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Summary: Science gateways have become a core part of the cyberinfrastructure ecosystem by increasing access to computational resources and providing community platforms for sharing and publishing education and research materials. While science gateways represent a promising solution for computational reproducibility, common methods for providing users with their user environments on gateways present challenges which are difficult to overcome. This article presents EasyScienceGateway: a new framework for providing user environments on science gateways to resolve these challenges, provides the technical details on implementing the framework on a science gateway based on Jupyter Notebook, and discusses our experience applying the framework to the CyberGIS‐Jupyter and CyberGIS‐Jupyter for Water gateways. [ABSTRACT FROM AUTHOR]

Calibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak. [ABSTRACT FROM AUTHOR]

Understanding the characteristics of the rapidly evolving geospatial software ecosystem in the United States is critical to enable convergence research and education that are dependent on geospatial data and software. This paper describes a survey approach to better understand geospatial use cases, software and tools, and limitations encountered while using and developing geospatial software. The survey was broadcast through a variety of geospatial-related academic mailing lists and listservs. We report both quantitative responses and qualitative insights. As 42% of respondents indicated that they viewed their work as limited by inadequacies in geospatial software, ample room for improvement exists. In general, respondents expressed concerns about steep learning curves and insufficient time for mastering geospatial software, and often limited access to high-performance computing resources. If adequate efforts were taken to resolve software limitations, respondents believed they would be able to better handle big data, cover broader study areas, integrate more types of data, and pursue new research. Insights gained from this survey play an important role in supporting the conceptualization of a national geospatial software institute in the United States with the aim to drastically advance the geospatial software ecosystem to enable broad and significant research and education advances. [ABSTRACT FROM AUTHOR]

North American bat species have been undergoing extreme population declines due to the White‐Nose Syndrome (WNS) epidemic caused by the spread of its pathogen, Pseudogymnoascus destructans. Existing models that represent the spread of the disease are limited in their scalability for use in management decisions or lacked the sophistication necessary to capture the complexity of WNS spread. Grounded in the theory of geoexpression, we exploit the latent structure of geographical process concurrency by implementing our modeling software using a concurrent Entity Component System architecture. We demonstrate our model's computational tractability for millions of individual bats. This work is significant because it lays the foundation for the use of advanced cyberinfrastructure and cyberGIS to address challenges in geographical wildlife epidemiology that can be understood using dynamic geographical Individual‐Based Models. [ABSTRACT FROM AUTHOR]

Biomass, for example, energy crops, forests, and agricultural residues, has emerged as a renewable energy option to alleviate the consumption of limited fossil fuel resources and the consequent environmental issues. Designing an effective and efficient biomass-to-biofuel supply chain involves sophisticated decision-making processes, often requiring collaborative work on data integration, model specification, scenario analysis, and coordinated implementation and management. To establish an integrated system for such work, challenges exist in (1) the limited interoperability between bioenergy models and geographic information systems (GIS); (2) interactive scenario construction, evaluation, and sharing; and (3) complex optimization problem solving that requires advanced cyberinfrastructure resources to support interactive decision-making. To resolve these challenges, this paper describes CyberGIS-BioScope, an interactive and collaborative cyberGIS-based spatial decision-making environment for biomass-to-biofuel supply chain optimization. The CyberGIS-BioScope takes advantage of cyberGIS capabilities to process and analyze spatial data and enhance visualization and sharing of optimization results. Meanwhile, the integrated environment makes the complex optimization model and advanced cyberinfrastructure resources easily accessible for agricultural scientists and decision-makers and thus accelerates their scientific discovery and decision-making processes. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Biomass supply chain optimization aims to facilitate large-scale production of biofuels by improving the efficiency and effectiveness of biomass feedstock provision. Most existing models are not web based, limited by the accessibility for real-world applications. A CyberGIS-enabled biomass supply chain decision support platform was developed to improve model accessibility and computational performance. The platform includes four major components: BioScope optimization model, GISolve middleware, high-performance cyberinfrastructure, and an interactive web interface. The workflow and functions of each component are provided to illustrate the development and usage of the platform. Case studies and associated system performance have been evaluated to demonstrate the utility of the CyberGIS-enabled decision support platform. Through leveraging cyberinfrastructure resources and interactive web-based interface, the platform enables solving complex biomass supply chain optimization problems. The improved computational performance could provide responsive decision support for group-based applications. [ABSTRACT FROM AUTHOR]

