Why Is Greenwich so Common? Quantifying the Uniqueness of Multivariate Observations

TY - CONF

T1 - Why Is Greenwich so Common? Quantifying the Uniqueness of Multivariate Observations

AU - Ballatore, Andrea

AU - Cavazzi, Stefano

N1 - Funding Information: This work was supported by Ordnance Survey. Publisher Copyright: © Andrea Ballatore and Stefano Cavazzi.

PY - 2023/9

Y1 - 2023/9

N2 - 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 uz, 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.

AB - 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 uz, 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.

KW - distinctiveness

KW - multivariate data

KW - outlier detection

KW - similarity

KW - uniqueness

UR - http://www.scopus.com/inward/record.url?scp=85172306781&partnerID=8YFLogxK

U2 - 10.4230/LIPIcs.GIScience.2023.15

DO - 10.4230/LIPIcs.GIScience.2023.15

M3 - Paper

AN - SCOPUS:85172306781

T2 - 12th International Conference on Geographic Information Science, GIScience 2023

Y2 - 12 September 2023 through 15 September 2023

ER -