Goals Project Focus  Applied spatial analysis model  Requires moderate GIS savvy  Customization: I.Seasonal II.Temporal III.Unique  Modernize US Army Mylar Overlay Process

5 5 Objectives Key Components:  Composite Cost Surfaces from Themes.  Standardized Classification.  Adaptable to any AOI.  Flexibility for Input and Output.  Cost Surfaces facilitate further spatial analysis (LCP/CD).  Cost Surfaces used to generate semantic data and hardcopy maps.

6 6 Background Military Geography Definitions “The application of geographical principles and knowledge to the solution of military problems.” (Jackman 1962) “It (Military Geography) links geography and the military science, and is a type of applied geography, employing the approaches, methods, techniques and concepts of the discipline to military affairs, places and regions.” (Palka 1995) “Geospatial intelligence (GEOINT) is the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on the Earth.” (US Army FM 3-24, 2006).

7 7 Background US Army Geospatial Terrain Analysis Development History “In the 1980s the U.S. Military finally recognized the need for routine input of terrain analysis for staff planning. Each army division and Corps has an organic engineer terrain-analysis team assigned.” (Guth 1998) “In 1972 the Defense Mapping Agency (now NGA) was formed from the Army, Navy and Air Force mapping and charting operations to consolidate map production and distribution for all branches of the military.” (Bacastow, Peuquet 1991) Logos used for educational purposes only

8 8 Background US Army Geospatial Terrain Analysis Development History “In concept, the role of the Army’s topographic field units is to provide tailored support for the field decision maker through synthesis, transformation, update and quick emergency substitutes when DMA data are not available or insufficient.” (Bacastow, Peuquet 1991) US Army Terrain Units Unit Functions Include*:  Creating terrain analysis products to support military operations  Creating Tactical Decision Aides (TDAs) based on operational variables  Pulling existing data from appropriate sources, creating TDAs and pushing products to end users. * CTIS Terrain Analysis Fact Sheet 2009

9 9 Background  Humanitarian Assistance  Peacekeeping  Nation/Security Assistance  Noncombatant Evacuation  Disaster Relief  Support of Domestic Civil Authority  Counter-drug Operations  Arms Control  Combating Terrorism Palka 1995 US Army Corps of Engineers Assisting a Trapped Motorist. For Educational Purpose only Military Operations Other than War (MOOTW):

10 10 Target Users Military Users:  US Army Terrain Analysis Teams Military Applications:  Combat Operations - Tactical Decision Aides (TDAs) I.Spatial Analysis: Least Cost Paths and Cost Distances. II.Semantic Data Generation: Avenues of Approach, No-Go Areas, Key Terrain, Barriers/Obstacles, Chokepoints.  Military operations other than war (MOOTW) I.Spatial Analysis - Military Land Management, Environmental Impacts.

11 11 Target Users Civilian Users:  Academic and Private Users Civilian Applications:  Search and Rescue Operations: I.Spatial Analysis: Least Cost Paths and Cost Distances.  Land Management I.Spatial Analysis - Public Land Management, Environmental Impacts, Suitability/Capability for Land Use, Off-Road Vehicle Impacts. II.Generation of Semantic Map Data for Presentations

12 12 Technical Approach  Evolution From Manual Process  Uses ESRI’s ModelBuilder  Based on FM 5-33 and NATO Reference Mobility Model II (NRMM) (Birkel 2003) Model Development

13 13 Technical Approach NATO Reference Mobility Model II (NRMM)*:  Developed in the 1970’s  Collection of Mobility Sub-Models  Predicts Physically Constrained Terrain  Calculates General Rates of Movement *(Birkel 2003) Limitations:  Focus on Force Controlled Speed  Time Constraints not Ease/Difficulty of Mobility  Engineering Point of View  Not a GIS solution

14 14 Technical Approach US Army Field Manual 5-33 Defines:  Traditional Mylar Overlay Process  Terrain Data Themes  Thematic Data Classification Scheme  Thematic Data Impact on Vehicle Movement  Inter-Theme Relationships  Analytical Approaches

15 15 Area of Interest  Representative Area of Interest (~ 2000 Km 2 )  Southern Arizona I.Horst and Graben (Basin and Range) II.Desert Scrub, Riparian Zones, High Mountain Alpine Terrain AOI for Proposed Model Development

16 16 Data Resources

17 17 Technical Approach Creating Composite Cost Surfaces  Map Algebra  Smoothing Algorithm  Data Classification

18 18 Objectives Using GIS to Improve CCM Cost Data:  Prevent Bin Category “Stepped” Data Values  Provide Smoothed Cost Surfaces  Improve CCM Data Depiction

19 19 Methodology

20 20 Methodology

21 21 Limitations Possible Restrictions and Limitations  Pre-Model Data Preparation Required I.Unique Data Aspects II.Dataset Variance III.Data Equivalency  Model Limited to Select Thematic Variables  Trial and Error approach Required  Requires Adaptive Optimization (Smoothing Algorithm, Order of Operations)

22 22 Methodology Anticipated Results  Composite CCM Cost Surfaces  Multi-Thematic Representation  Increased Level of Detail  Reproducible Results  Basis for Further Spatial Analysis (Least Cost Paths/Cost Distances)  Base Layer for Semantic Data Extraction and Depiction.  CCM Depiction for Spatio-Temporal Events

23 23 Questions Questions ? Andrew Thomas Grogan


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