Spatial Land Use/Cover Optimization with Genetic Algorithm: Incorporating Spatial Heteorogeneity

Authors: Yulun Zhou*, The Chinese University of Hong Kong, Bo Huang, The Chinese University of Hong Kong
Topics: Spatial Analysis & Modeling, Land Use and Land Cover Change
Keywords: Spatial Optimization, Spatial Statistics, Climate Change, Heat Stress
Session Type: Paper
Day: 4/13/2018
Start / End Time: 10:00 AM / 11:40 AM
Room: Grand Ballroom A, Astor, 2nd Floor
Presentation File: No File Uploaded

Extreme heat stress is an underestimated threat for the health conditions of all the urban dwellers, which will be 50% of the world’s population by 2030. Strengthened by the global warming trend and the urban heat island effect, cities will face more extreme and frequent heat waves. Based on the fundamental effect of land use and land cover (LULC) on the thermal environment, multi-objective spatial optimization methods have been proposed as an effective and efficient way for the smart planning of LULC considering the trade-off among multiple objectives and constraints. However, global relationships are mostly used as the evaluation function in existing optimization methods, which ignores the spatial heterogeneity within the relationship. While the spatial heterogeneity in LST-LULC relationship has been widely reported, in this paper, we propose a new optimization framework to slow down urban warming and reduce the related health risk with the smart planning of LULC, incorporating the state-of-the-art spatial statistical methods and the heuristic optimization methods. With the proposed methods, the optimization model understands the spatial difference in the environmental sensitivity of urban developments and is able to allocate urban development accordingly, which contributes to the equality of urban development from an environmental-friendly perspective.

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