Spatiotemporal Evolution of Specialized Villages in the Yellow River Basin and Its Influencing Factors

Q2 Social Sciences Papers in Applied Geography Pub Date : 2021-09-20 DOI:10.1080/23754931.2021.1977172
Jiajun Qiao, Chenguang Liu, Dong Han, Gang-Jun Liu, Shi‐Ning Zhou, Xiangyang Xin
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引用次数: 1

Abstract

Abstract We collected information on 35,353 specialized villages (SVs) from the nine provinces in the Yellow River Basin from 2008 to 2014. Using the nearest neighbor index (NNI), exploratory spatial data analysis (ESDA), kernel density estimation (KDE), and the geography detector (geodetector) method, we demonstrated the spatiotemporal evolution of these 35,353 SVs and analyzed the influencing factors that triggered change. We arrived at the following conclusion: (1) In the upstream area of the Yellow River Basin, these SVs were fewer in number and more dispersed, and the distribution of these SVs presented in a zonal region. The distribution of SVs in the middle and lower reaches was dense and blocky. The average observation distance of the SVs in the Yellow River Basin decreased from 3.17 km in 2008 to 2.74 km in 2014, and Moran’s I in the county level increased from 0.2640 in 2008 to 0.3449 in 2014, with the degree of concentration increasing. (2) These SV agglomerations in the Yellow River Basin were located in the Guanzhong region in the middle reaches and in the north Shandong province in the downstream region. The agglomeration degree in Guanzhong region was the highest, and the change from 2008 to 2014 grew increasingly stronger. The hotspot concentrated areas were distributed primarily in the downstream region of Shandong Province, the middle reaches of the Weihe Plain, and the Longdong Loess Plateau. The cold-spot concentrated areas were distributed mainly along the borders of Qinghai and Gansu, Inner Mongolia, and Shanxi province. (3) The natural environmental factors always had a great impact on the distribution of SVs, but this influence had decreased. In contrast, the local economic development level also played a very important role in the formation and development of SVs, and the influence of market conditions and technical conditions was gradually strengthening. The driving factors for the agglomeration of SVs in different regions were different and the differences were gradually increasing. The development model of SV was still an effective model to promote China’s Rural Revitalization. In the future, the research on the formation mechanism of SV agglomeration should be strengthened. According to the fact of regional development, the spatial interface theory provides a new path and thought to better study the internal mechanism of SV agglomeration.
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黄河流域特色村落时空演变及其影响因素
摘要/ Abstract摘要:对2008 - 2014年黄河流域9省35353个专业村进行了调查。采用最近邻指数(NNI)、探索性空间数据分析(ESDA)、核密度估计(KDE)和地理探测器(geodetector)方法,对35353个sv的时空演变进行了分析,并分析了引发变化的影响因素。结果表明:(1)在黄河流域上游地区,sv数量较少,分布较为分散,呈地带性分布;中下游sv呈密集块状分布;黄河流域sv的平均观测距离从2008年的3.17 km减少到2014年的2.74 km,县域Moran’s I从2008年的0.2640增加到2014年的0.3449,且集中程度增加。②黄河流域SV集聚区主要分布在中游关中地区和下游鲁北地区。关中地区的集聚程度最高,且2008 - 2014年的变化越来越强。热点集中区主要分布在山东省下游地区、渭河平原中游和陇东黄土高原。冷点集中区主要分布在青海与甘肃、内蒙古和山西交界地区。(3)自然环境因子对sv的分布始终具有较大的影响,但这种影响逐渐减弱。相比之下,当地经济发展水平对中小企业的形成和发展也起到了非常重要的作用,市场条件和技术条件的影响逐渐增强。不同地区中小企业集聚的驱动因素不同,且差异逐渐增大。SV的发展模式仍然是推动中国乡村振兴的有效模式。今后应加强对SV集聚形成机理的研究。从区域发展的实际出发,空间界面理论为更好地研究SV集聚的内在机制提供了新的路径和思路。
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来源期刊
Papers in Applied Geography
Papers in Applied Geography Social Sciences-Urban Studies
CiteScore
2.20
自引率
0.00%
发文量
19
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