Acta Geographica Sinica ›› 2014, Vol. 69 ›› Issue (12): 1753-1766.doi: 10.11821/dlxb201412002

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The spatial-temporal patterns of per capita share of grain at the county level in China: A comparation between registered population and resident population

LI Yating1,2, PAN Shaoqi2, MIAO Changhong1,2   

  1. 1. Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China;
    2. College of Environment and Planning, Henan University, Kaifeng 475004, China
  • Received:2013-08-30 Revised:2014-06-12 Online:2014-12-25 Published:2015-01-24
  • Contact: 苗长虹(1965-), 男, 河南鄢陵县人, 教授, 博士, 博士生导师, 中国地理学会会员(S110004313M), 研究方向为经济地理与区域发展。E-mail:
  • Supported by:
    National Natural Science Foundation of China, No.41401133, No.41430637, No.41329001; Humanity and Social Research Project of Education Ministry to Young Scholars, No.14YJC790092

Abstract: Per capita share of grain is a major indicator in studying the supply-and-demand equilibrium of grain. With the rapidly growing floating population in China, it is important for the decision-makers to accurately estimate the distribution of per capita share of grain and its dynamics. The variation of per capita share of grain at the finer spatial scale is ignored by most studies at national or provincial levels. Population data used in the calculation of the per capita share of grain are not consistent, but there is large difference between the size of registered population and resident population in some areas. This inconsistency will greatly influence the interpretation of the spatial pattern and trend of per capita share of grain as well as the food transportation policy. Based on the county-level data of registered population and resident population in China's fifth and sixth censuses, this paper conducts a comparative analysis of spatial-temporal patterns and trends of county-level per capita grain, when either registered or resident population is used. Several spatial data analysis methods are used, such as Global Moran's I, LISA, gravity centers curve and the thematic map series. The results show that: Firstly, per capita share of grain in China demonstrates obvious east-west and south-north divides. Per capita share of grain is significantly correlated over space no matter which population data is adopted. High-high clusters are concentrated in Northeast China, northwestern Xinjiang and parts of Central China. Low-low clusters are mainly distributed in coastal provinces in the middle and lower reaches of the Yangtze River and South China. Such a spatial agglomeration is growing over time. The spatial clusters of high values tend to form in Northeast China, while the cold spots have emerged in Southeast China. Secondly, spatial distribution of per capita share of grain differs a lot when two kinds of population data are used. Such differences grow over time, with most of them concentrated to the south of 800 mm rainfall line as well as in South China and Southwest China. This is in line with the fact that the active regions of floating population are mainly located in the eastern monsoon region to the south of 800 mm rainfall line. Although the spatial pattern of serious food shortage regions is consistent under either population standard, migration has intensified the degree of food shortage in these regions. At the same time, calculation result based on resident population shows that, more commodity grain can be exported by counties in Northeast China, Huang-Huai-Hai Region and Sichuan Province. Thirdly, the gravity center change curve of per-capita share of grain presents an "L" shape and has a greater increase and decrease to the north and southeast respectively. Integrating the thematic map series and gravity centers curve of per capita share of grain, we identify seven kinds of regions: large increase, moderate increase, small increase, zero growth, small decrease, moderate decrease and large decrease. Such patterns differ between the two population criteria.

Key words: spatialtemporal pattern, per capita share of grain, registered population, resident population