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Transitioning Experiences with Army Geo Spatial Center (AGC)

Transitioning Experiences with Army Geo Spatial Center (AGC). Pradeep Mohan 4 th Year PhD Student. Overview: Best Practices. Cascading Spatio-temporal pattern discovery. T1. T2. T3. Aggregate(T1,T2,T3). Assault(A). A.2. A.2. C.4. B.2. C.4. A.3. Bar Closing(B). A.3. C.1. B.2.

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Transitioning Experiences with Army Geo Spatial Center (AGC)

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  1. Transitioning Experiences with Army Geo Spatial Center (AGC) Pradeep Mohan 4th Year PhD Student

  2. Overview: Best Practices

  3. Cascading Spatio-temporal pattern discovery T1 T2 T3 Aggregate(T1,T2,T3) Assault(A) A.2 A.2 C.4 B.2 C.4 A.3 Bar Closing(B) A.3 C.1 B.2 B.1 Drunk Driving (C) C.1 C.2 B.1 C.3 A.1 C.3 C2 A.4 A.1 A.4 Cascading spatio-temporal pattern (CSTP) • Partially ordered subsets of ST event types. Bar Closing Drunk Driving • Located together in space. Assault • Occur in stages over time. Stages: Bar Closing, Assault , Drunk Driving, Hurricane, Climate change etc. Other Applications: Climate change, epidemiology, evacuation planning.

  4. Project: Cascade models for multi-scale pattern discovery J.W: Dr. J.A. Shine and Mr. J.P. Rogers (AGC) Cascade Pattern Discovery[1] Source Code: Matlab 2009b Test Case: Crime Data, Parameters Source code independent of Toolboxes Toolbox Dependencies .MAT Files (Test Cases) Shape Files • Patterns • Performance bottlenecks • Bugs • Other issues like visual display • Performance analysis • Pattern analysis • Parameter sensitivity Entire Process~2 Months 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

  5. Project: Cascade models for multi-scale pattern discovery J.W: Dr. J.A. Shine and Mr. J.P. Rogers (AGC) Pattern Visualization Pattern Data structure changes Performance Enhancement Faster Algorithms[3] Fixing Bugs Revised and Tested Code Parallelizable ? MPI Support in Matlab Toolbox independence ? Migration to C++ AGC Requirements Our Actions [3] (Ongoing) Pradeep Mohan, Shashi Shekhar, James A. Shine, James P. Rogers. Cascading spatio-temporal pattern discovery: A summary of results. (Journal Version)

  6. CrimeStat A Spatial Statistics Program for the Analysis of Crime Incident Locations

  7. 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.

  8. Project: Crime stat Libraries 1.0 J.W: Mr. Ron Wilson (NIJ) and Dr. Ned Levine (Ned Levine and Associates) • Performance Tuning • Outputs in several formats • Beta Testing-I Feedback • Documentation • Visual Outputs • Wrap up • Spatial Analysis • Spatial Interpolation • Journey to Crime • Distance Analysis • Alpha Testing Feedback Feedback • Documentation • Testing Framework • Feedback updates Feedback • Core Components • Spatial Description • Documentation Alpha Testing Feedback Vijay Gandhi~1 Year Beta Testing-I Beta Testing-II Chetan~1 Year NIJ+ Beta Testers Pradeep Mohan~1.5 Years Entire Process~2 .5Years • Algorithm Descriptions • Test Cases

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