Testing a Crime Control Model: Does Strategic and Directed Deployment of Police Officers Lead to Lower Crime?

Table 3

Autoregressive corrected models.

Variable	𝛽	𝑡	Probability
Model 1—effect of time on Part I crimes, city of Perris
Year	−23.372	−3.27	.005
Intercept	47333
Total 𝑅-square	0.804
Regress 𝑅-square	0.572
Log likelihood	−54.614
Durbin-Watson d	1.681		0.371
𝑁	11
Model 2—effect of the crime control model on Part I crimes, city of Perris
Year	−11.922	−1.83	.055
Crime control (1 = yes)	−90.119	−2.13	.035
Intercept	24407
Total 𝑅-square	0.864
Regress 𝑅-square	0.784
Log likelihood	−52.421
Durbin-Watson d	1.925		0.475
𝑁	11
Model 3—effect of the crime control model on Part 1 crimes, controlling for time and other cities
Year (time)	−3.790	−0.91	.185
Crime control (1 = yes)	−89.403	−1.49	.072
Perris	Reference
Coachella	−23.811	−0.44	.329
Lake Elsinore	−65.202	−1.20	.119
La Quinta	16.234	0.29	.386
Intercept	8119
Total 𝑅-square	0.573
Regress 𝑅-square	0.192
Log likelihood	−239.799
Durbin-Watson d	2.006		0.327
𝑁	44
Model 4—effect of the crime control model on property crimes, controlling for time and other cities
Year (time)	−1.400	−0.36	.358
Crime control (1 = yes)	−87.026	−1.51	.069
Perris	Reference
Coachella	−2.319	−0.05	.482
Lake Elsinore	−38.791	−0.74	.232
La Quinta	65.477	1.22	.115
Intercept	3241
Total 𝑅-square	0.624
Regress 𝑅-square	0.219
Log likelihood	−238.164
Durbin-Watson d	1.984		0.300
𝑁	44
Model 5—effect of the crime control model on violent crimes, controlling for time and other cities
Year (time)	−2.314	−4.05	.000
Crime control (1 = yes)	−4.293	−0.50	.309
Perris	Reference
Coachella	−22.134	−2.97	.002
Lake Elsinore	−27.050	−3.57	.000
La Quinta	−49.668	−6.44	.000
Intercept	4726
Total 𝑅-square	0.843
Regress 𝑅-square	0.600
Log likelihood	−154.565
Durbin-Watson d	1.950		0.342
𝑁	44

Note: statistical tests are 1-tailed, and 𝛼 = .10 for tests of regression coefficients.