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)

3.2.2.1. Logit Coefficients of Command Model

According to the results shown in Table 3, all of the factor variables are positively associated 

with the dependent variable of the first (i.e., command) model. 

Table 3. Logit Coefficients of Command Model with Dependent Variable Command-Level 

Managers’ Use of Crime Analysis

Variable

B

SE

z

P > | z |

Crime rates 

-0.002

0.004

-0.57

0.571

Agency size 

0.275

0.180

1.52

0.128

Unions

-0.200

0.249

-0.80

0.421

Budget 

-7.10e-07

1.39e-06

-0.51

0.610

Hierarchy

0.166

0.190

0.87

0.383

Crime analysis unit

0.636

0.275

2.31

0.021

Statistical analysis

0.417

0.121

3.43

0.001

Crime analysis

0.797

0.133

5.99

0.000

Intelligence analysis

0.363

0.127

2.86

0.004

Survey analysis

0.426

0.117

3.65

0.000

Patrol strategy analysis

0.641

0.124

5.16

0.000

Displacement analysis

0.177

0.121

1.47

0.142

τ

1

-2.217

0.456

τ

2

1.503

0.448

Note.  N = 352. Approximate likelihood-ratio test of parallel regression assumption: 

χ2 (12 df) = 15.67, p = .2067.

The only significant control variable is crime analysis unit, which is positively associated 

with the dependent variable. 

3.2.2.2. Logit Coefficients of Patrol Model 

According to the results shown in Table 4, all of the factor variables—except survey analysis 

and displacement/diffusion analysis—are positively associated with the dependent variable 

of the second model (i.e., patrol model). Among the control variables, crime analysis unit 

is the positively associated with the dependent variable, while size is negatively associated 

with the dependent variable.   

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33

Intelligence - Led Policing:

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

Table 4. Logit Coefficients of Patrol Model with Dependant Variable Patrol Officers’ Use of 

Crime Analysis

Variable

B

SE

z

P > | z |

Crime rates 

0.002

0.004

0.46

0.644    

Agency size

-0.520

0.173

-3.01   

0.003    

Unions

0.396

0.248

1.60   

0.111    

Budget 

    

1.33e-06

1.34e-06

0.99

0.321

Hierarchy

0.111

0.189

0.59   

0.557    

Crime analysis unit

0.629

0.275

2.29   

0.022     

Statistical analysis

0.315

0.119

2.65   

0.008     

Crime analysis

1.060

0.139

7.62   

0.000     

Intelligence analysis

0.357

0.126

2.82   

0.005     

Survey analysis

0.059

0.109

0.54   

0.592    

Patrol strategy analysis

0.273

0.117

2.33

0.020     

Displacement analysis

0.079

0.120

0.66

0.508

τ

1

-1.990

0.438

τ

2

1.880

0.437

Note.  N = 352. Approximate likelihood-ratio test of parallel regression assumption: 

χ2 (12 df) = 7.58, p = .8169.

3.2.2.3. Logit Coefficients of Detective Model

Table 5 shows that three of the factor variables (i.e., statistical analysis, crime analysis, and 

intelligence analysis) are positively associated with the dependent variable (i.e., detectives’ 

use of crime analysis). As in the previous models (i.e., command model and patrol model), 

crime analysis unit is positively significant when controlled.

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

Table 5. Logit Coefficients of Detective Model with Dependent Variable Detectives’ Use of 

Crime Analysis

Variable

B

SE

z

P > | z |

Crime rates

-0.003

0.004

-0.65

0.516

Agency size

-0.071

0.175

-0.41

0.683

Unions

0.425

0.252

1.69

0.092

Budget 

    -2.29e-08

     

1.36e-06

-0.02

0.987

Hierarchy

0.095 

0.020 

-0.50

0.618

Crime analysis unit

0.610

0.276

2.21

0.027

Statistical analysis

0.460

0.125

3.68

0.000

Crime analysis

1.184

0.147

8.05

0.000

Intelligence analysis

0.499

0.129

3.85

0.000

Survey analysis

0.211

0.114

1.84

0.065

Patrol strategy analysis

0.210

0.121

1.72

 0.085

Displacement analysis

0.0425

0.122

0.35

0.727

τ

1

-2.590

0.460

τ

2

1.350

0.441

Note.  N = 352. Approximate likelihood-ratio test of parallel regression assumption: 

χ2 (12 df) = 9.50, p = .6598.

4. Discussion and Conclusion

The  current  study  focused  on  the  association  of  crime  analysis  functions  with  the 

organizational  decision-making  process  at  three  ranks:  command-level  managers, 

patrol  officers,  and  detectives.  In  addition,  some  internal  (organizational)  and  external 

(environmental) determinants are controlled on the organizational decision-making process. 

Doing  so  allowed  the  researchers  to  partially  test  a  new  policing  model,  intelligence-led 

policing or, more specifically, Ratcliffe’s (2008) 3-i model. As mentioned previously, there 

are  three  components  (crime  analysis,  decision-making,  and  criminal  environment)  and 

three processes in the 3-i model that all begin with the letter i (interpretation, influence, and 

impact). In this model, crime intelligence analysts are assumed to interpret data from the 

criminal environment and then influence the decision- makers, who are assumed to make 

decisions or policies that impact crime and prolific offenders in the environment (i.e., the 

criminal environment). The current study was a partial testing of the 3-i model because only 

the perceived relationship of crime intelligence analysis and the decision-making components 

are explored, whereas the rest of the model is not studied because of insufficient proper data 

about criminal environment and criminals in that environment.

As indicated in earlier sections, although the findings in general seem to support the 

hypotheses, the findings indicate only an association based on the odds ratios between the 

variables—and not causality.  

Almost  all  of  the  crime  analysis  types  studied  were  found  to  be  significant,  as 

expected,  with  the  relevant  level  of  decision-making  within  the  organization.  Further,  the 

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