中国县域农村贫困的空间模拟分析

doi:10.12082/dqxxkx.2018.170352

摘要/Abstract

摘要：

以中国县级行政区划为研究单元,从自然和社会经济因素中选取贫困的影响因子,建立评价指标体系,利用GIS空间分析和 BP人工神经网络,模拟各县域的自然致贫指数和社会经济消贫指数,并在分析贫困内在形成原因的基础上,明晰了空间贫困的分布特征。结果显示：自然因素是现阶段中国县域主要的致贫原因,全国县域自然致贫指数的分布呈现出明显随纬度和经度地带性分布的规律,自北而南、自西而东逐次呈带状排列分布。社会经济因素对贫困起到一定的缓解作用,全国县域社会经济消贫指数的空间分布较为破碎,各省区内部县域社会经济消贫指数的变异系数均大大高于自然致贫指数的变异系数。全国贫困压力指数以“黑河-百色”一线为界,东中西差异显著,呈现“大分散、小聚集”的空间分布格局。本文识别的贫困县与国家确定的重点扶贫县在空间上具有较高的重合性。

关键词: 农村贫困, 空间模拟, BP神经网络, GIS, 中国

Abstract:

Poverty is a common problem during the development of human society, and is also one of grand challenge to achieve sustainable development for the developing countries. Rural poverty is a major problem in the process of building a well-off society in a all-around way in China. Thus, the identification and the measurement of poverty are premise and basis of the policies of poverty eradication and poverty alleviation under implementing the new regional development. From a geographical view of county scale, we select the major influencing factors of poverty from common natural and social factors to build an evaluation index system based on spatial poverty and its related theory. First, we use Pearson correlation analysis to differentiate the poverty leading factors and poverty elimination factors. Then, we use GIS and BP Neural Network to simulate Natural Impoverishing Index (NII) and Social Economic Poverty Alleviation Index (SEPAI). We compute Poverty Pressure Index (PPI) combining natural impoverishing index and social economic poverty alleviation index, and explore the spatial distribution characteristics of poverty, revealing spatial pattern of poverty and its differentiation mechanism. We put scientific and reliable theoretical foundation to poverty alleviation and development of rural regions in China. The results show that the natural factors, such as NPP, slope, elevation, terrain, are the major impoverishing index for the study area. The social economic factors, such as the public revenue, household saving, fixed assets, are the main factors to alleviate and eliminate poverty. From the view of spatial distribution, the higher NII were mainly distributed in the west and north of China, especially in Tibet plateau and the northwest of Xinjiang, but the lower NII counties were located in the east and south with the characteristics of zonal distribution of latitude and longitude. SEPAI is positively correlated with the local economic development level in the spatial distribution. The coefficient of variation of SEPAI in provinces are significantly higher than that of NII. The poverty distribution pattern of PPI show a tendency of "large dispersion, small aggregation" by dividing Heihe: Baise Line. The characteristics of PPI represents a globally strong spatial dependence with a Moran's I coefficient of 0.33. The poverty-stricken counties identified in this paper have a high coincidence with the national key poverty alleviation counties.

Key words: rural poverty, spatial simulation, BP Neural Network, GIS, China

冯娅娅, 潘竟虎, 杨亮洁. 中国县域农村贫困的空间模拟分析[J]. 地球信息科学学报, 2018, 20(3): 321-331.DOI:10.12082/dqxxkx.2018.170352

FENG Yaya,PAN Jinghu,YANG Liangjie. Analysis on Spatial Simulation of Rural Poverty at County Level in China[J]. Journal of Geo-information Science, 2018, 20(3): 321-331.DOI:10.12082/dqxxkx.2018.170352

