Improving Crime Count Forecasts Using Twitter and Taxi Data

IRTG 1792 Discussion Paper 2018-013

35 Pages Posted: 28 Feb 2018

See all articles by Lara Vomfell

Lara Vomfell

University of Warwick

Wolfgang K. Härdle

Blockchain Research Center; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; National Yang Ming Chiao Tung University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Stefan Lessmann

School of Business and Economics, Humboldt-University of Berlin

Date Written: February 28, 2018

Abstract

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 effectively 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.

Keywords: Predictive Policing, Crime Forecasting, Social Media Data, Spatial

JEL Classification: C14

Suggested Citation

Vomfell, Lara and Härdle, Wolfgang K. and Lessmann, Stefan, Improving Crime Count Forecasts Using Twitter and Taxi Data (February 28, 2018). IRTG 1792 Discussion Paper 2018-013, Available at SSRN: https://ssrn.com/abstract=3131517 or http://dx.doi.org/10.2139/ssrn.3131517

Lara Vomfell

University of Warwick ( email )

Gibbet Hill Rd.
Coventry, West Midlands CV4 8UW
United Kingdom

Wolfgang K. Härdle

Blockchain Research Center ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Yang Ming Chiao Tung University ( email )

No. 1001, Daxue Rd. East Dist.
Hsinchu City 300093
Taiwan

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Stefan Lessmann (Contact Author)

School of Business and Economics, Humboldt-University of Berlin ( email )

Unter den Linden 6
Berlin, Berlin 10099
Germany

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