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CrimeTelescope: crime hotspot prediction based on urban and social media data fusion

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Abstract

Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot prediction leverages past data in order to identify geographical areas susceptible of hosting crimes in the future. However, most of the existing techniques in crime hotspot prediction solely use historical crime records to identify crime hotspots, while ignoring the predictive power of other data such as urban or social media data. In this paper, we propose CrimeTelescope, a platform that predicts and visualizes crime hotspots based on a fusion of different data types. Our platform continuously collects crime data as well as urban and social media data on the Web. It then extracts key features from the collected data based on both statistical and linguistic analysis. Finally, it identifies crime hotspots by leveraging the extracted features, and offers visualizations of the hotspots on an interactive map. Based on real-world data collected from New York City, we show that combining different types of data can effectively improve the crime hotspot prediction accuracy (by up to 5.2%), compared to classical approaches based on historical crime records only. In addition, we demonstrate the usability of our platform through a System Usability Scale (SUS) survey on a full prototype of CrimeTelescope.

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Notes

  1. https://nycopendata.socrata.com/

  2. http://stamen.com/work/crimespotting/

  3. https://www.spotcrime.com/

  4. http://www.crimemapping.com/

  5. https://www.census.gov/ces/dataproducts/demographicdata.html

  6. http://oauth.net/

  7. https://developers.google.com/maps/

  8. https://nycopendata.socrata.com/

  9. https://foursquare.com/

  10. https://developer.foursquare.com/categorytree

  11. https://twitter.com/

  12. https://dev.twitter.com/streaming/public

  13. Note that the self-defined stop words are iteratively selected. More precisely, analyzing the LDA results allows us to detect frequent words that do not add any meaning to the documents and include them in the stop word list for another round of LDA training.

  14. 14 http://prediction.heaney.ch/

  15. https://docs.google.com/forms

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Acknowledgments

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 683253/GraphInt).

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Authors and Affiliations

  1. eXascale Infolab, Department of Computer Science, University of Fribourg, Bd de Perolles 90, 1700, Fribourg, Switzerland

    Dingqi Yang, Terence Heaney, Alberto Tonon & Philippe Cudré-Mauroux

  2. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    Leye Wang

Authors
  1. Dingqi Yang
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  2. Terence Heaney
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  3. Alberto Tonon
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  4. Leye Wang
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  5. Philippe Cudré-Mauroux
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Correspondence to Dingqi Yang.

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Yang, D., Heaney, T., Tonon, A. et al. CrimeTelescope: crime hotspot prediction based on urban and social media data fusion. World Wide Web 21, 1323–1347 (2018). https://doi.org/10.1007/s11280-017-0515-4

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