Youth Homicides in Boston

Description

Data about the number of youth homicides in Boston during the ‘Boston Gun Project’—a policing initiative aiming at lowering homicide victimization among young people in Boston.

Usage

data("BostonHomicide")

Format

A data frame containing 6 monthly time series and two factors coding seasonality and year, respectively.

homicides
time series. Number of youth homicides.
population
time series. Boston population (aged 25-44), linearly interpolated from annual data.
populationBM
time series. Population of black males (aged 15-24), linearly interpolated from annual data.
ahomicides25
time series. Number of adult homicides (aged 25 and older).
ahomicides35
time series. Number of adult homicides (aged 35-44).
unemploy
time series. Teen unemployment rate (in percent).
season
factor coding the month.
year
factor coding the year.

Details

The ‘Boston Gun Project’ is a policing initiative aiming at lowering youth homicides in Boston. The project began in early 1995 and implemented the so-called ‘Operation Ceasefire’ intervention which began in the late spring of 1996.

Source

Piehl et al. (2004), Figure 1, Figure 3, and Table 1.

From the table it is not clear how the data should be linearly interpolated. Here, it was chosen to use the given observations for July of the corresponding year and then use approx with rule = 2.

References

Piehl A.M., Cooper S.J., Braga A.A., Kennedy D.M. (2003), Testing for Structural Breaks in the Evaluation of Programs, The Review of Economics and Statistics, 85(3), 550-558.

Kennedy D.M., Piehl A.M., Braga A.A. (1996), Youth Violence in Boston: Gun Markets, Serious Youth Offenders, and a Use-Reduction Strategy, Law and Contemporary Problems, 59, 147-183.

Examples

library("strucchange")

data("BostonHomicide")
attach(BostonHomicide)

## data from Table 1
tapply(homicides, year, mean)
    1992     1993     1994     1995     1996     1997     1998 
3.083333 4.000000 3.166667 3.833333 2.083333 1.250000 0.800000 
populationBM[0:6*12 + 7]
[1] 12977 12455 12272 12222 11895 12038    NA
tapply(ahomicides25, year, mean)
    1992     1993     1994     1995     1996     1997     1998 
3.250000 4.166667 3.916667 4.166667 2.666667 2.333333 1.400000 
tapply(ahomicides35, year, mean)
     1992      1993      1994      1995      1996      1997      1998 
0.8333333 1.0833333 1.3333333 1.1666667 1.0833333 0.7500000 0.4000000 
population[0:6*12 + 7]
[1] 228465 227218 226611 231367 230744 228696     NA
unemploy[0:6*12 + 7]
[1] 20.2 18.8 15.9 14.7 13.8 12.6   NA
## model A
## via OLS
fmA <- lm(homicides ~ populationBM + season)
anova(fmA)
Analysis of Variance Table

Response: homicides
             Df  Sum Sq Mean Sq F value  Pr(>F)  
populationBM  1  14.364 14.3642  3.7961 0.05576 .
season       11  47.254  4.2959  1.1353 0.34985  
Residuals    64 242.174  3.7840                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## as GLM
fmA1 <- glm(homicides ~ populationBM + season, family = poisson)
anova(fmA1, test = "Chisq")
Analysis of Deviance Table

Model: poisson, link: log

Response: homicides

Terms added sequentially (first to last)

             Df Deviance Resid. Df Resid. Dev Pr(>Chi)  
NULL                            76    115.649           
populationBM  1   4.9916        75    110.657  0.02547 *
season       11  18.2135        64     92.444  0.07676 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model B & C
fmB <- lm(homicides ~ populationBM + season + ahomicides25)
fmC <- lm(homicides ~ populationBM + season + ahomicides25 + unemploy)

detach(BostonHomicide)