Analysis of human mobility patterns from GPS trajectories and contextual information

Katarzyna Siła-Nowicka, Jan Vandrol, Taylor Oshan, Jed A. Long, Urška Demšar, Stewart Fotheringham

Research output: Contribution to journalArticle

62 Citations (Scopus)

Abstract

Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries, or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. Although this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining, and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age, or gender of the tracked individuals. Further, we explore theorised ‘third places’, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.

Original languageEnglish (US)
Pages (from-to)881-906
Number of pages26
JournalInternational Journal of Geographical Information Science
Volume30
Issue number5
DOIs
StatePublished - May 3 2016

Fingerprint

Global positioning system
GPS
travel
trajectory
Trajectories
data collection method
residential location
Spatial distribution
Data mining
temporal analysis
data mining
urban area
integrated approach
segmentation
gender
Sensors
spatial distribution
interview
sensor
analysis

Keywords

  • Movement analysis
  • significant places
  • trajectories
  • trajectory segmentation
  • travel mode classification

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

Cite this

Analysis of human mobility patterns from GPS trajectories and contextual information. / Siła-Nowicka, Katarzyna; Vandrol, Jan; Oshan, Taylor; Long, Jed A.; Demšar, Urška; Fotheringham, Stewart.

In: International Journal of Geographical Information Science, Vol. 30, No. 5, 03.05.2016, p. 881-906.

Research output: Contribution to journalArticle

Siła-Nowicka, Katarzyna ; Vandrol, Jan ; Oshan, Taylor ; Long, Jed A. ; Demšar, Urška ; Fotheringham, Stewart. / Analysis of human mobility patterns from GPS trajectories and contextual information. In: International Journal of Geographical Information Science. 2016 ; Vol. 30, No. 5. pp. 881-906.
@article{9b9d187a6d36435abe24d7f038ef66c1,
title = "Analysis of human mobility patterns from GPS trajectories and contextual information",
abstract = "Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries, or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. Although this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining, and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age, or gender of the tracked individuals. Further, we explore theorised ‘third places’, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.",
keywords = "Movement analysis, significant places, trajectories, trajectory segmentation, travel mode classification",
author = "Katarzyna Siła-Nowicka and Jan Vandrol and Taylor Oshan and Long, {Jed A.} and Urška Demšar and Stewart Fotheringham",
year = "2016",
month = "5",
day = "3",
doi = "10.1080/13658816.2015.1100731",
language = "English (US)",
volume = "30",
pages = "881--906",
journal = "International Journal of Geographical Information Science",
issn = "1365-8816",
publisher = "Taylor and Francis Ltd.",
number = "5",

}

TY - JOUR

T1 - Analysis of human mobility patterns from GPS trajectories and contextual information

AU - Siła-Nowicka, Katarzyna

AU - Vandrol, Jan

AU - Oshan, Taylor

AU - Long, Jed A.

AU - Demšar, Urška

AU - Fotheringham, Stewart

PY - 2016/5/3

Y1 - 2016/5/3

N2 - Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries, or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. Although this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining, and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age, or gender of the tracked individuals. Further, we explore theorised ‘third places’, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.

AB - Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries, or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. Although this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining, and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age, or gender of the tracked individuals. Further, we explore theorised ‘third places’, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.

KW - Movement analysis

KW - significant places

KW - trajectories

KW - trajectory segmentation

KW - travel mode classification

UR - http://www.scopus.com/inward/record.url?scp=84961210738&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84961210738&partnerID=8YFLogxK

U2 - 10.1080/13658816.2015.1100731

DO - 10.1080/13658816.2015.1100731

M3 - Article

VL - 30

SP - 881

EP - 906

JO - International Journal of Geographical Information Science

JF - International Journal of Geographical Information Science

SN - 1365-8816

IS - 5

ER -