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

2. Overview: Best Practices PracticeAGCCrimeStat Libraries 1.0CrimeStat 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√√√ 2

3. 3 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. T1T2T3 B.2 B.1 C.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) C2 C.3 C.4 C.1 A.1 A.3 A.2 A.4 B.2 B.1

4. Project: Cascade models for multi-scale pattern discovery J.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 analysis b.Pattern analysis c.Parameter sensitivity a.Patterns b.Performance bottlenecks c.Bugs d.Other issues like visual display Entire Process~2 Months 4

5. Project: Cascade models for multi-scale pattern discovery J.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++ 5

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

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/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. 7

8. Project: Crime stat Libraries 1.0 J.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 Components  Spatial 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 Testing Feedback  Algorithm Descriptions  Test Cases Beta Testing-I  Documentation  Testing Framework  Feedback updates Feedback Beta Testing-II  Documentation  Visual Outputs  Wrap up Feedback 8