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Xiaochen Kang
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, Beijing 100039, China

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Journal article
Published: 30 October 2020 in ISPRS International Journal of Geo-Information
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Geographically weighted regression (GWR) introduces the distance weighted kernel function to examine the non-stationarity of geographical phenomena and improve the performance of global regression. However, GWR calibration becomes critical when using a serial computing mode to process large volumes of data. To address this problem, an improved approach based on the compute unified device architecture (CUDA) parallel architecture fast-parallel-GWR (FPGWR) is proposed in this paper to efficiently handle the computational demands of performing GWR over millions of data points. FPGWR is capable of decomposing the serial process into parallel atomic modules and optimizing the memory usage. To verify the computing capability of FPGWR, we designed simulation datasets and performed corresponding testing experiments. We also compared the performance of FPGWR and other GWR software packages using open datasets. The results show that the runtime of FPGWR is negatively correlated with the CUDA core number, and the calculation efficiency of FPGWR achieves a rate of thousands or even tens of thousands times faster than the traditional GWR algorithms. FPGWR provides an effective tool for exploring spatial heterogeneity for large-scale geographic data (geodata).

ACS Style

Dongchao Wang; Yi Yang; Agen Qiu; Xiaochen Kang; Jiakuan Han; Zhengyuan Chai. A CUDA-Based Parallel Geographically Weighted Regression for Large-Scale Geographic Data. ISPRS International Journal of Geo-Information 2020, 9, 653 .

AMA Style

Dongchao Wang, Yi Yang, Agen Qiu, Xiaochen Kang, Jiakuan Han, Zhengyuan Chai. A CUDA-Based Parallel Geographically Weighted Regression for Large-Scale Geographic Data. ISPRS International Journal of Geo-Information. 2020; 9 (11):653.

Chicago/Turabian Style

Dongchao Wang; Yi Yang; Agen Qiu; Xiaochen Kang; Jiakuan Han; Zhengyuan Chai. 2020. "A CUDA-Based Parallel Geographically Weighted Regression for Large-Scale Geographic Data." ISPRS International Journal of Geo-Information 9, no. 11: 653.

Journal article
Published: 11 July 2018 in ISPRS International Journal of Geo-Information
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Land use/land cover change (LUCC) analysis is a fundamental issue in regional and global geography that can accurately reflect the diversity of landscapes and detect the differences or changes on the earth’s surface. However, a very heavy computational load is often unavoidable, especially when processing multi-temporal land cover data with fine spatial resolution using more complicated procedures, which often takes a long time when performing the LUCC analysis over large areas. This paper employs a graph-based spatial decomposition that represents the computational loads as graph vertices and edges and then uses a balanced graph partitioning to decompose the LUCC analysis on spatial big data. For the decomposing tasks, a stream scheduling method is developed to exploit the parallelism in data moving, clipping, overlay analysis, area calculation and transition matrix building. Finally, a change analysis is performed on the land cover data from 2015 to 2016 in China, with each piece of temporal data containing approximately 260 million complex polygons. It took less than 6 h in a cluster with 15 workstations, which was an indispensable task that may surpass two weeks without any optimization.

ACS Style

Xiaochen Kang; Jiping Liu; Chun Dong; Shenghua Xu. Using High-Performance Computing to Address the Challenge of Land Use/Land Cover Change Analysis on Spatial Big Data. ISPRS International Journal of Geo-Information 2018, 7, 273 .

AMA Style

Xiaochen Kang, Jiping Liu, Chun Dong, Shenghua Xu. Using High-Performance Computing to Address the Challenge of Land Use/Land Cover Change Analysis on Spatial Big Data. ISPRS International Journal of Geo-Information. 2018; 7 (7):273.

Chicago/Turabian Style

Xiaochen Kang; Jiping Liu; Chun Dong; Shenghua Xu. 2018. "Using High-Performance Computing to Address the Challenge of Land Use/Land Cover Change Analysis on Spatial Big Data." ISPRS International Journal of Geo-Information 7, no. 7: 273.