Scale Selecting of Geographic National Conditions Information Statistical Grids with Spatial Autocorrelation——A Case Study of Vegetation Cover Information Statistics

doi:10.13474/j.cnki.11-2246.2017.0082

Abstract

Abstract: The difference in statistical grid scales will bring different statistical results, so it is important to select statistical grids for the geographical conditions information statistics. A method of choosing statistical grids scale of geographical conditions information with spatial autocorrelation being taken into account is proposed. By using geographic conditions census data and taking vegetation cover information statistics as an example, the study gets vegetation cover girds data, under the rule of maximum area and the rule of centric cell in scales of 50 m, 60 m, 70 m, 80 m, 90 m, 100 m, 250 m, 500 m and 1000 m, and meanwhile calculates vegetation cover statistical errors, analyzes changes of spatial autocorrelation of vegetation cover gird data at different scales to make scale selection, and then uses vegetation cover statistical errors to obtain an appropriate statistical grid scale of vegetation cover information statistics. The results show that for vegetation cover information statistics of geographic national conditions, the suitable scale is 250 m in areas with high degree of vegetation coverage, and 100 m in areas with low degree of vegetation coverage.

Key words: Spatial autocorrelation, scale selecting, statistical grids, vegetation cover, geographic national conditions

CLC Number:

P208

LIAN Shizhong, DING Lin, CHEN Jiangping. Scale Selecting of Geographic National Conditions Information Statistical Grids with Spatial Autocorrelation——A Case Study of Vegetation Cover Information Statistics[J]. 测绘通报, 2017, 0(3): 46-51,90.

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