Spatial-temporal variations of natural suitability of human settlement environment in the Three Gorges Reservoir Area—A case study in Fengjie County, China

Abstract

With rapid environmental degeneration and socio-economic development, the human settlement environment (HSE) has experienced dramatic changes and attracted attention from different communities. Consequently, the spatial-temporal evaluation of natural suitability of the human settlement environment (NSHSE) has become essential for understanding the patterns and dynamics of HSE, and for coordinating sustainable development among regional populations, resources, and environments. This study aims to explore the spatial-temporal evolution of NSHSE patterns in 1997, 2005, and 2009 in Fengjie County near the Three Gorges Reservoir Area (TGRA). A spatially weighted NSHSE model was established by integrating multi-source data (e.g., census data, meteorological data, remote sensing images, DEM data, and GIS data) into one framework, where the Ordinary Least Squares (OLS) linear regression model was applied to calculate the weights of indices in the NSHSE model. Results show that the trend of natural suitability has been first downward and then upward, which is evidenced by the disparity of NSHSE existing in the south, north, and central areas of Fengjie County. Results also reveal clustered NSHSE patterns for all 30 townships. Meanwhile, NSHSE has significant influence on population distribution, and 71.49% of the total population is living in moderate and high suitable districts.

Keywords

natural suitability of human settlement environment / ordinary least squares model / global and local spatial autocorrelation analyses / Three Gorges Reservoir Area (TGRA) / Fengjie County

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Acknowledgements

This work was supported in part by the Research Funding for the Continuous Work of Three Gorges Project sponsored by the Executive Office of Three Gorges Project Construction Committee (No. 0001792015CB50002). The authors would like to thank all the organizations for sharing data. We also wish to thank the three anonymous reviewers and Dr. Yanhua Xie of University of Wisconsin-Madison for their constructive comments.

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