CyberGIS for Geospatial Discovery and Innovation

Geographic information science and systems (GIS) have undergone rapid growth during the past several decades. This growing trend seems likely to persist into the foreseeable future driven by numerous diverse applications and enabled by steady progress of related technologies. As a geospatial data deluge permeates broad scientific and societal realms, to sustain the trend, however, requires GIS to be innovated based on synergistic integration of data-intensive and spatial approaches enabled by advanced cyberinfrastructure—a rapidly evolving infrastructure of communication, computing, and information technologies. Consequently, cyberGIS has been developed as a fundamentally new cyberinfrastructure and GIS modality comprising a seamless blending of advanced cyberinfrastructure, GIS, and spatial analysis and modeling capabilities and, thus, has enabled scientific advances and shown broad societal impacts while contributing to the advancement of cyberinfrastructure. For example, the U.S. National Science Foundation (NSF) has funded a major multi-institution initiative on cyberGIS software integration for sustained geospatial innovation—arguably the largest investment by NSF on related subjects during the past several years. Therefore, this book represents a timely effort to inform pertinent research communities about opportunities and challenges, roadmaps for research and development, and major progress, trends, and impacts of cyberGIS. The book serves as an authoritative source of information to fill the void of introducing this new, exciting, and growing field.

A CyberGIS approach is presented in this chapter where microscopic traffic simulation and gas dispersion simulation systems are combined in order to estimate atmospheric pollution for different scenarios. The combination of these two simulation models allows for detailed investigations of different situations such as the investigation of pollution impacts of different traffic infrastructure variants, as well as for prediction of expected pollution and whether pollutant thresholds will be exceeded. For different case studies, real data about traffic movements provided by the state government, a digital terrain model of the area as well as real measurements of atmospheric data have been used. The evaluation of the approach shows that variations in the settings, regarding traffic or atmospheric conditions, lead to different patterns of observed pollution. The CyberGIS environment described is used to run multiple simulations on a distributed cyberinfrastructure, where the high-end computational resources are available on servers in Europe and in North America.

This chapter describes some of the rapid developments in data collection and analysis through the processing of data collected and archived in real time that are capable of generating new insights into urban processes that in time, might lead to new theories of how cities function. It will focus on London both for its strategic importance as a global city, but also because its citizens are highly connected, and thus capable of generating a large number of datasets. These are individual-based and largely built from the bottom up. We believe that many of these aspects of London’s new data system will soon be replicated in other European cities, and it is clear that similar developments are already taking place in other world cities such as New York City and Singapore; it thus provides a useful basis on which to demonstrate another role of CyberGIS and its related technologies through the analysis of ‘big’ data, albeit in the urban domain.

In this chapter, we present and discuss an adaptable cyberinfrastructure (e-Infrastructure) for urban research. We illustrate the benefits of a loosely coupled service-oriented architecture-based design pattern for the internal architecture of this e-Infrastructure. This is presented in the context of the Australian Urban Research Infrastructure Network (AURIN), which provides an urban research environment across Australia supporting access to large amounts of highly distributed and heterogeneous data with accompanying analytical tools. The system is being reactively designed based on evolving and growing requirements from the community. We discuss the differences between more common spatial data infrastructures (SDIs) and eResearch infrastructures, and describe the unique AURIN environment set up to provide these additional features. The different aspects of loose coupling in internal architectures are examined in the context of the implemented components of the AURIN system. We conclude by discussing the benefits as well as challenges of this system architecture pattern for meeting the needs of urban researchers.

An increasingly rich array of geographic information, such as real-time sensor data, public web pages, social media data, and dynamic maps, are available over the Internet tobe integrated by multiple geographic information science (GIScience) applications that were not possible in the past. The conventional tightly coupled development approach is not suitable to integrate such widely distributed and dynamic data sources. The strictly standards-compliant interoperability, across all distributed geospatial information services, is inadequate; and it is not practical to satisfy the massive needs forvalue-added GIScience application integration from users and developers. Mashup has begun to play a major role in integrating applications. This chapter systematically examines the scientific and technical importance of mashup and illustrates how it enables the development of value-added GIScience applications. The Loma Linda University Medical Center (LLUMC) Advanced Emergency GIS (AEGIS)is used to demonstrate a mashup approach. AEGIS provides a Web-based visualization and decision-support tool to monitor emergencies, track victims and emergency-response personnel, and evaluate factors that impact emergency response. AEGIS achieves near-real-time map-based integration of data from more than a dozen disparate sources, including hospital locations and emergency room diversion status, highway traffic and incidents, location and availability of air ambulances and rescue airships, status of mobile telemedicine vehicles, and weather conditions, all with different data formats and update frequencies. AEGIS demonstrates the use of mashup as an effective means to achieve interoperability, and highlights the new conceptualization of geographic information as dynamic, distributed, and diverse.

