Wheeler, Andrew P.
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6684
Andrew Wheeler is an Assistant Professor of Criminology and Criminal Justice. His research interests include:
- Spatial Analysis of Crime and Criminal Behavior
- Evaluation of Policing and Crime Reduction Policies
- Practical Problems Faced by Crime Analysts
ORCID page
Browse
Recent Submissions
Item Using Risk Terrain Modeling to Predict Homeless Related Crime in Los Angeles, California(Elsevier Ltd, 2019-06-22) Yoo, Youngmin; Wheeler, Andrew P.; Yoo, Youngmin; Wheeler, Andrew P.We apply Risk Terrain Modeling (RTM) to identify the factors that predict homeless related crime at micro grid cells in Los Angeles, CA. We find that place based factors predicting whether homeless individuals are victimized or the offender being homeless are largely consistent with one another. Out of 26 different crime attractors and generators, prior drug arrests, homeless shelters, and bus stops are the three biggest factors in predicting homeless related crime. We show how the RTM model can effectively forecast future homeless related crimes as well. This suggests that targeted spatial strategies can reduce both homeless offending and victimization risk. Given that the majority of homeless individuals are only intermittently homeless, place based strategies may be a more an effective way to limit risk than strategies that focus on individuals. © 2019 Elsevier LtdItem Creating Optimal Patrol Areas Using the P-Median Model(Emerald Publishing Ltd.) Wheeler, Andrew P.; 0000-0003-2255-1316 (Wheeler, AP); Wheeler, Andrew P.Purpose: The purpose of this paper is to illustrate the use of the p-median model to construct optimal patrol areas. This can improve both time spent traveling to calls, as well as equalize call load between patrol areas. Design/methodology/approach: The paper provides an introduction to the use of integer linear programs to create optimal patrol areas, as many analysts and researchers in the author’s field will not be familiar with such models. The analysis then introduces a set of linear constraints to the p-median problem that are applicable to police agencies, such as constraining call loads to be equal and making patrol areas geographically contiguous. Findings: The analysis illustrates the technique on simplified simulated examples. The analysis then demonstrates the utility of the technique by showing how patrol areas in Carrollton, TX can be made both more efficient and equalize the call loads given the same number of patrol beats as currently in place. Originality/value: Unlike prior applications of creating patrol areas, this paper introduces linear constraints into the p-median problem, making it much easier to solve than programs that have non-linear or multiple objective functions. Supplementary code using open source software is also provided, allowing other analysts or researchers to apply the model to their own data. © 2018, Emerald Publishing Limited.