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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 4.

Difference-in-differences estimates of the effect of vacant-lot greening (all treatments) 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 −0.85***
(0.08)		 1.00
(0.02)		 −0.10**
(0.04)		 −1.04**
(0.32)		 −0.06
(0.04)		 −2.04*
(0.92)
Burglary −0.24***
(0.03)		 0.80***
(0.02)		 −0.06***
(0.02)		 −0.34***
(0.09)		 −0.04***
(0.01)		 −0.48*
(0.24)
Robbery −0.69***
(0.09)		 0.93**
(0.03)		 −0.08*
(0.04)		 −0.65
(0.37)		 −0.08*
(0.04)		 −1.31
(0.99)
Theft −0.07***
(0.02)		 0.96**
(0.01)		 −0.01
(0.01)		 −0.08
(0.09)		 −0.01
(0.01)		 0.14
(0.26)
Motor vehicle
theft		 0.53***
(0.15)		 0.90*
(0.04)		 0.10**
(0.03)		 1.40***
(0.34)		 0.06*
(0.03)		 2.66**
(0.91)
*

P < 0.05,

**

P < 0.01,

***

P < 0.001, based on z-score.

N = 30,075 (= 1203 groups * 25 times).

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