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

Authors Andrea Ballatore , Stefano Cavazzi

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Subject Classification

ACM Subject Classification
  • Mathematics of computing → Multivariate statistics
  • Information systems → Geographic information systems
Keywords
  • uniqueness
  • distinctiveness
  • similarity
  • outlier detection
  • multivariate data

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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.

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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) https://doi.org/10.4230/LIPIcs.GIScience.2023.15

Author Details

Andrea Ballatore
  • Department of Digital Humanities, King’s College London, UK
Stefano Cavazzi
  • Ordnance Survey, Southampton, UK

Funding

This work was supported by Ordnance Survey.

Supplementary Materials

References

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  2. Andrea Ballatore, Michela Bertolotto, and David C Wilson. A structural-lexical measure of semantic similarity for geo-knowledge graphs. ISPRS International Journal of Geo-Information, 4(2):471-492, 2015. URL: https://doi.org/10.3390/ijgi4020471.
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