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Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat. Near real-time and high spatial resolution mapping of human sentiments of heat exposure with Twitter data An integrated cyberGIS and machine learning framework for visualizing heat exposure with Twitter data Human sentiment of heat exposure mapping in the City of Chicago and the United States [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]

Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses. [ABSTRACT FROM AUTHOR]

The unprecedented availability of geospatial data and technologies is driving innovation and discovery but not without the risk of losing focus on the geographic foundations of space and place in this vast "cyber sea" of data and technology. There is a pressing need to educate a new generation of scientists and citizens who understand how space and place matter in the real world and who understand and can keep pace with technological advancements in the computational world. We define cyberliteracy for GIScience (cyberGIScience literacy) and outline eight core areas that serve as a framework for establishing the essential abilities and foundational knowledge necessary to navigate and thrive in this new technologically rich world. The core areas are arranged to provide multiple dimensions of learning ranging from a technological focus to a problem solving focus or a focus on GIScience or computational science. We establish a competency matrix as a means of assessing and evaluating levels of cyberGIScience literacy across the eight core areas. We outline plans to catalyze the collaborative development and sharing of instructional materials to embed cyberGIScience literacy in the classroom and begin to realize a cyberliterate citizenry and academe. Key Words: big data, computational thinking, geographic education, GIS, spatial thinking. [ABSTRACT FROM AUTHOR]

This paper surveys the rapid development of the Internet of Things, the massive data streams that are only now beginning to be generated from it, and the resulting opportunities and challenges that these data streams bring to geographic information analysis. These challenges arise because streaming data volumes cannot bt subjected to analysis using the standard repertoire of methods that have been designed to analyze static geospatial datasets. New approaches are needed, not to supplant, but to supplement, these existing tools. A focus is placed on the concept of data velocity (fast data) and its effects on sampling and inference. Innovative data ingestion strategies based on principles related to reservoir sampling and sketching are described. Dynamic temporal data flows present significant challenges to load balancing in distributed (e.g. cloud) parallel environments, even at exascale levels of performance. Further advances in the exploitation of data locality based on geographical concepts, as well as advanced processing methods based on edge and approximate computing, require further elucidation. Concepts are illustrated using a database compiled from a distributed sensor network of mobile radioactivity detectors. [ABSTRACT FROM AUTHOR]

This paper describes a multidimensional spatial scan statistics approach to comparing spatial movement patterns based on origin-destination (OD) representation. This approach aims to evaluate differences and similarities between the spatial distributions of a pair of OD movement datasets, and detect areas where the two spatial distributions differ the most. Specifically, two OD datasets being compared are modeled as a bivariate marked spatial point process in a multidimensional space, consisting of points representing individual OD movement records. Such multidimensional space is formed by the Cartesian product of the origins’ and the destinations’ geographic spaces. With this spatial data model, one can evaluate how two movement distributions differ from each other by testing against a random labeling null hypothesis. A multidimensional Bernoulli spatial scan statistics method is developed to detect OD region pairs with abnormally high concentrations of one movement dataset over the other. The existence and the spatial extents of these OD region pairs indicate whether and where the two movement distributions differ. Two case studies were conducted to evaluate the approach by comparing morning and afternoon taxi trips (individual movements), and county-to-county migration flows between age groups (aggregated movement flows), and demonstrated that areas with the most significant spatial distribution differences could be detected from large movement datasets. [ABSTRACT FROM AUTHOR]

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 usingTwitterdata. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data. [ABSTRACT FROM PUBLISHER]

Spatial objects can be interconnected and mutually dependent in complex ways. In Geographical Information Science, spatial objects’ topological relationships are not discussed together with their attributes’ dependencies, and the vagueness of spatial objects is often ignored during the spatial modelling process. To address this, a spatial fuzzy influence diagram (SFID) is introduced. Compared to the traditional statistical or fuzzy modelling approach, the influence diagram brings advantages in helping decision-makers structure complex interdependency problems. A questionnaire was developed to evaluate the applicability of using an influence diagram in modelling spatial objects’ dependencies. As a case study, an SFID is applied to tree-related electric outages. The result of the case study is represented as a vulnerability map of electrical networks. The map shows areas at risk due to tree-related electric outages. The results were first validated by using a visual comparison of the vulnerability map and electricity fault data. In the second validation step, the percentage of fault data, which has received values in different vulnerability categories, was calculated. The results of the case study can be used to support the decision-making process of electrical network maintenance and planning. [ABSTRACT FROM AUTHOR]

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intra- and interregional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, nonoverlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space. [ABSTRACT FROM PUBLISHER]

This article establishes a new methodological framework for parallelizing spatial data processing called parallel cartographic modeling, which extends the widely adopted cartographic modeling framework. Parallel cartographic modeling adds a novel component called a Subdomain, which serves as the elemental unit of parallel computation. Four operators are also added to express parallel spatial data processing, namely scheduler, decomposition, executor, and iteration. A parallel cartographic modeling language (PCML) is developed based on the parallel cartographic modeling framework, which is designed for usability, programmability, and scalability. PCML is a domain-specific language implemented in Python for the domain of cyberGIS. A key feature of PCML is that it supports automatic parallelization of cartographic modeling scripts; thus, allowing the analyst to develop models in the familiar cartographic modeling language in a Python syntax. PCML currently supports more than 70 operations and new operations can be easily implemented in as little as three lines of PCML code. Experimental results using the National Science Foundation-supported Resourcing Open Geospatial Education and Research computational resource demonstrate that PCML efficiently scales to 16 cores and can process gigabytes of spatial data in parallel. PCML is shown to support multiple decomposition strategies, decomposition granularities, and iteration strategies that be generically applied to any operation implemented in PCML. [ABSTRACT FROM AUTHOR]

