eprintid: 10193189
rev_number: 10
eprint_status: archive
userid: 699
dir: disk0/10/19/31/89
datestamp: 2024-06-10 11:16:02
lastmod: 2024-11-22 15:57:20
status_changed: 2024-06-10 11:16:02
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Mavrogeni, Mikaella
creators_name: Longley, Paul
creators_name: Van Dijk, Justin
title: The use of in-app data to drive geodemographic classification of activity patterns
ispublished: pub
divisions: UCL
divisions: B03
divisions: C03
divisions: F26
keywords: geodemographics, big data, temporal analytics, in-app data, geospatial
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/).
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.
date: 2023-04-19
date_type: published
publisher: GIS Research UK (GISRUK)
official_url: https://doi.org/10.5281/zenodo.7839567
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2283680
doi: 10.5281/zenodo.7839567
lyricists_name: Van Dijk, Justin
lyricists_id: JTVAN62
actors_name: Van Dijk, Justin
actors_id: JTVAN62
actors_role: owner
full_text_status: public
pres_type: paper
series: GISRUK Conference
publication: Proceedings of the 31st Conference of GIS Research UK (GISRUK)
volume: 31
place_of_pub: Glasgow, UK
event_title: 31st GISRUK Conference 2023
event_location: Glasgow, UK
book_title: Proceedings of the 31st Conference of GIS Research UK (GISRUK)
citation:        Mavrogeni, Mikaella;    Longley, Paul;    Van Dijk, Justin;      (2023)    The use of in-app data to drive geodemographic classification of activity patterns.                     In:  Proceedings of the 31st Conference of GIS Research UK (GISRUK).    GIS Research UK (GISRUK): Glasgow, UK.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10193189/1/GISRUK_2023_Mavrogeni.pdf