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Open AccessArticle

How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?

by Zengli Wang 1,2,3, Lin Liu 3,*, Hanlin Zhou 3 and Minxuan Lan 3
1
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
2
National Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China
3
Department of Geography and GIS, University of Cincinnati, Cincinnati, OH 45221, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 544; https://doi.org/10.3390/ijgi8120544
Received: 5 October 2019 / Revised: 10 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
(This article belongs to the Special Issue Using GIS to Improve (Public) Safety and Security)
Kernel density estimation (KDE) is widely adopted to show the overall crime distribution and at the same time obscure exact crime locations due to the confidentiality of crime data in many countries. However, the confidential level of crime locational information in the KDE map has not been systematically investigated. This study aims to examine whether a kernel density map could be reverse-transformed to its original map with discrete crime locations. Using the Epanecknikov kernel function, a default setting in ArcGIS for density mapping, the transformation from a density map to a point map was conducted with various combinations of parameters to examine its impact on the deconvolution process (density to point location). Results indicate that if the bandwidth parameter (search radius) in the original convolution process (point to density) was known, the original point map could be fully recovered by a deconvolution process. Conversely, when the parameter was unknown, the deconvolution process would be unable to restore the original point map. Experiments on four different point maps—a random point distribution, a simulated monocentric point distribution, a simulated polycentric point distribution, and a real crime location map—show consistent results. Therefore, it can be concluded that the point location of crime events cannot be restored from crime density maps as long as parameters such as the search radius parameter in the density mapping process remain confidential.
Keywords: kernel density estimation; density map; point map; deconvolution; confidential analysis kernel density estimation; density map; point map; deconvolution; confidential analysis
MDPI and ACS Style

Wang, Z.; Liu, L.; Zhou, H.; Lan, M. How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation? ISPRS Int. J. Geo-Inf. 2019, 8, 544.

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