Simplifying the interpretation of continuous time models for spatio-temporal networks

Gadd, S. C., Comber, A., Gilthorpe, M. S., Suchak, K. and Heppenstall, A. J. (2022) Simplifying the interpretation of continuous time models for spatio-temporal networks. Journal of Geographical Systems, 24(2), pp. 171-198. (doi: 10.1007/s10109-020-00345-z)

Abstract

Autoregressive and moving average models for temporally dynamic networks treat time as a series of discrete steps which assumes even intervals between data measurements and can introduce bias if this assumption is not met. Using real and simulated data from the London Underground network, this paper illustrates the use of continuous time multilevel models to capture temporal trajectories of edge properties without the need for simultaneous measurements, along with two methods for producing interpretable summaries of model results. These including extracting ‘features’ of temporal patterns (e.g. maxima, time of maxima) which have utility in understanding the network properties of each connection and summarising whole-network properties as a continuous function of time which allows estimation of network properties at any time without temporal aggregation of non-simultaneous measurements. Results for temporal pattern features in the response variable were captured with reasonable accuracy. Variation in the temporal pattern features for the exposure variable was underestimated by the models. The models showed some lack of precision. Both model summaries provided clear ‘real-world’ interpretations and could be applied to data from a range of spatio-temporal network structures (e.g. rivers, social networks). These models should be tested more extensively in a range of scenarios, with potential improvements such as random effects in the exposure variable dimension.

• Item Type: Articles
• Additional Information: Sarah Gadd is funded by the Economic and Social Research Council (ES/P000746/1). Alison Heppenstall is funded by an Economic and Social Research Council-Alan Turing fellowship (ES/R007918/1). Mark Gilthorpe is funded by the Alan Turing Institute (EP/N510129/1). Alexis Comber and Keiran Suchak received no specific funding for this work. This work uses data from TFL Open Data.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heppenstall, Professor Alison
• Authors: Gadd, S. C., Comber, A., Gilthorpe, M. S., Suchak, K., and Heppenstall, A. J.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Journal of Geographical Systems
• Publisher: Springer
• ISSN: 1435-5930
• ISSN (Online): 1435-5949
• Published Online: 26 July 2021
• Copyright Holders: Copyright © The Author(s) 2021
• First Published: First published in Journal of Geographical Systems 24(2):171–198
• Publisher Policy: Reproduced under a Creative Commons licence