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. Author manuscript; available in PMC: 2017 May 18.
Published in final edited form as: Urban Stud. 2015 Oct 20;53(15):3279–3295. doi: 10.1177/0042098015608058

Table 6.

Difference-in-differences estimates of the effect of LOG 2.0 community reuse treatment on crime.

Outcome
variable		 Model 1: Model 2: Model 3: Spatial Durbin model
GLS
regression		 Poisson
regression		 1/8 mile distance threshold 1/4 mile distance threshold
Coefficient IRR Direct
effect		 Indirect
effect		 Direct
effect		 Indirect
effect
Felony assault −0.93***
(0.16)		 1.01
(0.03)		 −0.27***
(0.07)		 −0.70**
(0.24)		 −0.18**
(0.06)		 −0.88
(0.48)
Burglary −0.19**
(0.06)		 0.83***
(0.04)		 −0.02
(0.04)		 −0.12
(0.10)		 −0.01
(0.04)		 −0.14
(0.19)
Robbery −0.22
(0.19)		 0.99
(0.06)		 −0.18*
(0.07)		 −0.45
(0.26)		 −0.11
(0.06)		 −0.24
(0.47)
Theft 0.00
(0.04)		 0.97
(0.02)		 −0.00
(0.02)		 −0.04
(0.06)		 0.00
(0.02)		 −0.03
(0.13)
Motor vehicle theft 0.79**
(0.27)		 0.94
(0.07)		 0.32***
(0.09)		 0.87**
(0.27)		 0.21**
(0.07)		 1.36**
(0.42)
*

P < 0.05,

**

P < 0.01,

***

P < 0.001, based on z-score.

N = 9625 (= 385 groups * 25 times).

Robust standard errors are shown for GLS, Poisson and SDM regression.