中国贫困村测度与空间分布特征分析

doi:10.11821/dlyj201612009

地理研究 ›› 2016, Vol. 35 ›› Issue (12): 2298-2308.doi: 10.11821/dlyj201612009

中国贫困村测度与空间分布特征分析

陈烨烽^1,^2,³(), 王艳慧^1,^2,³(), 王小林⁴

1. 首都师范大学资源环境与地理信息系统北京市重点实验室,北京 100048
2. 首都师范大学三维信息获取与应用教育部重点实验室,北京 100048
3. 首都师范大学城市环境过程与数字模拟国家重点实验室培育基地,北京 100048
4. 国务院扶贫办信息中心,北京 100028

收稿日期:2016-06-05 修回日期:2016-09-22 出版日期:2016-12-23 发布日期:2016-12-23
作者简介:
作者简介：陈烨烽（1991- ）,男,浙江绍兴人,硕士,主要从事GIS方法与应用研究。E-mail:13648313352@163.com
基金资助:
国家自然科学基金项目（40701147）;北京市自然科学基金项目（8132018）;“十二五”国家科技支撑计划项目（2012BAH33B00,2012BAH33B03,2012BAH33B05）

Measurement and spatial analysis of poverty-strickenvillages in China

Yefeng CHEN^1,^2,³(), Yanhui WANG^1,^2,³(), Xiaolin WANG⁴

1. Beijing Key Laboratory of Resource Environment and Geographic Information System, Capital Normal University, Beijing 100048, China
2. Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
3. State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, CapitalNormal University, Beijing 100048, China
4. Information Center of the State Council LeadingGroup Office of Poverty Alleviation and Development, Beijing 100028, China

Received:2016-06-05 Revised:2016-09-22 Online:2016-12-23 Published:2016-12-23

摘要/Abstract

摘要：

农村贫困问题是中国全面建成小康社会的主要障碍之一。面向当前国家瞄准贫困村和贫困人口的精准脱贫战略需求,基于“十二五”期间全国贫困村“整村推进”项目村数据,构建村级多维贫困综合测度模型,并利用加权核密度模型、空间自相关方法等,从不同尺度、不同视角系统测度并分析了研究区贫困村的相对贫困特征。结果表明：① 贫困程度上,“十二五”期间贫困村呈中间大,两头小的“橄榄型”结构,省级、县级尺度下贫困深度与当地经济、区位、政策、自然环境等因素相关;② 空间分布上,全国贫困村分布呈现出东部和西北部稀疏、中部和西南部密集的“夹层”形空间异质性格局,同时存在多个不同量级、呈“星点”式分布的贫困核心;③ 中国贫困村的多维贫困存在较强的全局空间依赖性,局部呈现为高—高区与低—低区集中式分布、高—低区与低—高区离散夹杂式分布,且整体表现为西高东低的“阶梯状”格局。

关键词: 贫困村, GIS, 多维贫困指数, 空间自相关, 加权核密度估计

Abstract:

Rural poverty is one of the most important obstacles to achieve the goal that builds a moderately prosperous society in an all-around way in China. To address the strategic requirement of targeted poverty reduction for the implementation of "Entire-village Advancement" to rid all Chinese of poverty by 2020, we integrate geographical contributing factors and socioeconomic ones to design a set of village-level evaluation indicator system, consisting of 6 dimensions and 20 indicators. And then based on the above indicator system, we build a comprehensive village-level poverty measurement model, using village-level archived "Entire-village Advancement" dataset that was collected during the "12th Five-year Plan" period (from 2011 to 2015) to assess multidimensional poverty index and relative poverty characteristics of each village. At last, we adopt spatial autocorrelation and weighted kernel density estimation to examine spatial distribution of the poverty-stricken villages at a multiscale of area-province-county. The results show that: (1) During the "12th Five-year Plan" period, the distribution of poverty levels for different villages statistically represents a geometrical olive-shape pattern that is larger in the middle and smaller at both ends; Meanwhile, the poverty depths of poverty-stricken villages at both provincial and county scales are closely related to their local economies, regional locations, local policies and natural environment, etc. (2) As far as the distribution of poverty-stricken villages is concerned, there exists a spatially heterogeneous distribution, presenting a typical sandwich-shaped structure that is sparse in both eastern and northwestern China while dense in both central and southwestern China, where different levels of dotted poverty kernels are scattered, and 3 first-level kernels, 6 secondary kernels and many tertiary kernels of poverty hotspots exist obviously. However, a grey poverty area called "transition zone", located between High-High and Low-Low poverty-stricken areas, is found in central China where the distribution of poverty-stricken villages does not show a significant spatial autocorrelation. (3) The multidimensional characteristics of poverty-stricken villages represent a globally strong spatial dependence with a Moran's I coefficient of 0. 55; however, both High-High areas and Low-Low areas are distributed intensively while both High-Low areas and Low-High areas are distributed discretely, overall showing a stepped distribution pattern that is "west-high vs. east-low". This study could help precisely target the poverty situation of the poverty-stricken villages in rural China, and also may provide a good understanding of the status and regional differences among villages at a village scale. On the other hand, it could serve as a scientific reference regarding decisions-making in both promoting intra-rural anti-poverty harmonious development and in constructing the new countryside of China.

