关键词: 地理空间, 点密度, 离散点, 哈希表, 数据挖掘

Abstract:

Due to the negative effects of commonly used kernel density estimator has many limitations on computing discrete point density, a point density algorithm based on projection and Hash-based data structure is purposed, which can quickly compute the true density of discrete points, by keeping the original location and discreteness of points. Coordinate of points in the nearest neighborhood grid are obtained by the rule of binning, then it calculates the number of points in each search neighborhood, density value and initial coordinate with discrete points for the output results. Groundwater resources of the continental United States extracted by USGS are used as experiment dataset, visualization output and the time complexity are used for validation indexes when compared with algorithm based on the kernel density. Results show that the algorithm improves the identification of density, obtains the true density value of discrete points, visual effect and the precision analysis are better than that of ArcGIS 10.4.1 kernel density and kde2d kernel density algorithm, the computation efficiency is better than common kde2d algorithm.

Key words: geographic spatial, point density, discrete points, Hash table, data mining