Reducing the number of contacts between passengers on an airplane can potentially curb the spread of infectious diseases. In this paper, a social force based pedestrian movement model is formulated and applied to evaluate the movement and contacts among passengers during boarding and deplaning of an airplane. Within the social force modeling framework, we introduce location dependence on the self-propelling momentum of pedestrian particles. The model parameters are varied over a large design space and the results are compared with experimental observations to validate the model. This model is then used to assess the different approaches to minimize passenger contacts during boarding and deplaning of airplanes. We find that smaller aircrafts are effective in reducing the contacts between passengers. Column wise deplaning and random boarding are found to be two strategies that reduced the number of contacts during passenger movement, and can potentially lower the likelihood of infection spread.

One of the most pressing global challenges for sustainable development is freshwater management. Sustainable water governance requires interdisciplinary knowledge about environmental and social processes as well as participatory strategies that bring scientists, managers, policymakers, and other stakeholders together to cooperatively produce knowledge and solutions, promote social learning, and build enduring institutional capacity. Cooperative production of knowledge and action is designed to enhance the likelihood that the findings, models, simulations, and decision support tools developed are scientifically credible, solutions‐oriented, and relevant to management needs and stakeholders' perspectives. To explore how interdisciplinary science and sustainable water management can be co‐developed in practice, the experiences of an international collaboration are drawn on to improve local capacity to manage existing and future water resources efficiently, sustainably, and equitably in the State of Pernambuco in northeastern Brazil. Systems are developed to model and simulate rainfall, reservoir management, and flood forecasting that allow users to create, save, and compare future scenarios. A web‐enabled decision support system is also designed to integrate models to inform water management and climate adaptation. The challenges and lessons learned from this project, the transferability of this approach, and strategies for evaluating the impacts on management decisions and sustainability outcomes are discussed.

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.

In this article, we report on our experiences with refactoring a spatial analysis library to support parallelization. Python Spatial Analysis Library (PySAL) is a library of spatial analytical functions written in the open-source language, Python. As part of a larger scale effort toward developing cyberinfrastructure of GIScience, we examine the particular case of choropleth map classification through alternative parallel implementations of the Fisher-Jenks optimal classification method using a multi-core, single desktop environment. The implementations rely on three different parallel Python libraries, PyOpenCL, Parallel Python, (PP) and Multiprocessing. Our results point to the dominance of the CPU-based Parallel Python and Multiprocessing implementations over the Graphical Processing Unit (GPU)-based PyOpenCL approach.

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