Key words: poverty-stricken villages, GIS, multidimensional poverty index, spatial autocorrelation, Weighted Kernel Density Estimation

陈烨烽, 王艳慧, 王小林. 中国贫困村测度与空间分布特征分析[J]. 地理研究, 2016, 35(12): 2298-2308.

Yefeng CHEN, Yanhui WANG, Xiaolin WANG. Measurement and spatial analysis of poverty-strickenvillages in China[J]. GEOGRAPHICAL RESEARCH, 2016, 35(12): 2298-2308.

图/表 9

图1

表1

村级多维贫困测度指标体系"

维度	指标编号	指标	指标释义	模型一	模型二
维度	指标编号	指标	指标释义	复相关系数R1	复相关系数R2
地理环境(X₁)	X₁₁	到最近乡镇集市的距离	行政村到最近乡镇集市的距离（km）	0.282	0.281
	X₁₂	地形类型	行政村地形类型（平原、丘陵、高原）	0.391	0.390
	X₁₃	遭受自然灾害频次	行政村当年所遭受的自然灾害次数/次	0.185	0.185
行政村特征(X₂)	X₂₁	贫困村类型	行政村类型（革命老区村、少数民族聚居村、边境地区村、其他）	0.213	0.213
行政村特征(X₂)	X₂₂	人口密度	行政村人口密度（人/km²）	0.301	0.299
生产和生活条件(X₃)	X₃₁	人均经济用地面积	行政村人均经济用地面积大小（亩/人）	0.184	0.184
	X₃₂	通路率	行政村内通机动车行驶道路自然村占总自然村数的比例（%）	0.428	0.426
	X₃₃	通电率	行政村内通电户数占总户数的比例（%）	0.584	0.459
	X₃₄	通电话率	行政村内通电话户数占总户数的比例（%）	0.622	0.477
		通电视率	行政村内桶电视户数占总户数的比例（%）	0.716	-
	X₃₅	安全饮用水比例	行政村内安全饮用水户数占总户数比例（%）	0.481	0.470
	X₃₆	卫生厕所比例	行政村内卫生厕所户数占总户数的比例（%）	0.423	0.418
	X₃₇	住危房比例	行政村内住危房户数占总户数的比例（%）	0.319	0.316
劳动力状况(X₄)	X₄₁	劳动力比例	行政村劳动力总数占该村总人口的比例（%）	0.254	0.250
	X₄₂	外出劳动力比例	行政村外出劳动力总数占该村劳动力总数的比例（%）	0.318	0.318
	X₄₃	劳动力文化素质	行政村初中及以上文化水平劳动力数占该村劳动力总数的比例（%）	0.348	0.344
		雨露计划参与率	行政村劳动力参与雨露计划人数占总劳动力人数的比例（%）	变量为常数	-
医疗卫生和社会保(X₅)	X₅₁	诊所数量	行政村诊所数量（个）	0.324	0.324
	X₅₂	每千人医生数	行政村每千人医生数量（个）	0.297	0.297
	X₅₃	参加新型农村合作医疗比例	参加新型农村合作医疗人数占整个行政村人数的比例（%）	0.295	0.295
	X₅₄	参加新型农村社会养老保险比例	参加新型农村社会养老保险人数占整个行政村人数的比例（%）	0.338	0.336
经济发展(X₆)	X₆₁	人均纯收入	行政村每年人均纯收入情况（元）	0.324	0.320

表1

表2

村级多维贫困测算指标标准化与权重分布"

维度	维度权重	指标编号	贫困程度					主观权重	客观权重	组合权重
维度	维度权重	指标编号	1	2	3	4	5	主观权重	客观权重	组合权重
地理环境(X₁)	0.260	X₁₁	<3	[3,7)	[7,11)	[11,18)	$≥$ 18	0.189	0.001	0.146
		X₁₂	平原	丘陵	山区	-	-	0.452	0.778	0.527
		X₁₃	0	-	1	2	>2	0.359	0.221	0.327
行政村特征(X₂)	0.075	X₂₁	否	-	一类	两类	三类	0.554	0.840	0.760
行政村特征(X₂)	0.075	X₂₂	[0,120)	[120,305)	[305,703)	[703,1001)	$≥$ 1001	0.446	0.160	0.240
生产和生活条件(X₃)	0.250	X₃₁	>7	(3,7]	(2,3]	(1,2]	[0,1]	0.263	0.000	0.069
		X₃₂	(0.95,1]	(0.9,0.95]	(0.8,0.9]	[0.5,0.8]	[0,0.5)	0.320	0.371	0.413
		X₃₃	(0.98,1]	(0.94,0.98]	(0.9,0.94]	[0.8,0.9]	[0,0.8)	0.214	0.021	0.074
		X₃₄	(0.95,1]	(0.9,0.95]	(0.8,0.9]	[0.5,0.8]	[0,0.5)	0.036	0.078	0.078
		X₃₅	(0.8,1]	(0.6,0.8]	(0.4,0.6]	[0.3,0.4]	[0,0.3)	0.110	1.042	0.338
		X₃₆	(0.6,1]	(0.4,0.6]	(0.3,0.4]	[0.2,0.3]	[0,0.2)	0.024	0.199	0.109
		X₃₇	[0,0.05)	[0.05,0.15)	[0.15,0.25)	[0.25,0.35]	(0.35,1]	0.114	0.137	0.151
劳动力状况(X₄)	0.199	X₄₁	(0.7,1]	(0.6,0.7]	(0.5,0.6]	[0.35,0.5]	[0,0.35)	0.504	0.183	0.368
		X₄₂	(0.5,1]	(0.4,0.5]	(0.3,0.4]	[0.2,0.3]	[0,0.2)	0.223	0.388	0.293
		X₄₃	(0.85,1]	(0.65,0.85]	(0.45,0.65]	[0.25,0.45]	[0,0.25)	0.272	0.429	0.339
医疗卫生和社会保障(X₅)	0.079	X₅₁	>3	3	2	1	0	0.170	0.024	0.056
		X₅₂	>5	(3,5]	(1,3]	(0,1]	0	0.346	0.005	0.080
		X₅₃	(95,100]	(90,95]	(80,90]	(0,80]	0	0.213	0.168	0.178
		X₅₄	(80,100]	(50,80]	(30,50]	(0,30]	0	0.271	0.803	0.686
经济发展(X₆)	0.136	X₆₁	>5543	(3961,5543]	(3169,3960]	[2300,3168]	[0,2300)	1	1	1

表2

图2

表3

片区与省级单位多维贫困情况统计表"

片区	乌蒙山区	滇西边境片区	四省藏区	滇桂黔石漠化片区	吕梁山片区	六盘山片区	武陵山区	秦巴山区	南疆三地州片区	罗霄山区	大兴安岭南麓山区	燕山太行山区	大别山区	西藏地区
$VPI 得分 ˉ$	61.12	60.39	60.00	59.14	58.57	56.48	55.79	55.18	55.12	54.51	51.96	51.75	48.90	无
$VPI 等级 ˉ$	3.62	3.54	3.53	3.44	3.41	3.22	3.15	3.10	3.09	3.04	2.84	2.80	2.58	无
省级单位	甘肃	云南	贵州	广西	湖南	青海	四川	新疆	江西	山西	宁夏	内蒙古	湖北	河北
$VPI 得分 ˉ$	58.94	58.57	57.46	57.14	55.88	55.58	55	54.59	53.5	53.36	52.84	52.81	52.52	52.26
$VPI 等级 ˉ$	3.43	3.39	3.3	3.27	3.13	3.14	3.08	3.04	2.95	2.95	2.89	2.89	2.93	2.85
省级单位	海南	陕西	吉林	重庆	河南	安徽	辽宁	黑龙江	山东	福建	江苏	浙江	广东	全国
$VPI 得分 ˉ$	50.53	50.4	49.85	49.75	49.29	49.05	46.94	46.7	46.29	45.62	43.82	43.37	39.53	55.08
$VPI 等级 ˉ$	2.69	2.66	2.67	2.62	2.47	2.59	2.35	2.4	2.33	2.29	2.13	2.12	1.65	3.09

