A Spatialization Method of Cross-scale Statistical Indicators Considering Grid Cell Attribute Classification
A technology of statistical indicators and unit attributes, applied in the field of geographic information science, can solve problems such as low accuracy
Active Publication Date: 2022-07-19
WUHAN UNIV
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[0006] In view of this, the present invention provides a cross-scale statistical index spatialization method that takes grid unit attribute classification into account, to solve or at least partially solve the technical problem of low precision existing in the methods in the prior art
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[0053] The purpose of the present invention is to solve the problem that statistical indicators lack grid-scale training data, and directly transfer the laws learned by administrative units to grids, resulting in low spatialization accuracy of statistical indicators due to downscaling, and to provide a grid-based method. Spatialization of cross-scale statistical indicators for cell attribute classification.
[0054] For achieving the above object, main design of the present invention is as follows:
[0055] First, analyze the correlation between the statistical indicators to be spatialized and multi-source data (which can be quantified at the grid unit scale) at the coarse-grained administrative unit scale (such as districts and counties), and select the statistical indicators to be spatialized with high correlation. Then, use the classification method to classify the grid statistics of various types of modeling auxiliary data, and determine the optimal number of classificatio...
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The invention discloses a cross-scale statistical index spatialization method considering the attribute classification of grid cells. First, the correlation between statistical indicators to be spatialized and multi-source data is analyzed at the scale of coarse-grained administrative units, and the statistical indicators to be spatialized are selected. The data with high correlation of the indicators is used as the modeling auxiliary data; then the grid statistics of various types of modeling auxiliary data are classified by the classification method, and the optimal number of classifications for each type of modeling auxiliary data is determined; then, At the administrative unit scale, construct the feature vector of grade proportion and input it into the regression model for training; then at the fine-grained grid unit scale, divide each grid unit according to the best grade of various auxiliary data to construct a feature vector and input it into the regression model Obtain the statistical index weight of each grid unit; finally, the total value of the statistical index to be spatialized in the administrative unit is distributed to each grid unit according to the weight to obtain the final grid statistical value. The method of the invention can greatly improve the prediction accuracy.
Description
technical field [0001] The invention relates to the field of geographic information science, including economic geography, population geography, environmental geography, etc., in particular to a cross-scale statistical index spatialization method that takes into account the classification of grid cell attributes. Background technique [0002] Spatialization of statistical indicators aims to reproduce the spatial distribution of statistical indicators in geographic grids or other divisions (such as hexagons, buildings, or communities), usually by converting the spatial expression of statistical indicators into coarse-grained administrative units. Convert to a fine-grained geographic grid. Spatialization of statistical indicators has been extensively studied on data such as population, GDP and grain output. It has important scientific significance and broad application prospects for finely depicting the spatial distribution of statistical indicators, rational allocation of aux...
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IPC IPC(8): G06K9/62G06Q10/06G06Q10/04G06Q50/26
CPCG06Q10/06393G06Q10/04G06Q50/26G06F18/241
Inventor 桂志鹏梅宇翱吴京航刘正廉
Owner WUHAN UNIV