Bars on blocks: A cellular automata model of crime and liquor licensed establishment density

Criminologists have extensively researched the problems generated by licensed establishments. Violent offending and disorderly behavior resulting from pubs, taverns, dance clubs and bars are of particular interest to this field of study. The relative density of these liquor establishments has been found to be associated with the level of violence and disorder in surrounding areas. A complex systems approach can be used to further understand the dynamic interplay between licensed establishments, violent offending and disorder, and urban planning decisions. The model presented here utilizes cellular automata as the mathematical framework to view the varying impact of liquor licensing density on crime. This study uses a sample of liquor establishments and crime data from the City of Vancouver in British Columbia. The cellular automata model incorporates transition rules which govern the change of city blocks from low-risk blocks to high-risk blocks. The results represented by a 50 × 50 cellular grid show that high-risk blocks multiply when liquor licenses are grouped. Two scenarios are presented to contrast the impact of grouping high-risk blocks which contain more liquor establishments and dispersing such blocks. A third scenario demonstrates how increasing the positive influence in a grouped scenario stops high-risk blocks from taking over the entire grid. Future iterations of this model will incorporate census data, public transportation data, land use data and entertainment districts from other cities to further analyze the effect of licensed establishments on the distribution of crime.

► This model demonstrates how density of liquor licenses impacts violence.
► Grouped liquor licensed establishments lead to violence.
► Un-grouped liquor license establishments reduce violence.
► Effective enforcement controls high-risk blocks in a grouped scenario.
► Effective enforcement eliminates high-risk blocks in an un-grouped scenario.