The use of Temporal Profiling and
Crime Mapping in a Street Lighting
Renewal Project in Leeds
Fiona McLaughlin
www.saferleeds.org.uk
Introduction
 Leeds City Council is undergoing a £300 million programme to
upgrade street lighting in the city. Although contractors plan the
overall sequence of work, Leeds City Council has the opportunity to
re-schedule the replacement of a small number of lighting columns,
up to a maximum of 500, each year.
 In mid 2006, the Street Lighting Contract Manager approached
Safer Leeds with a request to identify high crime areas that could
potentially benefit from improved street lighting.
 This paper discusses the use of crime mapping and temporal
profiling to identify suitable areas for street light renewal and
examines what happened to crime once the street lighting was
renewed.
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Using the SARA model
Why do we always start with Response?
 The response of street lighting replacement was happening anyway,
this was an opportunity to bring forward the replacement of a small
number of lighting columns in high crime areas.
But
 To give the project the best chance of success and make the most
of this ‘free’ (to Safer Leeds) opportunity it was essential to take two
steps backwards and start the SARA model at the beginning.
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Scanning
 Looking for best practice
 Farrington and Welsh (2002) conducted a systematic review of the
effects of improved street lighting on crime
 The British studies in the review showed encouraging results, but were
unclear about the most favourable circumstances for using improved
street lighting as a method of situational crime prevention
 The paper includes useful recommendations about project design
 Preliminary analysis to determine a suitable methodology for
selecting high crime areas
 Sufficiently sensitive to identify small areas
 Offences most likely to be affected by lighting levels
 Only crimes that happened during darkness
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Scanning
 Traditional methods of mid-point and aoristic profiling give a risk
profile for time of day, but do not identify the levels of risk during the
hours of darkness.
 However, these methods are a useful tool to assess which offences
have the greatest overnight risk.
Offence
Burglary Dwelling
Burglary Elsewhere
Criminal Damage
Theft from Person
Robbery
Theft from Vehicle
Theft of Vehicle
0
1.0
2.7
1.7
1.1
1.3
1.7
1.5
1
1.2
2.7
1.8
1.4
1.0
2.2
2.1
2
1.9
2.5
1.8
1.0
0.8
3.1
2.6
3
2.2
2.1
1.8
0.7
0.6
2.8
2.4
4
1.5
1.5
1.0
0.4
0.2
1.7
1.5
5
0.9
0.8
0.5
0.3
0.2
0.9
0.7
6
0.5
0.5
0.3
0.2
0.2
0.5
0.4
7
0.2
0.3
0.2
0.1
0.2
0.3
0.3
8
0.2
0.3
0.2
0.2
0.3
0.2
0.2
9
0.2
0.3
0.2
0.4
0.4
0.3
0.3
10
0.5
0.3
0.3
0.9
0.4
0.4
0.4
Hour
11 12
0.8 1.1
0.5 0.7
0.4 0.6
1.5 1.7
0.4 0.6
0.6 0.8
0.8 0.8
13
1.3
0.8
0.6
1.8
0.8
0.9
0.6
14
1.2
0.7
0.7
1.9
0.9
0.8
0.8
15
1.1
0.7
0.8
2.1
1.8
1.0
0.7
16
0.8
0.6
0.9
1.4
1.5
0.7
0.7
17
1.0
0.7
1.0
1.3
1.3
0.5
0.7
18
1.0
0.6
1.3
0.9
1.8
0.7
0.7
19
1.2
0.8
1.5
0.9
1.9
0.7
1.0
20
1.3
0.8
1.7
0.9
1.6
0.9
1.0
21
1.1
0.8
1.7
0.9
2.3
0.7
1.1
22
0.9
1.0
1.4
1.0
2.0
0.6
1.2
 As well as having a strong overnight temporal profile, offence types
chosen needed to reflect the partnership’s priority, which at that time
was PSA1.
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23
0.9
1.4
1.5
1.1
1.6
0.7
1.3
Why not include violent crime?
 Ideally, the study would only include offences either that occur on or
are commissioned from the street and this would include some
violent crimes. However, the complexity of causes underlying violent
crime means that improving street lighting alone is unlikely to reduce
offences.
 In the residential areas suitable for the installation of improved street
lighting much of the violence is of a domestic nature and happens
behind closed doors.
 The crime data available did not have a reliable street crime marker.
 An alternative method would have been to exclude violent crime
from the initial selection of high crime areas and then in just these
areas of interest assess which violent offences were street crimes.
This approach would require much less data cleansing.
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Analysis
 Mid-point and aoristic profiling gave a risk profile for time of day, but
did not identify the levels of risk during the hours of darkness.
 Overcoming this challenge required the development of a profiling
tool that used sunrise and sunset times.
 The use of average sunrise and sunset times month by month was a
possibility, but at mid-summer and mid-winter the difference in
day/night length between the start and end of the month is
significant.
 Daily sunrise and sunset times are predictable and it seemed
worthwhile to make an accurate calculation.
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Analysis
 Take the date/time from and date/time to information from the crime
record and compare it to the sunrise/sunset time for that day.
 This methodology would identify whether a crime occurred during
daylight, darkness or crossed both periods.
 Areas considered suitable for receiving upgraded street lighting
would have high levels of offences that happened during the hours
of darkness.
 Sunrise and sunset times obtained from the US Navy Observatory
http://aa.usno.navy.mil/data/docs/RS_OneYear.php
www.saferleeds.org.uk
Complete this form to get a table of sunrise/sunset times for your area
Copy this table and paste into Excel
Re-format the table in Excel
Organise the table layout to facilitate analysis
Compare the sunrise/sunset times with crime from/to times
Analysis
 The contractors work on a street by street basis so the method of
identifying high crime areas would have to be sufficiently sensitive to
work at small areas.
 The most obvious method would have been to use counts of crime
per street, but this does not always work particularly well in a city the
size of Leeds, there are too many streets with the same name.
 Mapping to census OA area and then selecting the areas with the
highest counts of crime during darkness was the simplest method,
having the advantage that these areas are fairly homogenous in
terms of population and household numbers.
 This identified 69 areas for further investigation, not all areas would
be suitable, e.g. where there are crime generators improving street
lighting would not be enough on its own to reduce vulnerability.
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Analysis
 Fifteen areas recommended for replacement street lighting.
 Contextual information was combined with the crime during
darkness information to provide a more complete picture of the area.
This included deprivation levels, community tensions and fear of
crime.
 There were some sources of information that should have been
included but that had no time stamp, most notably anti-social
behaviour (police and council).
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Response
 Contractors were only able to replace street lighting in five of the
fifteen areas because of limits to the amount of re-sequencing work
permitted and some areas already had street lighting to the required
standard.
 New lighting meets the requirements of the current British Standard
for street lighting and the specification of Leeds City Council.
 The work took place between September 2006 and August 2007.
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Assessment
 An evaluation study in February 2008 looked at the crime trends
following the work to replace street lighting.
 Crime fell by significant amounts during the study period, both in the
city as a whole and in the areas with improved street lighting.
 Crime during darkness fell by greater amounts in the areas with
improved street lighting than in the city as a whole and two different
comparison groups.
 There was no measurable displacement in crime during darkness
from areas with improved street lighting to neighbouring areas.
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Assessment
Year
Offences
Offences in Darkness
% Offences in Darkness
Recorded Crime in Leeds
2005
50,802
18,024
35.5%
2006
49,923
18,084
36.2%
2007
44,756
16,301
36.4%
Change
-11.9%
-9.6%
Recorded Crime in Re-Lit Areas
2005
329
162
49.2%
2006
309
122
39.5%
2007
185
67
36.2%
-43.8%
-58.6%
Change
The number of offences during darkness fell at a greater rate in the re-lit areas.
The proportion of offences during darkness remained relatively stable in Leeds
but fell significantly in the re-lit areas.
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Assessment
Period
Offences
Offences in Darkness
% Offences in Darkness
Recorded Crime in Leeds (‘Dark Months’ of October to January)
2005/06
18,278
9,221
50.4%
2006/07
17,267
8,675
50.2%
2007/08
14,982
7,451
49.7%
Change
-18.0%
-19.2%
Recorded Crime in Re-Lit Areas (‘Dark Months’ of October to January)
2005/06
124
88
80.0%
2006/07
105
66
62.9%
2007/08
63
28
44.4%
Change
-49.2%
-68.2%
The number of offences during darkness fell at a greater rate in the re-lit areas.
The proportion of offences during darkness remained relatively stable in Leeds
but fell significantly in the re-lit areas.
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Assessment
Period
Offences
Offences in Darkness
% Offences in Darkness
Recorded Crime in Leeds Re-lit Areas (‘Dark Months’ of October to January)
2005/06
124
88
80.0%
2006/07
105
66
62.9%
2007/08
63
28
44.4%
Change
-49.2%
-68.2%
Recorded Crime in Control Area 1 (‘Dark Months’ of October to January)
2005/06
268
161
60.1%
2006/07
226
135
59.7%
2007/08
215
115
53.5%
Change
-19.8%
-28.6%
The number of offences during darkness fell at a greater rate in the re-lit areas.
The proportion of offences during darkness fell more in the re-lit areas than in
the first control area.
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Assessment
Period
Offences
Offences in Darkness
% Offences in Darkness
Recorded Crime in Leeds Re-lit Areas (‘Dark Months’ of October to January)
2005/06
124
88
80.0%
2006/07
105
66
62.9%
2007/08
63
28
44.4%
Change
-49.2%
-68.2%
Recorded Crime in Control Area 2 (‘Dark Months’ of October to January)
2005/06
1,525
798
52.3%
2006/07
1,322
690
52.2%
2007/08
1,017
508
49.9%
Change
-33.3%
-36.3%
The number of offences during darkness fell at a greater rate in the re-lit areas.
The proportion of offences during darkness fell more in the re-lit areas than in
the second control area.
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Assessment
Period
Offences
Offences in Darkness
% Offences in Darkness
Recorded Crime in Leeds Re-lit Areas (‘Dark Months’ of October to January)
2005/06
124
88
80.0%
2006/07
105
66
62.9%
2007/08
63
28
44.4%
Change
-49.2%
-68.2%
Recorded Crime in Displacement Areas (‘Dark Months’ of October to January)
2005/06
643
343
53.3%
2006/07
613
350
57.1%
2007/08
427
220
51.5%
Change
-33.6%
-35.9%
The number of offences during darkness fell at a greater rate in the re-lit areas.
The proportion of offences during darkness fell more in the re-lit areas than in
the displacement areas.
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Summary
 Do not let starting at response get in the way of using the full SARA
model.
 Use analytical techniques that will identify areas that can benefit the
most from the chosen response.
 Diagnose hotspot types to make sure that the response is suitable
for that area.
 Design your analysis with the assessment in mind.
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Contact Us
Fiona McLaughlin, Principal Partnership Analyst
fiona.mclaughlin@leeds.gov.uk
Safer Leeds Partnership Team
PO Box 612
Leeds
LS2 7WH
www.saferleeds.org.uk
References
Farrington, D.P. and Welsh, B.C. (2002). Effects of improved street
lighting on crime: a systematic review. (Home Office Research Study
251.) London: Her Majesty’s Stationery Office.
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