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

Recommendations for a polar Earth science portal in the context of Arctic Spatial Data Infrastructure

In recent years, the Arctic has been one of the most dynamic environments on Earth. As a result, scientific inquiry into the region has produced a large amount of data, with future projections of the volume of data for the Arctic as well as the Antarctic expected to increase by an order of magnitude. On the receiving end of these data, a challenge remains as how to best manage, archive and distribute the scientific observations so that they may be easily studied, analyzed and modeled. Aim The aim of this study is to analyze infrastructure studies and standards development efforts for the Arctic environment in order to recommend a way forward for polar science data dissemination. Methodology In this study, a discussion will be presented on the development of scientific data standards for the Arctic. The methods for implementing infrastructure in the Arctic will be considered in relation to current trends and best practices in data management and cyberinfrastructure. Relevant publications, feedback from researchers who use the data, and workshop documents resulting from discussions about science data management will be used as the information upon which to base these recommendations. The needs, goals and trends of the science community as a whole will be considered in order to propose a way forward. Results Developing a Spatial Data Infrastructure (SDI) specific to the Arctic will allow for seamless sharing of heterogeneous data. Also, this study found that the unique aspects of mapping at the polar regions point the way to implementing a method of science data dissemination via a scientific data portal specific to both the north and south polar regions.In recent years, the Arctic has been one of the most dynamic environments on Earth. Changes in the Arctic climate have been occurring at nearly twice the rate of the rest the world during the last 100 years (IPCC, 2007). This has resulted in increased scientific inquiry into the Arctic, and beyond to the Antarctic, as both polar regions pose similar questions for scientists. Satellites have been launched, airborne missions are under way, and field expeditions have been undertaken to collect scientific data that may be used to study these areas of recent change. On the receiving end of these data, a challenge remains as how to best manage, archive and distribute the scientific observations so that they may be easily studied, analyzed and modeled. Due to the importance of the location of the measurements of Earth science data, working from a shared representation of geographic features in these areas facilitates the use of the data across different platforms. Such shared standards can be defined as Spatial Data Infrastructure (SDI). SDI provides a base upon which the data can be structured to allow for widespread use and understanding of the information. In the Arctic, shared geographic data standards, or Arctic SDI, has yet to be defined. Because of the increase of scientific inquiry in this area, defining an Arctic SDI would be beneficial. Additionally, developing a centralized interface from which to distribute the scientific observations from these areas would facilitate the research efforts underway. Such a distribution center would likely take the form of a scientific data portal or Earth browser, which would utilize the standards identified by the SDI. This interface could service science data from the Arctic as well as science data from the Antarctic because of the unique methods for mapping at the poles. Shared development initiatives towards a data portal between the Arctic and Antarctic data management communities would result in more unified and succinct polar science. Thus, defining an Arctic SDI and sharing portal development initiatives with the Antarctic community would be of great benefit to those seeking a better understanding of the changing Arctic

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

Web service-based exploration of Earth Observation time-series data for analyzing environmental changes

The increasing amount of Earth observation (EO) data requires a tremendous change, in order to property handle the number of observations and storage size thereof. Due to open data strategies and the increasing size of data archives, a new market has been developed to provide analysis and application-ready data, services, and platforms. It is not only scientists and geospatial processing specialists who work with EO data; stakeholders, thematic experts, and software developers do too. There is thus a great demand for improving the discovery, access, and analysis of EO data in line with new possibilities of web-based infrastructures. With the aim of bridging the gap between users and EO data archives, various topics have been researched: 1) user requirements and their relation to web services and output formats; 2) technical requirements for the discovery and access of multi-source EO time-series data, and 3) management of EO time-series data focusing on application-ready data. Web services for EO data discovery and access, time-series data processing, and EO platforms have been reviewed and related to the requirements of users. The diversity of data providers and web services requires specific knowledge of systems and specifications. Although service specifications for the discovery of EO data exist, improvements are still necessary to meet the requirements of different user personas. For the processing of EO time-series data, various data formats and processing steps need to be handled. Still, there remains a gap between EO time-series data access and analysis tools, which needs to be addressed to simplify work with such data. Within this thesis, web services for the discovery, access, and analysis of EO time-series data have been described and evaluated based on different user requirements. Standardized web services specifications, output and data formats are proposed, introduced and described to meet the needs of the different user personas

European Arctic Initiatives Compendium

Julkaistu versi

An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

abstract: Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making

D3.2. ENEON methodology for management and coordination and first plenary Workshop minutes

The report on the ENEON plenary Workshop (WS2) will gather the minutes and all the information regarding the plenary. The workshops will also be used to build a collection of frameworks and best practices across domains and stakeholders that will be collected in this deliverable. The deliverable also describes the new aspect about ENEON methodology for management and coordination. It is important to differentiate this deliverable from "D6.1 ConnectinGEO methodology" that deals with the gap analysis and priorities that uses the ENEON knowhow as input

Technologies for a FAIRer use of Ocean Best Practices

The publication and dissemination of best practices in ocean observing is pivotal for multiple aspects of modern marine science, including cross-disciplinary interoperability, improved reproducibility of observations and analyses, and training of new practitioners. Often, best practices are not published in a scientific journal and may not even be formally documented, residing solely within the minds of individuals who pass the information along through direct instruction. Naturally, documenting best practices is essential to accelerate high-quality marine science; however, documentation in a drawer has little impact. To enhance the application and development of best practices, we must leverage contemporary document handling technologies to make best practices discoverable, accessible, and interlinked, echoing the logic of the FAIR data principles [1]

Evaluating the Arctic SDI: An Assessment of the Foundations needed for Success

The Arctic encompasses eight countries and has a population of over four million people. With datasets produced by private and public stakeholders all over the world and noted gaps in data for many parts of the region, there is an opportunity to collaborate and create a unified Spatial Data Infrastructure (SDI) for the Arctic. This research identified a set of criteria for evaluating the long-term efficacy of the Arctic SDI from an organizational perspective and not from a user’s perspective. Through the external assessment, half of the countries were found to be strong contributors - almost equally contributing in terms of deliverables, resources and leadership to the Arctic SDI. These three themes developed based on a critical evaluation of the existing SDI literature. While the other half countries contributed noticeably less - due to a lack of deliverables, less participation in working groups or little or no resource contributions. Complementing theses (external) assessments, also internal reviews were conducted via semi-structured interviews, which obtained the participants’ view of the Arctic SDI collaboration potential successes and shortcomings. The interviewees identified opportunities, limitations and risks as they perceived them. Most of the issues associated with the opportunities, limitations and risks could be cross-validated with the external assessment criteria. However, the importance of communication was strongly emphasized in the interviews and was not represented by the external assessment criteria. The completion of both the external and internal assessments led to the multi-view framework that can be used to assess the long-term potential of the Arctic SDI. This evaluation tool can also be used for defining tasks and clarifying responsibilities for the next 5-year Memorandum of Understanding (2019-2024) or to assess the Arctic SDI to identify challenges and mitigation measures that would assist in its longevity. This tool can also be used for other regional SDIs to define MoUs and assess the potential for success