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

Authors: Alina Ristea, Ourania Kounadi, and Michael Leitner

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)


Abstract
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.

Cite as

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)


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@InProceedings{ristea_et_al:LIPIcs.GISCIENCE.2018.56,
  author =	{Ristea, Alina and Kounadi, Ourania and Leitner, Michael},
  title =	{{Geosocial Media Data as Predictors in a GWR Application to Forecast Crime Hotspots}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{56:1--56:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.56},
  URN =		{urn:nbn:de:0030-drops-93845},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.56},
  annote =	{Keywords: spatial crime prediction, street crime, population at risk, geographically weighted regression, geosocial media}
}
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