A Data-driven, High-performance and Intelligent CyberInfrastructure to Advance Spatial Sciences

abstract: In the field of Geographic Information Science (GIScience), we have witnessed the unprecedented data deluge brought about by the rapid advancement of high-resolution data observing technologies. For example, with the advancement of Earth Observation (EO) technologies, a massive amount of EO data including remote sensing data and other sensor observation data about earthquake, climate, ocean, hydrology, volcano, glacier, etc., are being collected on a daily basis by a wide range of organizations. In addition to the observation data, human-generated data including microblogs, photos, consumption records, evaluations, unstructured webpages and other Volunteered Geographical Information (VGI) are incessantly generated and shared on the Internet. Meanwhile, the emerging cyberinfrastructure rapidly increases our capacity for handling such massive data with regard to data collection and management, data integration and interoperability, data transmission and visualization, high-performance computing, etc. Cyberinfrastructure (CI) consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high-performance networks to improve research productivity and enable breakthroughs that are not otherwise possible. The Geospatial CI (GCI, or CyberGIS), as the synthesis of CI and GIScience has inherent advantages in enabling computationally intensive spatial analysis and modeling (SAM) and collaborative geospatial problem solving and decision making. This dissertation is dedicated to addressing several critical issues and improving the performance of existing methodologies and systems in the field of CyberGIS. My dissertation will include three parts: The first part is focused on developing methodologies to help public researchers find appropriate open geo-spatial datasets from millions of records provided by thousands of organizations scattered around the world efficiently and effectively. Machine learning and semantic search methods will be utilized in this research. The second part develops an interoperable and replicable geoprocessing service by synthesizing the high-performance computing (HPC) environment, the core spatial statistic/analysis algorithms from the widely adopted open source python package – Python Spatial Analysis Library (PySAL), and rich datasets acquired from the first research. The third part is dedicated to studying optimization strategies for feature data transmission and visualization. This study is intended for solving the performance issue in large feature data transmission through the Internet and visualization on the client (browser) side. Taken together, the three parts constitute an endeavor towards the methodological improvement and implementation practice of the data-driven, high-performance and intelligent CI to advance spatial sciences.Dissertation/ThesisDoctoral Dissertation Geography 201

Search improvement within the geospatial web in the context of spatial data infrastructures

El trabajo desarrollado en esta tesis doctoral demuestra que es posible mejorar la búsqueda en el contexto de las Infraestructuras de Datos Espaciales mediante la aplicación de técnicas y buenas prácticas de otras comunidades científicas, especialmente de las comunidades de la Web y de la Web Semántica (por ejemplo, Linked Data). El uso de las descripciones semánticas y las aproximaciones basadas en el contenido publicado por la comunidad geoespacial pueden ayudar en la búsqueda de información sobre los fenómenos geográficos, y en la búsqueda de recursos geoespaciales en general. El trabajo comienza con un análisis de una aproximación para mejorar la búsqueda de las entidades geoespaciales desde la perspectiva de geocodificación tradicional. La arquitectura de geocodificación compuesta propuesta en este trabajo asegura una mejora de los resultados de geocodificación gracias a la utilización de diferentes proveedores de información geográfica. En este enfoque, el uso de patrones estructurales de diseño y ontologías en esta aproximación permite una arquitectura avanzada en términos de extensibilidad, flexibilidad y adaptabilidad. Además, una arquitectura basada en la selección de servicio de geocodificación permite el desarrollo de una metodología de la georreferenciación de diversos tipos de información geográfica (por ejemplo, direcciones o puntos de interés). A continuación, se presentan dos aplicaciones representativas que requieren una caracterización semántica adicional de los recursos geoespaciales. El enfoque propuesto en este trabajo utiliza contenidos basados en heurísticas para el muestreo de un conjunto de recursos geopesaciales. La primera parte se dedica a la idea de la abstracción de un fenómeno geográfico de su definición espacial. La investigación muestra que las buenas prácticas de la Web Semántica se puede reutilizar en el ámbito de una Infraestructura de Datos Espaciales para describir los servicios geoespaciales estandarizados por Open Geospatial Consortium por medio de geoidentificadores (es decir, por medio de las entidades de una ontología geográfica). La segunda parte de este capítulo desglosa la aquitectura y componentes de un servicio de geoprocesamiento para la identificación automática de ortoimágenes ofrecidas a través de un servicio estándar de publicación de mapas (es decir, los servicios que siguen la especificación OGC Web Map Service). Como resultado de este trabajo se ha propuesto un método para la identificación de los mapas ofrecidos por un Web Map Service que son ortoimágenes. A continuación, el trabajo se dedica al análisis de cuestiones relacionadas con la creación de los metadatos de recursos de la Web en el contexto del dominio geográfico. Este trabajo propone una arquitectura para la generación automática de conocimiento geográfico de los recursos Web. Ha sido necesario desarrollar un método para la estimación de la cobertura geográfica de las páginas Web. Las heurísticas propuestas están basadas en el contenido publicado por os proveedores de información geográfica. El prototipo desarrollado es capaz de generar metadatos. El modelo generado contiene el conjunto mínimo recomendado de elementos requeridos por un catálogo que sigue especificación OGC Catalogue Service for the Web, el estandar recomendado por deiferentes Infraestructuras de Datos Espaciales (por ejemplo, the Infrastructure for Spatial Information in the European Community (INSPIRE)). Además, este estudio determina algunas características de la Web Geoespacial actual. En primer lugar, ofrece algunas características del mercado de los proveedores de los recursos Web de la información geográfica. Este estudio revela algunas prácticas de la comunidad geoespacial en la producción de metadatos de las páginas Web, en particular, la falta de metadatos geográficos. Todo lo anterior es la base del estudio de la cuestión del apoyo a los usuarios no expertos en la búsqueda de recursos de la Web Geoespacial. El motor de búsqueda dedicado a la Web Geoespacial propuesto en este trabajo es capaz de usar como base un motor de búsqueda existente. Por otro lado, da soporte a la búsqueda exploratoria de los recursos geoespaciales descubiertos en la Web. El experimento sobre la precisión y la recuperación ha demostrado que el prototipo desarrollado en este trabajo es al menos tan bueno como el motor de búsqueda remoto. Un estudio dedicado a la utilidad del sistema indica que incluso los no expertos pueden realizar una tarea de búsqueda con resultados satisfactorios

