Spatial Statistic Analysis And Its Application In Agricultural Land Classification

Spatial statistical analysis in less than 50 years of research and practice has developed into a random study of nature and the universal law of variables the scientific method. It widely used in geology, agriculture, soil and other research areas, and has achieved fruitful results. Compared with the geographic information systems and the traditional statistical analysis, spatial statistical analysis will not only focus on statistical analysis of numerical calculation, but also will effectively integrate data and geographic location. Spatial statistical analysis including spatial structure analysis, Kriging analysis, spatial autocorrelation analysis and spatial simulation, can be used to analyze the characteristics of the spatial coordinates of variables, and conduct process simulation and spatial interpolation.Based on national land resources survey project for the agricultural land classification issues source, to the Shaanxi Provincial Agricultural Land Classification results-based data, first, using Correlation Analysis and regression analysis, this study has validated the correlation between the various indicators of agricultural land classification, and then has established the regression equation, revealing a positive correlation between the variable relationship from the internal logic of data; Secondly, by the use of Inverse Distance to a Power, Kriging, such as the interpolation five interpolation of agricultural land and other natural quality index for interpolation, it has compared different methods of interpolation contour map and surface map, and then has tested the effectiveness of various methods interpolating; finally, it has established the one, two, three trends surface model of natural quality of agricultural land by using of trend analysis, followed one by one of the accuracy of testing and an appropriate test, then it has explained the difference in the quality of natural causes based on trends and the residual value of the margin of agricultural land and explained the distribution of other from the macro aspects of agricultural land, proving the correctness of agricultural land classification.