Intelligence led policing: how the use of crime intelligence analysis translates in to the decision-making

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International Journal of Security and Terrorism • Volume: 4 (1)

these crime analysis dimensions by making index variables; the authors of the current study 

created them as latent variables by performing factor analysis.  

The operationalization of crime analysis is based partially on O’Shea and Nicholls’ 

(2003) study. In their study, O’Shea and Nicholls conceptualized crime analysis with three 

main titles, which they referred to as the “dimensions of crime analysis” (p. 238). These three 

dimensions are as follows:  crime analysis functions, statistical methods, and data utilization. 

However, only the first two groups of variables were used as main independent variables in 

this study. There were 22 types of crime analysis activities in O’Shea and Nicholls’ (2003) 

survey, in which they ask respondents to indicate how frequently they undertook each type 

of crime analysis. The answers were coded as never, some, often, and very often. The types 

of crime analysis specified are the following:  target profile, victim, link, temporal, spatial, 

financial, flowcharting, program evaluation, case management, crime scene profiling, crime 

forecasting, crime trends, citizen surveys, victim surveys, employee surveys, environmental 

surveys, intelligence, productivity, civil litigation, patrol strategy, workload distribution, and 

displacement/diffusion  analyses.  In  the  second  dimension  (i.e.,  statistical  methods),  for 

each statistical method, respondents were asked to indicate how frequently they use the 

corresponding method. These methods are the use of the following:  frequencies, mean-

median-mode,  standard  deviation,  cross  tabulations,  correlation,  regression,  and  cluster 

analysis. The answers were coded as “never,” “some,” “often,” and “very often.” Using 

these two groups of variables, an exploratory factor analysis was performed.

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Table 1 shows the factor analysis results of the main independent variables. Based 

on  the  results,  six  factor  coefficients  were  created.  Factor  component  scores  less  than 

0.50 were not taken into account. These six factor components are as follows:  statistical 

analysis, crime analysis, intelligence analysis, survey analysis, patrol strategy analysis, and 

displacement/diffusion analysis. The rationale for using factor analysis is the possibility of a 

latent variable, which is not directly observable but can be assessed using indicators such 

as the frequency of employing specified crime analysis methods. 

When factor analysis was used (see Table 1), variables were clustered as follows:  

five  under  the  statistical  factor  correlated  highly  with  the  latent  variable  and  had  values 

ranging from 0.700 to 0.829; seven under the crime analysis factor correlated with the latent 

variable and had values ranging from 0.524 to 0.683; five under the intelligence analysis 

factor had component scores ranging from 0.556 to 0.756; four under the survey analysis 

factor  had  factor  scores  ranging  from  0.587  and  0.818;  three  under  the  patrol  strategy 

analysis component ranging from 0.663 to 0.744; and one correlated with the latent variable 

displacement/diffusion analysis and had a score of 0.523. 

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SPSS version 16.0 software was used.

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Intelligence - Led Policing:

How the Use Of Crime Intelligence Analysis Translates in to the Decision-Making 

Table 1. Exploratory Factor Analysis

Crime Analysis Type 

1

2

3

4

5

6

Standard deviation

0.829

Mean, median, mode

0.735

Regression

0.734

Cross tabulations

0.702

Correlation

0.700

Target profile analysis

0.683

Crime trends

0.675

Victim analysis

0.619

Crime forecasting

0.586

Frequencies

0.554

Spatial analysis

0.534

Temporal analysis

0.524

Financial analysis

0.756

Flowcharting

0.684

Program evaluation

0.569

Link analysis

0.564

Intelligence analysis

0.556

Citizen surveys

0.818

Victim surveys

0.799

Employee surveys

0.703

Environmental surveys

0.587

Productivity analysis

0.744

Workload distribution

0.731

Patrol strategy analysis

0.663

Displacement/diffusion analysis

0.523

Note. The scores from 1 to 6 represent the number of principal components and latent variables. The values in 

these (component) columns indicate the level of correlation depending on the exploratory factor analysis.

3. Statistical Analysis and Findings

3.1. Statistical Analysis

All  of  the  processes  regarding  data  merging  and  data  analysis  were  done  using  two 

statistical  software  packages:    Stata  version  SE10  and  SPSS  version  19.0.  Because  the 

dependent variables (i.e., command-level manager, patrol officer, and detective) have an 

ordinal/categorical level of measurement, it cannot be measured simply as an ordinary least 

squares (OLS) model (Agresti, 2002; Aldrich & Nelson, 1984; Long, 1997; Long & Freese, 

2006; McCullagh & Nelder, 1989; Powers & Xie, 1999), but an ordered logistic regression 

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