INTELLIGENT CYBERINFRASTRUCTURE FOR BIG DATA ENABLED HYDROLOGICAL MODELING, PREDICTION, AND EVALUATION

Most hydrologic data are associated with spatiotemporal information, which is capable of presenting patterns and changes in both spatial and temporal aspects. The demands of retrieving, managing, analyzing, visualizing, and sharing these data have been continuously increasing. However, spatiotemporal hydrologic data are generally complex, which can be difficult to work with knowledge from hydrology alone. With the assistance of geographic information systems (GIS) and web-based technologies, a solution of establishing a cyberinfrastructure as the backbone to support such demands has emerged. This interdisciplinary dissertation described the advancement of traditional approaches for organizing and managing spatiotemporal hydrologic data, integrating and executing hydrologic models, analyzing and evaluating the results, and sharing the entire process. A pilot study was conducted in Chapter 2, in which a globally shared flood cyberinfrastructure was created to collect, organize, and manage flood databases that visually provide useful information to authorities and the public in real-time. The cyberinfrastructure used public cloud services provided by Google Fusion Table and crowdsourcing data collection methods to provide location-based visualization as well as statistical analysis and graphing capabilities. This study intended to engage citizen-scientists and presented an opportunity to modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters eventually. An observationally based monthly evapotranspiration (ET) product was produced in Chapter 3, using the simple water balance equation across the conterminous United States (CONUS). The best quality ground- and satellite-based observations of the water budget components, i.e., precipitation, runoff, and water storage change were adopted, while ET is computed as the residual. A land surface model-based downscaling approach to disaggregate the monthly GRACE equivalent water thickness (EWT) data to daily, 0.125º values was developed. The derived ET was evaluated against three sets of existing ET products and showed reliable results. The new ET product and the disaggregated GRACE data could be used as a benchmark dataset for researches in hydrological and climatological changes and terrestrial water and energy cycle dynamics over the CONUS. The study in Chapter 4 developed an automated hydrological modeling framework for any non-hydrologists with internet access, who can organize hydrologic data, execute hydrologic models, and visualize results graphically and statistically for further analysis in real-time. By adopting Hadoop distributed file system (HDFS) and Apache Hive, the efficiency of data processing and query were significantly increased. Two lumped hydrologic models, lumped Coupled Routing and Excess STorage (CREST) model and HyMOD model, were integrated as a proof of concept in this web framework. Evaluation of selected basins over the CONUS were performed as a demonstration. Our vision is to simplify the processes of using hydrologic models for researchers and modelers, as well as to unlock the potential and educate the less experienced public on hydrologic models

