Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method
By Xun Liang, Xiaoping Liu, Xia Li, Yimin Chen, He Tian, Yao Yao. Landscape and Urban Planning, 2018
441 citations recorded by the source corpus.
Abstract Urban growth boundaries (UGBs) have been commonly regarded as a useful tool for controlling urban sprawl. There is a need to create models that can establish plausible UGBs for fast growing regions. Previous methods have merely focused on establishing a single UGB scenario over different time intervals, but rarely considered the influences of macro policy (e.g., future urban demand) and spatial policy (e.g., master plan) for regional planning. However, the spatial patterns of urban expansion are significantly affected by regional planning. In this paper, a CA-based method called the future land use simulation (FLUS) is applied to the delineation of UGBs. We argue that the delineation needs to integrate the top-down approach with CA for projecting complex land use changes under designed scenarios. The system dynamics model (SD) and cellular automaton model (CA) were interactively coupled in the FLUS model during the projection period. The top-down SD is used to project scenarios that relate to macro policy and socioeconomic status, and the bottom-up CA accounts for urban growth simulations under the influence of different driving factors and spatial planning policies. A morphological technology based on erosion and dilation is further proposed to generate the UGBs from the FLUS model’s simulated urban forms. The proposed UGB-FLUS model was applied to the establishment of UGBs in the Pearl River Delta region (PRD) from 2020 to 2050. The results demonstrate that the method can support urban planning by generating feasible patterns for UGBs under different planning scenarios.
Subfields: Atmospheric Science, Building and Construction, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/1ddea7707315d21753b96b6085e02f028fcad62b
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Citations in the corpus
- Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China (2021, methodology): “…for the same research question Models like CLUE-S and GeoSOS-FLUS have been widely used in different regions across the world (Fuchs et al., 2018; Liang et al., 2018b; Liu et al., 2017; Verburg et al., 2002) When ML models are used in other regions, driver data must be collected and structures…”
- Mapping global urban boundaries from the global artificial impervious area (GAIA) data (2020, methodology): “We chose the structuring element as a 7 × 7 window as suggested in Liang et al (2018).”