Spatiotemporal evolution and simulation optimization of rural settlements in Hotan Prefecture, China: A three-dimensional perspective

doi:10.1016/j.regsus.2026.100371

Abstract

Abstract:

The exploration of the evolutionary process and influence mechanism of rural settlements has significant implications for the promotion of sustainable rural development. However, existing research has yet to employ a three-dimensional perspective that organically combines scale, space, and morphology. In view of this, using Hotan Prefecture of China as the study area, we constructed a three-dimensional analysis framework to explore the spatiotemporal evolution characteristics of rural settlements from 2000 to 2020 through landscape pattern indices, kernel density analysis, and GeoDetector. Furthermore, the Geographical Simulation and Optimization System-Future Land Use Simulation (GeoSOS-FLUS) model was utilized to predict various scenarios affecting rural settlements in 2030. This study reveals that scale dynamics followed a clear pattern: values dropped sharply and then stabilized alongside ongoing growth. Spatial patterns showed clustering along rivers and transport corridors, with density gradients radiating outward. Morphological evolution is marked by gradual patch regularization and stabilized spatial configuration. GeoDetector analysis identified hydrological accessibility and cropland proximity as primary spatial determinants of rural settlements, demonstrating significant synergistic effects (q-value=0.457). Scenario projections yielded divergent outcomes: under the natural development scenario, rural settlements are projected to expand by 5.490% relative to 2020, with this expansion mainly driven by cropland encroachment and unused land development. In contrast, the urban-rural integration development scenario reveals a significantly lower predicted growth rate. Rural settlements are expected to adopt a more compact and clustered spatial layout. Under the cropland production development scenario, conversions of cropland to built-up land are constrained, substantially curbing the expansion rate of rural settlements. In summary, this study intends to offer scientific support for optimizing the spatial layout of rural settlements in arid oasis regions and advancing rural revitalization strategies.

Key words: Rural settlements, Spatiotemporal evolution, Landscape pattern index, Kernel density, GeoDetector, Hotan Prefecture

HAO Jiaxin, SHEN Liqiang, WANG Yuejian, CHEN Huanhuan. Spatiotemporal evolution and simulation optimization of rural settlements in Hotan Prefecture, China: A three-dimensional perspective[J]. Regional Sustainability, 2026, 7(4): 100371.

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Landscape pattern index	Description
Core area (CA)	It represents the total area covered by a given landscape patch type.
Patch density (PD)	It characterizes the overall landscape heterogeneity and fragmentation.
Landscape shape index (LSI)	It quantifies patch complexity by measuring the deviation between the shape of a given patch and a circle or square with an identical area.
Perimeter area ratio (PARA)	The higher the morphological complexity of the patches, the greater the PARA value.
Fractal dimension index (FRAC)	This index quantifies fractal dimensional complexity and overcomes the limitations of traditional integer dimensions. It can take non-integer values to reflect the irregular morphological features of patches.

Indicator	Indicator proxy	Impact mechanism	Index property	Reference
Digital elevation model (DEM)	X1	High-altitude regions hinder construction and agricultural activities, restricting settlement distribution.	Negative	Lin and Hou (2023); Ma et al. (2024)
Slope	X2	Construction costs rise substantially in steep-slope areas, and such terrain is also unfavourable for agricultural production.	Negative	Ji et al. (2022); Zhao et al. (2024)
Hydrological accessibility	X3	Settlements in arid areas are predominantly distributed near water, which constitutes their core pattern.	Positive	Zhang et al. (2024); Zou et al. (2024)
Cropland proximity	X4	In Hotan Prefecture, there is a strong spatial correlation between settlements and cropland.	Positive	Chen et al. (2017a); Rao et al. (2023)
Transportation convenience	X5	Settlements near transportation routes tend to cluster due to superior transport accessibility.	Positive	Zhou et al. (2021); Ma et al. (2022b)
Population density	X6	Settlements tend to be more concentrated in densely populated areas.	Positive	Huang et al. (2022); Ma et al. (2024)

Year	Patch count (patches)					PD (units/km²)	Patch area (km²)	Average patch area (km²)
Year	Level I	Level II	Level III	Level IV	Level V	PD (units/km²)	Patch area (km²)	Average patch area (km²)
2000	149	946	416	75	58	7.500	399.660	0.264
2010	73	268	215	48	35	3.800	197.820	0.240
2020	66	266	219	49	39	3.900	208.370	0.263

Landscape pattern index	q-value
Landscape pattern index	X1	X2	X3	X4	X5	X6
CA	0.143	0.127	0.438	0.373	0.187	0.115
PD	0.208	0.193	0.511	0.393	0.199	0.130
LSI	0.096	0.122	0.210	0.120	0.227	0.108
PARA	0.150	0.164	0.221	0.264	0.175	0.137
FRAC	0.128	0.135	0.113	0.158	0.127	0.094