Nearly 25 years ago, parallel computing techniques were first applied to vector spatial analysis methods. This initial research was driven by the desire to reduce computing times in order to support scaling to larger problem sets. Since this initial work, rapid technological advancement has driven the availability of High Performance Computing (HPC) resources, in the form of multi-core desktop computers, distributed geographic information processing systems, e.g. computational grids, and single site HPC clusters. In step with increases in computational resources, significant advancement in the capabilities to capture and store large quantities of spatially enabled data have been realized. A key component to utilizing vast data quantities in HPC environments, scalable algorithms, have failed to keep pace. The National Science Foundation has identified the lack of scalable algorithms in codified frameworks as an essential research product. Fulfillment of this goal is challenging given the lack of a codified theoretical framework mapping atomic numeric operations from the spatial analysis stack to parallel programming paradigms, the diversity in vernacular utilized by research groups, the propensity for implementations to tightly couple to under- lying hardware, and the general difficulty in realizing scalable parallel algorithms. This dissertation develops a taxonomy of parallel vector spatial analysis algorithms with classification being defined by root mathematical operation and communication pattern, a computational dwarf. Six computational dwarfs are identified, three being drawn directly from an existing parallel computing taxonomy and three being created to capture characteristics unique to spatial analysis algorithms. The taxonomy provides a high-level classification decoupled from low-level implementation details such as hardware, communication protocols, implementation language, decomposition method, or file input and output. By taking a high-level approach implementation specifics are broadly proposed, breadth of coverage is achieved, and extensibility is ensured. The taxonomy is both informed and informed by five case studies im- plemented across multiple, divergent hardware environments. A major contribution of this dissertation is a theoretical framework to support the future development of concrete parallel vector spatial analysis frameworks through the identification of computational dwarfs and, by extension, successful implementation strategies.

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms.