Suspect matching on spatial and
contextual aspects
Rob van der Veer, rvdveer@sentient.nl
8th National Crime Mapping Conference, 10 June 2010
Agenda
Introduction
Work history
Matching using associative memory
Series detection
Match case(s) to offender locations
Match case(s) to offenders description
Match case(s) to careers
Example and results
Do it yourself suspect matching
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Rob van der Veer, CEO Sentient
Data mining specialists since 1990
Own software: DataDetective
Customers:
–
–
–
–
–
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Fraud analysis (Tax offices)
Marketing (Delta Lloyd)
Market research (De Telegraaf)
Risk analysis (Cordares, KPN)
Product advice (Libraries)
Crime analysis (Police)
Broad co-operation
UvA, MapInfo, Hot ITem, Vicar Vision, ParaBots,
vtsPN, Police academy, Experian, VU
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Work history
1990: Company founded. Artificial Intelligence
research and application development
1996: First law enforcement application
2002: Amsterdam police force adopts DataDetective
2006: More police forces join
KDD2009: Third price ‘best data mining application’
2010: DataDetective proposed as national standard
2010: Government funds project to pilot
DataDetective at city councils
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Data sources
DataDetective
Find relations
Predict
GIS
Statistics,
charts
Cluster
Link analysis
Fuzzy query
Applications / BI systems
Query
Data+metadata for other tools
SPSS
Analyts’s Notebook
Google earth
5
Automatic reports and dashboards
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Application: Crime type clustering
Problems on queens day
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Application: link analysis
Onderzoek relatie tussen verdachten (rood) van geweld en slachtoffers (wit)
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Application: Profile Analysis
Application: find interacting factors
1.683
794
47,18%
Geslacht
Man
Vrouw
1.116
656
58,78%
567
138
24,34%
leeftijdskl 12-17
ja
nee
ja
nee
175
130
74,29%
941
526
55,90%
21
10
47,62%
546
128
23,44%
nationaliteit XXXXXXXXXX
ja
cbs-buurt YYYYYYYYYYY
geboorteland NEDERLAND
nee
leeftijdskl 18-24
ja
nee
ja
nee
390
252
64,62%
551
274
49,73%
135
44
32,59%
411
84
20,44%
geboortegemeente Goirle
Ooit getrouwd?
22
153
20
110
90,91% 71,90%
cbs-wijk Wijk XXXXXXXXX)
ja
nee
nee
ja
105
80
76,19%
285
172
60,35%
190
129
67,89%
361
145
40,17%
Links/rechtshandig
rechts
links
29
76
28
52
96,55% 68,42%
ja
nee
12
123
9
35
75,00% 28,46%
burg.staat Gescheiden
ja
nee
28
333
23
122
82,14% 36,64%
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Application:prediction models
Personal file
Dataminingmodel
Risk of fire arm
(0-100%)
Report
Domestic violence
Dataminingmodel
Risk for escalation
(0-100%)
Neighbourhood, surroundings,
infrastructure,
households
Dataminingmodel
What risks to expect
Dataminingmodel
Risk for an event
(0-100%)
Exact location
Day, time, season,
weather prediction
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The weather report for tomorrow…
… for street robbery.
Application: Where/when analysis
Tue afternoon
Using knife
Hot spots (location, day of week, time)
Th, Fr, Sa
Late afternoon
Th, Fr
Evening
We
age 12-15
Street robbery
Su to Mo
Grabbing purse
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Application: geographic trend analysis
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Matching using associative memory
Input case(s)
Matched with processed train data
Associated cases
Conclusion
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Matching algorithm
Height
category
Age
Series detection
Clusters indicate
trends and series
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Match case(s) to offender locations
‘geographic profiling’
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Match case(s) to offender descriptions
Match witness descriptions to known offenders
Applications:
Selecting foto’s to show
Narrow down search
Field test 1996: 50% more hits
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Match case(s) to careers
Series
Match
Similar cases in past
Suspect of four
similar cases
Suspects of
similar cases
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Profile typical suspect
What are typical features of the suspects of past
cases that are similar to the problem at hand?
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Example
Summer 2009: Series:
Tilburg area
Car break-ins in car parks
Laptops stolen
Matched to similar incidents in past
Printed photos of suspects of those similar incidents
Photo of suspect X matched CCTV footage
Patrolling offers briefed with photos
August 2009: X spotted during patrol and taken in.
Was part of international gang.
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Results
Field tests show a 50% increase in search hit
Experiments with crime data show a similar gain
While initially hesitant, use increases constantly
Also applied by non-technical users
User surveys report an efficiency gain of factor 20
User surveys report a quicker response
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Do it yourself suspect matching
1. Select case(s) and possible suspects
2. Score suspects with geographic profile probability
3. Score suspects with similarity to description
a)
b)
c)
Find the governing witness description
Assign score 100 to exact matching suspects
Relax search criteria and assign lower scores for matches
4. Score suspects with matching careers
a)
b)
c)
Find the governing MO, location and time plus derived features
Match-score past incidents using the method above
Select the best matching incidents and score suspects depending
on how many best matching past incidents they have
5. Combine the scores to sort and select suspects
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More information
Rob van der Veer
rvdveer@sentient.nl
+31 20 5300 330
Hedda Roos
Amsterdam police force
+31 20 5598 495
hedda.roos@amsterdam.politie.nl
www.sentient.nl/?crime
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