This paper describes CyberGIS Gateway as an online problem-solving environment for multiple science communities to conduct data-rich geospatial research and education. CyberGIS Gateway is a key modality in the CyberGIS software environment. Scalable gateway application integration has been the focus of CyberGIS Gateway in order to efficiently develop highly interactive online geographic information systems (GIS) user interface components and couple a rich collection of heterogeneous and distributed geospatial data and analytical services for advanced cyberGIS capabilities on advanced cyberinfrastructure. An open mashup and service API approach is developed to address the integration challenges in CyberGIS Gateway application development. This approach is applied and evaluated in developing several representative cyberGIS data and analytical applications. The experience gained from the integration practice is shared. The education and outreach activities in CyberGIS Gateway are presented to illustrate the impact of CyberGIS Gateway in GIScience communities and the effective collaboration within the science gateway community. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

CyberGIS – defined as cyberinfrastructure-based geographic information systems (GIS) – has emerged as a new generation of GIS representing an important research direction for both cyberinfrastructure and geographic information science. This study introduces a 5-year effort funded by the US National Science Foundation to advance the science and applications of CyberGIS, particularly for enabling the analysis of big spatial data, computationally intensive spatial analysis and modeling (SAM), and collaborative geospatial problem-solving and decision-making, simultaneously conducted by a large number of users. Several fundamental research questions are raised and addressed while a set of CyberGIS challenges and opportunities are identified from scientific perspectives. The study reviews several key CyberGIS software tools that are used to elucidate a vision and roadmap for CyberGIS software research. The roadmap focuses on software integration and synthesis of cyberinfrastructure, GIS, and SAM by defining several key integration dimensions and strategies. CyberGIS, based on this holistic integration roadmap, exhibits the following key characteristics: high-performance and scalable, open and distributed, collaborative, service-oriented, user-centric, and community-driven. As a major result of the roadmap, two key CyberGIS modalities – gateway and toolkit – combined with a community-driven and participatory approach have laid a solid foundation to achieve scientific breakthroughs across many geospatial communities that would be otherwise impossible. [ABSTRACT FROM AUTHOR]

Parallel spatially explicit agent-based models (SE-ABM) exploit high-performance and parallel computing to simulate spatial dynamics of complex geographic systems. The integration of parallel SE-ABM with CyberGIS could facilitate straightforward access to massive computational resources and geographic information systems to support pre- and post-simulation analysis and visualization. However, to benefit from CyberGIS integration, parallel SE-ABM must overcome the challenge of communication management for orchestrating many processor cores in parallel computing environments. This paper examines and addresses this challenge by describing a generic framework for the management of inter-processor communication to enable parallel SE-ABM to scale to high-performance parallel computers. The framework synthesizes four interrelated components: agent grouping, rectilinear domain decomposition, a communication-aware load-balancing strategy, and entity proxies. The results of a series of computational experiments based on a template agent-based model demonstrate that parallel computational efficiency diminishes as inter-processor communication increases, particularly when scaling a fixed-size model to thousands of processor cores. Therefore, effective communication management is crucial. The communication framework is shown to efficiently scale up to 2048 cores, demonstrating its ability to effectively scale to thousands of processor cores to support the simulation of billions of agents. In a simulated scenario, the communication-aware load-balancer reduced both overall simulation time and communication percentage improving overall computational efficiency. By examining and addressing inter-processor communication challenges, this research enables parallel SE-ABM to efficiently use high-performance computing resources, which reduces the barriers for synergistic integration with CyberGIS. [ABSTRACT FROM AUTHOR]

Agent-based models (ABM) allow for the bottom-up simulation of dynamics in complex adaptive spatial systems through the explicit representation of pattern–process interactions. This bottom-up simulation, however, has been identified as both data- and computing-intensive. While cyberinfrastrucutre provides such support for intensive computation, the appropriate management and use of cyberinfrastructure (CI)-enabled computing resources for ABM raise a challenging and intriguing issue. To gain insight into this issue, in this article we present a service-oriented simulation framework that supports spatially explicit agent-based modeling within a CI environment. This framework is designed at three levels: intermodel, intrasimulation, and individual. Functionalities at these levels are encapsulated into services, each of which is an assembly of new or existing services. Services at the intermodel and intrasimulation levels are suitable for generic ABM; individual-level services are designed specifically for modeling intelligent agents. The service-oriented simulation framework enables the integration of domain-specific functionalities for ABM and allows access to high-performance and distributed computing resources to perform simulation tasks that are often computationally intensive. We used a case study to investigate the utility of the framework in enabling agent-based modeling within a CI environment. We conducted experiments using supercomputing resources on the TeraGrid – a key element of the US CI. It is indicated that the service-oriented framework facilitates the leverage of CI-enabled resources for computationally intensive agent-based modeling. [ABSTRACT FROM AUTHOR]

