耦合FLUS和Markov的快速发展城市土地利用空间格局模拟方法

doi:10.12082/dqxxkx.2022.210359

Abstract

Abstract:

Modeling urban land use change is important for future regional planning and sustainable development. Previous FLUS-based studies are mostly based on larger grid scales. How to simulate the complex land use change processes in rapidly developing cities and explore the driving mechanisms of land use change still need further exploration. This paper constructs an urban land use pattern simulation framework coupled with FLUS and Markov and innovatively introduces house price to characterize socio-economic attributes. We take Shenzhen as the study area to simulate future urban land use spatial patterns under different development scenarios based on small grid scale (30 m) land use classification data and multi-source spatial variables such as basic geography data, road and river networks, and point-of-interest data. Finally, we analyze the land use change drivers using random forest models. The results show that the coupled FLUS and Markov method proposed in this paper has higher accuracy (FoM = 0.22) and simulate the land use change processes more accurately in rapidly developing cities, compared to traditional CA models (RFA-CA and Logistic-CA). The mapping results of multi-scenario land use patterns verify the importance of ecological control lines in the process of urban development, further illustrating the reference value of the proposed simulation framework for future urban planning layout. Hospital infrastructure, entertainment venues, and bus stop, road network density have a greater impact on urban development than natural factors (e.g., elevation, slope), while the distance to coastline limits land use change processes to some extent within Shenzhen. The model constructed in this study and the fine mapping results can provide a reference basis and theoretical foundation for related research on urban regional planning and spatial pattern simulation.

Key words: multi-source data, rapidly developing cities, land use patterns, Markov model, FLUS model, scenario settings, simulation of land use spatial patterns, land use change driving mechanisms

WANG Xudong, YAO Yao, REN Shuliang, SHI Xuguo. A Coupled FLUS and Markov Approach to Simulate the Spatial Pattern of Land Use in Rapidly Developing Cities[J].Journal of Geo-information Science, 2022, 24(1): 100-113.DOI:10.12082/dqxxkx.2022.210359

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类别	空间变量	分辨率/m	含义
自然因素	高程/m	30	地形高程条件
自然因素	坡度/°		地形坡度条件
	到河流距离/m		像元几何中心到最近河流的欧氏距离
	到海岸线距离/m		像元几何中心到最近海岸线的欧氏距离
交通因素	到主要道路距离/m		像元几何中心到最近主要道路的欧氏距离
交通因素	到铁路距离/m		像元几何中心到最近铁路的欧氏距离
	路网密度/(km/km²)		每个像元周边的道路分布密度
	到地铁站距离/m	30	像元几何中心到最近地铁站的欧氏距离
	公交站分布密度/(个/km²)		每个像元周边的公交站分布密度
社会经济因素	到医疗设施距离/m		像元几何中心到最近医疗设施的欧氏距离
社会经济因素	到区县中心距离/m		像元几何中心到最近区县中心的欧氏距离
	到学校距离/m		像元几何中心到最近学校的欧氏距离
	到娱乐场所距离/m		像元几何中心到最近娱乐场所的欧氏距离
	房屋价格分布/元	30	每个像元所在位置的房价精细制图

	RFA-CA	Logistic-CA	FLUS
总体精度	0.842	0.843	0.850
Kappa系数	0.744	0.746	0.757
FoM	0.198	0.198	0.220

	城市用地	植被	水体	裸地
2030年	1074.441	787.715	293.127	38.749
2040年	1151.236	712.587	291.338	38.872

A Coupled FLUS and Markov Approach to Simulate the Spatial Pattern of Land Use in Rapidly Developing Cities

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