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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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Author Details

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

Funding

This research was funded by the Austrian Science Fund (FWF) through the Doctoral College GIScience at the University of Salzburg (DK W 1237-N23).

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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) https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.56

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.

Subject Classification

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

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References

  1. Meshrif Alruily. Using text mining to identify crime patterns from arabic crime news report corpus. Thesis, DeMontfort University, 2012. URL: http://hdl.handle.net/2086/7584.
  2. Johannes Bendler, Tobias Brandt, Sebastian Wagner, and Dirk Neumann. Investigating crime-to-twitter relationships in urban environments-facilitating a virtual neighborhood watch. In ECIS 2014, 2014. URL: http://aisel.aisnet.org/ecis2014/proceedings/track11/10/.
  3. Chris Brunsdon, A Stewart Fotheringham, and Martin E Charlton. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4):281-298, 1996. Google Scholar
  4. Joel M Caplan and Leslie W Kennedy. Risk terrain modeling compendium. Rutgers Center on Public Security, Newark, 2011. Google Scholar
  5. Spencer Chainey, Lisa Tompson, and Sebastian Uhlig. The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1):4-28, 2008. Google Scholar
  6. Xinyu Chen, Youngwoon Cho, and Suk young Jang. Crime prediction using twitter sentiment and weather. In Systems and Information Engineering Design Symposium (SIEDS), 2015, pages 63-68. IEEE, 2015. Google Scholar
  7. JE Eck, S Chainey, JG Cameron, M Leitner, and RE Wilson. Mapping crime: Understanding hot spots. national institute of justice. Washington, DC, 2005. Google Scholar
  8. A Stewart Fotheringham, Chris Brunsdon, and Martin Charlton. Geographically weighted regression: the analysis of spatially varying relationships. John Wiley &Sons, 2003. Google Scholar
  9. Matthew S Gerber. Predicting crime using twitter and kernel density estimation. Decision Support Systems, 61:115-125, 2014. URL: http://dx.doi.org/10.1016/j.dss.2014.02.003.
  10. Philip Glasner and Michael Leitner. Evaluating the impact the weekday has on near-repeat victimization: A spatio-temporal analysis of street robberies in the city of vienna, austria. ISPRS International Journal of Geo-Information, 6(1):3, 2016. Google Scholar
  11. Ourania Kounadi, Alina Ristea, Michael Leitner, and Chad Langford. Population at risk: using areal interpolation and twitter messages to create population models for burglaries and robberies. Cartography and Geographic Information Science, pages 1-15, 2017. URL: http://dx.doi.org/15230406.2017.1304243.
  12. Nick Malleson and Martin A Andresen. The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science, 42(2):112-121, 2015. URL: http://dx.doi.org/10.1080/15230406.2014.905756.
  13. Nick Malleson and Martin A Andresen. Exploring the impact of ambient population measures on london crime hotspots. Journal of Criminal Justice, 46:52-63, 2016. Google Scholar
  14. Walt L Perry. Predictive policing: The role of crime forecasting in law enforcement operations. Rand Corporation, 2013. Google Scholar
  15. Alina Ristea, Justin Kurland, Bernd Resch, Michael Leitner, and Chad Langford. Estimating the spatial distribution of crime events around a football stadium from georeferenced tweets. ISPRS International Journal of Geo-Information, 7(2):43, 2018. URL: http://www.mdpi.com/2220-9964/7/2/43.
  16. Xiaofeng Wang, Matthew S Gerber, and Donald E Brown. Automatic crime prediction using events extracted from twitter posts. In International conference on social computing, behavioral-cultural modeling, and prediction, pages 231-238. Springer, 2012. Google Scholar
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