Transitioning Experiences with Army Geo Spatial Center (AGC)

Pradeep Mohan

4th Year PhD Student

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Overview: Best Practices

Practice AGC CrimeStat Libraries 1.0 CrimeStat 3.2

Function Level Documentation

√

Parameter sensitivity analysis √

Pattern Analysis with test datasets

√

Well defined Test Cases √ √

Unit Testing √

Integration Testing √ √

Alpha/ Beta Testing √ √

API Documentation √ √

Testing on Multiple Platforms √

Multiple Data formats √ √ √

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Cascading Spatio-temporal pattern discovery

Stages: Bar Closing, Assault , Drunk Driving, Hurricane, Climate change etc.

Cascading spatio-temporal pattern (CSTP)Bar Closing

Assault

Drunk Driving

Partially ordered subsets of ST event types.

Located together in space.

Occur in stages over time.

Other Applications: Climate change, epidemiology, evacuation planning.

T1 T2 T3

B.2

B.1C.1

C.2 C.3

C.4

A.1

A.3

A.2

A.4

Assault(A)

Drunk Driving (C)

Bar Closing(B)

Aggregate(T1,T2,T3)

C2C.3

C.4

C.1

A.1

A.3

A.2

A.4

B.2B.1

Project: Cascade models for multi-scale pattern discoveryJ.W: Dr. J.A. Shine and Mr. J.P. Rogers (AGC)

[1] Pradeep Mohan, Shashi Shekhar, James A. Shine, James P. Rogers. Cascading spatio-temporal pattern discovery: A summary of results. In Proc. of 10th SIAM International Data Mining (SDM) 2010, Columbus, OH, USA[2] J.A. Shine, J.P.Rogers, S.Shekhar, P.Mohan. Cascade models for multi scale pattern discovery: An Extended abstract. In USARMY ERDC Conference 2009, Memphis, TN

Cascade Pattern Discovery[1]Source Code: Matlab 2009b

Test Case: Crime Data,Parameters

Source code independent of Toolboxes

Toolbox Dependencies

Shape Files

.MAT Files (Test Cases)

AGC

a. Performance analysisb. Pattern analysisc. Parameter sensitivity

a. Patternsb. Performance bottlenecksc. Bugsd. Other issues like visual display

Entire Process~2 Months

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Project: Cascade models for multi-scale pattern discoveryJ.W: Dr. J.A. Shine and Mr. J.P. Rogers (AGC)

[3] (Ongoing) Pradeep Mohan, Shashi Shekhar, James A. Shine, James P. Rogers. Cascading spatio-temporal pattern discovery: A summary of results. (Journal Version)

AGC Requirements

Pattern Visualization

Performance Enhancement

Fixing Bugs

Parallelizable

Toolbox independence

Our Actions

Pattern Data structure changes

Faster Algorithms[3]

Revised and Tested Code

? MPI Support in Matlab

? Migration to C++

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CrimeStat

A Spatial Statistics Program for the Analysis of Crime Incident Locations

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Our Contributions• Crime Stat Libraries 1.0[1]

– Set of .NET components distributed by NIJ

• Crime Stat v 3.2– Statistical Simulation functions for Spatial Analysis Routines

• Scalability to Large Datasets– Self-Join Index[2]

[1] http://www.spatial.cs.umn.edu/projects/crimestat-pub/beta/ [2] Pradeep Mohan, Shashi Shekhar, Ned Levine, Ronald E. Wilson, Betsy George, Mete Celik, Should SDBMS support the join index ?:

A Case Study from Crimestat. In Proc. of 16th ACM SIGSPATIAL International Conference on

Advances in Geographic Information Systems (ACM GIS 2008), California, USA,2008.

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Project: Crime stat Libraries 1.0J.W: Mr. Ron Wilson (NIJ) and Dr. Ned Levine (Ned Levine and Associates)

Pradeep Mohan~1.5 Years

Entire Process~2 .5Years

Vijay Gandhi~1 Year

Chetan~1 Year

Core ComponentsSpatial Description

Spatial Analysis Spatial Interpolation Journey to Crime Distance Analysis Alpha Testing Feedback

Performance Tuning Outputs in several formats Beta Testing-I Feedback

NIJ+ Beta Testers

Documentation

Alpha TestingFeedback

Algorithm Descriptions Test Cases

Beta Testing-I

Documentation Testing Framework Feedback updatesFeedback

Beta Testing-II

Documentation Visual Outputs Wrap upFeedback

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