Unveiling intra-urban complexity and identifying urban cores through the lens of living structure using point-of-interest data.
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| Title: | Unveiling intra-urban complexity and identifying urban cores through the lens of living structure using point-of-interest data. |
|---|---|
| Authors: | Ren, Zheng1 (AUTHOR), Ma, Ding2 (AUTHOR) dingma@szu.edu.cn, Jiang, Bin3 (AUTHOR), Seipel, Stefan1 (AUTHOR) |
| Source: | Geo-Spatial Information Science. Feb2026, Vol. 29 Issue 1, p530-545. 16p. |
| Subjects: | Recursive partitioning, Central business districts, Geospatial data, Complexity (Philosophy), Pareto distribution |
| Geographic Terms: | China |
| Abstract: | The intra-urban space is essentially an organized structure of complexity that consists of centers at different hierarchical levels or scales. This kind of complexity can be measured from the perspective of living structure inspired by Christopher Alexander's organic view of space. Previous studies have revealed that the living structure can be used to characterize the structural complexity of photos, satellite images and urban systems. However, its potential to measure intra-urban complexity using massive point-based datasets remains underexplored. This study introduces a recursive method to analyze intra-urban complexity using massive point-of-interest (POI) data. By recursively decomposing urban substructures, we quantified structural complexity based on the livingness of substructures using a unified criterion. Our findings indicate that cities or intra-urban areas with higher livingness exhibit greater structural complexity. The resulting substructures exhibit power-law distributions and align closely with human activity patterns across multiple spatial scales in four large cities in China. Remarkably, intra-urban structures can be effectively understood with no more than four levels of recursive decomposition. Furthermore, we found that the urban centers or core areas can be effectively located using the proposed method. These insights underscore the potential of living structure as a framework for understanding and measuring the organized complexity of intra-urban spaces. [ABSTRACT FROM AUTHOR] |
| Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192503484 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unveiling intra-urban complexity and identifying urban cores through the lens of living structure using point-of-interest data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ren%2C+Zheng%22">Ren, Zheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Ding%22">Ma, Ding</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> dingma@szu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Bin%22">Jiang, Bin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Seipel%2C+Stefan%22">Seipel, Stefan</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Geo-Spatial+Information+Science%22">Geo-Spatial Information Science</searchLink>. Feb2026, Vol. 29 Issue 1, p530-545. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Recursive+partitioning%22">Recursive partitioning</searchLink><br /><searchLink fieldCode="DE" term="%22Central+business+districts%22">Central business districts</searchLink><br /><searchLink fieldCode="DE" term="%22Geospatial+data%22">Geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Complexity+%28Philosophy%29%22">Complexity (Philosophy)</searchLink><br /><searchLink fieldCode="DE" term="%22Pareto+distribution%22">Pareto distribution</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The intra-urban space is essentially an organized structure of complexity that consists of centers at different hierarchical levels or scales. This kind of complexity can be measured from the perspective of living structure inspired by Christopher Alexander's organic view of space. Previous studies have revealed that the living structure can be used to characterize the structural complexity of photos, satellite images and urban systems. However, its potential to measure intra-urban complexity using massive point-based datasets remains underexplored. This study introduces a recursive method to analyze intra-urban complexity using massive point-of-interest (POI) data. By recursively decomposing urban substructures, we quantified structural complexity based on the livingness of substructures using a unified criterion. Our findings indicate that cities or intra-urban areas with higher livingness exhibit greater structural complexity. The resulting substructures exhibit power-law distributions and align closely with human activity patterns across multiple spatial scales in four large cities in China. Remarkably, intra-urban structures can be effectively understood with no more than four levels of recursive decomposition. Furthermore, we found that the urban centers or core areas can be effectively located using the proposed method. These insights underscore the potential of living structure as a framework for understanding and measuring the organized complexity of intra-urban spaces. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10095020.2025.2525494 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 530 Subjects: – SubjectFull: Recursive partitioning Type: general – SubjectFull: Central business districts Type: general – SubjectFull: Geospatial data Type: general – SubjectFull: Complexity (Philosophy) Type: general – SubjectFull: Pareto distribution Type: general – SubjectFull: China Type: general Titles: – TitleFull: Unveiling intra-urban complexity and identifying urban cores through the lens of living structure using point-of-interest data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ren, Zheng – PersonEntity: Name: NameFull: Ma, Ding – PersonEntity: Name: NameFull: Jiang, Bin – PersonEntity: Name: NameFull: Seipel, Stefan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10095020 Numbering: – Type: volume Value: 29 – Type: issue Value: 1 Titles: – TitleFull: Geo-Spatial Information Science Type: main |
| ResultId | 1 |
