Urban land uses, socio-demographic attributes and commuting: A multilevel modeling approach

Antipova, Anzhelika; Wang, Fahui; Wilmot, Chester G.

doi:10.1016/j.apgeog.2011.02.001

Cited by 113 publications

(54 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, from these examples to a recent study in 2011 by Antipova, Wang, and Wilmot [32], the research focus was on commute trips. In 2014, studies began to apply multilevel modeling to all daily trips regardless of travel purpose [33] or to business and non-business trips together [34].…”

Section: Lee Et Al 2009mentioning

confidence: 99%

Full Random Coefficients Multilevel Modeling of the Relationship between Land Use and Trip Time on Weekdays and Weekends

Gim

2017

Sustainability

View full text Add to dashboard Cite

Interests in weekend trips are increasing, but few have studied how they are affected by land use. In this study, we analyze the relationship between compact land use characteristics and trip time in Seoul, Korea by comparing two research models, each of which uses the weekday and weekend data of the same travelers. To secure sufficient numbers of subjects and groups, full random coefficients multilevel models define the trip as level one and the neighborhood as level two, and find that level-two land use characteristics account for less variation in trip time than level-one individual characteristics. At level one, weekday trip time is found to be reduced by the choice of the automobile as a travel mode, but not by its ownership per se. In addition, it becomes reduced if made by high income travelers and extended to travel to quality jobs. Among four land use characteristics at level two, population density, road connectivity, and subway availability are shown to be significant in the weekday model. Only subway availability has a positive relationship with trip time and this finding is consistent with the level-one result that the choice of automobile alternatives increases trip time. The other land use characteristic, land use balance, turns out to be a single significant land use variable in the weekend model, implying that it is concerned mainly with non-work, non-mandatory travel.

show abstract

Section: Lee Et Al 2009mentioning

confidence: 99%

Full Random Coefficients Multilevel Modeling of the Relationship between Land Use and Trip Time on Weekdays and Weekends

Gim

2017

Sustainability

View full text Add to dashboard Cite

show abstract

“…Researchers have found that there is a significant association between commuting activity and land use types [28]. Using Twitter messages, Han et al (2015) [29] proposed a new analytic method to identify spatiotemporal differences in the level of geographical awareness of Twitter users living in each U.S. city.…”

Section: Mapping Urban Dynamics With Social Media and Social Sensor Datamentioning

confidence: 99%

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Wang

Tsou

et al. 2016

Sustainability

View full text Add to dashboard Cite

Abstract:In fast-growing cities, especially large cities in developing countries, land use types are changing rapidly, and different types of land use are mixed together. It is difficult to assess the land use types in these fast-growing cities in a timely and accurate way. To address this problem, this paper presents a multi-source data mining approach to study dynamic urban land use patterns. Spatiotemporal social media data reveal human activity patterns in different areas, social media text data reflects the topics discussed in different areas, and Points of Interest (POI) reflect the distribution of urban facilities in different regions. Human activity patterns, topics of discussion on social media, and the distribution of urban facilities in different regions were combined and analyzed to infer urban land use patterns. We collected 9.5 million geo-tagged Chinese social media (Sina-Weibo) messages from January 2014 to July 2014 in the urban core areas of Beijing and compared them with 385,792 commercial Points of Interest (POI) from Datatang (a Chinese digital data content provider). To estimate urban land use types and patterns in Beijing, a regular grid of 400 m × 400 m was created to divide the urban core areas into 18,492 cells. By analyzing the temporal frequency trends of social media messages within each cell using K-means clustering algorithm, we identified seven types of land use clusters in Beijing: residential areas, university dormitories, commercial areas, work areas, transportation hubs, and two types of mixed land use areas. Text mining, word clouds, and the distribution analysis of POI were used to verify the estimated land use types successfully. This study can help urban planners create up-to-date land use patterns in an economic way and help us better understand dynamic human activity patterns in a city.

show abstract

“…These qualitative findings are supported by a multivariate regression analysis that aims to explain CEP in terms of job market proximity, residential compactness, and inherent regional differences (Antipova et al, 2011). The following independent variables were included.…”

Section: Explaining 2001 Cep Valuesmentioning

confidence: 78%

Sustainability and change in the institutionalized commute in Belgium: Exploring regional differences

et al. 2012

View full text Add to dashboard Cite

de Bruxelles 61, B-5000 Namur, Belgique This paper examines regional differences in commute-energy performance in Belgium, and explores their relationships with spatial characteristics such as the distribution of population and housing, the metropolitan influence of the Brussels agglomeration, and the compactness of cities and towns. We also investigate contradictions between Belgian state-wide commute policy and regional differences in average commuting distance and mode choice. Against a background of long-term federal measures that traditionally encourage longdistance commuting in Belgium, we find striking discrepancies between the structure and the development of commuting patterns in the three administrative regions of Flanders, Wallonia and Brussels. Residents of Brussels show the most sustainable commuting patterns, due to the metropolitan spatial structure.Residents of Wallonia represent the least sustainable commute. Given the rather weak regional economy of Wallonia compared with Flanders, commuters must frequently seek employment far from their residence. Population changes and consequent developments in the housing market seem to exacerbate this competitive disadvantage, since most growth occurs in relatively remote rural areas that are nevertheless within reach of the main employment centres.

show abstract

Urban land uses, socio-demographic attributes and commuting: A multilevel modeling approach

Cited by 113 publications

References 45 publications

Full Random Coefficients Multilevel Modeling of the Relationship between Land Use and Trip Time on Weekdays and Weekends

Full Random Coefficients Multilevel Modeling of the Relationship between Land Use and Trip Time on Weekdays and Weekends

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Sustainability and change in the institutionalized commute in Belgium: Exploring regional differences

Contact Info

Product

Resources

About