表3

图3

图4

图5

图6

参考文献 24

[1]	Park A, Wang S, Wu G.Regional poverty targeting in China. Journal of Public Economics, 2002, 86(1): 123-153. doi: 10.1016/S0047-2727(01)00108-6
[2]	高帅, 毕洁颖. 农村口动态多维贫困: 状态持续与转变. 中国人口·资源与环境, 2016, 26(2): 76-83. doi: 10.3969/j.issn.1002-2104.2016.02.010
	[Cao Shuai, Bi Jieying. Dynamic multidimensional poverty in rural China: Persistence and transition. China Population, Resources and Environment. 2016, 26(2): 76-83.] doi: 10.3969/j.issn.1002-2104.2016.02.010
[3]	刘彦随, 周扬, 刘继来. 中国农村贫困化地域分异特征及其精准扶贫策略. 中国科学院院刊, 2016, 31(3): 269-278.
	[Liu Yansui, Zhou Yang, Liu Jilai.Regional differentiation characteristics of rural poverty and targeted poverty alleviation strategy in China. Bulletin of Chinese Academy of Sciences, 2016, 31(3): 269-278.]
[4]	国家统计局住户调查办公室. 中国农村2011贫困监测报告. 北京: 中国统计出版社, 2012.
	[Household Survey Department of National Bureau of Statistics. Poverty Monitoring Report of Rural China 2011. Beijing: China Statistics Press, 2012.]
[5]	Tsui K.Multidimensional poverty indices. Social Choice and Welfare, 2002, 19(1): 69-93. doi: 10.1007/s355-002-8326-3
[6]	王小林, Alkire S.中国多维贫困测量: 估计和政策含义. 中国农村经济, 2009, (12): 4-10.
	[Wang Xiaolin, Alkire S.Measurement of multidimensional poverty in China: Estimation and policy implication. Chinese Rural Economy, 2009, (12): 4-10.]
[7]	UNDP. Human Development Report 2013. New York: United Nations Development Program, 2013. doi: 10.3917/afco.246.0164
[8]	Alkire S, Foster J.Counting and multidimensional poverty measurement. Journal of Public Economics, 2011, 95(7-8): 476-487. doi: 10.1016/j.jpubeco.2010.11.006
[9]	刘艳华, 徐勇. 中国农村多维贫困地理识别及类型划分. 地理学报, 2015, 70(6): 993-1007. doi: 10.11821/dlxb201506012
	[Liu Yanhua, Xu Yong.Geographical identification and classification of multi-dimensional poverty in rural China. Acta Geographica Sinica, 2015, 70(6): 993-1007.] doi: 10.11821/dlxb201506012
[10]	赵莹, 刘小鹏, 郭永杰. 集中连片特困地区多维贫困的度量及动态演变: 以宁夏西吉县为例. 宁夏大学学报: 自然科学版, 2015, 36(1): 73-78.
	[Zhao Ying, Liu Xiaopeng, Guo Yongjie.Measurement on the multidimensional poverty in concentrate contiguous destitute areas and its dynamic evolution trend: Taking Xiji county of Ningxia for example. Journal of Ningxia University: Natural Science Edition, 2015, 36(1): 73-78.]
[11]	Deaton B, Niman E.An empirical examination of the relationship between mining employment and poverty in the appalachian region. Applied Economics, 2011, 44(3): 303-312.
[12]	罗庆, 李小建. 国外农村贫困地理研究进展. 经济地理, 2014, 34(6): 1-8.
	[Luo Qing, Li Xiaojian.The research progress of foreign rural poverty geography. Economic Geography, 2014, 34(6): 1-8.]
[13]	Curtis K J, Voss P R, Long D D.Spatial variation in poverty-generating processes: Child poverty in the United States. Social Science Research, 2012, 41(1): 146-159. doi: 10.1016/j.ssresearch.2011.07.007 pmid: 23017703
[14]	Sowunmi F A, Akinyosoye V O, Okoruwa V O, et al.The landscape of poverty in Nigeria: A spatial analysis using senatorial districts-level data. American Journal of Economics, 2012, 2(5): 61-74. doi: 10.5923/j.economics.20120205.01
