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Smart Crime Pattern Analysis Using the Geographical Analysis Machine

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Title: Smart Crime Pattern Analysis Using the Geographical Analysis Machine


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

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
Residential Crimes
5
Street Crime Locations
6
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7
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8
Spot any patterns?
  • Mapping the raw data is virtually useless unless
    the patterns are
  • blindingly obvious

9
GAM GEM
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
11
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12
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13
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14
GAM Results Surface
15
GAM results for Street Crime
16
GAM results for Street Crime II
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
Random results
19
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20
But why not build the search for local
association into the circle search used in GAM?
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
Geology Map
23
railway
buffer polygon
24
Combined Geology and Railway Buffer Map
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
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
House Value
28
Ethnicity
29
Old People
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
Residential Crime (Mode 1)
32
Residential Crime (mode 3)
33
Residential Crimes
  • The most common combination of coverages for
    clusters of residential crime
  • high house values
  • lots of old people

34
Street Crime
35
Street Crime II
36
Related Coverages
  • For both base populations the most commonly
    related coverages are
  • high house values
  • high proportion of white residents

37
If you want to try out Smart Analysis on the Web
http//www.ccg.leeds.ac.uk/smart/intro.html
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_at_geog.leeds.ac.uk
  • ian_at_geog.leeds.ac.uk
  • j.macgill_at_geog.leeds.ac.uk
  • http//www.ccg.leeds.ac.uk/smart/intro.html
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