A GIS-Based K-Mean Clustering Algorithm for Characteristic Towns in China

Zuo Zhang, Yuqian Dou, Chi Zhan () and Qiumei Mao
Additional contact information
Zuo Zhang: Central China Normal University
Yuqian Dou: Central China Normal University
Chi Zhan: Central China Normal University
Qiumei Mao: Central China Normal University

A chapter in Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate, 2021, pp 1175-1186 from Springer

Abstract: Abstract The unbalanced development among regions and city-towns has attracted considerable attention due to the rapid China’s urbanization process. What promote the characteristic town, a key node in China's urban system, to be the new driving force of China's urbanization development and national economic adjustment are population, living environment, rural vitality restoration and economic innovation. However, the unitary development of characteristic towns will exert an adverse impact on the long-term economic development. On the condition that previous academic work focuses less on the spatial clustering, spatial features based on the Chinese city-town system are thus introduced in this paper, and then the basic data is integrated through the Orange platform for statistical analysis. Finally, on the basis of the spatial featural visualization obtained through nearest-neighbor search, the K-mean algorithm and GIS tool are applied to perform the visualization and conduct spatial analysis of characteristic town. The results indicate that: (1) premised on the large gap in the towns’ quantity, the distance between characteristic towns and prefecture-level cities as well as major cities is quite different in diverse regions; (2) the characteristic towns can be divided into four types in space, where this four spatial clusters are gradually increasing from east to west; (3) different spatial characteristic types require various unfolding policies.

Keywords: Characteristic towns; K-mean; Clustering; GIS (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-3587-8_79

Ordering information: This item can be ordered from
http://www.springer.com/9789811635878

DOI: 10.1007/978-981-16-3587-8_79

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().