Scatter of each variable and crime

For now, I am doing only comunas, to see if it leads anywhere and get the code running. Once I can do it with barrios, the sample sizes will increase.

The color in these scatters is each comuna, each point is a day.

Regression of each variable and crime

Naive Regressions

Here I just regress amount of crime in a day, to the measure of PM2.5 of each model.

Dependent variable:
crime
(1) (2) (3) (4) (5) (6)
mean 0.006
(0.004)
median 0.002
(0.004)
max 0.007***
(0.002)
min 0.007
(0.006)
q25 0.005
(0.005)
q75 0.002
(0.004)
Constant 1.681*** 1.757*** 1.511*** 1.741*** 1.729*** 1.765***
(0.106) (0.100) (0.100) (0.066) (0.091) (0.107)
Observations 7,515 7,515 7,515 7,515 7,515 7,515
R2 0.0002 0.00004 0.001 0.0002 0.0001 0.00002
Adjusted R2 0.0001 -0.0001 0.001 0.0001 -0.00002 -0.0001
Residual Std. Error (df = 7513) 3.285 3.285 3.283 3.285 3.285 3.285
F Statistic (df = 1; 7513) 1.605 0.285 10.051*** 1.451 0.884 0.169
Note: p<0.1; p<0.05; p<0.01

Here I do the same, but add a “comuna” fixed effect.

Dependent variable:
crime
(1) (2) (3) (4) (5) (6)
mean -0.005
(0.005)
median -0.008*
(0.005)
max 0.005**
(0.002)
min -0.016**
(0.007)
q25 -0.012**
(0.005)
q75 -0.004
(0.004)
Observations 7,515 7,515 7,515 7,515 7,515 7,515
R2 0.0001 0.0004 0.001 0.001 0.001 0.0002
Adjusted R2 -0.002 -0.001 -0.001 -0.001 -0.001 -0.002
F Statistic (df = 1; 7500) 0.971 2.986* 5.183** 5.545** 4.683** 1.162
Note: p<0.1; p<0.05; p<0.01