1. 1 Smart Crime Pattern Analysis Using the Geographical Analysis Machine Ian Turton, Stan Openshaw, James Macgill CCG, University of Leeds email: ian@geog.leeds.ac.uk

2. 2 Crime Pattern Analysis Automated Smart Easy to use Easy to understand

3. Being SMART is not just a matter of methodology but also involves access, usability, relevancy, and result communication factors

4. 4 Residential Crimes

5. 5 Street Crime Locations

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7. 7

8. 8 Spot any patterns? Mapping the raw data is virtually useless unless the patterns are blindingly obvious

9. 9 GAM & GEM

10. 10 GAM creates a density surface of weighted evidence of clustering which is used to suggest locations, intensities, and patterns of clustering that exists on the map

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14. 14 GAM Results Surface

15. 15 GAM results for Street Crime

16. 16 GAM results for Street Crime II

17. 17 That could be random chance! Each run examines 433,714 different circles So you might expect some circles by random chance GAM lets you test that

18. 18 Random results

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20. 20 But why not build the search for local association into the circle search used in GAM?

21. 21 Building a Geographical Explanations Machine- GEM/1 Explanation here is to be interpreted in the traditional geographical sense of there being a possibly interesting localized spatial association between clusters and certain GIS data layers Maps do not cause patterns to appear BUT they do contain clues as to the processes that do if only we were clever enough to spot and decode them

22. 22 Rock A Rock B Rock C Rock D Geology Map

23. 23 railway 2 km buffer polygon

24. 24 Combined Geology and Railway Buffer Map Rock A Rock B Rock C Rock D 2 km

25. 25 Combinations of Attributes If we have 8 attributes with 10 classes each There are 3160 permutations of 2 classes from 80 compared with 24,040,016 if any 5 are used Smart searches are essential –use GA to generate possible combinations of interest

26. 26 Back to Baltimore Visit the US Census Bureau Web site Download Census variables at block level Aggregate to block groups Split variables to quartiles Export as text files from arcview

27. 27 House Value

28. 28 Ethnicity

29. 29 Old People

30. 30 Run GEM Similar web interface simple ASCII text files same visual output I have used chloropleth maps as psuedo coverages you could use other information –distance to main roads –neighbourhood watch areas

31. 31 Residential Crime (Mode 1)

32. 32 Residential Crime (mode 3)

33. 33 Residential Crimes The most common combination of coverages for clusters of residential crime high house values lots of old people

34. 34 Street Crime

35. 35 Street Crime II

36. 36 Related Coverages For both base populations the most commonly related coverages are high house values high proportion of white residents

37. 37 If you want to try out Smart Analysis on the Web http://www.ccg.leeds.ac.uk/smart/intro.html

38. 38 Future developments GAM and GEM fail eventually as more coverages and time periods are added The CCG is currently developing new methods of driving the search process –Genetic Algorithms –Swarm based optimization

39. Further Info: Email stan@geog.leeds.ac.uk ian@geog.leeds.ac.uk j.macgill@geog.leeds.ac.uk http://www.ccg.leeds.ac.uk/smart/intro.html