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Safely Plotting Continuous Variables on a Map

  • Peter-Paul de WolfEmail author
  • Edwin de Jonge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11126)

Abstract

A plotted spatial distribution of a variable is an interesting type of statistical output favored by many users. Examples include the spatial distribution of people that make use of child care, of the amount of electricity used by businesses or of the exhaust of certain gasses by industry. However, a spatial distribution plot may be exploited to link information to a single unit of interest. Traditional disclosure control methods and disclosure risk measures can not readily be applied to this type of maps. In previous papers [5, 6] we discussed plotting the distribution of a dichotomous variable on a cartographic map. In the present paper we focus on plotting a continuous variable and derive a suitable risk measure, that not only detects unsafe areas, but also contains a recipe to repair them. We apply the risk measure to the spatial distribution of the energy consumption of enterprises to test and describe its properties.

Keywords

Cartographic map Disclosure risk Spatial distribution Continuous variable 

References

  1. 1.
    Chainey, S., Reid, S., Stuart, N.: When is a hotspot a hotspot? A procedure for creating statistically robust hotspot maps of crime. In: Kidner, D., Higgs, G., White, S. (eds.) Innovations in GIS 9: Socio-Economic Applications of Geographic Information Science, pp. 21–36. Taylor and Francis, Abingdon (2002)CrossRefGoogle Scholar
  2. 2.
    Danese, M., Lazzari, M., Murgante, B.: Kernel density estimation methods for a geostatistical approach in seismic risk analysis: the case study of Potenza Hilltop Town (Southern Italy). In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008. LNCS, vol. 5072, pp. 415–429. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-69839-5_31CrossRefGoogle Scholar
  3. 3.
    Duncan, G.T., Stokes, S.L.: Disclosure risk vs. data utility: the RU confidentiality map as applied to topcoding. Chance 17(3), 16–20 (2004)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Hundepool, A., et al.: Statistical Disclosure Control. Wiley Series in Survey Methodology. Wiley, Hoboken (2012). ISBN 978-1-119-97815-2CrossRefGoogle Scholar
  5. 5.
    de Jonge, E., de Wolf, P.-P.: Spatial smoothing and statistical disclosure control. In: Domingo-Ferrer, J., Pejić-Bach, M. (eds.) PSD 2016. LNCS, vol. 9867, pp. 107–117. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45381-1_9CrossRefGoogle Scholar
  6. 6.
    de Wolf, P.P., de Jonge, E.: Location related risk and utility. Presented at UNECE/Eurostat Worksession Statistical Data Confidentiality, 20–22 September, Skopje (2017). https://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2017/3_LocationRiskUtility.pdf
  7. 7.
    Worton, B.J.: Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70(1), 164–168 (1989)CrossRefGoogle Scholar
  8. 8.
    Zhou, Y., Dominici, F., Louis, T.A.: A smoothing approach for masking spatial data. Ann. Appl. Stat. 4(3), 1451–1475 (2010).  https://doi.org/10.1214/09-AOAS325MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Statistics NetherlandsThe HagueThe Netherlands

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