Calibrating spatial interaction models from GPS tracking data: an example of retail behaviour

Siła-Nowicka, K. and Fotheringham, A. S. (2019) Calibrating spatial interaction models from GPS tracking data: an example of retail behaviour. Computers, Environment and Urban Systems, 74, pp. 136-150. (doi: 10.1016/j.compenvurbsys.2018.10.005)

172130.pdf - Accepted Version



Global Positioning System (GPS) technology has changed the world. We now depend on it for navigating vehicles, for route finding and we use it in our everyday lives to extract information about our locations and to track our movements. The latter use offers a potential alternative to more traditional sources of movement data through the construction of trip trajectories and, ultimately, the construction of origin-destination flow matrices. The advantage of being able to use GPS-derived movement data is that such data are potentially much richer than traditional sources of movement data both temporally and spatially. GPS-derived movement data potentially allow the calibration of spatial interaction models specific to very short time intervals, such as daily or even hourly, and for user-specified origins and destinations. Ultimately, it should be possible to calibrate continuously updated models in near real-time. However, the processing of GPS data into trajectories and then origin-destination flow matrices is not straightforward and is not well understood. This paper describes the process of transferring GPS tracking data into matrices that can be used to calibrate spatial interaction models. An example is given using retail behaviour in two towns in Scotland with an origin-constrained spatial interaction model calibrated for each day of the week and under different weather conditions (normal, rainy, windy). Although the study is small in terms of individuals and spatial context, it serves to demonstrate a future for spatial interaction modelling free from the tyranny of temporally static and spatially predefined data sets.

Item Type:Articles
Additional Information:This work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7-PEOPLE-2010-ITN-264994).
Glasgow Author(s) Enlighten ID:Sila-Nowicka, Ms Katarzyna
Authors: Siła-Nowicka, K., and Fotheringham, A. S.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:Computers, Environment and Urban Systems
ISSN (Online):1873-7587
Published Online:08 November 2018
Copyright Holders:Copyright © 2018 Elsevier Ltd.
First Published:First published in Computers, Environment and Urban Systems 74:136-150
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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