Kernel Density Map Based on MapReduce

Hua AI, Qiang LIU, Pei-wen LIU, Yao-sen HUANG, Chen CHEN, Wei-qing LI, Hao LUO

Abstract


Geoprocessing plays an important role in Geographical Information Systems. In this paper, we propose a spatial processing parallel algorithm based on MapReduce architecture. The method is addressed in kernel density computation, and it also can cover other commonly used raster map processing, and the statistical calculation of the raster data, such as slope, buffer, European distribution, interpolation.

Keywords


MapReduce, Kernel density, Map, Parallel.


DOI
10.12783/dtcse/cnsce2017/8916

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