Advertisement

Crime Mapping On-line: Public Perception of Privacy Issues

  • Ourania Kounadi
  • Kate Bowers
  • Michael Leitner
Article

Abstract

The Web 2.0 technology introduced dynamic web mapping, which in turn has dramatically changed the distribution and use of geographical information in our society. Some of the many advantages of online mapping include the fast information dissemination to the public, the interactivity between the users and the map interface, as well as the frequent and easy database updates. However, the theme of these maps may entail privacy risks at times. Such examples include the crime mapping initiatives where crime information is considered as private and sensitive. With respect to privacy disclosure risks, this study employs a survey design to investigate the public’s perception of location privacy when information about crimes is being displayed on public maps. Participants from the London Borough of Camden were recruited and their views were analysed by means of a questionnaire accompanied by mapping material (n = 201). Participants expressed a clear concern when practical implications related to the crime maps were revealed to them. Further, the majority of participants, if given the choice, would opt for a medium-risk protection method in terms of risk of privacy violation. Lastly, in regards to the impact that different visualizations may have on local crime perception the study revealed that the current police.uk cartographic technique creates the perception of more unsafe neighbourhoods than an alternative hot routes density map.

Keywords

Confidentiality Location privacy Public perception and crime mapping 

Notes

Acknowledgments

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

References

  1. Abdel Malik, P., Boulos, M. N. K., & Jones, R. (2008). The perceived impact of location privacy: A web-based survey of public health perspectives and requirements in the UK and Canada. BMC Public Health, 8.Google Scholar
  2. Benisch, M., Kelley, P. G., Sadeh, N., & Cranor, L. F. (2011). Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs. Personal and Ubiquitous Computing, 15(7), 679–694.CrossRefGoogle Scholar
  3. Beresford, A. R., & Stajano, F. (2003). Location privacy in pervasive computing. IEEE CS and IEEE Communications Society, 1, 46–55.Google Scholar
  4. Blumberg, A. J., & Eckersley, P. (2009). On locational privacy, and how to avoid losing it forever. Electronic Frontier Foundation: White Paper. retrieved at https://www.eff.org/wp/locational-privacy.
  5. Bowers, K. J., & Johnson, S. D. (2005). Domestic Burglary Repeats and Space-Time Clusters The Dimensions of Risk. European Journal of Criminology, 2(1), 67–92.CrossRefGoogle Scholar
  6. Bridwell, S. A. (2007). The dimensions of locational privacy. Societies and Cities in the Age of Instant Access, 88, 209–225.CrossRefGoogle Scholar
  7. Brownstein, J. S., Cassa, C. A., Kohane, I. S., & Mandl, K. D. (2006). An unsupervised classification method for inferring original case locations from low-resolution disease maps. International Journal of Health Geographics, 5(1), 56.CrossRefGoogle Scholar
  8. Burrough, P., Craglia, M., Masser, I., & Rhind, D. (1997). Decision makers’ perspectives on European geographic information policy issues. Transactions in GIS, 2(1), 61–71.CrossRefGoogle Scholar
  9. Cassa, C. A., Grannis, S. J., Overhage, J. M., & Mandl, K. D. (2006). A context-sensitive approach to anonymizing spatial surveillance data: Impact on outbreak detection. Journal of the American Medical Informatics Association, 13(2), 160–165.CrossRefGoogle Scholar
  10. Cassa, C. A., Wieland, S. C., & Mandl, K. D. (2008). Re-identification of home addresses from spatial locations anonymized by Gaussian skew. International Journal of Health Geographics, 7(45).Google Scholar
  11. Chaplin, R., Flatley, J., & Smith, K. (2011). Crime in England and Wales 2010/11: Findings from the British Crime Survey and police recorded crime (2nd Edition). Home Office Statistical Bulletin 10/11. London: Home Office.Google Scholar
  12. Curtis, A. J., Mills, J. W., & Leitner, M. (2006). Spatial confidentiality and GIS: re-engineering mortality locations from published maps about Hurricane Katrina. International Journal of Health Geographics, 5(1), 44.CrossRefGoogle Scholar
  13. Duffy, B., & Rowden, L. (2005). You are what you read? London: MORI.Google Scholar
  14. Duffy, B., Wake, R., Burrows, T., & Bremner, P. (2008). Closing the gaps–crime and public perceptions. International Review of Law Computers & Technology, 22(1–2), 17–44.CrossRefGoogle Scholar
  15. Duwe, G. (2009). Residency restrictions and sex offender recidivism: Implications for public safety. Geography & Public Safety, 2(1), 6–8.Google Scholar
  16. Graham, C. (2012). Anonymisation: managing data protection risk code of practice. Information Commissioner’s Office. Google Scholar
  17. Groff, E. R., Kearley, B., Fogg, H., Beatty, P., Couture, H., & Wartell, J. (2005). A randomized experimental study of sharing crime data with citizens: Do maps produce more fear? Journal of Experimental Criminology, 1(1), 87–115.CrossRefGoogle Scholar
  18. Hampton, K. H., Fitch, M. K., Allshouse, W. B., Doherty, I. A., Gesink, D. C., Leone, P. A., et al. (2010). Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking. American Journal of Epidemiology, 172(9), 1062–1069.CrossRefGoogle Scholar
  19. Hohl, K., Bradford, B., & Stanko, E. A. (2010). Influencing trust and confidence in the London Metropolitan Police. British Journal of Criminology, 50, 491–513.CrossRefGoogle Scholar
  20. ICO. (2012). Crime-mapping and geo-spatial crime data: privacy and transparency principles. Information: Commissioner’s Office.Google Scholar
  21. Johnson, S. D., & Bowers, K. J. (2004). The Burglary as Clue to the Future The Beginnings of Prospective Hot-Spotting. European Journal of Criminology, 1(2), 237–255.CrossRefGoogle Scholar
  22. Kar, B., Crowsey, R. C., & Zale, J. J. (2013). The Myth of Location Privacy in the United States: Surveyed Attitude Versus Current Practices. The Professional Geographer, 65(1), 47–64.CrossRefGoogle Scholar
  23. Kounadi, O., Lampoltshammer, T. J., Leitner, M., & Heistracher, T. (2013). Accuracy and privacy aspects in free online reverse geocoding services. Cartography and Geographic Information Science, 40(2), 140–153.CrossRefGoogle Scholar
  24. Krumm, J. (2007). Inference attacks on location tracks (In A. LaMarca, M. Langheinrich & K. Truong (Eds.), Pervasive Computing (Vol. 4480, pp. 127–143)). Berlin Heidelberg: Springer.CrossRefGoogle Scholar
  25. Kwan, M. P., Casas, I., & Schmitz, B. C. (2004). Protection of geoprivacy and accuracy of spatial information: How effective are geographical masks? Cartographica: The International Journal for Geographic Information and Geovisualization, 39(2), 15–28.CrossRefGoogle Scholar
  26. Leitner, M., & Curtis, A. (2004). Cartographic guidelines for geographically masking the locations of confidential point data. Cartographic Perspectives, 49, 22–39.CrossRefGoogle Scholar
  27. Leitner, M., Mills, J. W., & Curtis, A. (2007). Can Novices to Geospatial Technology Compromise Spatial Confidentially? Kartographische Nachrichten, 57(2), 78–84.Google Scholar
  28. Likert, R. (1932). A Technique for the Measurement of Attitudes. Archives of Psychology, 140: 1–55.Google Scholar
  29. Minamisava, R., Nouer, S. S., de Morais Neto, O. L., Melo, L. K., & Andrade, A. L. S. (2009). International Journal of Health Geographics. International Journal of Health Geographics, 8, 66.CrossRefGoogle Scholar
  30. police.uk (2012). Anonymisation Methodology. Available at http://data.police.uk/about/#anonymisation. Accessed 29 May 2014.
  31. Quinton, P. (2011). The impact of information about crime and policing on public perceptions: The results of a randomised controlled trial. London: NPIA.Google Scholar
  32. Ratcliffe, J. H. (2002). Damned if you don’t, damned if you do: Crime mapping and its implications in the real world. Policing and Society, 12(3), 211–225.CrossRefGoogle Scholar
  33. Ray, K., Davidson, R., Husain, F., Vegeris, S., Vowden, K., & Karn, J. (2012). Perceptions of the policing and crime mapping ‘Trailblazers’. Home Office, Policing research and analysis, Research Report, 67.Google Scholar
  34. Sampson, F., & Kinnear, F. (2010). Plotting Crimes: Too True to Be Good? The Rationale and Risks behind Crime Mapping in the UK. Policing: A Journal of Policy and Practice, 4(1), 15–27.CrossRefGoogle Scholar
  35. Tita, G. E., & Radil, S. M. (2011). Spatializing the social networks of gangs to explore patterns of violence. Journal of Quantitative Criminology, 27(4), 521–545.CrossRefGoogle Scholar
  36. Tompson, L., & Chainey, S. (2012). Engagement, empowerment and transparency: publishing crime statistics using online crime mapping. Policing: A Journal of Policy and Practice, 6(3):228–239.Google Scholar
  37. Tompson, L., Partridge, H., & Shepherd, N. (2009). Hot Routes: Developing a New Technique for the Spatial Analysis of Crime. Crime Mapping: A Journal of Research and Practice, 1(1), 77–96.Google Scholar
  38. Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries - A test of the near repeat hypothesis. British Journal of Criminology, 43(3), 615–633.CrossRefGoogle Scholar
  39. Wartell, J. (2001). Evaluating a Crime Mapping Web Site. Crime Mapping News, 3(3), 1–4.Google Scholar
  40. Wartell, J., & McEwen, J. T. (2001). Privacy in the Information Age: A Guide for Sharing Crime Maps and Spatial Data Series: Research Report. Institute for Law and Justice. Google Scholar
  41. Weatherburn, D., Matka, E., & Lind, B. (1996). Crime Perception and Reality. Crime and Justice Bulletin, 28.Google Scholar
  42. Zhou, Y. J., Dominici, F., & Louis, T. A. (2010). A Smoothing Approach for Masking Spatial Data. Annals of Applied Statistics, 4(3), 1451–1475.CrossRefGoogle Scholar
  43. Zimmerman, D. L., & Pavlik, C. (2008). Quantifying the effects of mask metadata disclosure and multiple releases on the confidentiality of geographically masked health data. Geographical Analysis, 40(1), 52–76.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Ourania Kounadi
    • 1
  • Kate Bowers
    • 2
  • Michael Leitner
    • 1
    • 3
  1. 1.Doctoral College GIScience Department of Geoinformatics – Z_GISUniversity of SalzburgStatutory cityAustria
  2. 2.Department of Security and Crime ScienceUniversity College LondonLondonUK
  3. 3.Department of Geography and AnthropologyLouisiana State UniversityBaton RougeUSA

Personalised recommendations