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.

Rebecca C. Vandewalleab , William C. Barleyc , Anand Padmanabhanabd, Daniel S. Katzd & Shaowen Wangab* a Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL, USAb CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana-Champaign, Urbana, IL, USAc Department of Communication, University of Illinois at Urbana-Champaign, Urbana, IL, USAd National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USARebecca Vandewalle is a PhD student at the University of Illinois at Urbana-Champaign. Her research interests include spatially-explicit agent-based modeling, spatial network analysis, coupled human and natural systems in emergency contexts, and cyberGIS.William C. Barley is an Assistant Professor in the Department of Communication at the University of Illinois Urbana-Champaign. His research interests include organizational communication, collaboration and coordination, data representation, and field studies of technology design, adoption, and use.Anand Padmanabhan is a Research Associate Professor in the Department of Geography and Geographic Information Science at the University of Illinois Urbana-Champaign. His research interests include distributed systems, cyberinfrastructure, and cyberGIS.Daniel S. Katz is Assistant Director for Scientific Software and Applications at the National Center for Supercomputing Applications and Research Associate Professor in Computer Science, Electrical and Computer Engineering, and the School of Information Sciences at the University of Illinois Urbana-Champaign. His research interests include the interaction of people and software.Shaowen Wang is a Professor and Head of the Department of Geography and Geographic Information Science; and an Affiliate Professor of the Department of Computer Science, Department of Urban and Regional Planning, and School of Information Sciences at the University of Illinois at Urbana-Champaign. His research interests include geographic information science and systems (GIS), advanced cyberinfrastructure and cyberGIS, complex environmental and geospatial problems, computational and data sciences, high-performance and distributed computing, and spatial analysis and modeling.CONTACT Shaowen Wang [email protected] 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.

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.

Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US using Twitter data. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data.

Summary 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.

SUMMARY Social media have experienced a spectacular rise in popularity, attracting hundreds of millions of users and generating unprecedented amount of content that increasingly contain location and place information. Collectively, the massive location information in these data provides an excellent opportunity to better understand many geographic phenomena and geospatial dynamics in a timely fashion. Recent studies capitalizing on social networking and media data show significant societal impacts in many areas including prediction of stock market and infectious disease surveillance. However, because location‐based social media data are often massive, generated dynamically, and unstructured, significant computation, data, and visualization challenges need to be resolved. This research aims to demonstrate the use of massive social media data to interactively analyze spatiotemporal events across spatial and temporal scales, by establishing a data‐driven framework using cyberGIS—geographic information systems (GIS) based on advanced cyberinfrastructure—to resolve aforementioned challenges. Specifically, FluMapper—an application on the CyberGIS Gateway—is employed as a case study to demonstrate the data‐driven framework and seamless integration of massive location‐based social media data and spatial analytical services within the online problem solving environment of the Gateway. FluMapper presents integrated results from two complementary spatial analyses: (i) an interactive exploration of spatial distribution of flu risk and (ii) dynamic mapping of movement patterns, across multiple spatial, and temporal scales. The seamless integration of these two analyses through the framework illustrates the potential of cyberGIS to resolve the compute and data challenges of analyzing near real‐time social media data in an efficient and scalable manner and to support interactive visualization. Copyright © 2014 John Wiley & Sons, Ltd.

This paper describes CyberGIS Gateway, an online problem-solving environment, for multiple science communities to conduct data-rich geospatial research and education.

Viewshed analysis, often supported by geographic information system, is widely used in many application domains. However, as terrain data continue to become increasingly large and available at high resolutions, data-intensive viewshed analysis poses significant computational challenges. General-purpose computation on graphics processing units (GPUs) provides a promising means to address such challenges. This article describes a parallel computing approach to data-intensive viewshed analysis of large terrain data using GPUs. Our approach exploits the high-bandwidth memory of GPUs and the parallelism of massive spatial data to enable memory-intensive and computation-intensive tasks while central processing units are used to achieve efficient input/output (I/O) management. Furthermore, a two-level spatial domain decomposition strategy has been developed to mitigate a performance bottleneck caused by data transfer in the memory hierarchy of GPU-based architecture. Computational experiments were designed to evaluate computational performance of the approach. The experiments demonstrate significant performance improvement over a well-known sequential computing method, and an enhanced ability of analyzing sizable datasets that the sequential computing method cannot handle.

The field of Culturomics exploits “big data” to explore human society at population scale. Culturomics increasingly needs to consider geographic contexts and, thus, this research develops a geospatial visual analytical approach that transforms vast amounts of textual data into emotional heatmaps with fine-grained spatial resolution. Fulltext geocoding and sentiment mining extract locations and latent “tone” from text-based data, which are combined with spatial analysis methods - kernel density estimation and spatial interpolation - to generate heatmaps that capture the interplay of location, topic, and tone toward narrative impacts. To demonstrate the effectiveness of the approach, the complete English edition of Wikipedia is processed using a supercomputer to extract all locations and tone associated with the year of 2003. An emotional heatmap of Wikipedia's discussion of “armed conflict” for that year is created using the spatial analysis methods. Unlike previous research, our approach is designed for exploratory spatial analysis of topics in text archives by incorporating multiple attributes including the prominence of each location mentioned in the text, the density of a topic at each location compared to other topics, and the tone of the topics of interest into a single analysis. The generation of such fine-grained emotional heatmaps is computationally intensive particularly when accounting for the multiple attributes at fine scales. Therefore a CyberGIS platform based on national cyberinfrastructure in the United States is used to enable the computationally intensive visual analytics.

GIS (Geographic Information Systems) play an important role to acquire and communicate geospatial knowledge based on spatial data and the use of spatial analysis, modeling, and visualization. The assurance of the validity and quality of spatial data handling and analysis remains a great challenge, in part, because of sophisticated procedures are often required for collaborative geospatial problem-solving and decision making. These procedures, when specified as knowledge derivation workflows, require carefully configured parameters and spatiotemporal specifications guided by specific contexts and purposes. The information of spatial data lineage and related analysis workflow is defined as spatial provenance in this research. Such information is often not well recorded or managed during spatial data handling and related analysis. This paper presents a provenance-aware GIS architecture that incorporates spatial provenance to address this shortcoming and facilitate the assurance of validity and quality of spatial data handling and analysis. Spatial provenance in this architecture is generated and managed to allow queries on data lineage and workflow information to support geospatial problem-solving. Basic elements of spatial provenance are captured using a spatial provenance model. The illustration of the provenance-aware GIS architecture and its proof-of-concept implementation reveals the similarity and difference in the use of spatial provenance in GIS applications. Overall, the architecture and implementation described in the paper demonstrates the necessity and feasibility of introducing provenance into GIS.

Agent technologies represent a promising approach for the integration of interorganizational capabilities across distributed, networked environments. However, knowledge sharing interoperability problems can arise when agents incorporating differing ontologies try to synchronize their internal information. Moreover, in practice, agents may not have a common or global consensus ontology that will facilitate knowledge sharing and integration of functional capabilities. We propose a method to enable agents to develop a local consensus ontology during operation time as needed. By identifying similarities in the ontologies of their peer agents, a set of agents can discover new concepts/relations and integrate them into a local consensus ontology on demand. We evaluate this method, both syntactically and semantically, when forming local consensus ontologies with and without the use of a lexical database. We also report on the effects when several factors, such as the similarity measure, the relation search level depth, and the merge order, are varied. Finally, experimenting in the domain of agent-supported Web service composition, we demonstrate how our method allows us to successfully autonomously form service-oriented local consensus ontologies.