Combining police perceptions
with police records of serious
crime areas
Robert Haining and Jane Law
Department of Geography
University of Cambridge
Outline
1. Purpose of the study
2. High Intensity Crime Areas (HIA)
3. Constructing HIA:
1. Senior Police Officer perceptions
2. Empirically defined from police records
3. Comparing the two maps
4. Data Modelling:
1. Model specification
2. Results: modelling each map independently
3. Results: combining the two maps
5. Discussion
1. Purpose of the Study
• Map the location of serious crime areas (HIA) in
Sheffield using two valid but different data
sources: senior police officers knowledge and
database records;
• Go beyond descriptive mapping; “getting behind”
the two maps using statistical models, in order to
try to better understand the two spatial
distributions;
• Examine ways of combining different types of
knowledge.
2. High Intensity Crime Areas (HIA)
• Definition
– HIA = areas of English cities that experience
high levels of violent (drug related) crime often
perpetrated by people resident in the area 
special policing problems including witness
intimidation. (Home Office, 1997)
– More than just “hot spots” (i.e. it is type rather
than level of offending that is important).
“HIA appear to represent a particularly
extreme manifestation of the co-existence in
an area of serious crime, the victims of
those crimes and the particularly dangerous
individuals and groups responsible for
committing those crimes” (Craglia et al
2001, p.1925)
• Conceptual roots
– Social disorganization theory:
• Breakdown of internal social networks…
• Population turnover…
• Socio-economic or ethnic heterogeneity…
….leading to anti-social and eventually
criminal behaviour (Shaw and McKay, 1942)
• Powerlessness of some communities to influence
decisions made externally and that ultimately have
damaging consequences for them. (Bursik and
Grasmick, 1993)
=> in UK context some areas become local
authority “dumping grounds” for problem families.
(Bottoms and Wiles, 2002)
- what may start out as “non-serious”
offences (vandalism etc) carried out by
youth gangs and petty offenders evolve into
something more substantial – often linked
to the very profitable drugs market.
- motivated offenders go to greater lengths
and violence becomes a feature of crime in
such communities.
3. Constructing HIA (1998)
3.1 Senior Police Officer Perceptions (PHIA).
STEP 1: South Yorkshire Police’s Performance
Review Department were given a list of factors:
•
•
•
•
•
serious gang culture;
high propensity to violence;
drugs problem;
transient population;
hostility towards police.
=> Identification of BCU-K
STEP 2: BCU-K commander asked to
identify the precise areas within BCU-K on
street plans. Members of CID and
uniformed police asked to identify the
particular difficulties associated with these
areas. Comparisons made with other areas
and BCUs.
=> No guidelines on the number
of PHIA but there had to be a clear “step
change” in the problems encountered.
STEP 3: PHIA areas transferred to 2001
Census output areas (av: 125 households).
An output area was in a PHIA if any part of
it lay inside a PHIA:
Output area i = 1 if it overlapped some part
of a PHIA.
Output area i = 0 if it did not overlap any
part of a PHIA.
There were 337 output areas in BCU-K; 47
were defined as PHIA.
3.2 Empirically defined from police records
(EHIA).
STEP 1: From SYP’s recorded offence and
offender database for 1998:
- all offences of murder, attempted murder,
manslaughter, robbery, supplying drugs, offences
involving use of firearms, serious assault and
wounding (by output area);
- all accused offenders (by place of residence).
Both data sets (offences and offenders) in the form
of counts by output area.
Count statistics at the Output
Area level
Average Standard Minimum Maximum
deviation
Offence
counts
1.24
2.18
0
15
Offender
counts
5.79
4.80
0
31
STEP 2: Convert both counts for each output area
to standardised scores (subtract mean and divide
by standard deviation).
STEP 3: Sum the two standardised scores for each
output area.
STEP 4: Rank. The first 47 are defined as EHIA.
Cross tabulation for serious offence and offender
counts by output area.
Offender scores
 -1.21,
 -0.79
> -0.79,
 -0.16
> -0.16,
 0.67
> 0.67,
 5.25
= -0.57
72
(0)
49
(0)
29
(0)
18
(2)
> -0.57,
 -0.11
15
(0)
32
(0)
24
(0)
14
(4)
> -0.11,
 0.81
6
(0)
10
(0)
24
(2)
16
(15)
> 0.81,
 6.30
1
(0)
5
(2)
6
(6)
16
(16)
Offence
scores
1. The number in brackets indicates the number of output areas classified as EHIA.
2. Row and column intervals refer to quartiles but because of the lumpiness of the
distribution, with many output areas with 0 counts especially for serious offences, the
row and column sums do not correspond to 25% of data values.
