Assessing the socio-demographic representativeness of mobile phone application data

Sinclair, M. , Maadi, S., Zhao, Q. , Hong, J. , Ghermandi, A. and Bailey, N. (2023) Assessing the socio-demographic representativeness of mobile phone application data. Applied Geography, 158, 102997. (doi: 10.1016/j.apgeog.2023.102997)

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Emerging forms of mobile phone data generated from the use of mobile phone applications have the potential to advance scientific research across a range of disciplines. However, there are risks regarding uncertainties in the socio-demographic representativeness of these data, which may introduce bias and mislead policy recommendations. This paper addresses the issue directly by developing a novel approach to assessing socio-demographic representativeness, demonstrating this with two large independent mobile phone application datasets, Huq and Tamoco, each with three years data for a large and diverse city-region (Glasgow, Scotland) home to over 1.8 million people. We advance methods for detecting home location by including high-resolution land use data in the process and test representativeness across multiple dimensions. Our findings offer greater confidence in using mobile phone app data for research and planning. Both datasets show good representativeness compared to the known population distribution. Indeed, they achieve better population coverage than the ‘gold standard’ random sample survey which is the alternative source of data on population mobility in this region. More importantly, our approach provides an improved benchmark for assessing the quality of similar data sources in the future.

Item Type:Articles
Additional Information:The work was made possible by ESRC's SDAI funding [ES/W012979/1] and ESRC's on-going support for the Urban Big Data Centre (UBDC) [ES/L011921/1 and ES/S007105/1].
Glasgow Author(s) Enlighten ID:Hong, Dr Jinhyun and Zhao, Dr Qunshan and Bailey, Professor Nick and Sinclair, Dr Michael and Maadi, Mr Saeed
Authors: Sinclair, M., Maadi, S., Zhao, Q., Hong, J., Ghermandi, A., and Bailey, N.
College/School:College of Social Sciences > School of Social and Political Sciences
College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:Applied Geography
ISSN (Online):1873-7730
Published Online:13 July 2023
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in Applied Geography 158:102997
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190698Urban Big Data Research CentreNick BaileyEconomic and Social Research Council (ESRC)ES/L011921/1S&PS - Urban Big Data
304042UBDC Centre TransitionNick BaileyEconomic and Social Research Council (ESRC)ES/S007105/1S&PS - Administration