Heller, S. B. 2014. "Summer jobs reduce violence among disadvantaged youth." Science 346, no. 6214.
Hunt, G. P. and Laidler, K. J. 2001. "Alcohol and Violence in the Lives of Gang Members." Alcohol Research & Health 25, no. 1. Accessed November 17, 2016.
Moser, Whet. 2014. "How a Chicago Summer Job Program Reduced Violent Crime." Chicago Magazine/A Chicago Tribute Media Group website, Chicago Politics & City Life, Dec 15, 2014. Accessed March 28, 2016.
Toomey, T. L., Erickson, D. J., Carlin, B. P., Lenk, K. M., Quick, H. S., Jones, A. M., and Harwood, E. M. 2012. "The Association between Density of Alcohol Establishments and Violent Crime within Urban Neighborhoods." Alcoholism: Clinical & Experimental Research 36, no. 8. Accessed March 28, 2016.
Ventura County Behavioral Health Department, 2008. "Issue Briefing: Alcohol Retail Outlet Density Affects Neighborhood Crime and Violence." This publication is one in a series from Ventura County Behavioral Health, Alcohol & Drug Programs -- Prevention Services. www.venturacountylimits.org. Accessed March 28, 2016.
Willits, D., Broidy, L., Gonzales, A., and Denman, K. 2011. "Place and Neighborhood Crime: Examining the Relationship between Schools, Churches, and Alcohol Related Establishments and Crime." Final Report to the Justice Research Statistics Association. New Mexico Statistical Analysis Center, Institute for Social Research, University of New Mexico. Accessed March 28, 2016.
Routine activity theory
Routine activity theory argues three things need to converge for crime to occur: motivated offenders, potential victims, and an unguarded environment. More crime is expected in places that promote this convergence. The consumption of alcohol at bars, for example, can encourage motivated offenders by decreasing inhibitions and increasing aggression. At the same time, excessive drinking can reduce a person's ability to guard themselves or their belongings.
Felson, M. and Cohen, L. 1979. "Social change and crime rate trends: A routine activity approach." American Sociological Review 44, no. 4
Felson, M. 2009. Crime and Everyday Life. 2nd Edition. Sage Publications, Thousand Oaks, CA.
Social disorganization theory
Social disorganization theory argues that neighborhoods with strong social networks, stable residential populations, a sense of community, and the ability/resources to promote the common good are likely to have less crime. While the research linking the presence of alcohol establishments to community disorganization is sparse, it is possible that the coming and going of clientele to neighborhoods where these businesses exist would reduce social cohesion among neighbors making them less willing and less able to intervene in criminal activities.
Kubrin, C. E. and Weitzer, R. 2003. "New directions in social disorganization theory." Journal of Research in Crime and Delinquency 40.
Shaw, C. R. and McKay, H. D. 1942. Juvenile delinquency in urban areas. University of Chicago Press, Chicago.
Snowden, A. J. and Freiburger, T. L. 2015 forthcoming. "Alcohol outlets, social disorganization, and robberies: Accounting for neighborhood characteristics and alcohol outlet types." Social Science Research.
2014 Violent Crime Data
Obtained from the City of Chicago data portal, https://data.cityofchicago.org
In keeping with the requirements of the City of Chicago Data Portal terms of data use, note the following: This case study describes analyses using data that have been modified for use from its original source, www.cityofchicago.org, the official website of the City of Chicago. The City of Chicago makes no claims as to the content, accuracy, timeliness, or completeness of any of the data provided at this site. The data provided at this site is subject to change at any time. It is understood that the data provided at this site is being used at one's own risk.
Summary of tools
This case study demonstrates a number of analytical methods that can be adapted to many different application areas, allowing you to answer a variety of questions.
Where are the highest and lowest point densities?
Where are the statistically significant clusters of violent crime, liquor establishments, elm trees, boating accidents, flu cases, or foreclosures?
Where do high and low values cluster together?
Where are the statistically significant clusters of poverty, unemployment, wealth, beer drinkers, lead levels, or college graduates?
Where do the areas of interest in multiple maps overlap?
Where are the intersections among high crime areas, high liquor vendor areas, and high poverty areas? Where are high lead levels and poor educational outcomes spatially congruent?
What are the space-time trends?
Where are the new, intensifying, and sporadic hot spots for crime, traffic accidents, IED events, tweets, or 911 calls?
Which features are within or near other features?
Which schools are close to the remediation areas? Which homes fall within the flood zone? Which ZIP Codes are within the county?
In addition, you used the data-enrichment capabilities to get poverty and unemployment rate data. A wide variety of data including demographics, consumer spending, occupation, and landscape data can be obtained for point, line, or polygon geometries.
A number of resources are available to help you learn more about the analyses demonstrated in this case study:
- What is spatial analysis in ArcGIS?
- Spatial Statistics resources
- Learn more about hot spot analysis
- Learn more about emerging space time hot spot analysis
- Spatial Data Mining I: Essentials of Cluster Analysis, Video
- Spatial Data Mining II: A Deep Dive Into Space-Time Analysis, Video
- Feature Overlay
- Combining Data
- Performing analysis with ArcGIS Online
The resources below include guidelines and best practices for building custom model and script tools: