PROGRESS IN GEOGRAPHY ›› 2015, Vol. 34 ›› Issue (1): 110-117.doi: 10.11820/dlkxjz.2015.01.013

• Application of GIS • Previous Articles     Next Articles

Spatiotemporal characteristics and influencing factors of inflow population in Guangdong from 2000 to 2010

LI Yuejiao1,2, YANG Xiaohuan1, CAI Hongyan1, YU Yuefei3   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Anhui Normal University, Wuhu 241000, Anhui, China
  • Received:2014-05-01 Revised:2014-10-01 Online:2015-01-25 Published:2015-01-25
  • About author:10.11820/dlkxjz.2015.01.013

Abstract: Since the economic reform and opening up, Guangdong Province has been one of the provinces in China that has the largest floating population inflow. Data from the sixth census show that Guangdong had 21497787 inflow population from other provinces, which accounted for 20.61% of the total population in Guangdong in 2010. The number of inflow population in Guangdong ranked the first in all provinces, autonomous regions, and municipalities in China. Using data from the fifth and sixth national population census in 2000 and 2010 and spatial autocorrelation method (global autocorrelation, local autocorrelation, and cold and hot spot analyses), we analyzed the spatial- temporal characteristics and influencing factors of inflow population in Guangdong Province during the first decade of the 21st century. The results show that: (1) From 2000 to 2010, the number of inflow population in Guangdong increased sharply, but the distribution pattern of the inflow population was stable. The concentration of the inflow population slightly decreased from 2000 to 2010. (2) The number of inflow population in Guangdong Province was closely related to economic development, but the distribution pattern had a clear relationship with local industrial transfer policies.

Key words: Guangdong Province, inflow population, influencing factor, spatial autocorrelation, spatiotemporal pattern