Distribution of Chinese traditional villages and influencing factors for regionalization
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Ciência Rural |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000700801 |
Resumo: | 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. |
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Distribution of Chinese traditional villages and influencing factors for regionalizationtraditional/ historical rural settlementsdominant factorscluster analysisdistribution regionsgeographic information system.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.Universidade Federal de Santa Maria2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000700801Ciência Rural v.51 n.7 2021reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20200124info:eu-repo/semantics/openAccessWu,YunongWu,MengqiWang,ZhexiaoZhang,BeimingLi,ChangzuoZhang,Bineng2021-04-08T00:00:00ZRevista |
dc.title.none.fl_str_mv |
Distribution of Chinese traditional villages and influencing factors for regionalization |
title |
Distribution of Chinese traditional villages and influencing factors for regionalization |
spellingShingle |
Distribution of Chinese traditional villages and influencing factors for regionalization Wu,Yunong traditional/ historical rural settlements dominant factors cluster analysis distribution regions geographic information system. |
title_short |
Distribution of Chinese traditional villages and influencing factors for regionalization |
title_full |
Distribution of Chinese traditional villages and influencing factors for regionalization |
title_fullStr |
Distribution of Chinese traditional villages and influencing factors for regionalization |
title_full_unstemmed |
Distribution of Chinese traditional villages and influencing factors for regionalization |
title_sort |
Distribution of Chinese traditional villages and influencing factors for regionalization |
author |
Wu,Yunong |
author_facet |
Wu,Yunong Wu,Mengqi Wang,Zhexiao Zhang,Beiming Li,Changzuo Zhang,Bin |
author_role |
author |
author2 |
Wu,Mengqi Wang,Zhexiao Zhang,Beiming Li,Changzuo Zhang,Bin |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Wu,Yunong Wu,Mengqi Wang,Zhexiao Zhang,Beiming Li,Changzuo Zhang,Bin |
dc.subject.por.fl_str_mv |
traditional/ historical rural settlements dominant factors cluster analysis distribution regions geographic information system. |
topic |
traditional/ historical rural settlements dominant factors cluster analysis distribution regions geographic information system. |
description |
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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000700801 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000700801 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-8478cr20200124 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
dc.source.none.fl_str_mv |
Ciência Rural v.51 n.7 2021 reponame:Ciência Rural instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Ciência Rural |
collection |
Ciência Rural |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1749140556068421632 |