1. Mapping and analysis for public safety: An Overview

2. Motivation  Identifying events (e.g. Bar closing, football games) that lead to increased crime.  Crime generators and attractors  Predicting crime events  Identifying location and time where a serial offender would commit his next crime. Predicting the next target of a burglary offender  Identification of patrol routes  Force deployment to mitigate crime hotspots. Courtsey: www.startribune.comwww.startribune.com http://www.dublincrime.com/blog/wp- content/MappingOurMeanStreets.jpg

3. Scientific Domain: Environmental Criminology Courtsey: http://www.popcenter.org/learning/60steps/index.cfm?stepNum=16http://www.popcenter.org/learning/60steps/index.cfm?stepNum=16 Crime pattern theory Routine activity theory and Crime Triangle Courtsey: http://www.popcenter.org/learning/60steps/index.cfm?stepnum=8http://www.popcenter.org/learning/60steps/index.cfm?stepnum=8  Crime Event: Motivated offender, vulnerable victim (available at an appropriate location and time), absence of a capable guardian.  Crime Generators : offenders and targets come together in time place, large gatherings (e.g. Bars, Football games)  Crime Attractors : places offering many criminal opportunities and offenders may relocate to these areas (e.g. drug areas)

4. What is spatiotemporal data mining ? Process of discovering interesting, useful and non-trivial patterns from spatiotemporal data. Traditional Data MiningSpatiotemporal data mining (STDM) Data mining Tasks Frequent patterns (e.g. Associations, Sequential association, frequent graphs) ST Frequent patterns (e.g. ST Co-occurrence, ST Sequences and Cascading ST patterns) Clustering Hotspot Analysis Anomaly detection Classification/ Regression ST Classification / ST (auto) Regression ST Outliers

5. STDM pattern families Spatial outliers: sensor (#9) on I-35 Nest locationsDistance to open water Vegetation durability Water depth Location prediction: nesting sites Co-occurrence PatternsHotspots www.sentient.nl/crimeanabody.html

6. Projects : Mapping and Analysis for public safety  US DoJ/NIJ- Mapping and analysis for Public Safety  CrimeStat.NET Libaries 1.0 : Modularization of CrimeStat, a tool for the analysis of crime incidents.  Performance tuning of Spatial analysis routines in CrimeStat  CrimeStat 3.2a - 3.3: Addition of new modules for spatial analysis.  US DOD/ ERDC/ AGC – Cascade models for multi scale pattern discovery  Designed new interest measures and formulated pattern mining algorithms for identifying patterns from large crime report datasets.  US DOD – Spatial network hotspot discovery  New algorithms to discover hotspots along street networks

7. CrimeStat  A Spatial statistics software to analyze crime incident locations.  It provides modules for spatial statistics, space-time analysis, finding patterns:  Hotspot Analysis  Spatial Modeling  Crime Travel Demand  Used widely by law enforcement agencies throughout the country.  Popular among Public Health agencies and research groups throughout the country.

8. CrimeStat  Used by law enforcement all over the country (e.g. Redlands Police Department, Baltimore County)  File down loads: Fall 2010 65,875 (Source: http://www.icpsr.umich.edu/CrimeStat/about.html ) http://www.icpsr.umich.edu/CrimeStat/about.html  6 Releases since 1999

9. Our Contributions Crime Stat Libraries 1.0 [1] – Set of.NET components distributed by NIJ – Credits: http://www.icpsr.umich.edu/CrimeStat/files/Documentation_for_CrimeStat_Libraries_1.0.pdf http://www.icpsr.umich.edu/CrimeStat/files/Documentation_for_CrimeStat_Libraries_1.0.pdf Crime Stat v 3.2-3.3 – Statistical Simulation functions for Spatial Analysis Routines –Credits: http://www.icpsr.umich.edu/CrimeStat/files/CrimeStat3.3updatenotesPartI.pdfhttp://www.icpsr.umich.edu/CrimeStat/files/CrimeStat3.3updatenotesPartI.pdf 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.

10. Real Crime Datasets Lincoln, NE Dataset  Years 2002- 2007  > 40 Crime types  > 200 Sub types  Average size of each year ~ 40000 Real Data

11. Cascading spatio-temporal pattern (CSTP)  Output: CSTP  Partially ordered subsets of ST event types.  Located together in space.  Occur in stages over time. Aggregate(T1,T2,T3) Time T1 Assault(A)Drunk Driving (C)Bar Closing(B) Time T3>T2Time T2 > T1 BA C CSTP: P1 a  Input: Crime reports with location and time.

12. Lincoln, NE crime dataset: Case study  Is bar closing a generator for crime related CSTP ?  Observation: Crime peaks around bar-closing! Bar locations in Lincoln, NE  Is bar closing a crime generator ?  Are there other generators (e.g. Saturday Nights )? Questions K.S Test: Saturday night significantly different than normal day bar closing (P-value = 1.249x10 -7, K =0.41)

13. Lincoln, NE crime dataset: Case study {Bar Closing} {Vandalism} {Assault} Spatial Neighborhood Gen-CPRCPIMax-CPR 1 Mile0.03860.022830.0386 2.5 Miles0.184910.045390.18491  Temporal Neighbor Size = 1 hr  Dataset Years 2002-2006

14. Lincoln, NE crime dataset: Case study Crimes considered: Assault and Vandalism Probability of a Bar closing generating a crime in Lincoln City = 0.038 Probability of a Lincoln city downtown Bar closing generating a crime = 0.0862

15. Lincoln, NE crime dataset: Case study  Only bar closings that also generate assaults  Downtown subsetting may decrease/ increase chances. Probability of a Vandalism after Bar closing in Lincoln City = 0.022 Probability of a Vandalism after a downtown Bar closing = 0.0397

16. Lincoln, NE crime dataset: Case study  Only bar closings that also generate Vandalism  Downtown subsetting may decrease/ increase chances Probability of an Assault after Bar closing in Lincoln City = 0.029 Probability of an Assault after a downtown Bar closing = 0.021

17. Spatial Network Hotspots Geometric Hotspot Network Hotspot