Geosocial Media Data as Predictors in a GWR Application to Forecast Crime Hotspots (Short Paper)

Authors Alina Ristea , Ourania Kounadi , Michael Leitner

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Alina Ristea
  • Department of Geoinformatics – Z_GIS, Doctoral College GIScience, University of Salzburg, Austria
Ourania Kounadi
  • Department of Geo-information Processing, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, Netherlands
Michael Leitner
  • Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, USA

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Alina Ristea, Ourania Kounadi, and Michael Leitner. Geosocial Media Data as Predictors in a GWR Application to Forecast Crime Hotspots (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 56:1-56:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


In this paper we forecast hotspots of street crime in Portland, Oregon. Our approach uses geosocial media posts, which define the predictors in geographically weighted regression (GWR) models. We use two predictors that are both derived from Twitter data. The first one is the population at risk of being victim of street crime. The second one is the crime related tweets. These two predictors were used in GWR to create models that depict future street crime hotspots. The predicted hotspots enclosed more than 23% of the future street crimes in 1% of the study area and also outperformed the prediction efficiency of a baseline approach. Future work will focus on optimizing the prediction parameters and testing the applicability of this approach to other mobile crime types.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • spatial crime prediction
  • street crime
  • population at risk
  • geographically weighted regression
  • geosocial media


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