Abstract

ABSTRACT: Traditional Villages (TVs) are typical and representative of the agricultural civilization in millions of Chinese villages. The distribution of TVs shows spatial heterogeneity, based on the complexity and diversity of several influencing factors. In this study, 6,819 Chinese TVs were identified and the influencing factors that affect their distribution were screened in terms of three indicator groups: climatic, geographic, and humanity-related factors. Additionally, the K-means clustering algorithm clustered the TVs into different distribution regions. The quantitative relationships between the dominant influencing factors of different distribution regions were revealed to ensure a lucid understanding of the regional distribution of TVs. The results indicated that 1) climatic factors have the greatest impact on the spatial distribution of TVs, followed by geographic factors, particularly the elevation, and then by human factors, of which ethnic distribution played a relatively important role. 2) Twenty-one TV clustering distributions were obtained, which were classified into eight regions of TV distribution with different dominant influencing factors. Management and protective strategies were formulated based on the attribute analysis of influencing factors in each region. The obtained results delineated homogeneous TV distribution regions via the clustering method to achieve an accurate statistical analysis of the influencing factors. This study proposes a new perspective and reference for managing and protecting the diversity, continuity, and integrity of TVs across administrative regions.

Highlights

  • Urbanization, producer and consumer behavior, and extensive economic growth have resulted in the serious degradation of the living environment in villages (ANTROP, 2004; TAN & LI, 2013) such that there is a disappearance of important natural and cultural heritages specific to certain villages (YANG et al, 2018)

  • We focused on three issues: what is the spatial distribution of Traditional Villages (TVs) under the influence of differences and synergy?; what are the dominant factors?; and how do we carry out the scientific management and protection zoning of TVs based on the dominant factors? The purpose of this study was to: 1) further understand the distribution status and formation environment of TVs and 2) identify the distribution of TV management regions under the influence of similar environmental factors and determine the main influencing factors within each region to guide subsequent planning and policy formulation

  • The grouping analysis tool in ArcGIS was used to generate the TV cluster distribution map, which is shown in figure 3

Read more

Summary

Introduction

Urbanization, producer and consumer behavior, and extensive economic growth have resulted in the serious degradation of the living environment in villages (ANTROP, 2004; TAN & LI, 2013) such that there is a disappearance of important natural and cultural heritages specific to certain villages (YANG et al, 2018). Several studies have evaluated the influencing factors and laws that govern the distribution of ancient settlements from an archaeological perspective (PARSONS, 1972; DEMJÁN & DRESLEROVÁ, 2016; FANTA et al, 2020) These studies have emphasized the relationship between early complex rural settlements and specific environments (GREEN & PETRIE, 2018), often focusing on a fixed historical period, with an emphasis on the protection of relics. With the development of computer and remote sensing technologies, spatial analysis technology based on geographic information systems (GIS) and mathematical modelling have been used to quantitatively analyze the spatial patterns of TVs (SEVENANT & ANTROP, 2007; BRAGA et al, 2016; YANG et al, 2018; FANTA et al, 2020) These studies often adopt the methods of the case study and lack ofquantitative analysis of large samples of TVs. even macroscale studies have not considered in-depth analyses of the internal mechanisms that drive the differences and similarities in the TV distribution due to different dominant factors

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.