eprintid: 1475701
rev_number: 30
eprint_status: archive
userid: 608
dir: disk0/01/47/57/01
datestamp: 2016-03-01 15:40:15
lastmod: 2021-09-26 22:45:46
status_changed: 2016-12-23 11:38:56
type: article
metadata_visibility: show
creators_name: Shen, J
creators_name: Cheng, T
title: A framework for identifying activity groups from individual space-time profiles
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F44
keywords: Activity pattern; region of
interest; behaviour similarity;
clustering; time geography
note: © 2016 The Author (s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Permission is granted subject to the terms of the License under which the work was published. Please check the License conditions for the work which you wish to reuse. Full and appropriate attribution must be given. This permission does not cover any third party copyrighted material which may appear in the work requested.
abstract: Datasets collecting the ever-changing position of moving individuals are usually big and possess high spatial and temporal resolution to reveal activity patterns of individuals in greater detail. Information about human mobility, such as ‘when, where and why people travel’, is contained in these datasets and is necessary for urban planning and public policy making. Nevertheless, how to segregate the users into groups with different movement and behaviours and generalise the patterns of groups are still challenging. To address this, this article develops a theoretical framework for uncovering space-time activity patterns from individual’s movement trajectory data and segregating users into subgroups according to these patterns. In this framework, individuals’ activities are modelled as their visits to spatio-temporal region of interests (ST-ROIs) by incorporating both the time and places the activities take place. An individual’s behaviour is defined as his/her profile of time allocation on the ST-ROIs she/he visited. A hierarchical approach is adopted to segregate individuals into subgroups based upon the similarity of these individuals’ profiles. The proposed framework is tested in the analysis of the behaviours of London foot patrol police officers based on their GPS trajectories provided by the Metropolitan Police.
date: 2016-01-29
date_type: published
official_url: http://dx.doi.org/10.1080/13658816.2016.1139119
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
article_type_text: Article in Press
verified: verified_manual
elements_id: 1113640
doi: 10.1080/13658816.2016.1139119
lyricists_name: Cheng, Tao
lyricists_id: TCHEN23
actors_name: Cheng, Tao
actors_id: TCHEN23
actors_role: owner
full_text_status: public
publication: International Journal of Geographical Information Science
volume: 30
number: 9
pagerange: 1785-1805
issn: 1362-3087
citation:        Shen, J;    Cheng, T;      (2016)    A framework for identifying activity groups from individual space-time profiles.                   International Journal of Geographical Information Science , 30  (9)   pp. 1785-1805.    10.1080/13658816.2016.1139119 <https://doi.org/10.1080/13658816.2016.1139119>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1475701/1/A%20framework%20for%20identifying%20activity%20groups%20from%20individual%20space%20time%20profiles.pdf