Spatial Relationships between Violence, Drug Markets and Alcohol Outlets in Boston

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1 Spatial Relationships between Violence, Drug Markets and Alcohol Outlets in Boston Robert Lipton 1, Jason Goldstick 1,Xiaowen Yang 2 Anthony Braga 3 and Manya Newton 1 1 University of Michigan, 2 MIT, 3 Harvard This research was made possible by a grant from NIAAA, R21AA18204

2 'Tobler's first law of geography' may be summarized as "everything is related to everything else, but near things are more related than distant things

3 What is a spatial lag? Measures collected from geographic units are rarely independent and characteristics of one area may affect violence in another. X 1 e.g: Poverty in one area may affect violence in another (spatial lags). X 2 Y 3 X 4 X 5 X 6

4 Goal The goal of this project is to determine how place based, environmental features such as alcohol outlets are related to rates of violence.

5 Why? Understanding the social mechanisms that underlie these associations will aid in the development of environmental violence prevention strategies, such as changes in zoning, community action and education, and policing.

6 Figure 1: Conceptual Model Population Characteristics Population b Violence e Density Density s e Environmental Characteristics

10 Data Elements Census block groups were used in this analysis, n=544 Three sources of data were used in this analysis: 1) Boston Police Department (BPD) 2006 data on homicide and aggravated assault incidents, drug arrests and 911 citizen emergency calls for service 2) U.S. Census data, including population estimates (Census, 2000) 3) Massachusetts Alcohol Beverage Control Commission (MABCC) alcohol outlet data (MABCC, 2009),

11 Alcohol Outlet Density. Data on the locations of alcohol outlets were obtained from the Massachusetts Alcohol Beverage Control commission (ABCC) and are available for all the years of study ( ) proposed in this study. Outlet locations are geocoded to street address of the establishment. Numbers of active alcohol l outlets t by Census tract t were tabulated for off-premise establishments (license type 20 or 21), restaurants that serve alcohol (license type 41 or 47), and bars and pubs (license type 23, 40, 42, 48, 61 or 75).

14 Drug market activity: at the census block group level, was approximated using two different sources -- police response to illegal drug sales, possession and transport/distribution, (see figure 1c) as obtained from police incident/arrest reports(committee on Data and Research for Policy on Illegal Drugs, 2001) and resident response to drug activity as measured by drug related 911 calls (BPD, 2008). Both measurements have advantages and disadvantages.

16 Empty Household percentage for block groups

18 Violence: Based on crime incident id data, we calculate l the violent crime rate by summing three categories of violent crime incidence (counts of homicide, robbery and aggravated assault), then divide the sum by the population in each census block group and multiplying the results by 1,000 Although homicide and aggravated assaults are treated as different crime types, we combined them into a single measure of serious assaultive violence

22 N=55 N=489 Variables Mean Std. Mean Std. % population <= 16 years old % population >= 65 years old * % minority % families with income <poverty level % unemployed in labor force % families with female head % households with income lower than 25K Mean collective efficacy Count of restaurants which sell all types of alcohol Count of restaurants which sell wine and malt 0.636* Count of package alcohol outlets Count of drug possession incidents Count of drug distribution incidents lag value of % housing units occupied by renter lag value of % housing units vacant lag value of % >=25 years old w/ >= associate it deg ** lag value of % households with income lower than 25K lag value of % families with female head lag value of % unemployed in labor force lag value of % households with income lower than 25K lag value of % minority lag value of % population >= 65 years old * lag value of % population <= 16 years old lag vaue of count of drug distribution incidents lag value of count of drug possion incidents lag value of count of package alcohol outlets 0.419* Comparison of highest decile for aggravated assaults and murder to all other deciles. * measures which were not found to be significantly different between the top decile and all others, at an alpha of.05 using a pooled T test. ** italicized rows indicate significant differences in which the top decile was significantly lower than all other deciles. lag value of count of restaurants which sell all types of alcohol lag value of count of restaurants which sell wine and malt

23 Descriptive Results: -The overall mean number of violent crimes per 1,000 persons was (sd = 12.53) for the City of Boston. -The block group-level crime rate ranged from a low of 0 to a high of 99 incidents per thousand persons. When comparing the highest decile block groups (n=55) to all others (n=489) for violent crime rate -We found that the highest decile block groups, (hot spots), had significantly higher mean values for most independent variables when compared to all other block groups. -This was true for socio-demographic measures, place-based measures (alcohol l outlets and drug arrests) and both socio-demographic and place-based spatially lagged measures. -In areas adjacent to target area violent crimes, these measures were found to be significantly higher in the highest decile.

