PROGRESS IN GEOGRAPHY ›› 2008, Vol. 27 ›› Issue (3): 152-160.doi: 10.11820/dlkxjz.2008.03.021

• Original Articles • Previous Articles     Next Articles

Str ata Efficiency and Optimization str ategy of Str atified Sampling on Spatial Population

CAO Zhidong1,2, WANG Jinfeng1, LI Lianfa1, JIANG Chengsheng1   

  1. 1. State Key Laboratory of Resources &|Environmental Information System| Institute of Geographic Sciences &|Natural Resources Research, Chinese Academy of Sciences| Beijing 100101, China;
    2. Graduate School of Chinese Academy of Sciences| Beijing 100039| China
  • Received:2008-01-01 Revised:2008-03-01 Online:2008-05-25 Published:2008-05-25


Efficiency of stratified sampling for geospatial population is restricted by spatial autocorrelation. Strata efficiency origins from two aspects: the first is spatial auto- correlation, which makes sampling with dispersed distribution improve the accuracy; and the second is priori knowledge, which can make the variance smaller within strata than within the overall population. The strata efficiency for knowledge strata is more outstanding than that of arbitrary strata only in the geographical object with strong spatial auto- correlation; when the spatial auto- correlation is weak, knowledge will not be preferred to the arbitrary strata. Spatial auto - correlation has an important influence on stratified sampling design: Although a stratified statostoc always "gains" in terms of accuracy, the implementation of the technique is conditional, expensive and sometime unnecessary. This is often overlooked in practical application. Different stratified sampling surveys for the ratio of thin - non - cultivated component in Shandong Province are simulated by using Mento Carlo method. Simulated results validate the influence of spatial: auto - correlation on different stratified methods. Finally, this paper proposes optimization strategy of strata selection for geospatial objects.

Key words: geographical object, optimization strategy, spatial autocorrelation, strata efficiency, stratified sampling