Redrawing hot spots of crime in Dallas, Texas

Andrew Palmer Wheeler and Sydney Reuter
Additional contact information
Andrew Palmer Wheeler: University of Texas at Dallas

No nmq8r, SocArXiv from Center for Open Science

Abstract: In this work we evaluate the predictive capability of identifying long term, micro place hot spots in Dallas, Texas. We create hot spots using a hierarchical clustering algorithm, using law enforcement cost of crime estimates as weights. Relative to the much larger current hot spot areas defined by the Dallas Police Department, our identified hot spots are much smaller (under 3 square miles), and capture crime harm at a higher density per the Predictive Accuracy Index statistic. We also show that the hierarchical clustering algorithm captures a wide array of hot spot types; some one or two addresses, some street segments, and others an agglomeration of larger areas. This suggests identifying hot spots based on a specific unit of aggregation (e.g. addresses, street segments), may be less efficient than using a hierarchical clustering technique in practice. Code and data to reproduce the analysis can be downloaded from https://www.dropbox.com/sh/kcask6pinaaaz4v/AAC4CXk6NzUweyld2n4OznzWa?dl=0

Date: 2020-03-30
New Economics Papers: this item is included in nep-law and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/5e80e9b41ab2430077e03102/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:nmq8r

DOI: 10.31219/osf.io/nmq8r

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().