Abstract: Cyberinfrastructure science and engineering gateways have become an important modality to connect science and engineering communities and cyberinfrastructure. The use of cyberinfrastructure through gateways is fundamental to the advancement of science and engineering. However, learning science gateway technologies and developing science gateways remain a significant challenge, given that science gateway technologies are still actively evolving and often include a number of sophisticated components. A geosciences gateway must be designed to accommodate legacy methods that geoscientists use in conventional computational tools. The research described in this paper establishes an open-source toolkit—SimpleGrid for learning and developing science gateways based on a service-oriented architecture using a component-based approach that allows flexible separation and integration of the components between geocomputation applications and cyberinfrastructure. The design and implementation of SimpleGrid is based on the National Science Foundation TeraGrid—a key element of the U.S. and world cyberinfrastructure. This paper illustrates our experience of using SimpleGrid and a spatial interpolation method in a tutorial to teach TeraGrid science gateways. [Copyright &y& Elsevier]

Cyberinfrastructure (CI) represents the integrated information and communication technologies for distributed information processing and coordinated knowledge discovery, and is promising to revolutionize how science and engineering are conducted in the twenty-first century. The value of bridging CI and GIScience is significant to advance CI and benefit GIScience research and education, particularly in distributed geographic information processing (DGIP). This article presents a holistic framework that bridges CI and GIScience by integrating CI capabilities to empower GIScience research and education and establish generic DGIP services supported by CI. The framework, the TeraGrid GIScience Gateway, is based on a CI science gateway approach developed on the National Science Foundation (NSF) TeraGrid - a key element of US and world CI. This gateway develops a unifying service-oriented framework with respect to its architecture, design, and implementation as well as its integration with the TeraGrid. The functions of the gateway focus on enabling parallel and distributed processing for geographical analysis, managing the complexity of TeraGrid software environment, and establishing a Web-based GIS for the GIScience community to gain shared and collaborative access to TeraGrid-based geospatial processing services. The gateway implementation uses Web 2.0 technologies to create a highly configurable and interactive multiuser environment. Two case studies, Bayesian geostatistical modeling and a spatial statistic [image omitted] for detecting local clustering, are used to demonstrate the gateway functions and user environment. The service transformation for these analyses is applied to create a shared, decentralized, and collaborative geographical analysis environment in which GIScience community users can contribute new analysis services and reuse existing gateway services. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical approach that has been developed to capture the computational intensity and computing resource requirements of geographical data and analysis methods. These requirements are then transformed into a common framework, a grid-based representation of a spatial computational domain, which supports the efficient use of emerging cyberinfrastructure environments. Two key types of transformational functions (data-centric and operation-centric) are identified and their relationships are explained. The application of the approach is illustrated using two geographical analysis methods: inverse distance weighted interpolation and the [image omitted] spatial statistic. We describe the underpinnings of these two methods, present their conventional sequential algorithms, and then address their latent parallelism based on a spatial computational domain representation. Through the application of this theoretical approach, the development of domain decomposition methods is decoupled from specific high-performance computer architectures and task scheduling implementations, which makes the design of generic parallel processing solutions feasible for geographical analyses. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including geographic information systems (GIS), cyberinfrastructure, and high-performance computing of spatial modeling.

Emergency evacuation is a critical response to deadly disasters such as hurricanes, floods, and earthquakes, etc. However, mass emergency evacuation itself is a complex process that sometimes could lead to chaotic situations and unintended consequences. In many emergency scenarios, mass evacuation is necessary to cope with severe public threats within tight spatiotemporal ranges. To better understand complex phenomena like mass evacuation, and study possible consequences, agent-based models (ABMs) have been widely developed in previous work. Existing models simulate individual behaviors, posing computational challenges when applied to large geographic areas and sophisticated behaviors. A key strategy for resolving such computational challenges is to partition transportation networks into smaller regions and resolve corresponding computational costs by taking advantage of advanced cyberinfrastructure and cyberGIS. In this study, a novel network partition algorithm is developed to improve the scalability of agent-based modeling of mass evacuation based on a cutting-edge cyberGIS-enabled computational framework that exploits the spatial movement patterns of emergency evacuation. Specifically, the algorithm is termed as Voronoi Clustering based on Target-Shift, or ViCTS. It is enlightened by network Voronoi diagrams and designed to resolve computational scalability challenges caused by the unique characteristics of evacuation traffic. We conducted a set of computational experiments with real street network data in various evacuation scenarios to test the effectiveness and efficiency of the algorithm. Computational experiments show that ViCTS outperforms a widely used network partition algorithm for microscopic traffic simulation in terms of achieving optimal computational performance by balancing computational loads and reducing communications across high-performance parallel computing resources. • Conventional network partition algorithms result in suboptimal computational performance. • Voronoi diagrams and target-shifting proximity are employed to resolve computational challenges. • The ViTCS algorithm achieves optimal computational performance, and enables scalable agent-based modeling of mass evacuation. [ABSTRACT FROM AUTHOR]

