Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division
Abstract Discovering the correlation relationships of spatial facilities in urban functional regions is of great significance in analyzing urban planning and promoting urban development. However, existing regional co-location pattern mining methods do not fully consider the attributes of urban funct...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-08-01
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| Series: | Data Science and Engineering |
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| Online Access: | https://doi.org/10.1007/s41019-024-00256-9 |
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| author | Yuqing Liu Lizhen Wang Peizhong Yang Lihua Zhou |
| author_facet | Yuqing Liu Lizhen Wang Peizhong Yang Lihua Zhou |
| author_sort | Yuqing Liu |
| collection | DOAJ |
| description | Abstract Discovering the correlation relationships of spatial facilities in urban functional regions is of great significance in analyzing urban planning and promoting urban development. However, existing regional co-location pattern mining methods do not fully consider the attributes of urban functional regions. Consequently, they fail to effectively capture the relationships between spatial facilities and regions, resulting in patterns lacking practical application value and interpretability. To address this issue, this paper proposes a novel regional co-location pattern (RCP) mining method based on urban functional region division. First, an ontology-based method for urban functional region division is proposed. On the basis of dividing urban functional regions, the expected mean distance is generated to adaptively calculate the neighbor relationships between instances in each region, and a parallel algorithm is designed to quickly mine RCPs in urban functional regions. Then, the correlation coefficient ( $$CC_{RP}$$ C C RP ) measures the association strength between RCP and function type to provide interpretability for RCPs. Finally, extensive experiments on real-world datasets are conducted to verify the effectiveness of urban functional region partition and to demonstrate the superiority of the proposed RCP mining method. |
| format | Article |
| id | doaj-art-96f5dcfc93bd463aa193d80c1aa75674 |
| institution | Kabale University |
| issn | 2364-1185 2364-1541 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Data Science and Engineering |
| spelling | doaj-art-96f5dcfc93bd463aa193d80c1aa756742024-12-08T12:39:02ZengSpringerOpenData Science and Engineering2364-11852364-15412024-08-019446448510.1007/s41019-024-00256-9Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region DivisionYuqing Liu0Lizhen Wang1Peizhong Yang2Lihua Zhou3School of Information Science and Engineering, Yunnan UniversitySchool of Information Science and Engineering, Yunnan UniversitySchool of Information Science and Engineering, Yunnan UniversitySchool of Information Science and Engineering, Yunnan UniversityAbstract Discovering the correlation relationships of spatial facilities in urban functional regions is of great significance in analyzing urban planning and promoting urban development. However, existing regional co-location pattern mining methods do not fully consider the attributes of urban functional regions. Consequently, they fail to effectively capture the relationships between spatial facilities and regions, resulting in patterns lacking practical application value and interpretability. To address this issue, this paper proposes a novel regional co-location pattern (RCP) mining method based on urban functional region division. First, an ontology-based method for urban functional region division is proposed. On the basis of dividing urban functional regions, the expected mean distance is generated to adaptively calculate the neighbor relationships between instances in each region, and a parallel algorithm is designed to quickly mine RCPs in urban functional regions. Then, the correlation coefficient ( $$CC_{RP}$$ C C RP ) measures the association strength between RCP and function type to provide interpretability for RCPs. Finally, extensive experiments on real-world datasets are conducted to verify the effectiveness of urban functional region partition and to demonstrate the superiority of the proposed RCP mining method.https://doi.org/10.1007/s41019-024-00256-9Spatial data miningRegional co-location pattern (RCP)OntologyUrban functional region |
| spellingShingle | Yuqing Liu Lizhen Wang Peizhong Yang Lihua Zhou Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division Data Science and Engineering Spatial data mining Regional co-location pattern (RCP) Ontology Urban functional region |
| title | Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division |
| title_full | Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division |
| title_fullStr | Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division |
| title_full_unstemmed | Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division |
| title_short | Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division |
| title_sort | mining interpretable regional co location patterns based on urban functional region division |
| topic | Spatial data mining Regional co-location pattern (RCP) Ontology Urban functional region |
| url | https://doi.org/10.1007/s41019-024-00256-9 |
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