Volume II-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 7-11, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-7-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 7-11, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-7-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  10 Jul 2015

10 Jul 2015

SPATIOTEMPORAL DOMAIN DECOMPOSITION FOR MASSIVE PARALLEL COMPUTATION OF SPACE-TIME KERNEL DENSITY

A. Hohl1,2, E. M. Delmelle1,2, and W. Tang1,2 A. Hohl et al.
  • 1Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
  • 2Center for Applied Geographic Information Science, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA

Keywords: Domain Decomposition, Parallel Computing, Space-Time Analysis, Octtree, Kernel Density Estimation

Abstract. Accelerated processing capabilities are deemed critical when conducting analysis on spatiotemporal datasets of increasing size, diversity and availability. High-performance parallel computing offers the capacity to solve computationally demanding problems in a limited timeframe, but likewise poses the challenge of preventing processing inefficiency due to workload imbalance between computing resources. Therefore, when designing new algorithms capable of implementing parallel strategies, careful spatiotemporal domain decomposition is necessary to account for heterogeneity in the data. In this study, we perform octtree-based adaptive decomposition of the spatiotemporal domain for parallel computation of space-time kernel density. In order to avoid edge effects near subdomain boundaries, we establish spatiotemporal buffers to include adjacent data-points that are within the spatial and temporal kernel bandwidths. Then, we quantify computational intensity of each subdomain to balance workloads among processors. We illustrate the benefits of our methodology using a space-time epidemiological dataset of Dengue fever, an infectious vector-borne disease that poses a severe threat to communities in tropical climates. Our parallel implementation of kernel density reaches substantial speedup compared to sequential processing, and achieves high levels of workload balance among processors due to great accuracy in quantifying computational intensity. Our approach is portable of other space-time analytical tests.