PROGRESS IN GEOGRAPHY ›› 2009, Vol. 28 ›› Issue (5): 663-672.doi: 10.11820/dlkxjz.2009.05.003

• Original Articles • Previous Articles     Next Articles

Review on Soft Spatial Data and its Spatial Interpolation Methods

LUO Ming1,2, PEI Tao1,3   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Key Laboratory of Geographic Information Science, Ministry of Education, Shanghai 20062, China
  • Online:2009-09-25 Published:2009-09-25


In recent years, as the observation technologies develop rapidly, both type and number of spatial data is increasing, and information retrieved from spatial data expands increasingly, among which includes a large number of qualitative information, for instance, land-use type data, vegetation type data, topographic feature data, which some experts called soft information or soft data. These so-called soft data often have associations with the predicted target variable, even could become one of most important factors that influence the spatial distribution of target variable obviously in some cases, therefore, they can help improve prediction of target variable theoretically. However, in respect that non-numerical soft data can’t be calculated directly and is neglected by traditional spatial interpolation methods, connotative useful information can not be utilized sufficiently and effectively, which results in a mass of wasted information. Lately, soft spatial interpolation technology was proposed, aimed to integrate soft spatial data as auxiliary or second information to help improve interpolation accuracy. According to the characteristics and categories of soft spatial data, this paper aimed to review on soft spatial interpolation methods and their applications. Firstly, we summarized some “harden” methods, hardening the soft spatial data to hard data. Then, we discussed several different type soft spatial interpolation methods afterward, such as simple kriging, cokriging, indicator kriging, ordinary kriging, stratified kriging, kriging with external drift regression, bayesian maximum entropy, inverse distance weighted. After that, prospects of application of soft data and soft spatial interpolations were proposed in the last part.

Key words: bayesian maximum entropy, geostatistics, kriging, regression, soft data, spatial interpolation