Author(s)

Gao Xiang ¹, Wu Xiaoguang ¹, Xu Jingxin ¹, Zheng Wenjing ¹, Yun Hao ¹

Affiliation(s)

¹ Inner Mongolia Autonomous Region Institute of Territorial Spatial Planning, Hohhot, 010013, Inner Mongolia Autonomous Region, China

Corresponding Author

Gao Xiang

ABSTRACT

Arid and semi-arid regions are widely distributed across the globe, and their governance is a key component in achieving the United Nations Sustainable Development Goals, attracting significant international attention. Understanding the characteristics of rural population dynamics in such regions and exploring their development pathways can help effectively guide various production and living activities, thereby promoting harmonious coexistence between humans and nature. Using the Inner Mongolia Autonomous Region as a case study, this paper integrates multi-source spatiotemporal big data and employs geostatistical and machine learning methods to investigate the intrinsic relationships between human activities and environmental factors from multiple dimensions. Based on the data analysis, three main conclusions are drawn. First, rural human activities in the arid and semi-arid areas of Inner Mongolia exhibit spatial heterogeneity and are broadly associated with six environmental factors. Second, based on three indicators—resident population distribution, human activity intensity, and resident population migration—the town-level units within the study area can be classified into three types: shrinking towns, stagnant towns, and developing towns. Third, significant differences exist in the development levels of environmental factors across units with different human activity patterns. Among these, economic, transportation, and public service factors show the greatest disparities, while climatic and locational factors exhibit relatively smaller differences. The findings of this study offer localized insights from China to support research and decision-making in arid and semi-arid regions.

KEYWORDS

Arid and Semi-Arid Areas, Rural Areas, Human Activities; Environmental Factors, Pattern Clustering, Internal Connection

CITE THIS PAPER

Gao Xiang, Wu Xiaoguang, Xu Jingxin, Zheng Wenjing, Yun Hao, Decision Support for Population Regulation in Arid and Semi-Arid Rural Areas Based on Big Data Methods: A Case Study of Inner Mongolia. Environment, Resource and Ecology Journal (2025) Vol. 9: 125-135. DOI: http://dx.doi.org/10.23977/erej.2025.090115.

REFERENCES

[1] Zhang, T.; Chen, Y. The Effects of Landscape Change on Habitat Quality in Arid Desert Areas Based on Future Scenarios: Tarim River Basin as a Case Study. Front. Plant Sci. 2022, 13. https://doi.org/10.3389/fpls.2022.1031859.
[2] Li X.; He X.; Yang G.; Liu H.; Long A.; Chen F.; Liu B.; Gu X. Land use/cover and landscape pattern changes in Manas River Basin based on remote sensing. ijabe 2020, 13 (5), 141–152. https://doi.org/10.25165/j.ijabe. 20201305. 4783.
[3] Li, C.; Wang, Y.; Wu, X.; Cao, H.; Li, W.; Wu, T. Reducing Human Activity Promotes Environmental Restoration in Arid and Semi-Arid Regions: A Case Study in Northwest China. Science of the Total Environment 2021, 768, 144525. https://doi.org/10.1016/j.scitotenv.2020.144525.
[4] Li, C.; Cai, G.; Du, M. Big Data Supported the Identification of Urban Land Efficiency in Eurasia by Indicator SDG 11.3.1. ISPRS International Journal of Geo-Information 2021, 10 (2), 64. https://doi.org/10.3390/ijgi10020064.
[5] Wu, J.; Yang, X. Assessment of Ecological Carrying Capacity in Xilingol League Based on Three-Dimensional Ecological Footprint Model. Sustainability 2025, 17 (1), 128. https://doi.org/10.3390/su17010128.
[6] Hamerly, G.; Elkan, C. Learning the k in K-Means. In Advances in Neural Information Processing Systems; MIT Press, 2003; Vol. 16.
[7] Wang, K.; Deng, L.; Shangguan, Z.; Chen, Y.; Lin, X. Sustainability of Eco-Environment in Semi-Arid Regions: Lessons from the Chinese Loess Plateau. Environmental Science & Policy 2021, 125, 126–134. https://doi.org/10.1016/j.envsci.2021.08.025.
[8] McGrail, M. R. Spatial Accessibility of Primary Health Care Utilising the Two Step Floating Catchment Area Method: An Assessment of Recent Improvements. Int J Health Geogr 2012, 11 (1), 50. https://doi.org/10.1186/1476-072X-11-50.

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