Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in China
Understanding mixing patterns in urban networks is crucial for exploring the connectivity relationships between nodes and revealing the connection tendencies. Based on multi-source data (Baidu index data, investment data of listed companies, high-speed rail operation data, and highway network data)...
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/4/2024 |
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| author | Kaiqi Zhang Lujin Jia Sheng Xu |
| author_facet | Kaiqi Zhang Lujin Jia Sheng Xu |
| author_sort | Kaiqi Zhang |
| collection | DOAJ |
| description | Understanding mixing patterns in urban networks is crucial for exploring the connectivity relationships between nodes and revealing the connection tendencies. Based on multi-source data (Baidu index data, investment data of listed companies, high-speed rail operation data, and highway network data) from 2017 to 2019 across seven national-level urban agglomerations, this study introduces complex network assortativity coefficients to analyze the mechanisms of urban relationship formation from two dimensions, structural features and socioeconomic attributes, to evaluate how these features shape urban agglomeration networks and reveal the distribution of network assortativity coefficients across urban agglomerations to classify diverse developmental patterns. The results show that the sampled cities exhibit heterogeneous characteristics following a stretched exponential distribution in urban structural features and a log-normal distribution in socioeconomic attributes, demonstrating significant resource mixing patterns. Different types of urban agglomeration networks display distinct assortativity characteristics. Information network mixing patterns within urban agglomerations are insignificant; investment relationships, high-speed rail, and highway networks demonstrate significant centripetal mixing patterns. The assortativity coefficients of urban agglomerations follow a unified general probability density distribution, suggesting that urban agglomerations objectively tend toward centripetal agglomeration. |
| format | Article |
| id | doaj-art-e473805374b244b2ad884a1a9973bbe1 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e473805374b244b2ad884a1a9973bbe12025-08-20T03:12:08ZengMDPI AGApplied Sciences2076-34172025-02-01154202410.3390/app15042024Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in ChinaKaiqi Zhang0Lujin Jia1Sheng Xu2College of Economics and Management, Chang’an University, Xi’an 710064, ChinaCollege of Economics and Management, Chang’an University, Xi’an 710064, ChinaCollege of Economics and Management, Chang’an University, Xi’an 710064, ChinaUnderstanding mixing patterns in urban networks is crucial for exploring the connectivity relationships between nodes and revealing the connection tendencies. Based on multi-source data (Baidu index data, investment data of listed companies, high-speed rail operation data, and highway network data) from 2017 to 2019 across seven national-level urban agglomerations, this study introduces complex network assortativity coefficients to analyze the mechanisms of urban relationship formation from two dimensions, structural features and socioeconomic attributes, to evaluate how these features shape urban agglomeration networks and reveal the distribution of network assortativity coefficients across urban agglomerations to classify diverse developmental patterns. The results show that the sampled cities exhibit heterogeneous characteristics following a stretched exponential distribution in urban structural features and a log-normal distribution in socioeconomic attributes, demonstrating significant resource mixing patterns. Different types of urban agglomeration networks display distinct assortativity characteristics. Information network mixing patterns within urban agglomerations are insignificant; investment relationships, high-speed rail, and highway networks demonstrate significant centripetal mixing patterns. The assortativity coefficients of urban agglomerations follow a unified general probability density distribution, suggesting that urban agglomerations objectively tend toward centripetal agglomeration.https://www.mdpi.com/2076-3417/15/4/2024urban networkassortativity coefficientmixing patternstructural featuresocioeconomic attribute |
| spellingShingle | Kaiqi Zhang Lujin Jia Sheng Xu Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in China Applied Sciences urban network assortativity coefficient mixing pattern structural feature socioeconomic attribute |
| title | Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in China |
| title_full | Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in China |
| title_fullStr | Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in China |
| title_full_unstemmed | Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in China |
| title_short | Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in China |
| title_sort | recognizing mixing patterns of urban agglomeration based on complex network assortativity coefficient a case study in china |
| topic | urban network assortativity coefficient mixing pattern structural feature socioeconomic attribute |
| url | https://www.mdpi.com/2076-3417/15/4/2024 |
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