Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data
The rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/9/486 |
| _version_ | 1797487486957518848 |
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| author | Heping Jiang Shijia Luo Jiahui Qin Ruihua Liu Disheng Yi Yusi Liu Jing Zhang |
| author_facet | Heping Jiang Shijia Luo Jiahui Qin Ruihua Liu Disheng Yi Yusi Liu Jing Zhang |
| author_sort | Heping Jiang |
| collection | DOAJ |
| description | The rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly focused on the perspective of static networks and have not understood the dynamic spatial interaction patterns of Chinese cities. Therefore, this paper proposes a research framework to explore the urban dynamic spatial interaction patterns. Firstly, we establish a dynamic urban spatial interaction network according to monthly migration data. Then, the dynamic community detection algorithm, combined with the Louvain and Jaccard matching method, is used to obtain urban communities and their dynamic events. We construct event vectors for each urban community and use hierarchical clustering to cluster event vectors to obtain different types of spatial interaction patterns. Finally, we divide the urban dynamic interaction into three urban spatial interaction modes: fixed spatial interaction pattern, long-term spatial interaction pattern, and short-term spatial interaction pattern. According to the results, we find that the cities in well-developed areas (eastern China) and under-developed areas (northwestern China) mostly show fixed spatial interaction patterns and long-term spatial interaction patterns, while the cities in moderately developed areas (central and western China) often show short-term spatial interaction patterns. The research results and conclusions of this paper reveal the inter-monthly urban spatial interaction patterns in China, provide theoretical support for the policy making and development planning of urban agglomeration construction, and contribute to the coordinated development of national and regional cities. |
| first_indexed | 2024-03-09T23:49:20Z |
| format | Article |
| id | doaj.art-0e1984ea480e4239bb1bdda1011eb9e3 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T23:49:20Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-0e1984ea480e4239bb1bdda1011eb9e32023-11-23T16:37:22ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-09-0111948610.3390/ijgi11090486Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration DataHeping Jiang0Shijia Luo1Jiahui Qin2Ruihua Liu3Disheng Yi4Yusi Liu5Jing Zhang6College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaCollege of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaThe rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly focused on the perspective of static networks and have not understood the dynamic spatial interaction patterns of Chinese cities. Therefore, this paper proposes a research framework to explore the urban dynamic spatial interaction patterns. Firstly, we establish a dynamic urban spatial interaction network according to monthly migration data. Then, the dynamic community detection algorithm, combined with the Louvain and Jaccard matching method, is used to obtain urban communities and their dynamic events. We construct event vectors for each urban community and use hierarchical clustering to cluster event vectors to obtain different types of spatial interaction patterns. Finally, we divide the urban dynamic interaction into three urban spatial interaction modes: fixed spatial interaction pattern, long-term spatial interaction pattern, and short-term spatial interaction pattern. According to the results, we find that the cities in well-developed areas (eastern China) and under-developed areas (northwestern China) mostly show fixed spatial interaction patterns and long-term spatial interaction patterns, while the cities in moderately developed areas (central and western China) often show short-term spatial interaction patterns. The research results and conclusions of this paper reveal the inter-monthly urban spatial interaction patterns in China, provide theoretical support for the policy making and development planning of urban agglomeration construction, and contribute to the coordinated development of national and regional cities.https://www.mdpi.com/2220-9964/11/9/486urban spatial interaction networkdynamic spatial interaction patternsdynamic community detectionBaidu migration data |
| spellingShingle | Heping Jiang Shijia Luo Jiahui Qin Ruihua Liu Disheng Yi Yusi Liu Jing Zhang Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data ISPRS International Journal of Geo-Information urban spatial interaction network dynamic spatial interaction patterns dynamic community detection Baidu migration data |
| title | Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data |
| title_full | Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data |
| title_fullStr | Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data |
| title_full_unstemmed | Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data |
| title_short | Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data |
| title_sort | exploring the inter monthly dynamic patterns of chinese urban spatial interaction networks based on baidu migration data |
| topic | urban spatial interaction network dynamic spatial interaction patterns dynamic community detection Baidu migration data |
| url | https://www.mdpi.com/2220-9964/11/9/486 |
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