TY  - GEN
N1  - This is an Open Access article published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).
A1  - Mavrogeni, Mikaella
A1  - Longley, Paul
A1  - Van Dijk, Justin
PB  - GIS Research UK (GISRUK)
KW  - geodemographics
KW  -  big data
KW  -  temporal analytics
KW  -  in-app data
KW  -  geospatial
Y1  - 2023/04/19/
CY  - Glasgow, UK
TI  - The use of in-app data to drive geodemographic classification of activity patterns
AV  - public
N2  - We use location data from multiple mobile phone applications to describe daily, weekly, seasonal and annual activity patterns. Geodemographics, or ?the analysis of people by where they live?, provides an organising framework, extended to represent the ways in which neighbourhood residents interact with workplaces, recreational and leisure destinations and transport infrastructure. We evaluate how in-app location data can be incorporated into geodemographic analysis to better understand the flux of activity patterns that characterise densely populated areas throughout the day. Limitations and net benefits of in-app location data are critically assessed to evaluate the ways in which activity-based geodemographics are robust, effective and safe to use when characterising the population at large.
UR  - https://doi.org/10.5281/zenodo.7839567
ID  - discovery10193189
T3  - GISRUK Conference
ER  -