[15]	Bird K, Shepherd A.Livelihoods and chronic poverty in semi-arid Zimbabwe. World Development, 2003, 31(3): 591-610. doi: 10.1016/S0305-750X(02)00220-6
[16]	Epprecht M, Muller D, Minot N.How Remote are Viernam's ethnic minorities? An analysis of spatial patterns of poverty and inequality. The Annals of Regional Science, 2011, 46(2): 349-368. doi: 10.1007/s00168-009-0330-7
[17]	李远远, 云俊. 多属性综合评价指标体系理论综述. 武汉理工大学学报: 信息与管理工程版, 2009, 31(2): 305-309. doi: 10.3963/j.issn.1007-144X.2009.02.035
	[Li Yuanyuan, Yun Jun.Synthetic research on the theory of multi-attribute comprehensive evaluation index system. Journal of Wuhan University of Technology: Information & Management Engineering, 2009, 31(2): 305-309.] doi: 10.3963/j.issn.1007-144X.2009.02.035
[18]	刘小鹏, 苏胜亮, 王亚娟, 等. 集中连片特殊困难地区村域空间贫困测度指标体系研究. 地理科学, 2014, 34(4): 447-453.
	[Liu Xiaopeng, Su Shengliang, Wang Yajuan, et al.The index system of spatial poverty of village level to monitor in concentrated contiguous areas with particular difficulties. Scientia Geographica Sinica, 2014, 34(4): 447-453.]
[19]	裴银宝, 刘小鹏, 李永红, 等. 六盘山特困片区村域空间贫困调查与分析: 以宁夏西吉县为例. 农业现代化研究, 2015, 36(5): 748-754. doi: 10.13872/j.1000-0275.2015.0130
	[Pei Yinbao, Liu Xiaopeng, Li Yonghong, et al.Investigation and analysis of villages in extreme spatial poverty in Liupan Mountain contiguous areas: A case study of Xiji, Ningxia province. Research of Agriculture Moderniztion, 2015, 36(5): 748-754.] doi: 10.13872/j.1000-0275.2015.0130
[20]	田伟. 连片特困区乡村多维贫困及综合治理研究: 以湘西州永顺县高坪乡为例. 湖南: 吉首大学硕士学位论文, 2014. doi: 10.3969/j.issn.1673-291X.2014.12.014
	[Tian Wei.Study on comprehensive treatment and contiguous rural poor area of multidimensional poverty. Hunan: Master Dissertation of Jishou University, 2014.] doi: 10.3969/j.issn.1673-291X.2014.12.014
[21]	吕香亭. 综合评价指标筛选方法综述. 合作经济与科技, 2009, (3): 54. doi: 10.3969/j.issn.1672-190X.2009.06.030
	[Lv Xiangting.Survey of the screening methods of comprehensive evaluation index. Co-operative Economy & Science, 2009, (3): 54.] doi: 10.3969/j.issn.1672-190X.2009.06.030
[22]	姜昱汐, 迟国泰, 严丽俊. 基于最大熵原理的线性组合赋权方法. 运筹与管理, 2011, 20(1): 53-59. doi: 10.3969/j.issn.1007-3221.2011.01.010
	[Jiang Yuxi, Chi Guotai, Yan Lijun.The linear combination weights method based on maximum entropy principle. Operations Research and Management Science, 2011, 20(1): 53-59.] doi: 10.3969/j.issn.1007-3221.2011.01.010
[23]	Robert H.Spatial Data Analysis: Theory and Practice. Cambridge: Cambridge University Press, 2003. doi: 10.1017/CBO9780511754944
[24]	Goodchild F, Anselin L, Appelbaum P, et al.Toward spatially integrated social science. International Regional Socience Review, 2000, 23(2): 139-159. doi: 10.1177/016001760002300201

中国贫困村测度与空间分布特征分析

Measurement and spatial analysis of poverty-strickenvillages in China

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