24 Descriptive results (cont.) -In this regard, alcohol outlets, in all categories, were significantly more prevalent in the highest decile. -Further, drug-related arrests, both for possession and trafficking, were significantly higher in this decile. -Both alcohol outlets and drug arrests were found be related to violent crimes, both within a given block group and adjacent to these block groups.

25 Parametric Modeling We developed nested Quasi- Poisson regression models in which socio-demographic measures were first assessed; we then included first- order environmental measures -- alcohol l outlets t and illicit it drug arrests -- and finally included spatially lagged measures for all variables in the first two models. Separate complete models were also run comparing weekend and weekday violence.

26 Table 1. Nested Poisson Regression models, model 1 Model 2 Model 3 Model 4 Model 5 contribution for each group of variables to an overall model. Census based variables: %( under 16, over 65, minority, below poverty line, unemployed, families with female head, household under 25k income, with at least an AA degree, housing vacant, housing occupied by renter.) Environmental variables: #( restaurants selling any type of alcohol, restaurants selling beer or wine, liquor stores, outlets that are neither bars or restaurants, drug distribution arrests, drug possession arrests)... Lag values for census and environmental variables... above. (Lagged variables measure the relationship between adjacent area independent variables and target area violent crime.) R 2 =.39 R 2 =.51 R 2 =.60 % Dev = 46.02% % Dev = 55.32% % Dev = 63.34%

27 Nested Poisson regression models Demographics Model Add in drug/alcohol (2) (1) Estimate Pr(> z ) Estimate Pr(> z ) Intercept < <0.001 % Under < % over % Minority < <0.001 % below poverty line % Unemployed % Families with female head % Household under $25k income % With at least associate's degree < <0.001 % Housing vacant < <0.001 % Housing occupied by renter # Restaurants selling any type of alcohol <0.001 # Restaurants selling beer and/or wine # Package alcohol outlets # Alcohol outlets that are neither stores nor restaurants # Drug distribution incidents # Drug possession incidents <0.001 R^2 = R^2 = % Dev = 46.02% % Dev = 55.32%

28 (Nested Poisson continued) Lag Model Lag Weekday Lag Week end lag value % occupied by renter lag value % housing vacant lag value % with at least associate's degree lag value % houses under $25k income lag value % families with female head lag value % unemployed lag value % below poverty line lag value % minority lag value % over lag value % under lag value drug distribution incidents < lag value drug possession incidents lag value alcohol outlets neither restaurant or store lag value package alcohol outlets lag value restaurants selling all types of alcohol < < lag value restaurants selling beer and wine R^2 = % Dev = 46.02% R^2 = R^2 = R^2 = R^2 = % Dev = 55.32% % Dev = 63.34% % Dev = 62.76% % Dev 45.22%

32 Conclusions: Alcohol l outlets, t both the density and type, were found to be related to violent crime in a differentiated and complex way. The effect of alcohol outlets on crime is often conceived of as a marker for other population or environmental features of places related to violence; in this paper we give evidence that there is a sizeable effect attributable to alcohol outlets that cannot be explained solely by such features. In addition, we found that spatial effects due to the environment of neighboring block groups were significantly related to violent crime, controlling for drug trafficking and drug possession arrests.

33 Further, areas of high violent crime were found to be associated with significantly higher values for socio-demographic, alcohol outlet and drug arrest measures. Such hot spot identification may help in identifying micro-environments, such as face blocks or intersections with place-level characteristics that facilitate t violent behavior.

34 Regardless of specific alcohol outlet or drug relationships found, we have shown that place-based factors contribute to a more comprehensive and useful understanding of the context in which violent crime occurs. State and city alcohol control and policing policies can be strengthened, building on such information. Future research efforts will also benefit from this more nuanced understanding of alcohol outlets as well as changes in violence crime and outlets through time.

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