Efficient end-to-end parallel/distributed processing of vector-based spatial data has been a long-standing research question in GIS community. The irregular and data intensive nature of the underlying computation has impeded the research in this space. We have created an open-architecture-based system named Crayons for Azure cloud platform using state-of-the-art techniques. The design and development of Crayons system is an engineering feat both due to (i) the emerging nature of the Azure cloud platform which lacks traditional support for parallel processing, and (ii) the tedious exploration of design space for suitable techniques for parallelizing various workflow components including file I/O, partitioning, task creation, and load balancing. Crayons is an open-source system available for both download and online access, to foster academic activities. We believe Crayons to be the first distributed GIS system over cloud capable of end-to-end spatial overlay analysis. We demonstrate how Azure platforms storage, communication, and computation mechanisms can support high performance computing (HPC) application development. Crayons scales well for sufficiently large data sets, achieving end-to-end absolute speedup of over 28-fold employing 100 Azure processors. For smaller, more irregular workload, it still yields over 9-fold absolute speedup.

Recent years have seen the emergence of many new and valuable spatial datasets such as trajectories of cell-phones and Global Positioning System (GPS) devices, vehicle engine measurements, global climate models simulation data, volunteered geographic information (VGI), geo-social media, and tweets. The value of these datasets is already evident through many societal applications including disaster management and disease outbreak prediction. However, these location-aware datasets are of a volume, variety, and velocity that exceed the capability of current CyberGIS technologies. We refer to these datasets as Spatial Big Data. In this chapter, we define spatial big data in terms of its value proposition and user experience which depends on the computational platform, use-case, and dataset at hand. We compare spatial big data with traditional spatial data and with other types of big data. We then provide an overview of the current efforts, challenges and opportunities available when spatial big data is enabled via next-generation CyberGIS. Our discussion includes current accomplishments and opportunities from both an analytics and an infrastructure perspective.

This chapter introduces a CyberGIS solution that aims at resolving the big data challenges in the discovery, search, visualization and interoperability of geospatial data. We describe a service-oriented architecture to make heterogeneous geospatial resources easily sharable and interoperable. OGC standards for sharing vector data, raster data, sensor observation data etc. are adopted in such an infrastructure because of their widespread popularity in the GIScience community. Three supporting techniques include: (1) a novel method that combines real-time Web crawling and meta-cataloging in support of quick identification and discovery of distributed geospatial services; (2) an ontology-enabled semantic search framework to enhance the relevancy search and ranking; (3) multi-dimensional visualization of diverse interrelated dataset for discovering underlying patterns and decision-making. Finally, we introduce two applications: Landsat Image Service Archive (LISA) and the ESIP (Earth Science Information Partnership) Semantic Web Testbed to demonstrate the applicability of proposed techniques in various Earth Science domains.

Agent-based models (ABMs) (also referred to as multi-agent systems, or MAS, in literature) are widely used to model complex adaptive systems (CAS) through representing dynamic non-linear interactions amongst a large number of heterogeneous agents and their environments. However, these models often oversimplify real-life decisions and lack the cognitive authenticity present in real-world interactions. In this paper we argue that although role-playing games (RPGs) and massive multi-player online games were developed separately from ABMs, both exhibit high levels of spatially situated participation or collaboration, social interaction, and knowledge construction. We describe an online map-based gaming platform which simulates spatial scenarios as MAS using human participants as the decision agents. We use our prototype to demonstrate and discuss challenges that cyberGIS faces towards the facilitation of massively multi-user computational resources and methods along with the opportunities for a cyberGIS framework to provide improved understanding of complex systems.

While high-performance computing is a fundamental component of CyberGIS, equally important is establishing a fundamental connection between CyberGIS and the various user communities requiring it. This involves the sharing, communication, and collaboration of authoritative, relevant spatial science not only among GIS specialists within their respective organizations, but across related scientific disciplines, between government agencies, and even to interested citizens seeking easy access to complex spatial analysis through a tailored, simplified user experience. In order to best to achieve such effective sharing and collaboration, one must also seek to understand the advantages and limitations of cloud computing in the context of spatial computation. We briefly introduce some key concepts of cloud GIS, followed by several use cases ranging from optimizing community resource allocation decisions, to coastal and marine spatial planning, to assessing solar energy potential in urban areas, to understanding river and watershed dynamics. These examples underscore the great potential for CyberGIS to provide as a fundamental component an environment for users of varying background and abilities an environment in which to perform and evaluate spatial analyses in a “community playground” of datasets, maps, scripts, web-based geoprocessing services, and GIS analysis models. Indeed, exposing the power of spatial analysis to a larger audience (the non-GIS audience) may be the biggest long-term value of CyberGIS, helping it toward the ultimate goals of facilitating communication and collaboration, breaking down barriers between institutions, disciplines and cultures, and fostering a better connection between CyberGIS and its many communities.

In the age of Web 2.0, many online environments support user-generated content and social media integration; however, to enable collaborative problem-solving in CyberGIS we must go beyond information dissemination and provide an integrated analytic-deliberative collaboratory—an environment for high-performance collaboration. The Structured Participation Toolkit (SPT) allows structured participation methods to be seamlessly integrated into the CyberGIS Gateway to support large-scale, asynchronous participation; structured analytic deliberation; consensus-building and decision-making; as well as to provide an open, transparent decision repository and participation metrics for reporting and analysis. The SPT is deployed as pluggable graphical user interface widgets to enable cross-platform and cross-domain communication based on a service-oriented architecture approach.