Consumer mobility and shopping mall visitation data from smartphone tracking in Johannesburg, South Africa

This dataset, sourced from Fetch Analytics, captures observations of consumer visits (<i>n</i> = 87,204,056) to three major shopping malls in Johannesburg, South Africa, from January 2022 to December 2023. The dataset collected using smartphone signal tracking provides insights into consumer behaviour. Key variables include mall name, visit frequency, distance travelled from origin (home) to destination (mall), and socio-demographic indicators such as gender, age, income, and Living Standard Measure (LSM). The dataset allows for granular analyses of how spatial and socio-economic factors influence shopping patterns in a fragmented retail landscape. This dataset is valuable for researchers investigating consumer behaviour, spatial economics, and urban retail planning.

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Publication Details

Journal
Data in Brief
Published
2026-03-03
DOI
https://doi.org/10.1016/j.dib.2026.112648
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Consumer mobility and shopping mall visitation data from smartphone tracking in Johannesburg, South Africa

John K Karuitha, Yewande Adewunmi, Koech Cheruiyot, Kalasipa Moenyane et al.
Data in Brief
Human Mobility and Location-Based Analysis
article

Consumer mobility and shopping mall visitation data from smartphone tracking in Johannesburg, South Africa

John K Karuitha, Yewande Adewunmi, Koech Cheruiyot, Kalasipa Moenyane, Prisca Simbanegavi, Kelly Cohen
article en

Abstract

This dataset, sourced from Fetch Analytics, captures observations of consumer visits (<i>n</i> = 87,204,056) to three major shopping malls in Johannesburg, South Africa, from January 2022 to December 2023. The dataset collected using smartphone signal tracking provides insights into consumer behaviour. Key variables include mall name, visit frequency, distance travelled from origin (home) to destination (mall), and socio-demographic indicators such as gender, age, income, and Living Standard Measure (LSM). The dataset allows for granular analyses of how spatial and socio-economic factors influence shopping patterns in a fragmented retail landscape. This dataset is valuable for researchers investigating consumer behaviour, spatial economics, and urban retail planning.

Data in BriefVol. 66
University of the Witwatersrand (ZA), Karatina University (KE), Gauteng Department of Agriculture and Rural Development (ZA)
Openalex Percentile: Top 39%
Human Mobility and Location-Based Analysis
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Consumer mobility and shopping mall visitation data from smartphone tracking in Johannesburg, South Africa — John K Karuitha, Yewande Adewunmi, et al. · Data in Brief (2026) | TGRS Research Map | TGRS