Abstract
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.
Highlights
Recent advances in geospatial technologies have generated an avalanche of new sources and quantities of georeferenced data [1,2]
Moving into a distributed and high performance computing context requires architectures that can support interoperability between components and services, as well as exploit the new capabilities of the available hardware. This is not a simple matter of installing the desktop version of the Python-based Spatial Analysis Library (PySAL) library on new hardware, but rather a number of fundamental challenges arise that must be addressed if the potential of high performance computing (HPC) for spatial analysis are to be realized
The PySAL provenance architecture [31] is designed based on the following principles: (1) it should be light-weighted; (2) it should support easy integration with spatial analytical and visualization modules within and outside of the realm of PySAL; (3) it should facilitate automated invocation of PySAL
Summary
Recent advances in geospatial technologies have generated an avalanche of new sources and quantities of georeferenced data [1,2]. Several examples of flexible delivery formats of PySAL are discussed that include desktop programs (stand-alone and plug-in), web-based applications (web services, web-based spatial data management), and a decision support system (the Complex Systems Framework CSF) These provide specific examples of a larger flexible methodological framework to explore and explain spatial area data patterns through new techniques for space-time and spatial econometric analysis. Geospatial visual analytics to spatial econometric modelling; (2) a software implementation that is modular, open source, and cross-platform; and (3) the delivery of functionality through multiple user interfaces This toolbox is delivered through traditional free standing desktop software, toolbox extensions to commercial Geographic Information Systems, and integration into web-based applications such as web services and a dashboard system for decision support. The paper closes with a discussion of future directions for this work
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