Patch-Level and Neighborhood-Dependency Spatial Optimization Method (PNO): Application to Urban Land-Use Planning to Facilitate Both Socio-Economic and Environmental Development in Beijing
Rapid urban expansion and chaotic urban land-use patterns cause many socio-economic and environmental issues, e.g., traffic congestion and urban heat islands; thus, scientific planning considering land-use trade-offs and layout optimization is highly required for resolving these issues, especially i...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/14/1/33 |
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| Summary: | Rapid urban expansion and chaotic urban land-use patterns cause many socio-economic and environmental issues, e.g., traffic congestion and urban heat islands; thus, scientific planning considering land-use trade-offs and layout optimization is highly required for resolving these issues, especially in the urban renewal stage. However, previous spatial optimization methods were weak in processing land-use patches and ignored their neighborhood dependency, leading to fragmented and inapplicable optimization results. Accordingly, this study proposes a patch-level and neighborhood-dependency spatial optimization method (PNO) to adjust urban land-use patterns considering multiple optimization targets (i.e., improving population and economy but controlling land surface temperature). The PNO represents land-use patterns in a graph structure, quantifies land-use patterns’ impacts on the population, economy, and land surface temperature, defines the spatiotemporal constraints of land-use optimization considering neighborhood-dependency and optimization sequences, and finally optimizes land uses and their spatial layouts based on a multi-objective genetic algorithm. Experiments were conducted in the urban area of Beijing, and the results suggested that, after optimization, the population and GDP can be improved by 667,323 people (4.72%) and USD 10.69 billion in products (2.75%) in the study area; meanwhile, the land surface temperature can be reduced by 0.12 °C (−0.32%). Through comparison, the proposed PNO outperforms previous spatial optimization methods, e.g., NSGA-II, in processing land-use patches as well as their neighborhoods. Taking the land-use map in 2022 as a reference, the PNO optimization results are more consistent with actual land-use changes (consistency of 25%), compared to the existing spatial optimization results (consistency of 10.6%). Thus, PNO is more applicable to land-use planning in urban renewal circumstances. |
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| ISSN: | 2220-9964 |