Beyond home neighborhood: Mobility, activity and temporal variation of socio-spatial segregation. (February 2022)
- Record Type:
- Journal Article
- Title:
- Beyond home neighborhood: Mobility, activity and temporal variation of socio-spatial segregation. (February 2022)
- Main Title:
- Beyond home neighborhood: Mobility, activity and temporal variation of socio-spatial segregation
- Authors:
- Xian, Shi
Qi, Zhixin
Yip, Ngai-ming - Abstract:
- Abstract: Recent studies on socio-spatial segregation have revealed the uneven segregation experiences of individuals within their daily life contexts. However, little is known about its temporal variations across the week. The advancement of GIS and GPS tracking technology also poses methodological challenges in processing rich mobility–activity data efficiently in identifying the socio-spatial segregation patterns. With data collected by a mobile phone app that ran on the participants' mobile phone for a whole week, this paper integrates the spatial, temporal, mobility, and activity dimensions with the demographic data to segregation patterns of the participants and assesses segregation at the individual level. Our findings indicate that the socio-spatial segregation level decreased in the daytime and increased at night, and this pattern was consistent across a week. However, no significant differences are found between different age groups, occupation, housing types and home neighborhood types. To improve the efficiency of data processing, this paper employs decision tree algorithms supplemented by the analysis of variance and Tukey's honestly significant difference test to identify meaningful mobility–activity patterns with significant intergroup differences. It is able to pinpoint temporal and spatial activity-mobility patterns that crosscut home location, location of workplace, and socioeconomic status. It also helps connect residential segregation and segregation thatAbstract: Recent studies on socio-spatial segregation have revealed the uneven segregation experiences of individuals within their daily life contexts. However, little is known about its temporal variations across the week. The advancement of GIS and GPS tracking technology also poses methodological challenges in processing rich mobility–activity data efficiently in identifying the socio-spatial segregation patterns. With data collected by a mobile phone app that ran on the participants' mobile phone for a whole week, this paper integrates the spatial, temporal, mobility, and activity dimensions with the demographic data to segregation patterns of the participants and assesses segregation at the individual level. Our findings indicate that the socio-spatial segregation level decreased in the daytime and increased at night, and this pattern was consistent across a week. However, no significant differences are found between different age groups, occupation, housing types and home neighborhood types. To improve the efficiency of data processing, this paper employs decision tree algorithms supplemented by the analysis of variance and Tukey's honestly significant difference test to identify meaningful mobility–activity patterns with significant intergroup differences. It is able to pinpoint temporal and spatial activity-mobility patterns that crosscut home location, location of workplace, and socioeconomic status. It also helps connect residential segregation and segregation that goes beyond the home neighborhoods. … (more)
- Is Part Of:
- Journal of transport geography. Volume 99(2022)
- Journal:
- Journal of transport geography
- Issue:
- Volume 99(2022)
- Issue Display:
- Volume 99, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 99
- Issue:
- 2022
- Issue Sort Value:
- 2022-0099-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-02
- Subjects:
- Socio-spatial segregation -- Mobility -- Activity -- Housing tenure status -- Decision tree algorithms
Transportation -- Periodicals
Telecommunication -- Periodicals
Transport -- Périodiques
Télécommunications -- Périodiques
Telecommunication
Transportation
Periodicals
388 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09666923 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.jtrangeo.2022.103304 ↗
- Languages:
- English
- ISSNs:
- 0966-6923
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5069.950000
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- 21014.xml