Automated web-based analysis and visualization of spatiotemporal data

Most data are associated with a place, and many are also associated with a moment in time, a time interval, or another linked temporal component. Spatiotemporal data (i.e., data with elements of both space and time) can be used to assess movement or change over time in a particular location, an approach that is useful across many disciplines. However, spatiotemporal data structures can be quite complex, and the datasets very large. Although GIS software programs are capable of processing and analyzing spatial information, most contain no (or minimal) features for handling temporal information and have limited capability to deal with large, complex multidimensional spatiotemporal data. A related problem is how to best represent spatiotemporal data to support efficient processing, analysis, and visualization. In the era of "big data," efficient methods for analyzing and visualizing large quantities of spatiotemporal data have become increasingly necessary. Automated processing approaches, when made scalable and generalizable, can result in much greater efficiency in spatiotemporal data analysis. The growing popularity of web services and server-side processing methods can be leveraged to create systems for processing spatiotemporal data on the server, with delivery of output products to the client. In many cases, the client can be a standard web browser, providing a common platform from which users can interact with complex server-side processing systems to produce specific output data and visualizations. The rise of complex JavaScript libraries for creating interactive client-side tools has enabled the development of rich internet applications (RIA) that provide interactive data exploration capabilities and an enhanced user experience within the web browser. Three projects involving time-series tsunami simulation data, potential human response in a tsunami evacuation scenario, and large sets of modeled time-series climate grids were conducted to explore automated web-based analysis, processing, and visualization of spatiotemporal data. Methods were developed for efficient handling of spatiotemporal data on the server side, as well as for interactive animation and visualization tools on the client side. The common web browser, particularly when combined with specialized server side code and client side RIA libraries, was found to be an effective platform for analysis and visualization tools that quickly interact with complex spatiotemporal data. Although specialized methods were developed to for each project, in most cases those methods can be generalized to other disciplines or computational domains where similar problem sets exist

Emerging Informatics

The book on emerging informatics brings together the new concepts and applications that will help define and outline problem solving methods and features in designing business and human systems. It covers international aspects of information systems design in which many relevant technologies are introduced for the welfare of human and business systems. This initiative can be viewed as an emergent area of informatics that helps better conceptualise and design new world-class solutions. The book provides four flexible sections that accommodate total of fourteen chapters. The section specifies learning contexts in emerging fields. Each chapter presents a clear basis through the problem conception and its applicable technological solutions. I hope this will help further exploration of knowledge in the informatics discipline

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Developing a Framework for Stigmergic Human Collaboration with Technology Tools: Cases in Emergency Response

Information and Communications Technologies (ICTs), particularly social media and geographic information systems (GIS), have become a transformational force in emergency response. Social media enables ad hoc collaboration, providing timely, useful information dissemination and sharing, and helping to overcome limitations of time and place. Geographic information systems increase the level of situation awareness, serving geospatial data using interactive maps, animations, and computer generated imagery derived from sophisticated global remote sensing systems. Digital workspaces bring these technologies together and contribute to meeting ad hoc and formal emergency response challenges through their affordances of situation awareness and mass collaboration. Distributed ICTs that enable ad hoc emergency response via digital workspaces have arguably made traditional top-down system deployments less relevant in certain situations, including emergency response (Merrill, 2009; Heylighen, 2007a, b). Heylighen (2014, 2007a, b) theorizes that human cognitive stigmergy explains some self-organizing characteristics of ad hoc systems. Elliott (2007) identifies cognitive stigmergy as a factor in mass collaborations supported by digital workspaces. Stigmergy, a term from biology, refers to the phenomenon of self-organizing systems with agents that coordinate via perceived changes in the environment rather than direct communication. In the present research, ad hoc emergency response is examined through the lens of human cognitive stigmergy. The basic assertion is that ICTs and stigmergy together make possible highly effective ad hoc collaborations in circumstances where more typical collaborative methods break down. The research is organized into three essays: an in-depth analysis of the development and deployment of the Ushahidi emergency response software platform, a comparison of the emergency response ICTs used for emergency response during Hurricanes Katrina and Sandy, and a process model developed from the case studies and relevant academic literature is described