Geosocial media data as predictors in a GWR application to forecast crime hotspots

Alina Ristea, O. Kounadi, Michael Leitner

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication10th International Conference on Geographic Information Science, GIScience 2018
EditorsSester M Griffin A.L. Winter S.
PublisherDagstuhl
Volume114
ISBN (Print)9783959770835
DOIs
Publication statusPublished - 1 Aug 2018
Event10th International Conference on Geographic Information Science, GIScience 2018 - RMIT University, Melbourne, Australia
Duration: 28 Aug 201831 Aug 2018
Conference number: 10

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume114

Conference

Conference10th International Conference on Geographic Information Science, GIScience 2018
Abbreviated titleGIScience 2018
CountryAustralia
CityMelbourne
Period28/08/1831/08/18

Keywords

  • Geographically weighted regression
  • Geosocial media
  • Population at risk
  • Spatial crime prediction
  • Street crime
  • ITC-GOLD

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  • Cite this

    Ristea, A., Kounadi, O., & Leitner, M. (2018). Geosocial media data as predictors in a GWR application to forecast crime hotspots. In S. M. Griffin A.L. Winter S. (Ed.), 10th International Conference on Geographic Information Science, GIScience 2018 (Vol. 114). (Leibniz International Proceedings in Informatics, LIPIcs; Vol. 114). Dagstuhl. https://doi.org/10.4230/LIPIcs.GIScience.2018.56