Summary: The interdisciplinary field of cyberGIS (geographic information science and systems (GIS) based on advanced cyberinfrastructure) has a major focus on data‐ and computation‐intensive geospatial analytics. The rapidly growing needs across many application and science domains for such analytics based on disparate geospatial big data poses significant challenges to conventional GIS approaches. This paper describes CyberGIS‐Jupyter, an innovative cyberGIS framework for achieving data‐intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on ROGER, the first cyberGIS supercomputer. The framework adapts the Notebook with built‐in cyberGIS capabilities to accelerate gateway application development and sharing while associated data, analytics, and workflow runtime environments are encapsulated into application packages that can be elastically reproduced through cloud‐computing approaches. As a desirable outcome, data‐intensive and scalable geospatial analytics can be efficiently developed and improved and seamlessly reproduced among multidisciplinary users in a novel cyberGIS science gateway environment. [ABSTRACT FROM AUTHOR]

Creating online data repositories that follow Findable, Accessible, Interoperable, and Reusable (FAIR) principles has been a significant focus in the research community to address the reproducibility crisis facing many computational fields, including environmental modeling. However, less work has focused on another reproducibility challenge: capturing modeling software and computational environments needed to reproduce complex modeling workflows. Containerization technology offers an opportunity to address this need, and there are a growing number of strategies being put forth that leverage containerization to improve the reproducibility of environmental modeling. This research compares ten such approaches using a hydrologic model application as a case study. For each approach, we use both quantitative and qualitative metrics for comparing the different strategies. Based on the results, we discuss challenges and opportunities for containerization in environmental modeling and recommend best practices across both research and educational use cases for when and how to apply the different containerization-based strategies. • Different approaches for reproducible environmental modeling are evaluated. • The approaches emphasize the use of containerization technologies. • The SUMMA hydrologic model is used for evaluating the approaches. • Results are used to recommend best practices for computational reproducibility. • Best practices are recommended for different common modeling use cases. [ABSTRACT FROM AUTHOR]

Building cyberinfrastructure for the reuse and reproducibility of large-scale hydrologic modeling studies requires overcoming a number of data management and software architecture challenges. The objective of this research is to advance the cyberinfrastructure needed to overcome some of these challenges to make such computational hydrologic studies easier to reuse and reproduce. We present novel cyberinfrastructure capable of integrating HydroShare (an online data repository), CyberGIS-Jupyter for Water and high performance computing (HPC) resources (computational environments), and the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic modeling framework through its application programming interface for orchestrating model runs. The cyberinfrastructure is demonstrated for a complex computational modeling study on a contiguous United States dataset. We present and discuss key capabilities of the cyberinfrastructure including (1) containerization for portability across compute environments, (2) Globus for large data transfers, (3) a Jupyter gateway to HPC environments, and (4) Jupyter notebooks for capturing the modeling workflows. • Presents novel cyberinfrastructure for complex hydrologic modeling studies. • Focuses on the challenges introduced by computationally and data intensive studies. • Uses Globus for large data transfers between scientific cloud services. • Leverages containerization for model portability across compute environments. • Combines model APIs and Jupyter notebooks to document modeling workflows. [ABSTRACT FROM AUTHOR]

Cyberinfrastructure needs to be advanced to enable open and reproducible environmental modeling research. Recent efforts toward this goal have focused on advancing online repositories for data and model sharing, online computational environments along with containerization technology and notebooks for capturing reproducible computational studies, and Application Programming Interfaces (APIs) for simulation models to foster intuitive programmatic control. The objective of this research is to show how these efforts can be integrated to support reproducible environmental modeling. We present first the high-level concept and general approach for integrating these three components. We then present one possible implementation that integrates HydroShare (an online repository), CUAHSI JupyterHub and CyberGIS-Jupyter for Water (computational environments), and pySUMMA (a model API) to support open and reproducible hydrologic modeling. We apply the example implementation for a hydrologic modeling use case to demonstrate how the approach can advance reproducible environmental modeling through the seamless integration of cyberinfrastructure services. • New approaches are needed to support open and reproducible environmental modeling. • Efforts should focus on integrating existing cyberinfrastructure to build new systems. • Our focus is on integrating repositories, computational environments, and model APIs. • An example implementation is shown using HydroShare, JupyterHub, and pySUMMA. • We demonstrate how the approach fosters reproducibility using a modeling case study. [ABSTRACT FROM AUTHOR]