Police Perceptions of the Spatial
Distribution of Residential Burglary
Lindsay M McLaughlin, Shane D Johnson, and Ken Pease
Background
• Part of a larger research project done over the last
year in Derbyshire Constabulary – Predictive
Mapping
• Some initial resistance towards using predictive
mapping because many officers told us that they
‘know where crime is’
• Can police officers accurately identify hotspots?
Previous Findings
• Ratcliffe and McCullagh (2001): Perceptions of the
most vulnerable areas for residential burglary
were 60% accurate
• Pilot Study, McLaughlin et al. (in press): Police
officers correctly identified 60% of residential
burglary hotspots
Problems with Methodology
• X marks the spot
Aims of Research
•
Measure accuracy using new methodology
•
Determine if confidence can help identify
accurate officers
Methodology
• Survey of 53 front line officers in ‘A’ Division,
Derbyshire Constabulary (5 Sections)
Measures Used
– Accuracy: Officers asked to identify the top 10 cells
(250m square) that had an ‘above average’ amount of
residential burglary in them for the last year
– Confidence: Officers asked to rate each of the 10 cells
with a confidence score between 1 (least confident) and
10 (very confident)
0
0.5
1
2
3
Kilometers
4
6
Long Eaton
Last Year
8
8
8
4
¯
6
9
6
9
10
Creating a Hotspot Map for Actual Crime:
How Kernel Density Estimation works
High crime density
Low crime density
X
Zero crime density
X
One crime
Residential burglary for the last year
18 cells
46 cells
246 cells
92 cells
799 cells
Hot cells illustrated another way:
Number of Cells as Defined by KDE
1,000
800
600
Hottest
Cells
400
200
0
Low Crime Density
High Crime Density
Let’s look at an example
1= 0.0433
2= 0.0432
3= 0.0555
4= 0.0665
5= 0.0001
6= 0.0000
7= 0.0000
9= 0.0044
10= 0.0565
Mean= 0.027
Findings: Police Perceptions
• Top 10% of map = 249 cells
• Identified the lowest intensity value (critical Z)
Measuring individual responses:
• 35% of responses were above this cut off point
Measuring mean value across officers:
• 38% of officers had a mean value that was equal
to or greater than this cut off point
Confidence versus Accuracy
High Accuracy
Pearson’s correlation (r)
= non significant
Spearman’s rho (rs)
= non significant
Low Accuracy
Confidence
does
not predict
accuracy
Low Confidence
High Confidence
Summary – what should you take away?
•
Effective deployment of resources depends on the
accurate identification of high crime areas
But…
•
38% of officers can identify hot areas – but why should
they be expected to be 100% accurate?
•
Confidence does not help identify accurate officers
Ways Forward
•
Explore factors other than ‘confidence’
•
Systematic approach, e.g. predictive mapping
Contact details:
Lindsay McLaughlin or Shane Johnson
Jill Dando Institute of Crime Science
University College London
Second Floor
Brook House
2 - 16 Torrington Place
London WC1E 7HN
e shane.johnson@ucl.ac.uk
e l.mclaughlin@ucl.ac.uk