Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach
Spatial structure is a fundamental characteristic of cities that influences the urban functioning to a large extent. While administrative partitioning is generally done in the form of static spatial division, understanding a more temporally dynamic structure of the urban space would benefit urban pl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-12-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | http://www.mdpi.com/2220-9964/5/12/240 |
| _version_ | 1818465998759526400 |
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| author | Meng Zhou Yang Yue Qingquan Li Donggen Wang |
| author_facet | Meng Zhou Yang Yue Qingquan Li Donggen Wang |
| author_sort | Meng Zhou |
| collection | DOAJ |
| description | Spatial structure is a fundamental characteristic of cities that influences the urban functioning to a large extent. While administrative partitioning is generally done in the form of static spatial division, understanding a more temporally dynamic structure of the urban space would benefit urban planning and management immensely. This study makes use of a large-scale mobile phone positioning dataset to characterize the diurnal dynamics of the interaction-based urban spatial structure. To extract the temporally vibrant structure, spatial interaction networks at different times are constructed based on the movement connections of individuals between geographical units. Complex network community detection technique is applied to identify the spatial divisions as well as to quantify their temporal dynamics. Empirical analysis is conducted using data containing all user positions on a typical weekday in Shenzhen, China. Results are compared with official zoning and planned structure and indicate a certain degree of expansion in urban central areas and fragmentation in industrial suburban areas. A high level of variability in spatial divisions at different times of day is detected with some distinct temporal features. Peak and pre-/post-peak hours witness the most prominent fluctuation in spatial division indicating significant change in the characteristics of movements and activities during these periods of time. Findings of this study demonstrate great potential of large-scale mobility data in supporting intelligent spatial decision making and providing valuable knowledge to the urban planning sectors. |
| first_indexed | 2024-04-13T20:52:31Z |
| format | Article |
| id | doaj.art-43b99a92d46c437291d620ff4ce3f468 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-04-13T20:52:31Z |
| publishDate | 2016-12-01 |
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| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-43b99a92d46c437291d620ff4ce3f4682022-12-22T02:30:26ZengMDPI AGISPRS International Journal of Geo-Information2220-99642016-12-0151224010.3390/ijgi5120240ijgi5120240Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network ApproachMeng Zhou0Yang Yue1Qingquan Li2Donggen Wang3Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, ChinaShenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, ChinaDepartment of Geography, Hong Kong Baptist University, Kowloon Tong, Hong Kong, ChinaSpatial structure is a fundamental characteristic of cities that influences the urban functioning to a large extent. While administrative partitioning is generally done in the form of static spatial division, understanding a more temporally dynamic structure of the urban space would benefit urban planning and management immensely. This study makes use of a large-scale mobile phone positioning dataset to characterize the diurnal dynamics of the interaction-based urban spatial structure. To extract the temporally vibrant structure, spatial interaction networks at different times are constructed based on the movement connections of individuals between geographical units. Complex network community detection technique is applied to identify the spatial divisions as well as to quantify their temporal dynamics. Empirical analysis is conducted using data containing all user positions on a typical weekday in Shenzhen, China. Results are compared with official zoning and planned structure and indicate a certain degree of expansion in urban central areas and fragmentation in industrial suburban areas. A high level of variability in spatial divisions at different times of day is detected with some distinct temporal features. Peak and pre-/post-peak hours witness the most prominent fluctuation in spatial division indicating significant change in the characteristics of movements and activities during these periods of time. Findings of this study demonstrate great potential of large-scale mobility data in supporting intelligent spatial decision making and providing valuable knowledge to the urban planning sectors.http://www.mdpi.com/2220-9964/5/12/240urban structurespatial divisionmobile phone positioning datacommunity detectiondiurnal dynamics |
| spellingShingle | Meng Zhou Yang Yue Qingquan Li Donggen Wang Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach ISPRS International Journal of Geo-Information urban structure spatial division mobile phone positioning data community detection diurnal dynamics |
| title | Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach |
| title_full | Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach |
| title_fullStr | Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach |
| title_full_unstemmed | Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach |
| title_short | Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach |
| title_sort | portraying temporal dynamics of urban spatial divisions with mobile phone positioning data a complex network approach |
| topic | urban structure spatial division mobile phone positioning data community detection diurnal dynamics |
| url | http://www.mdpi.com/2220-9964/5/12/240 |
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