Spatial data analytics on heterogeneous multi- and many-core parallel architectures using python
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Abstract
Parallel vector spatial analysis concerns the application of parallel computational methods to facilitate vector-based spatial analysis. The history of parallel computation in spatial analysis is reviewed, and this work is placed into the broader context of high-performance computing (HPC) and parallelization research. The rise of cyber infrastructure and its manifestation in spatial analysis as CyberGIScience is seen as a main driver of renewed interest in parallel computation in the spatial sciences. Key problems in spatial analysis that have been the focus of parallel computing are covered. Chief among these are spatial optimization problems, computational geometric problems including polygonization and spatial contiguity detection, the use of Monte Carlo Markov chain simulation in spatial statistics, and parallel implementations of spatial econometric methods. Future directions for research on parallelization in computational spatial analysis are outlined.
| Publication type | Book chapter |
|---|---|
| Publication Subtype | Book Chapter |
| Title | Spatial data analytics on heterogeneous multi- and many-core parallel architectures using python |
| DOI | 10.1007/978-3-319-17885-1_1569 |
| Year Published | 2017 |
| Language | English |
| Publisher | Springer |
| Contributing office(s) | Astrogeology Science Center |
| Larger Work Type | Book |
| Larger Work Title | Encyclopedia of GIS |
| Google Analytic Metrics | Metrics page |