@inproceedings{discovery10193189,
            note = {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/).},
         journal = {Proceedings of the 31st Conference of GIS Research UK (GISRUK)},
            year = {2023},
           title = {The use of in-app data to drive geodemographic classification of activity patterns},
          series = {GISRUK Conference},
         address = {Glasgow, UK},
          volume = {31},
           month = {April},
       booktitle = {Proceedings of the 31st Conference of GIS Research UK (GISRUK)},
       publisher = {GIS Research UK (GISRUK)},
          author = {Mavrogeni, Mikaella and Longley, Paul and Van Dijk, Justin},
        abstract = {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.},
             url = {https://doi.org/10.5281/zenodo.7839567},
        keywords = {geodemographics, big data, temporal analytics, in-app data, geospatial}
}