Improving Crime Count Forecasts Using Twitter and Taxi Data

Crime prediction is crucial to criminal justice decision makers and efforts to prevent crime. The paper evaluates the explanatory and predictive value of human activity patterns derived from taxi trip, Twitter and Foursquare data. Analysis of a six-month period of crime data for New York City shows...

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Bibliographic Details
Published inarXiv.org
Main Authors Vomfell, Lara, Härdle, Wolfgang Karl, Lessmann, Stefan
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.09.2020
Subjects
Crime
Crime prevention
Statistics - Applications
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Summary:Crime prediction is crucial to criminal justice decision makers and efforts to prevent crime. The paper evaluates the explanatory and predictive value of human activity patterns derived from taxi trip, Twitter and Foursquare data. Analysis of a six-month period of crime data for New York City shows that these data sources improve predictive accuracy for property crime by 19% compared to using only demographic data. This effect is strongest when the novel features are used together, yielding new insights into crime prediction. Notably and in line with social disorganization theory, the novel features cannot improve predictions for violent crimes.
ISSN:2331-8422
DOI:10.48550/arxiv.2009.03703