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Why Is Greenwich so Common? Quantifying the Uniqueness of Multivariate Observations (Short Paper)

Authors: Andrea Ballatore and Stefano Cavazzi

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


Abstract
The concept of uniqueness can play an important role when the assessment of an observation’s distinctiveness is essential. This article introduces a distance-based uniqueness measure that quantifies the relative rarity or commonness of a multi-variate observation within a dataset. Unique observations exhibit rare combinations of values, and not necessarily extreme values. Taking a cognitive psychological perspective, our measure defines uniqueness as the sum of distances between a target observation and all other observations. After presenting the measure u and its corresponding standardised version u_z, we propose a method to calculate a p value through a probability density function. We then demonstrate the measure’s behaviour in a case study on the uniqueness of Greater London boroughs, based on real-world socioeconomic variables. This initial investigation indicates that u can support exploratory data analysis.

Cite as

Andrea Ballatore and Stefano Cavazzi. Why Is Greenwich so Common? Quantifying the Uniqueness of Multivariate Observations (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 15:1-15:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{ballatore_et_al:LIPIcs.GIScience.2023.15,
  author =	{Ballatore, Andrea and Cavazzi, Stefano},
  title =	{{Why Is Greenwich so Common? Quantifying the Uniqueness of Multivariate Observations}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{15:1--15:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.15},
  URN =		{urn:nbn:de:0030-drops-189109},
  doi =		{10.4230/LIPIcs.GIScience.2023.15},
  annote =	{Keywords: uniqueness, distinctiveness, similarity, outlier detection, multivariate data}
}
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