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Geospatial analyses of distributed data from surveys and sensors are often stored and managed in diverse regional , national and global repositories. The nature of scientific processes requires composition of these resources in a meaningful order to solve a specific geoscience problem. These tasks can be viewed as scientific workflows. Web based interfaces(More)
Point-in-Polygon (PIP) test is fundamental to spatial databases and GIS. Motivated by the slow response times in joining large-scale point locations with polygons using traditional spatial databases and GIS, we have designed and developed an end-to-end system completely on Graphics Processing Units (GPUs) to associate points with the polygons that they fall(More)
With the increasing availability of locating and navigation technologies on portable wireless devices, huge amounts of location data are being captured at ever growing rates. Spatial and temporal aggregations in an Online Analytical Processing (OLAP) setting for the large-scale ubiquitous urban sensing data play an important role in understanding urban(More)
Cluster computing, Cloud computing and GPU computing play overlapping and complementary roles in parallel processing of geospatial data within the general HPC framework. The fast increasing hardware capacities of modern personal computers equipped with chip multiprocessor CPUs and massively parallel GPUs have made high performance computing of large-scale(More)
Normalized difference vegetation index (NDVI) Beta diversity Bray–Cutis dissimilarity index Taxonomic rank Ecoregion Phenology Mantel test a b s t r a c t Finding an effective method to quantify species compositional changes in time and space has been an important task for ecologists and biogeographers. Recently, exploring regional floristic patterns using(More)
a r t i c l e i n f o Keywords: Beta diversity MODIS NDVI Mantel test Multivariate distance WWF ecoregion Biogeographical Evolutionary history Considerable amount of research on the relationships between species diversity and productivity at different spatial, ecological, and taxonomic scales has been conducted. However, the overall trend of the correlation(More)
R-Trees are popular spatial indexing techniques that have been widely adopted in many geospatial applications. As commodity GPUs (Graphics Processing Units) are increasingly becoming available on personal workstations and cluster computers, there are considerable research interests in applying the massive data parallel GPGPU (General Purpose computing on(More)
Global remote sensing and large-scale environmental modeling have generated huge amounts of raster geospatial data. While the inherent data parallelism of large-scale raster geospatial data allows straightforward coarse-grained parallelization at the chunk level on CPUs, it is largely unclear how to effectively exploit such data parallelism on massively(More)