Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/230724 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
IRTG 1792 Discussion Paper No. 2018-013
Verlag: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Zusammenfassung: 
Data from social media has created opportunities to understand how and why people move through their urban environment and how this relates to criminal activity. To aid resource allocation decisions in the scope of predictive policing, the paper proposes an approach to predict weekly crime counts. The novel approach captures spatial dependency of criminal activity through approximating human dynamics. It integrates point of interest data in the form of Foursquare venues with Twitter activity and taxi trip data, and introduces a set of approaches to create features from these data sources. Empirical results demonstrate the explanatory and predictive power of the novel features. Analysis of a six-month period of real-world crime data for the city of New York evidences that both temporal and static features are necessary to eectively account for human dynamics and predict crime counts accurately. Furthermore, results provide new evidence into the underlying mechanisms of crime and give implications for crime analysis and intervention.
Schlagwörter: 
Predictive Policing
Crime Forecasting
Social Media Data
Spatial Econometrics
JEL: 
C00
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.