Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework
The urban structure is the spatial reflection of various economic and cultural factors acting on the urban territory. Different from the physical structure, urban structure is closely related to the population mobility. Taxi trajectories are widely distributed, completely spontaneous, closely relate...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/10/4/227 |
| _version_ | 1797538706904580096 |
|---|---|
| author | Yan Zhang Xiang Zheng Min Chen Yingbing Li Yingxue Yan Peiying Wang |
| author_facet | Yan Zhang Xiang Zheng Min Chen Yingbing Li Yingxue Yan Peiying Wang |
| author_sort | Yan Zhang |
| collection | DOAJ |
| description | The urban structure is the spatial reflection of various economic and cultural factors acting on the urban territory. Different from the physical structure, urban structure is closely related to the population mobility. Taxi trajectories are widely distributed, completely spontaneous, closely related to travel needs, and massive in data volume. Mining it not only can help us better understand the flow pattern of a city, but also provides a new perspective for interpreting the urban structure. On the basis of massive taxi trajectory data in Chengdu, we introduce a network science approach to analysis, propose a new framework for interaction analysis, and model the intrinsic connections within cities. The spatial grid of fine particles and the trajectory connections between them are used to resolve the urban structure. The results show that: (1) Based on 200,000 taxi trajectories, we constructed a spatial network of traffic flow using the interaction analysis framework and extracted the cold hot spots among them. (2) We divide the 400 traffic flow network nodes into 6 communities. Community 2 has high centrality and density, and belongs to the core built-up area of the city. (3) A traffic direction field is proposed to describe the direction of the traffic flow network, and the direction of traffic flow roughly presents an inflow from northeast to southwest and an outflow from southeast to northwest of the study area. The interaction analysis framework proposed in this study can be applied to other cities or other research areas (e.g., population migration), and it could extract the directional nature of the network as well as the hierarchical structure of the city. |
| first_indexed | 2024-03-10T12:34:14Z |
| format | Article |
| id | doaj.art-cb2bf888f5f843179f0872421738240b |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T12:34:14Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-cb2bf888f5f843179f0872421738240b2023-11-21T14:24:31ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-04-0110422710.3390/ijgi10040227Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis FrameworkYan Zhang0Xiang Zheng1Min Chen2Yingbing Li3Yingxue Yan4Peiying Wang5State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, ChinaSchool of Information Management, Wuhan University, Wuhan 430072, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430072, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430072, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430072, ChinaSchool of Architecture, Hunan University, Changsha 410006, ChinaThe urban structure is the spatial reflection of various economic and cultural factors acting on the urban territory. Different from the physical structure, urban structure is closely related to the population mobility. Taxi trajectories are widely distributed, completely spontaneous, closely related to travel needs, and massive in data volume. Mining it not only can help us better understand the flow pattern of a city, but also provides a new perspective for interpreting the urban structure. On the basis of massive taxi trajectory data in Chengdu, we introduce a network science approach to analysis, propose a new framework for interaction analysis, and model the intrinsic connections within cities. The spatial grid of fine particles and the trajectory connections between them are used to resolve the urban structure. The results show that: (1) Based on 200,000 taxi trajectories, we constructed a spatial network of traffic flow using the interaction analysis framework and extracted the cold hot spots among them. (2) We divide the 400 traffic flow network nodes into 6 communities. Community 2 has high centrality and density, and belongs to the core built-up area of the city. (3) A traffic direction field is proposed to describe the direction of the traffic flow network, and the direction of traffic flow roughly presents an inflow from northeast to southwest and an outflow from southeast to northwest of the study area. The interaction analysis framework proposed in this study can be applied to other cities or other research areas (e.g., population migration), and it could extract the directional nature of the network as well as the hierarchical structure of the city.https://www.mdpi.com/2220-9964/10/4/227traffic flowfine-grained spatial gridcomplex networkLouvain algorithmurban structurespace hot spot analysis |
| spellingShingle | Yan Zhang Xiang Zheng Min Chen Yingbing Li Yingxue Yan Peiying Wang Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework ISPRS International Journal of Geo-Information traffic flow fine-grained spatial grid complex network Louvain algorithm urban structure space hot spot analysis |
| title | Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework |
| title_full | Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework |
| title_fullStr | Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework |
| title_full_unstemmed | Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework |
| title_short | Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework |
| title_sort | urban fine grained spatial structure detection based on a new traffic flow interaction analysis framework |
| topic | traffic flow fine-grained spatial grid complex network Louvain algorithm urban structure space hot spot analysis |
| url | https://www.mdpi.com/2220-9964/10/4/227 |
| work_keys_str_mv | AT yanzhang urbanfinegrainedspatialstructuredetectionbasedonanewtrafficflowinteractionanalysisframework AT xiangzheng urbanfinegrainedspatialstructuredetectionbasedonanewtrafficflowinteractionanalysisframework AT minchen urbanfinegrainedspatialstructuredetectionbasedonanewtrafficflowinteractionanalysisframework AT yingbingli urbanfinegrainedspatialstructuredetectionbasedonanewtrafficflowinteractionanalysisframework AT yingxueyan urbanfinegrainedspatialstructuredetectionbasedonanewtrafficflowinteractionanalysisframework AT peiyingwang urbanfinegrainedspatialstructuredetectionbasedonanewtrafficflowinteractionanalysisframework |