Functional distributional clustering using spatio-temporal data

Venkatasubramaniam, A., Evers, L. , Thakuriah, P. and Ampountolas, K. (2023) Functional distributional clustering using spatio-temporal data. Journal of Applied Statistics, 50(4), pp. 909-926. (doi: 10.1080/02664763.2021.2001443)

Abstract

This paper presents a new method called the functional distributional clustering algorithm (FDCA) that seeks to identify spatially contiguous clusters and incorporate changes in temporal patterns across overcrowded networks. This method is motivated by a graph-based network composed of sensors arranged over space where recorded observations for each sensor represent a multi-modal distribution. The proposed method is fully non-parametric and generates clusters within an agglomerative hierarchical clustering approach based on a measure of distance that defines a cumulative distribution function over temporal changes for different locations in space. Traditional hierarchical clustering algorithms that are spatially adapted do not typically accommodate the temporal characteristics of the underlying data. The effectiveness of the FDCA is illustrated using an application to both empirical and simulated data from about 400 sensors in a 2.5 square miles network area in downtown San Francisco, California. The results demonstrate the superior ability of the the FDCA in identifying true clusters compared to functional only and distributional only algorithms and similar performance to a model-based clustering algorithm.

• Item Type: Articles
• Additional Information: KA and PT would like to acknowledge support by the UK Economic and Social Research Council (ESRC) (grant number ES/S007105/1, ES/L011921/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ampountolas, Dr Konstantinos and Thakuriah, Professor Piyushimita and Evers, Dr Ludger
• Authors: Venkatasubramaniam, A., Evers, L., Thakuriah, P., and Ampountolas, K.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Mathematics and Statistics > Statistics; College of Social Sciences > School of Social and Political Sciences
• Journal Name: Journal of Applied Statistics
• Publisher: Taylor & Francis
• ISSN: 0266-4763
• ISSN (Online): 1360-0532
• Published Online: 16 November 2021
• Copyright Holders: Copyright © 2021 Informa UK Limited, trading as Taylor and Francis Group
• First Published: First published in Journal of Applied Statistics 50(4): 909-926
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304042		UBDC Centre Transition	Nick Bailey	Economic and Social Research Council (ESRC)	ES/S007105/1	S&PS - Administration
190698		Urban Big Data Research Centre	Nick Bailey	Economic and Social Research Council (ESRC)	ES/L011921/1	S&PS - Urban Big Data