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Cited by 43Human Mobility and Location-Based AnalysisGeographic Information Systems StudiesMobile Crowdsensing and Crowdsourcing

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Assessing the socio-demographic representativeness of mobile phone application data

Open access
Sinclair, Michael, Maadi, Saeed, Zhao, Qunshan, Hong, Jinhyun, Ghermandi, Andrea, Bailey, Nick·2023·Applied Geography
DOI 10.1016/j.apgeog.2023.102997
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Abstract

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.

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  • These representativeness and exclusion factors can affect the absolute levels of mobility observed and the proportional presence of different mobility types.

    “These factors can affect the absolute levels of mobility observed and the proportional presence of different mobil-ity types (Sinclair et al. 2023).”

    From Visitation patterns across mobility groups: wandering, commuting, and exploring (2026) · cites this paper as Sinclair et al. 2023

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Research works printed but not linked: 2

  1. 2021, SHSScottish household survey, 1999-Scottish Household Survey
  2. Wakefield, 2021, Location data collection firm admits privacy breach, BBC News

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Metadata

Authors
Sinclair, M., Maadi, S., Zhao, Q., Hong, J., Ghermandi, A., & Bailey, N.
Affiliations
  • Urban Big Data Centre (GB)
  • University of Seoul (KR)
  • University of Haifa (IL)
Corresponding author
Sinclair, Michael
ORCID
Venue
Applied Geography
Publisher
Elsevier BV
Published
2023-09
Volume
158
Pages
102997
ISSN
0143-6228 (ISSN-L)
DOI
10.1016/j.apgeog.2023.102997
OpenAlex
W4384162608
Cited by
43
References
37
Citations by year
2026: 16 · 2025: 10 · 2024: 13 · 2023: 4
FWCI
8.83
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Geography (publishedVersion, cc-by)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
Funders
Economic and Social Research Council (ES/S007105/1, ES/L011921/1, ES/W012979/1)
Topics
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
  • Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
Keywords
data bias, land use data, population coverage, human mobility

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