A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

abstract: Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations

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

A Web-based spatial decision supporting system for land management and soil conservation

Abstract. Today it is evident that there are many contrasting demands on our landscape (e.g. food security, more sustainable agriculture, higher income in rural areas, etc.) as well as many land degradation problems. It has been proved that providing operational answers to these demands and problems is extremely difficult. Here we aim to demonstrate that a spatial decision support system based on geospatial cyberinfrastructure (GCI) can address all of the above, so producing a smart system for supporting decision making for agriculture, forestry, and urban planning with respect to the landscape. In this paper, we discuss methods and results of a special kind of GCI architecture, one that is highly focused on land management and soil conservation. The system allows us to obtain dynamic, multidisciplinary, multiscale, and multifunctional answers to agriculture, forestry, and urban planning issues through the Web. The system has been applied to and tested in an area of about 20 000 ha in the south of Italy, within the framework of a European LIFE+ project (SOILCONSWEB). The paper reports – as a case study – results from two different applications dealing with agriculture (olive growth tool) and environmental protection (soil capability to protect groundwater). Developed with the help of end users, the system is starting to be adopted by local communities. The system indirectly explores a change of paradigm for soil and landscape scientists. Indeed, the potential benefit is shown of overcoming current disciplinary fragmentation over landscape issues by offering – through a smart Web-based system – truly integrated geospatial knowledge that may be directly and freely used by any end user (www.landconsultingweb.eu). This may help bridge the last very important divide between scientists working on the landscape and end users

Shaping digital earth applications through open innovation – setting the scene for a digital earth living lab

Science and policy increasingly request for sustainable development and growth. Similarly, Digital Earth undergoes a paradigm shift to an open platform that actively supports user engagement. While the public becomes able to contribute new content, we recognize a gap in user-driven validation, feedback and requirements capture, and innovative application development. Rather than defining Digital Earth applications top down, we see a need for methods and tools that will help building applications bottom up and driven by community needs. These should include a technology toolbox of geospatial and environmental enablers, which allow to access functional building blocks and content in multiple ways, but – equally important – enable the collaboration within partially unknown stakeholder networks. The validation and testing in real-life scenarios will be a central requirement when approaching the Digital Earth 2020 goals, which were articulated recently. We particularly argue to follow a Living Lab approach for co-creation and awareness rising in relation to environmental and geospatial matters. We explain why and how such a Digital Earth Living Lab could lead to a sustainable approach for developing, deploying, and using Digital Earth applications and suggest a paradigm shift for Virtual Globes becoming forums for research and innovation

Semantic Geodemography and Urban Interoperability

Nowadays there exists an increasing interest on the use of the information collected by cities coming from different resources as data with dynamic nature like the one provided by sensor networks, as static data associated to the socio-technical system that the city performs. As well as the Semantic Sensor Web allows the standardization of data, it is essential to give an appropriate dealing to geo-demographic data. In this paper, an approach to the semantization of the geo-demographic information is presented, with the aim of achieving interoperability within other systems of the geospatial cyberinfrastructure. Furthermore, fundamental aspects of the creation of ontologies by starting from socio-demographical systems are discussed and the process is illustrated with a case study.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606

Urban Knowledge Extraction, Representation and Reasoning as a Bridge from Data City towards Smart City

Urban Data management represents a major challenge in the field of Smart Cities. Its understanding is essential for the development of better smart services, which are a persistent demand in urban policies. From all the sources of data available, those that involve a collective processing of urban information (by the citizens or other collectives) deliver in fact, useful insights into social perception. Such is the case, for example, of data collected from mobile networks. Prior to the design of sociotechnical artifacts in cities, it seems important to extract the qualitative and quantitative opinions, sentiment and feedbacks present in these data. In this paper we present three solutions for mining these contents through Knowledge Extraction methods, as a previous step to the prospection of new smart services.Ministerio de Economía y Competitividad TIN2013-41086-

Digital catchment observatories: A platform for engagement and knowledge exchange between catchment scientists, policy makers, and local communities

Increasing pressures on the hydrological cycle from our changing planet have led to calls for a refocus of research in the sciences of hydrology and water resources. Opportunities for new and innovative research into these areas are being facilitated by advances in the use of cyberinfrastructure, such as the development of digital catchment observatories. This is enabling research into hydrological issues such as flooding to be approached differently. The ability to combine different sources of data, knowledge, and modeling capabilities from different groups such as scientists, policy makers, and the general public has the potential to provide novel insights into the way individual catchments respond at different temporal and spatial scales. While the potential benefits of the digital catchment observatory are large, this new way of carrying out research into hydrological sciences is likely to prove challenging on many levels. Along with the obvious technical and infrastructural challenges to this work, an important area for consideration is how to enable a digital observatory to work for a range of potential end-users, paving the way for new areas of research through developing a platform effective for engagement and knowledge exchange. Using examples from the recent local-scale hydrological exemplar in the Environmental Virtual Observatory pilot project (http://www.evo-uk.org), this commentary considers a number of issues around the communication between and engagement of different users, the use of local knowledge and uncertainty with cloud-based models, and the potential for decision support and directions for future research

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

The Brokering Approach for Multidisciplinary Interoperability: A Position Paper

Global sustainability research requires an integrated multi-disciplinary effort underpinned by a cyber infrastructure able to harness big data and heterogeneous information systems across disciplines. Two approaches are possible to achieve the interoperability desired across such systems and data: federating, and brokering. This position paper argues that the former is appropriate to single discipline or domain environments, but that brokering is more scalable and effective in complex multi-disciplinary domains. The paper identifies the principles of brokering, and gives examples of practical implementation relating to data discovery, semantic searching, and data access achieved in the EuroGEOSS project. The value of the EuroGEOSS brokering approach has been demonstrated in extending the data resources available through the Global Earth Observation System of Systems (GEOSS) from a few hundred to over 28 million in a matter of 3 months. Brokering offers therefore a real chance to facilitate truly multi-disciplinary big data science and address the scientific challenges of our time