A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data

Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computa...

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Vydané v:	International journal of geographical information science : IJGIS Ročník 31; číslo 10; s. 2068 - 2097
Hlavní autori:	Zhang, Guiming, Zhu, A-Xing, Huang, Qunying
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Abingdon Taylor & Francis 03.10.2017 Taylor & Francis LLC
Predmet:	Adaptive algorithms Adaptive kernel density estimation Algorithms Bandwidths Big Data Computer applications Cores Data management Data mining Data processing Datasets Fractals GPU/CUDA Graphics processing units Multiprocessing OpenMP optimization Pattern analysis Shopping centers Spatial analysis spatial big data
ISSN:	1365-8816, 1362-3087, 1365-8824
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