Journal of Geographical Sciences ›› 2012, Vol. 22 ›› Issue (6): 1079-1100.doi: 10.1007/s11442-012-0984-4

• Human-Environment Interactions • Previous Articles     Next Articles

Spatial analysis and districting of the livestock and poultry breeding in China

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

  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 the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475001, Henan, China
  • Received:2012-05-15 Revised:2012-06-18 Online:2012-12-15 Published:2013-02-07


The capacity of livestock breeding in China has increased rapidly since 1949, and the total output of meat, poultry and eggs maintains the world’s top first in recent 20 years. Livestock emissions and pollution is closely associated with its population and spatial distribution. This paper aims to investigate the spatial patterns of livestock and poultry breeding in China. Using statistical yearbook and agricultural survey in 2007, the county-level populations of livestock and poultry are estimated as equivalent standardized pig index (ESP), per cultivated land pig index (PCLP) and per capita pig index (PCP). With the help of spatial data analysis (ESDA) 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, PCLP is clustering locally but not significant globally. Furthermore, the thematic map series (TMS) and related gravity centers curve (GCC) are introduced to explore the spatial patterns of livestock and poultry in China. The indicators are classified into 16 levels, and the GCCs for the three indicators from level 1 to 16 are discussed in detail. For districting purpose, each interval between gravity centers of near levels for all the three indicators is calculated, and the districting types of each indicator are obtained by merging adjacent levels. The districting analysis for the three indicators shows that there exists a potential uniform districting scheme for China’s livestock and poultry breeding. As a result, the China’s livestock and poultry breeding would be classified into eight types: extremely sparse region, sparse region, relatively sparse region, normally sparse region, normal region, relatively concentrated region, concentrated region and highly concentrated region. It is also found that there exists a clear demarcation line between the concentrated and the sparse regions. 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, spatial autocorrelation, gravity centers curve, spatial patterns, demarcation, the thematic map series, sparse region, concentrated region