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Documents authored by Kounadi, Ourania

Document
DOI: 10.4230/LIPIcs.GIScience.2021.II.1
Adaptive Voronoi Masking: A Method to Protect Confidential Discrete Spatial Data

Authors: Fiona Polzin and Ourania Kounadi

Published in: LIPIcs, Volume 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II

Abstract
Geomasks assure the protection of individuals in a discrete spatial point data set by aggregating, transferring or altering original points. This study develops an alternative approach, referred to as Adaptive Voronoi Masking (AVM), which is based on the concepts of Adaptive Aerial Elimination (AAE) and Voronoi Masking (VM). It considers the underlying population density by establishing areas of K-anonymity in which Voronoi polygons are created. Contrary to other geomasks, AVM considers the underlying topography and displaces data points to street intersections thus decreasing the risk of false-identification since residences are not endowed with a data point. The geomasking effects of AVM are examined by various spatial analytical results and are compared with the outputs of AAE, VM, and Donut Masking (DM). VM attains the best efficiency for the mean centres whereas DM does for the median centres. Regarding the Nearest Neighbour Hierarchical Cluster Analysis and Ripley’s K-function, DM demonstrates the strongest performance since its cluster ellipsoids and clustering distance are the most similar to those of the original data. The extend of the original data is preserved the most by VM, while AVM retains the topology of the point pattern. Overall, AVM was ranked as 2nd in terms of data utility (i) and also outperforms all methods regarding the risk of false re-identification (ii) because no data point is moved to a residence. Furthermore, AVM maintains the Spatial K-anonymity (iii) which is also done by AAE and partly by DM. Based on the performance combination of these factors, AVM is an advantageous technique to mask geodata.
Cite as

Fiona Polzin and Ourania Kounadi. Adaptive Voronoi Masking: A Method to Protect Confidential Discrete Spatial Data. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part II. Leibniz International Proceedings in Informatics (LIPIcs), Volume 208, pp. 1:1-1:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{polzin_et_al:LIPIcs.GIScience.2021.II.1,
  author =	{Polzin, Fiona and Kounadi, Ourania},
  title =	{{Adaptive Voronoi Masking: A Method to Protect Confidential Discrete Spatial Data}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
  pages =	{1:1--1:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-208-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{208},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.II.1},
  URN =		{urn:nbn:de:0030-drops-147606},
  doi =		{10.4230/LIPIcs.GIScience.2021.II.1},
  annote =	{Keywords: Geoprivacy, location privacy, geomasking, Adaptive Voronoi Masking, Voronoi Masking, Adaptive Aerial Elimination, Donut Geomasking, ESDA}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.56
Geosocial Media Data as Predictors in a GWR Application to Forecast Crime Hotspots (Short Paper)

Authors: Alina Ristea, Ourania Kounadi, and Michael Leitner

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

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.
Cite as

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)

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@InProceedings{ristea_et_al:LIPIcs.GISCIENCE.2018.56,
  author =	{Ristea, Alina and Kounadi, Ourania and Leitner, Michael},
  title =	{{Geosocial Media Data as Predictors in a GWR Application to Forecast Crime Hotspots}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{56:1--56:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.56},
  URN =		{urn:nbn:de:0030-drops-93845},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.56},
  annote =	{Keywords: spatial crime prediction, street crime, population at risk, geographically weighted regression, geosocial media}
}
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