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参考文献 26

[1]	Labar K, Bresson F.A multidimensional analysis of poverty in China from 1991 to 2006[J]. China Economic Review, 2011,22(4):646-668. doi: 10.1016/j.chieco.2011.08.005
[2]	杨振,江琪,刘会敏,等.中国农村居民多维贫困测度与空间格局[J].经济地理,2015,35(12):148-153. doi: 10.15957/j.cnki.jjdl.2015.12.021
	[Yang Z, Jiang Q, Liu H M, et al.Multi-dimensional poverty measure and spatial pattern of Chinese rural residents[J]. Economic Geography, 2015,35(12):148-153.] doi: 10.15957/j.cnki.jjdl.2015.12.021
[3]	罗庆,李小建.国外农村贫困地理研究进展[J].经济地理,2014,34(6):1-8.
	[Luo Q, Li X J.The progress of geographical research on foreign rural poverty[J]. Economic Geography, 2014,34(6):1-8.]
[4]	蔡亚庆,王晓兵,杨军,等.我国农户贫困可持续性及决定因素分析——基于相对和绝对贫困线的再审视[J].农业现代化研究,2016,37(1):9-16. doi: 10.13872/j.1000-0275.2015.0170
	[Cai Y Q, Wang X B, Yang J, et al.The persistence of poverty and its determinants: Evidence under the absolute and relative poverty line[J]. Research of Agricultural Modernization, 2016,37(1):9-16.] doi: 10.13872/j.1000-0275.2015.0170
[5]	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[J].中国科学院院刊,2016,31(3):269-287.
	[Liu Y S, Zhou Y, Liu J L.Regional differentiation characteristics of rural poverty and targeted poverty alleviation strategy in China[J]. Bulletin of Chinese Academy of Sciences, 2016,31(3):269-287.]
[6]	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[J].地理科学,2014,34(4):447-453.
	[Liu X P, Su S L, Wang Y J, et al.Study on the index system of spatial poverty at village level monitoring in concentrated contiguous areas with particular difficulties[J]. Scientia Geographica Sinica, 2014,34(4):447-453.]
[7]	刘艳华,徐勇.中国农村多维贫困地理识别及类型划分[J].地理学报,2015,70(6):993-1007. doi: 10.11821/dlxb201506012
	[Liu Y H, Xu Y.Geographical identification and classification of multi-dimensional poverty in rural China[J]. Acta Geographica Sinica, 2015,70(6):993-1007.] doi: 10.11821/dlxb201506012
[8]	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.
	[Chen Y F, Wang Y H, Wang X L.Measurement and spatial analysis of poverty-stricken villages in China[J]. Geographical Research, 2016,35(12):2298-2308.]
[9]	Ravallion M, Wodon Q.Poor areas, or only poor people?[J]. Journal of Regional science, 1999,39(4):689-711. doi: 10.1111/0022-4146.00156
[10]	王永明,王美霞,吴殿廷,等.贵州省乡村贫困空间格局与形成机制分析[J].地理科学,2017,37(2):217-227.
	[Wang Y M, Wang M X, Wu D T, et al.Spatial patterns and determinants of rural poverty: A case of Guizhou Province, China[J]. Scientia Geographica Sinica, 2017,37(2):217-227.]
[11]	潘竟虎,胡艳兴.基于夜间灯光数据的中国多维贫困空间识别[J].经济地理,2016,36(11):124-131.
	[Pan J H, Hu Y X.Spatial identification of multidimensional poverty in China based on nighttime light remote sensing data[J]. Economic Geography, 2016,36(11):124-131.]
[12]	王艳慧,钱乐毅,陈烨烽,等.生态贫困视角下的贫困县多维贫困综合度量[J].应用生态学报,2017,28(8):2677-2686. doi: 10.13287/j.1001-9332.201708.003
	[Wang Y H, Qian Y Y, Chen Y F, et al.Multidimensional and comprehensive poverty measurement of poverty-stricken counties from the perspective of ecological poverty[J]. Chinese Journal of Applied Ecology, 2017,28(8):2677-2686.] doi: 10.13287/j.1001-9332.201708.003
[13]	刘小鹏,李永红,王亚娟,等.县域空间贫困的地理识别研究——以宁夏泾源县为例[J].地理学报,2017,72(3):545-557.
	[Liu X P, Li Y H, Wang Y J, et al.Geographical identification of spatial poverty at county scale: A case study of Ningxia Jingyan country[J]. Acta Geographica Sinica, 2017,72(3):545-557.]
[14]	Liu Y H, Xu Y.A Geographic identification of multidimensional poverty in rural China under the framework of sustainable livelihoods analysis[J]. Applied Geography, 2016,73:62-76. doi: 10.1016/j.apgeog.2016.06.004
[15]	李双成,许月卿,傅小锋.基于GIS和ANN的中国区域贫困化空间模拟分析[J].资源科学,2005,27(4):76-81. doi: 10.3321/j.issn:1007-7588.2005.04.014
	[Li S C, Xu Y Q, Fu X F.Spatial simulation using GIS and ANN for regional pauperization in China[J]. Resources Science, 2005,27(4):76-81.] doi: 10.3321/j.issn:1007-7588.2005.04.014
[16]	曾永明,张果.基于GIS和BP神经网络的区域农村贫困空间模拟分析——一种区域贫困程度测度新方法[J].地理与地理信息科学,2011,27(2):70-75.
	[Zeng Y M, Zhang G.Spatial simulation in regional rural poverty based on GIS and BP neural Network: A new appraisement method on regional rural poverty[J]. Geography and Geo-Information Science, 2011,27(2):70-75.]
[17]	刘一明,胡卓玮,赵文吉,等.基于BP神经网络的区域贫困空间特征研究——以武陵山连片特困区为例[J].地球信息科学学报,2015,17(1):69-77. doi: 10.3724/SP.J.1047.2015.00069
	[Liu Y M, Hu Z W, Zhao W J, et al.Research on spatial characteristics of regional poverty based on BP neural network: A case study of Wuling Mountain Area[J]. Journal of Geo-information Science, 2015,17(1):69-77.] doi: 10.3724/SP.J.1047.2015.00069
[18]	Sørensen R, Zinko U, Seibert J.On the calculation of the topographic wetness index: Evaluation of different methods based on field observations[J]. Hydrology and Earth System Sciences, 2006,10:101-112. doi: 10.5194/hess-10-101-2006
[19]	Ceccato P, Gobron N, Flasse S, et al.Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach[J]. Remote Sensing of Environment, 2002,82(2-3):188-197. doi: 10.1016/S0034-4257(02)00037-8
[20]	Yu Bailang, Shi Kaifang, Hu Yingjie, et al.Poverty evaluation using NPP-VIIRS Nighttime Light Composite Data at the county level in China[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015,8(3):1217-1229. doi: 10.1109/JSTARS.2015.2399416
[21]	国家统计局农村社会经济调查司.中国县域统计年鉴[M].北京:中国统计出版社, 2015.
	[Department of Rural Survey of National Bureau of Statistics. China Statistical Yearbook(County-Level)[M]. Beijing: China Statistics Press, 2015.]
[22]	李云良,张奇,李淼,等.基于BP神经网络的鄱阳湖水位模拟[J].长江流域资源与环境,2015,24(2):233-240. doi: 10.11870/cjlyzyyhj201502008
	[Li Y L, Zhang Q, Li M, et al.Using BP neutral networks for water level simulation in Poyang Lake[J]. Resources and Environment in the Yangtze Basin, 2015,24(2):233-240.] doi: 10.11870/cjlyzyyhj201502008
[23]	Huang J K, Zhang Q, Rozelle S, et al.Determinants of rural poverty reduction and pro-poor economic growth in China[M]. India: Poorest & Hungry Assessments Analyses and Actions, 2009.
[24]	康晓光. 中国贫困与反贫困理论[M].南宁:广西人民出版社,1995.
	[Kang X G.The poverty and anti-poverty theory of China[M]. Nanning: Guangxi People's Press, 1995.]
[25]	周侃,王传胜.中国贫困地区时空格局与差别化脱贫政策研究[J].中国科学院院刊,2016,31(1):101-111.
	[Zhou K, Wang C S.Study on spatial-temporal patterns of poverty-stricken areas and their differential policies of poverty alleviation in China[J]. Bulletin of Chinese Academy of Sciences, 2016,31(1):101-111.]
[26]	刘彦随,李进涛.中国县域农村贫困化分异机制的地理探测与优化决策[J].地理学报,2017,72(1):161-173.
	[Liu Y S, Li J T.Geographical detection and optimization decisions of the differenstiation mechanisms of rural poverty in China[J]. Acta Geographica Sinica, 2017,72(1): 161-173.]