Map of counts
offences
offenders
3.3 Comparing the two maps
(a) Map overlay
(b) Spatially adjusted bivariate correlation
(Clifford and Richardson, 1985)
(a) Map of the 1998 HIA and overlay
(b) Spatially adjusted bivariate correlation
Number
of bins
5
8
10
15
20
25
30
Break
distance
(metres)
2091
1307
1046
698
523
419
348
p-value
.001
.015
.012
.020
.022
.035
.016
940.4
486.4
525.7
449.4
436.1
368.4
475.8
Effective
sample
size
4. Data Modelling
4.1.1 Statistical model specification
Y(i) ~ Bernoulli (p(i))
logit (p(i)) = 0 + 1 X1(i) + … + k Xk(i) + (i)
Y(i) = 0 (output area i is not in an HIA) or 1 (....i is in an HIA)
p(i) = probability that output area i is in an HIA.
X1,…,Xk are the explanatory variables.
Model 1: An ordinary logistic regression
model: (i) = 0 (Fitted in S-PLUS)
Model 2: A logistic regression model with spatial
random effects: (i) = SCAR(i) which is a
conditional spatial autoregressive model. (Fitted in
WinBUGS)
This model allows us to recognize the spatial
relationships between the output areas and
to allow for the effects of spatially
autocorrelated missing variables. The
logit(p(i)) are now random variables.
SCAR
 s2 , 
S(1)
S(n)
S(2)
Y(1)
logit[p(1)]
X(1)
Y(2)
logit[p(2)]
X(2)
Y(n)
logit[p(n)]
X(n)
4.1.2 Explanatory Variables
terrace: % terrace housing;
turnover: % of residents with different addresses
one year before Census;
male unemployment 16-24;
households with no car or van;
long term unemployment;
lone parent families;
households renting from local authority or
housing association;
index of ethnic heterogeneity;
index of socio-economic heterogeneity.
4.2 Results: modelling each map
independently
Posterior means with credible intervals of
parameters / Final models
Police defined
HIA
Index of ethnic heterogeneity
(1, CI: 2.5%, 97.5%)
0.236
(0.147, 0.349)
0.019
(0.005, 0.034)
NA
0.034
(0.005, 0.066)
0.109
(0.049, 0.175)
0.113
(0.056, 0.169)
NA
No car/van
(2, CI: 2.5%, 97.5%)
Turnover
(3, CI: 2.5%, 97.5%)
Lone parent
(4, CI: 2.5%, 97.5%)
Gamma
(, CI: 2.5%, 97.5%)
0.985
(0.940, 1)
Final (parsimonious) model
Model (2)
NA
NA
Empirically
defined HIA
Model (1)
4.3 Combining PHIA and EHIA maps
• Approach 1: Treat PHIA as “prior beliefs”
and update using the current set of data.
=> no interesting results
• Approach 2: Treat the two maps as repeated
measurements on the same set of outcomes.
Result: repeated measurements approach
Posterior means with credible
intervals of parameters / Final
model
Police and
empirically defined
HIA
Index of ethnic heterogeneity
(1, CI: 2.5%, 97.5%)
0.050
(0.039, 0.062)
No car/van
(2, CI: 2.5%, 97.5%)
0.026
(0.004, 0.047)
Turnover
(3, CI: 2.5%, 97.5%)
0.047
(0.005, 0.089)
Lone parent
(4, CI: 2.5%, 97.5%)
0.074
(0.028, 0.120)
Gamma
(, CI: 2.5%, 97.5%)
Final (parsimonious) model
NA
Model (1) with
repeated
measurements
Repeated Measurements model results:
maps
N
(a)
not modelled HIA
modelled HIA
(b)
Probability
0.34 - 0.60
0.60 - 0.70
0.70- 0.83
(a) 47 HIA output areas
obtained from repeated
measurements model
(b) Associated model probabilities
(posterior means)
5. Discussion
• The construction of good quality geocoded
offence/offender databases creates
opportunities both for academic research
and for the police and other services
involved in local crime and disorder
partnerships;
BUT
• Tendency to use such databases for descriptive
mapping and;
• To treat the database evidence as definitive when
compared to the police’s own knowledge
(Ratcliffe and McCullagh 2001).
• Modelling offers an opportunity to
understand the social, economic and
demographic factors that underlie particular
offence/offender geographies. (Academic
importance; strategic benefit to the police?)
• Formally integrating both recorded crime
data and the police’s own experience using
GIS- or statistically-based methodologies
may provide an effective way to exploit
geocoded crime data:
Complementarity of the two information
sources?
Strengths
-contains detail over a
wide area that will
Database probably not be known
consistently by officers;
- consistent procedures
for recording.
Police
officers
Weaknesses
- incompleteness (and with a
geography);
-problems with locational
referencing;
-influenced by short term
fluctuations and
displacement effects.
- operational knowledge; influenced by
- accumulated experience
- attitudes (see Rengert
and Palfrey 1997);
- what is/not remembered;
- particular experiences.
and there are several ways of combining such information
from purely map-based (overlay) to model-based.
• BUT for the police there are challenges:
– Institutional:
• Data sharing protocols;
• Need to be undertaken regularly if to be of practical value;
• Need to be embedded into operational procedures.
– Technical:
• Rapid data collection and processing;
• Need for appropriate analytical software;
• GIS and statistical expertise (web-based services?).