地理学报 ›› 2012, Vol. 67 ›› Issue (10): 1383-1398.doi: 10.11821/xb201210009

• 生态与旅游地理 • 上一篇    下一篇

中国畜禽养殖的空间格局与重心曲线特征分析

付强1,2,3, 诸云强2, 孙九林1,2, 孔云峰1,3   

  1. 1. 河南大学环境与规划学院, 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. 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
  • 收稿日期:2012-01-20 修回日期:2012-05-22 出版日期:2012-10-20 发布日期:2012-12-19
  • 通讯作者: 诸云强(1977-),男,江西广丰人,博士,副研究员。中国地理学会会员(S110009034M),主要研究方向为地学数据共享,资源环境信息系统。E-mail:zhuyq@lreis.ac.cn E-mail:zhuyq@lreis.ac.cn
  • 作者简介:付强(1980-),男,河南新乡人,博士研究生,讲师。中国地理学会会员(S110009033M),研究方向为GIS、RS及其应用和空间数据分析。E-mail:fuq@lreis.ac.cn;ffuqiang@gmail.com
  • 基金资助:

    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)

Spatial Patterns and Gravity Centers Curve of Livestock and Poultry Breeding in China

FU Qiang1,2,3, ZHU Yunqiang2, SUN Jiulin1,2, KONG Yunfeng1,3   

  1. 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
  • Received:2012-01-20 Revised:2012-05-22 Online:2012-10-20 Published:2012-12-19
  • Supported by:

    Key Program of Special Science Research in Environmental Protection Public Welfare Industry, No.201009017; Research Plan of LREIS, No. 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

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.

Key words: livestock and poultry breeding, spatial autocorrelation, gravity centers curve, spatial patterns, demarcation line, the thematic map series, China