Spatial Patterns and the Regional Differences of Rural Settlements in Jilin Province, China

Xiaoyan Li, Huiying Li, Yingnan Zhang and Limin Yang
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Xiaoyan Li: College of Earth Sciences, Jilin University, Changchun 130012, China
Huiying Li: College of Earth Sciences, Jilin University, Changchun 130012, China
Yingnan Zhang: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100049, China
Limin Yang: College of Earth Sciences, Jilin University, Changchun 130012, China

Sustainability, 2017, vol. 9, issue 12, 1-16

Abstract: The spatial patterns of rural settlements are important for understanding the drivers of land use change and the relationship between human activity and environmental processes. It has been suggested that the clustering of houses decreases the negative effects on the environment and promotes the development of the countryside, but few empirical studies have quantified the spatial distribution patterns of houses. Our aim was to explore the regional differences in rural settlement patterns and expand our understanding of their geographic associations, and thus contribute to land use planning and the implementation of the policy of “building a new countryside”. We used spatial statistical methods and indices of landscape metrics to investigate different settlement patterns in three typical counties within different environments in Jilin Province, Northeast China. The results indicated that rural settlements in these three counties were all clustered, but to a varied degree. Settlement density maps and landscape metrics displayed uniformity of the settlement distributions within plain, hill, and mountainous areas. Influenced by the physical environment, the scale, form, and degree of aggregation varied. Accordingly, three types of rural settlements were summarized: a low-density, large-scale and sparse type; a mass-like and point-scattered type; and a low-density and high cluster-like type. The spatial patterns of rural settlements are the result of anthropogenic and complex physical processes, and provide an important insight for the layout and management of the countryside.

Keywords: Ripley’s K function; point pattern analysis; spatial statistics; geographic association (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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