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中国畜禽养殖的空间格局与重心曲线特征分析
引用本文:付强,诸云强,孙九林,孔云峰.中国畜禽养殖的空间格局与重心曲线特征分析[J].地理学报,2012,67(10):1383-1398.
作者姓名:付强  诸云强  孙九林  孔云峰
作者单位:1. 河南大学环境与规划学院,河南开封475001;中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101;省部共建黄河中下游数字地理技术教育部重点实验室(河南大学),河南开封475001
2. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京,100101
3. 河南大学环境与规划学院,河南开封475001;中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101
4. 河南大学环境与规划学院,河南开封475001;省部共建黄河中下游数字地理技术教育部重点实验室(河南大学),河南开封475001
基金项目:环保公益性行业科研专项重点项目(201009017);资源与环境信息系统国家重点实验室自主部署创新研究计划项目(088RA900KA)~~
摘    要:以中国畜禽养殖的空间格局为研究目标,利用2007年分县的统计数据及农业调查数据,构建标准猪、地均猪、人均猪等指标,使用GeoDa、ArcGIS等软件,借助全局和局部空间自相关分析、空间分布图系、重心曲线等方法,对中国县域畜禽养殖空间分布规律、空间格局进行分析.主要结论:①空间聚类趋势分析表明,标准猪和人均猪在全国和局部聚集特征都显著,而地均猪在全国的聚集特征不明显,局部有聚集特征;②虽然标准猪、地均猪和人均猪等不同的刻画方式对应着不同的分布图系、重心曲线和不同的分区方案,但是却存在着潜在的统一分区方案.只是,每一分区中各级别重心的归属依据与相邻级别重心的间距进行调整.由此,中国畜禽养殖可分为畜养极疏区、稀疏区、相对稀疏区、一般稀疏区、一般区、相对密集区、密集区、高密区等8个区;③存在着一条畜禽养殖疏密分界线,该线自内蒙古新巴尔虎左右旗交界处到海南省东方市西海岸.

关 键 词:畜禽养殖  空间自相关  重心曲线  空间格局  分界线  分布图系  中国

Spatial Patterns and Gravity Centers Curve of Livestock and Poultry Breeding in China
FU Qiang , ZHU Yunqiang , SUN Jiulin , KONG Yunfeng.Spatial Patterns and Gravity Centers Curve of Livestock and Poultry Breeding in China[J].Acta Geographica Sinica,2012,67(10):1383-1398.
Authors:FU Qiang  ZHU Yunqiang  SUN Jiulin  KONG Yunfeng
Institution:1, 3 (1. College of Environment and Planning, Henan University, Kaifeng 475001, Henan, China; 2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; 3. Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475001, Henan, China)
Abstract:This paper aims to examine the spatial distribution patterns of livestock and poultry breeding in China. Using statistical data from Chinese yearbooks and agricultural survey in 2007, the county-level populations of livestock and poultry are estimated in terms of equivalent standardized pig index, per cultivated land pig index and per capita pig index. With the help of spatial data analysis tools in Geoda and ArcGIS software, especially the Moran’s I and LISA statistics, the nationwide global and local clustering trends of the three indicators are examined respectively. The Moran’s I and LISA analysis shows that ESP and PCP are significantly clustering both globally and locally. However, the per cultivated land pig index is clustering locally but not significant globally. Furthermore, the thematic map series and related gravity centers curve are introduced to explore the spatial patterns of livestock and poultry in China. Based on 1-16 levels of the thematic map design, the centers curve for each indicator are discussed in detail. For districting purpose, each level of the three indicators is adjusted by the intervals between gravity centers of near levels, and the level is classified into one of district types. The districting analysis for three indicators shows that there exists a potential uniform districting scheme for China’s livestock and poultry breeding (eight districts in China). As a result, the China’s livestock and poultry breeding would be classified into eight districts: extremely sparse area, sparse area, relatively sparse area, normally sparse area, normal area, relatively concentrated area, concentrated area and highly concentrated area. It is also found that there exists a clear demarcation line between the concentrated and the sparse regions of livestock and poultry breeding in China. The line starts from the county boundary between Xin Barag Left Banner and Xin Barag Right Banner, Inner Mongolia Autonomous Region to the west coast of Dongfang County, Hainan Province.
Keywords:livestock and poultry breeding  spatial autocorrelation  gravity centers curve  spatial patterns  demarcation line  the thematic map series  China
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