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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

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Crime Pattern Analysis

• Automated

• Smart

• Easy to use

• Easy to understand

Being SMART is not just a matter of methodology but also involves access, usability, relevancy, and

result communication factors

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Residential Crimes

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Street Crime Locations

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Spot any patterns?Mapping the raw data is virtually useless unless the patterns are

blindingly obvious

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GAM & GEM

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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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GAM Results Surface

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GAM results for Street Crime

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GAM results for Street Crime II

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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

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Random results

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If you want to try out WWW-GAM

http://www.ccg.leeds.ac.uk/smart/intro.html

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But why not build the search for local association into the circle search used

in GAM?

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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

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Rock A

Rock B Rock C

Rock D

Geology Map

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railway

2 km

buffer polygon

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Combined Geology and Railway Buffer Map

Rock A

Rock B Rock C

Rock D2 km

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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

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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

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House Value

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Ethnicity

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Old People

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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

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Residential Crime (Mode 1)

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Residential Crime (mode 3)

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Residential Crimes

• The most common combination of coverages for clusters of residential crime

• high house values

• lots of old people

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Street Crime

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Street Crime II

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Related Coverages

• For both base populations the most commonly related coverages are

• high house values

• high proportion of white residents

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If you want to try out Smart Analysis on the Web

http://www.ccg.leeds.ac.uk/smart/intro.html

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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

Further Info: Emailstan@geog.leeds.ac.uk

ian@geog.leeds.ac.ukj.macgill@geog.leeds.ac.uk

http://www.ccg.leeds.ac.uk/smart/intro.html