New indices to capture the evolution characteristics of urban expansion structure and form
A quantitative description is the basis for correctly understanding the urban expansion process. Previous approaches were dedicated to identifying expansion types based on the boundary sharing rate, thereby depicting the evolution of urban expansion. These methods, however, focused on describing nei...
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| Autores principales: | , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/5cd9912336da47e0ba2a74a0795c52c2 |
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| Sumario: | A quantitative description is the basis for correctly understanding the urban expansion process. Previous approaches were dedicated to identifying expansion types based on the boundary sharing rate, thereby depicting the evolution of urban expansion. These methods, however, focused on describing neighborhood relations and ignored urban global expansion structure and form information. In this study, we first propose a new index, the location centrality index (LCI), to capture the expansion structure characteristics by incorporating an “area-inverse distance” weighting algorithm and geometric features. Then, we propose another index, the location centrality aggregation index (LCAI), to depict the heterogeneous evolution of urban form by considering the attribute of the new patch. The location centrality and location aggregation types are identified to reflect the effect of new patches on the urban expansion structure and form based on LCI and LCAI, respectively. Two variants of LCI and LCAI are also proposed to reflect the global bottom-up characteristics of urban expansion. The LCI and LCAI were verified using four periods of Landsat images (1995, 2000, 2005, and 2010) of the Wuhan metropolitan area, China. The results show that the overall development trend of the expansion structure in the Wuhan metropolitan area was toward decentralization. The urban form had become generally separated, but tended toward aggregation from 2005 to 2010. Our findings also reveal that the LCI fills the gap in the dynamic assessment of urban expansion with previous indices by explicitly uncovering global structure characteristics. The LCAI achieves better performance than previous indices in identifying heterogeneous aggregation. |
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