This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas. We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems. [ABSTRACT FROM AUTHOR]

Computational and data sciences are transforming the entire science enterprise. In the arena of GIS, this is represented by the emergence of cyberGIS. We provide an overview of applying the cyberGIS approach to spatial analysis for health studies. We emphasize that cyberGIS is not just aserviceto traditional spatial analyses, but itself is an alternative approach to problem solving. Some fundamental and profound distinctions of cyberGIS approaches in health-GIS include the following: (1) they may greatly reduce the reliance on models or assumptions, and instead seek actual empirical evidence through mining a large amount of data orvirtualempirical evidence generated through computation; (2) they tend to be non-parametric and tend to generate local solution; (3) they are scalable to high-resolution and less aggregated data; (4) they tend to be stochastic rather than deterministic; and (5) with these approaches, the large amount of data may not be only from input data-sets, but also from analytical workflows. We described the kernel ratio estimation for local intensity estimation, therestrictedandcontrolledMonte Carlo for data disaggregation, andunrestrictedandcontrolledMonte Carlo for statistical significance evaluation as examples of the cyberGIS approaches in health-GIS. [ABSTRACT FROM PUBLISHER]

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]

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. [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]

Landscapes are complex adaptive spatial systems driven by biophysical and socioeconomic processes that are shaped by the cumulative behavior of interacting, but independent decision-makers. Agent-based modeling has been identified as an effective paradigm for the study of such systems because of its ability to represent behavior and interaction at the individual level. These interactions are inherently associated with cause-effect relations in the transition of land use systems through time. Elucidating specific cause and effect relations from model results, however, poses a significant challenge to the research community because of complicated model structures and the complex processes that drive system dynamics. In this article, we call for provenance-based agent-based models of land use dynamics to support the capture and tracking of complex cause-effect relations. We illustrate the importance of explicitly considering provenance in agent-based modeling through the development of a spatially explicit agent-based land use simulation framework. This framework is designed to simulate complex land use dynamics in southwest Montana, USA. Land managers are modeled as geospatial agents who interact with their spatially explicit environments. These agents exchange opinions about land use controls through social networks in an attempt to form consensus on land use policies. The heterogeneous opinion behavior of landowner agents and complicated social structural relationships produce complex dynamics. We conduct experiments to demonstrate the need to explicitly consider provenance in agent-based models of land use systems and its ability to promote our understanding of complex adaptive land use systems. [ABSTRACT FROM AUTHOR]

In this article we present a parallel agent-based model (ABM) to support large-scale simulations of land use change. ABMs are a commonly used simulation approach for the investigation of land use systems. The computationally intense nature of these models, however, often prohibits the development of models that fully capture the complex dynamics of land use systems when using typical desktop computing environments. The search for scientific understanding and solutions to real-world problems is, therefore, often limited by an inability to explore a wide range of scales or the impact of complex interactions. Parallel computing provides a potential solution for this limitation issue. Our ABM is designed using parallel computing to simulate the formation of large-scale land use opinions within spatially explicit environments. Agents, environments, and interactions among agents are distributed among processors through parallel computing strategies, including spatial domain decomposition, ghost zones, and synchronization. We examine the computational performance of the model within a supercomputing environment. It is demonstrated that by leveraging increasingly available high-performance parallel computing resources large-scale ABMs of land use systems can be developed and, ultimately, underlying processes that drive these systems better understood. [ABSTRACT FROM AUTHOR]

Cyberinfrastructure (CI) integrates distributed information and communication technologies for coordinated knowledge discovery. The purpose of this article is to develop a CyberGIS framework for the synthesis of CI, geographic information systems (GIS), and spatial analysis (broadly including spatial modeling). This framework focuses on enabling computationally intensive and collaborative geographic problem solving. The article describes new trends in the development and use of CyberGIS while illustrating particular CyberGIS components. Spatial middleware glues CyberGIS components and corresponding services while managing the complexity of generic CI middleware. Spatial middleware, tailored to GIS and spatial analysis, is developed to capture important spatial characteristics of problems through the spatially explicit representation of computing, data, and communication intensity (collectively termed computational intensity), which enables GIS and spatial analysis to locate, allocate, and use CI resources effectively and efficiently. A CyberGIS implementation—GISolve—is developed to systematically integrate CI capabilities, including high-performance and distributed computing, data management and visualization, and virtual organization support. Currently, GISolve is deployed on the National Science Foundation TeraGrid, a key element of the U.S. and worldwide CI. A case study, motivated by an application in which geographic patterns of the impact of global climate change on large-scale crop yields are examined in the United States, focuses on assessing the computational performance of GISolve on resolving the computational intensity of a widely used spatial interpolation analysis that is conducted in a collaborative fashion. Computational experiments demonstrate that GISolve achieves a high-performance, distributed, and collaborative CyberGIS implementation. [ABSTRACT FROM AUTHOR]

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]

High performance computing has undergone a radical transformation during the past decade. Though monolithic supercomputers continue to be built with significantly increased computing power, geographically distributed computing resources are now routinely linked using high-speed networks to address a broad range of computationally complex problems. These confederated resources are referred to collectively as a computational Grid. Many geographical problems exhibit characteristics that make them candidates for this new model of computing. As an illustration, we describe a spatial statistics problem and demonstrate how it can be addressed using Grid computing strategies. A key element of this application is the development of middleware that handles domain decomposition and coordinates computational functions. We also discuss the development of Grid portals that are designed to help researchers and decision makers access and use geographic information analysis tools. [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.