Using spatial heterogeneity to strengthen the neighbourhood effects of urban growth simulation models

Zhai, Shuting; Feng, Yongjiu; Yan, Xinlei; Wei, Yongliang; Wang, Rong; Li, Pengshuo

doi:10.1080/14498596.2021.1982783

Cited by 5 publications

(1 citation statement)

References 59 publications

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“…The CA model has a distinctive feature known as its neighborhood effect. The neighborhood effect refers to the influence of the states of neighboring cells on the state update of a central cell, reflecting the local interactions between the central cell and its adjacent cells [33,34]. This imparts the CA model with the ability for local self-organization evolution, which plays a crucial role in the model [35].…”

Section: Introductionmentioning

confidence: 99%

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

Liu,

Luo,

Wang

et al. 2024

IJGI

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Land use/cover change (LUCC) refers to the phenomenon of changes in the Earth’s surface over time. Accurate prediction of LUCC is crucial for guiding policy formulation and resource management, contributing to the sustainable use of land, and maintaining the health of the Earth’s ecosystems. LUCC is a dynamic geographical process involving complex spatiotemporal dependencies. Existing LUCC simulation models suffer from insufficient spatiotemporal feature learning, and traditional cellular automaton (CA) models exhibit limitations in neighborhood effects. This study proposes a cellular automaton model based on spatiotemporal feature learning and hotspot area pre-allocation (VST-PCA). The model utilizes the video swin transformer to acquire transformation rules, enabling a more accurate capture of the spatiotemporal dependencies inherent in LUCC. Simultaneously, a pre-allocation strategy is introduced in the CA simulation to address the local constraints of neighborhood effects, thereby enhancing the simulation accuracy. Using the Chongqing metropolitan area as the study area, two traditional CA models and two deep learning-based CA models were constructed to validate the performance of the VST-PCA model. Results indicated that the proposed VST-PCA model achieved Kappa and FOM values of 0.8654 and 0.4534, respectively. Compared to other models, Kappa increased by 0.0322–0.1036, and FOM increased by 0.0513–0.1649. This study provides an accurate and effective method for LUCC simulation, offering valuable insights for future research and land management planning.

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Section: Introductionmentioning

confidence: 99%

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

Liu,

Luo,

Wang

et al. 2024

IJGI

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Urban growth scenario projection using heuristic cellular automata in arid areas considering the drought impact

Tang,

Feng,

Lei

et al. 2024

J. Arid Land

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Heterogeneous spatial models in R: spatial regimes models

Piras

Sarrias

2023

J Spat Econometrics

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This paper presents the progress made so far in the development of the R package hspm. The package hspm aims at implementing a variety of models and methods to control for heterogeneity in spatial models. Spatial heterogeneity can be specified in different ways, ranging from exogenous (or endogenous) spatial regimes models, to models with coefficients that potentially vary for each observations (i.e., continuous heterogeneity). We focus on a few R functions that allow for the estimation of a general spatial regimes model, as well as all of the nested specifications deriving from it. The models are estimated by instrumental variables and generalized method of moments techniques.

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Using spatial heterogeneity to strengthen the neighbourhood effects of urban growth simulation models

Cited by 5 publications

References 59 publications

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

Urban growth scenario projection using heuristic cellular automata in arid areas considering the drought impact

Heterogeneous spatial models in R: spatial regimes models

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