Using Risk Terrain Modeling to Predict Homeless Related Crime in Los Angeles, California

dc.contributor.authorYoo, Youngmin
dc.contributor.authorWheeler, Andrew P.
dc.contributor.utdAuthorYoo, Youngmin
dc.contributor.utdAuthorWheeler, Andrew P.
dc.descriptionDue to copyright restrictions and/or publisher's policy full text access from Treasures at UT Dallas is limited to current UTD affiliates (use the provided Link to Article).
dc.description.abstractWe 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 Ltd
dc.description.departmentSchool of Economic, Political and Policy Sciences
dc.identifier.bibliographicCitationYoo, Y., and A. P. Wheeler. 2019. "Using risk terrain modeling to predict homeless related crime in Los Angeles, California." Applied Geography 109: art. 102039, doi: 10.1016/j.apgeog.2019.102039
dc.publisherElsevier Ltd
dc.rights©2019 Elsevier Ltd. All Rights Reserved.
dc.source.journalApplied Geography
dc.subjectHomeless persons
dc.subjectRisk Terrain Modeling
dc.subjectVictims of crimes surveys
dc.subjectDrug abuse and crime
dc.subjectShelters for the homeless
dc.subjectBus stops
dc.titleUsing Risk Terrain Modeling to Predict Homeless Related Crime in Los Angeles, California


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