一级指标	二级指标	单位	数据类型
自然环境	净初级生产力（NPP）X1	gc/m²	遥感数据
	集水指数X2	-	观测数据
	地形破碎度X3	m	空间栅格
	平均高程X4	m	空间栅格
	平均坡度X5	-	空间栅格
	植被湿度指数（GVMI）X6	-	遥感数据
社会经济	人均公共财政收入X7	元/人	统计数据
	人均居民储蓄余额X8	元/人	统计数据
	农民人均纯收入X9	元/人	统计数据
	文盲率X10	%	统计数据
	每万人卫生机构床位数X11	床	统计数据
	平均夜间灯光X12	-	遥感数据

	X1	X2	X3	X4	X5	X6
相关性系数	-0.186	0.050	-0.155	-0.176	-0.279	-0.145
显著性水平	0.004	0.243	0.015	0.006	0.000	0.021
	X7	X8	X9	X10	X11	X12
相关性系数	0.890	0.727	0.764	0.020	0.501	0.421
显著性水平	0.000	0.000	0.000	0.388	0.000	0.000

NPP	集水指数	地形破碎度	高程	平均坡度	GVMI	等级
1	1	0.0656	0.0520	0.0792	1	1（低）
0.5530	0.5609	0.1564	0.1500	0.1860	0.7457	2（较低）
0.4005	0.3681	0.2887	0.2992	0.3194	0.5780	3（中）
0.2785	0.2441	0.5460	0.5632	0.5308	0.4508	4（较高）
0.1579	0.1423	1	1	1	0.2937	5（高）

贫困指数	六盘山区	秦巴山区	武陵山区	乌蒙山区	滇桂黔石漠化区	滇西边境山区	大兴安岭南麓山区	燕山太行山区	吕梁山区	大别山区	罗霄山区	四省藏区	南疆三地州	西藏
NII	2.82	2.72	2.29	2.33	2.10	1.99	2.99	2.91	2.84	2.39	2.29	3.61	3.66	4.34
SEPAI	0.82	1.06	0.94	0.60	0.67	0.86	0.79	1.02	0.94	0.68	0.87	0.92	0.99	-
PPI	2.64	2.68	2.26	2.95	2.26	2.82	2.09	2.44	2.33	1.95	2.01	3.15	3.00	-

人均公共财政收入	人均居民储蓄额	农民人均纯收入	文盲率	每万人卫生机构床位数	平均夜间灯光强度	等级
0.0364	0.0531	0.0453	0.1834	0.0845	0.0254	1（低）
0.0951	0.1118	0.0964	0.2473	0.1443	0.0873	2（较低）
0.1925	0.2094	0.2021	0.3262	0.2358	0.1986	3（中）
0.5077	0.3902	0.4310	0.5363	0.4052	0.4509	4（较高）
1	1	1	1	1	1	5（高）