Introduction to SuperGIS Spatial Statistical Analyst

Preface

Based on a complete and content-rich geodatabase, the Geographic Information System (GIS) is an integrated software for capturing, editing, updating, storing, searching, processing, analyzing and displaying geodata. It has been commonly applied in science and social science. With the increasing importance and popularity of GIS, the demands for various functions of GIS from experts of different fields are also diversified, expecting software they are using is having stronger and more practical analysis functions in analyzing data and also expecting geostatistical methods with better spatial data analysis and estimations being added to GIS in order to understand the variability and dependence of spatial data and estimations of un-sampled points. Furthermore, the ability of GIS for supporting decision analyses of spatial data could be enhanced.

Geostatistics has been extensively applied in different fields, including social science and science. Based on the regionalized variable theory, it uses a variogram to process the data structure analysis of a regionalized random variable to obtain the spatial dependence of this regionalized variable. It also uses Kriging methods to estimate the best linear unbiased estimation of un-sampled values. If we could integrate the GIS for storing, displaying, processing, and operating spatial data with geostatistics, GIS can not only operate the display and queries of spatial data and attribute data and some basic statistical functions, but also improves its spatial data analysis functions and estimation functions. We may hence understand the spatial variability and dependence of spatial data and process spatial estimations and simulations in order to increase the ability of GIS for supporting decision analyses of spatial data. Therefore, geostatistical calculating components are important and necessary tools to GIS applications.