贵州区域性整体贫困的复杂空间社会网络
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摘要
为贵州省相关部门制定贫困退出策略提供参考。选取2017年贵州8个市州67个县区687个乡镇为研究对象,建立节点和边构成群体关系的邻接矩阵,通过Fruchterman Reingold布局,绘制贵州省8个市州所辖贫困县区社会网络图和贵州省16个深度贫困县所辖乡镇复杂网络图。结果表明:1)贵州贫困市州占全省市州总数的88.89%;深度贫困县区、乡镇和村的占比分别为23.88%、37.99%和40.25%。2)8个市州深度贫困县区、乡镇及村的占比总计以黔东南州最大,为80.78%;其次是毕节市,为48.09%;遵义市最小,为2.30%。贫困市州的贫困强度为毕节市>黔东南州>六盘水市>安顺市>铜仁市>黔南州>黔西南州>遵义市。3)贫困县区属Ⅰ区(强核心区)的有8个,辖175个乡镇,网络的度为0.396 6,网络凝聚力很高;Ⅱ区(核心区)的有5个,辖56个乡镇,网络的度为0.297 3,网络凝聚力比较高;Ⅲ区(强半边缘区)的有3个,辖18个乡镇,网络的度为0.101 4,网络凝聚力高。结合贵州退贫现状与问题提出退出贫困的"四步走"建议。
In order to provide references for the development of poverty alleviation strategies in relevant departments of Guizhou Province,in 2017,687 townships in 67 counties and districts of eight cities and prefectures in Guizhou were selected as research objects,and the adjacency matrix of group relationship formed by nodes and edges was established.Through the layout of Fruchterman Reingold,the social network map of poverty-stricken counties under the jurisdiction of eight cities and prefectures in Guizhou Province was drawn,and also a complex network map of townships under the jurisdiction of 16 deep poverty counties in Guizhou Province.Results:1)The poverty-stricken cities accounted for 88.89%of the total number of provinces and cities;the proportion of deep poverty-stricken counties,townships and villages was 23.88%,37.99% and 40.25%respectively.2)The proportion of deep poverty-stricken counties,townships and villages in Qiandongnan Prefecture was the largest,up to 80.78%,followed by Bijie City,which was 48.09%,and the last was Zunyi City,merely 2.30%.The poverty intensity followed the sequence of Bijie City > Qiandongnan Prefecture > Liupanshui City > Anshun City >Tongren City> Qiannan Prefecture> Qianxinan Prefecture> Zunyi City.3)There were 175 townships belonging to eight counties in AreaⅠ(strong core area),with a network degree of 0.396 6 and a high degree of network cohesion.There were 56 townships belonging to five counties in AreaⅡ(core area),with a network of 0.297 3,and a relatively high network cohesion.There were 18 townships belonging to three counties in AreaⅢ(Strong semi-edge area),with a network degree of 0.101 4,and high degree of network cohesion.Combined with the status quo and problems of poverty alleviation in Guizhou,a fourstep proposal was proposed to withdraw from poverty.
In order to provide references for the development of poverty alleviation strategies in relevant departments of Guizhou Province,in 2017,687 townships in 67 counties and districts of eight cities and prefectures in Guizhou were selected as research objects,and the adjacency matrix of group relationship formed by nodes and edges was established.Through the layout of Fruchterman Reingold,the social network map of poverty-stricken counties under the jurisdiction of eight cities and prefectures in Guizhou Province was drawn,and also a complex network map of townships under the jurisdiction of 16 deep poverty counties in Guizhou Province.Results:1)The poverty-stricken cities accounted for 88.89%of the total number of provinces and cities;the proportion of deep poverty-stricken counties,townships and villages was 23.88%,37.99% and 40.25%respectively.2)The proportion of deep poverty-stricken counties,townships and villages in Qiandongnan Prefecture was the largest,up to 80.78%,followed by Bijie City,which was 48.09%,and the last was Zunyi City,merely 2.30%.The poverty intensity followed the sequence of Bijie City > Qiandongnan Prefecture > Liupanshui City > Anshun City >Tongren City> Qiannan Prefecture> Qianxinan Prefecture> Zunyi City.3)There were 175 townships belonging to eight counties in AreaⅠ(strong core area),with a network degree of 0.396 6 and a high degree of network cohesion.There were 56 townships belonging to five counties in AreaⅡ(core area),with a network of 0.297 3,and a relatively high network cohesion.There were 18 townships belonging to three counties in AreaⅢ(Strong semi-edge area),with a network degree of 0.101 4,and high degree of network cohesion.Combined with the status quo and problems of poverty alleviation in Guizhou,a fourstep proposal was proposed to withdraw from poverty.
引文
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[2]王飞.城镇化、城乡收入差距与传统农区农村剩余劳动力就地转移的空间差异——基于ESDA方法和面板DOLS模型的实证研究[J].中国农业资源与区划,2016(10):26-34.
[3]成作成,龚新蜀.西部地区自我发展能力的测度与实证分析[J].西北人口,2013(2):110-115.
[4]甄茂成,张景秋,杨广林.基于复杂社会网络的商业银行网点布局特征——以北京市中国银行为例[J].地理科学进展,2013,32(12):1733-1741.
[5]褚建勋,黄晟鹏,纪娇娇.基于SNA的国家科技奖合作网络演化研究[J].科学学研究,2016,34(8):1137-1144.
[6] WOUTER DE NOOY,ANDREJ MRVAR,VLADIMIR BATAGELJ.Exploratory Social Network Analysis with Pajek[M].Second Edition.Cambridge:Cambrideg University Press,2011.
[7]林聚任.社会网络分析:理论、方法与应用[M].北京:北京师范大学出版社,2009.
[8]曾帅.有好项目赶紧拿出来!贵州省科技厅面向深度贫困县征集科技项目并给予支持[DB/OL].(2019-01-16)[2019-02-18].http://jgz.app.todayguizhou.com//news/news-news_detail-news_id-11515114933552.html.
[9]孙志刚.在全省深度贫困地区脱贫空间工作推进大会上的讲话[EB/OL].(2017-08-06)[2019-02-18].http://fpb.zunyi.gov.cn/ldjh/201708/t20170812_494684.html.
[10]谌贻琴.政府工作报告——2019年1月27日在贵州省第十三届人民代表大会第二次会议上[DB/OL].(2019-01-27)[2019-02-18].https://www.toutiao.com/a6656634004059456012/.
[11]贵州省发展和改革委员会.贵州省国民经济和社会发展第十三个五年规划纲要[DB/OL].(2017-03-01)[2019-02-18].http://fgw.guizhou.gov.cn/zwgk/zdgk/ghjh/201707/t20170726_2750931.html.
[12]贵州省发展和改革委员会,贵州省扶贫开发办公室.贵州省“十三五”脱贫攻坚专项规划[DB/OL].(2017-03-01)[2019-02-18].http://58.16.65.217/upflie/gydq/gzsfgw/jcms/jcms_files/jcms1/web16/site/attach/0/1703231000232562968.pdf.
[13]余高辉,杨建梅,曾敏刚.QQ群好友关系的复杂网络研究[J].华南理工大学学报(社会科学版),2011,13(14):20-23.
[14]赵宇鸾,李秀彬,张颖.黔桂喀斯特山地与山区类型划分技术与应用[J].地理信息科学,2017,19(7):934-940.
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