Wenpeng Feng

TL;DR: The parallel map projection framework presented in this study is based on a layered architecture that couples capabilities of cloud computing and high-performance computing accelerated by Graphics Processing Units and provides considerable acceleration for re-projecting vector-based big spatial data.

TL;DR: This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis.

TL;DR: In this article, a spatially explicit analytical framework that integrates remotely sensed data and spatial analyses approaches to support the estimation of mangrove biomass and carbon stock and their spatial patterns in West Africa is presented.

TL;DR: A cyber-enabled SDSS framework is developed to facilitate the computationally challenging fieldwork design that requires the efficacious selection of base camps and plots for the inventory of mangroves in the Zambezi River Delta, Mozambique.

TL;DR: This paper reviews the use of parallel computing for geocomputational modeling by focusing on four aspects: spatial statistics, spatial optimization, spatial simulation, and cartography and geovisualization, and designs a case study of a spatial agent-based model to show how parallel computing can be exploited to empower advanced geocomputable modeling.