中国县域农村贫困的空间模拟分析

doi:10.12082/dqxxkx.2018.170352

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (3): 321-331.doi: 10.12082/dqxxkx.2018.170352

• 地理空间分析综合应用 • 上一篇下一篇

中国县域农村贫困的空间模拟分析

冯娅娅(), 潘竟虎^*(), 杨亮洁

西北师范大学地理与环境科学学院,兰州 730070

收稿日期:2017-07-29 修回日期:2017-12-13 出版日期:2018-03-20 发布日期:2018-03-20
通讯作者: 潘竟虎 E-mail:fengyaya_1102@163.com;panjh_nwnu@nwnu.edu.cn
作者简介:
作者简介：冯娅娅（1991-）,女,甘肃天水人,硕士生,主要从事生态环境遥感研究。E-mail：fengyaya_1102@163.com
基金资助:
国家自然科学基金项目（41661025）;甘肃省高等学校科研项目（2016A-001）;西北师范大学青年教师科研能力提升计划（NWNU-LKQN-16-7）

Analysis on Spatial Simulation of Rural Poverty at County Level in China

FENG Yaya(), PAN Jinghu^*(), YANG Liangjie

College of Geographic and Environmental Science, Northwest Normal University, Lanzhou 730070, China

Received:2017-07-29 Revised:2017-12-13 Online:2018-03-20 Published:2018-03-20
Contact: PAN Jinghu E-mail:fengyaya_1102@163.com;panjh_nwnu@nwnu.edu.cn
Supported by:
National Natural Science Foundation of China, No.41661025;Project of Educational Commission of Gansu Province of China, No.2016A-001;Research Ability Promotion Project for Young Teachers of Northwest Normal University, No.NWNU-LKQN-16-7.

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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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）

中国县域农村贫困的空间模拟分析

Analysis on Spatial Simulation of Rural Poverty at County Level in China

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