Geospatial information infrastructures

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreflectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeflexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments

FOSS4G 2016 Proceedings: Academic Program - selected papers and posters

This Conference Proceedings is a collection of selected papers and posters submitted to the Academic Program of the International Conference for Free and Open Source Software for Geospatial (FOSS4G 2016), 24th to 26th August 2016 in Bonn, Germany. Like in previous FOSS4G conferences on national and international level the academic papers and posters cover an extensive wide range of topics reflecting the contribution of the academia to this field by the development of open source software components, in the design of open standards, in the proliferation of web-based solutions, in the dissemination of the open principles important in science and education, or in the collection and the hosting of freely available geo-data

Geospatial queries on data collection using a common provenance model

Altres ajuts: Xavier Pons is the recipient of an ICREA Academia Excellence in Research Grant (2016-2020)Lineage information is the part of the metadata that describes "what", "when", "who", "how", and "where" geospatial data were generated. If it is well-presented and queryable, lineage becomes very useful information for inferring data quality, tracing error sources and increasing trust in geospatial information. In addition, if the lineage of a collection of datasets can be related and presented together, datasets, process chains, and methodologies can be compared. This paper proposes extending process step lineage descriptions into four explicit levels of abstraction (process run, tool, algorithm and functionality). Including functionalities and algorithm descriptions as a part of lineage provides high-level information that is independent from the details of the software used. Therefore, it is possible to transform lineage metadata that is initially documenting specific processing steps into a reusable workflow that describes a set of operations as a processing chain. This paper presents a system that provides lineage information as a service in a distributed environment. The system is complemented by an integrated provenance web application that is capable of visualizing and querying a provenance graph that is composed by the lineage of a collection of datasets. The International Organization for Standardization (ISO) 19115 standards family with World Wide Web Consortium (W3C) provenance initiative (W3C PROV) were combined in order to integrate provenance of a collection of datasets. To represent lineage elements, the ISO 19115-2 lineage class names were chosen, because they express the names of the geospatial objects that are involved more precisely. The relationship naming conventions of W3C PROV are used to represent relationships among these elements. The elements and relationships are presented in a queryable graph

GIS-based Software Infrastructure to Model PV Generation in Fine-grained Spatio-temporal Domain

Nowadays, we are moving forward to more sustainable societies, where a crucial issue consists on reducing footprint and greenhouse emissions. This transition can be achieved by increasing the penetration of distributed renewable energy sources together with a smarter use of energy. To achieve it, new tools are needed to plan the deployment of such renewable systems by modelling variability and uncertainty of their generation profiles. In this paper, we present a distributed software infrastructure for modelling and simulating energy production of Photovoltaic (PV) systems in urban context. In its core, it performs simulations in a spatio-temporal domain exploiting Geographic Information Systems together with meteorological data to estimate Photovoltaic generation profiles in real operating conditions. This solution provides results in real-sky conditions with different time-intervals: i) yearly, ii) monthly and iii) sub-hourly. To evaluate the accuracy of our simulations, we tested the proposed software infrastructure in a real world case study. Finally, experimental results are presented and compared with real energy production data collected from PV systems deployed in the case study area

Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research

Scientific reproducibility is essential for the advancement of science. It allows the results of previous studies to be reproduced, validates their conclusions and develops new contributions based on previous research. Nowadays, more and more authors consider that the ultimate product of academic research is the scientific manuscript, together with all the necessary elements (i.e., code and data) so that others can reproduce the results. However, there are numerous difficulties for some studies to be reproduced easily (i.e., biased results, the pressure to publish, and proprietary data). In this context, we explain our experience in an attempt to improve the reproducibility of a GIScience project. According to our project needs, we evaluated a list of practices, standards and tools that may facilitate open and reproducible research in the geospatial domain, contextualising them on Peng’s reproducibility spectrum. Among these resources, we focused on containerisation technologies and performed a shallow review to reflect on the level of adoption of these technologies in combination with OSGeo software. Finally, containerisation technologies proved to enhance the reproducibility and we used UML diagrams to describe representative work-flows deployed in our GIScience project.This work has been funded by the Generalitat Valenciana through the “Subvenciones para la realización de proyectos de I+D+i desarrollados por grupos de investigación emergentes” programme (GV/2019/016) and by the Spanish Ministry of Economy and Competitiveness under the subprogrammes Challenges-Collaboration 2014 (RTC-2014-1863-8) and Challenges R+D+I 2016 (CSO2016-79420-R AEI/FEDER, EU). Sergio Trilles has been funded by the postdoctoral programme PINV2018 - Universitat Jaume I (POSDOC-B/2018/12) and stays programme PINV2018 - Universitat Jaume I (E/2019/031)

Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research

Scientific reproducibility is essential for the advancement of science. It allows the results of previous studies to be reproduced, validates their conclusions and develops new contributions based on previous research. Nowadays, more and more authors consider that the ultimate product of academic research is the scientific manuscript, together with all the necessary elements (i.e., code and data) so that others can reproduce the results. However, there are numerous difficulties for some studies to be reproduced easily (i.e., biased results, the pressure to publish, and proprietary data). In this context, we explain our experience in an attempt to improve the reproducibility of a GIScience project. According to our project needs, we evaluated a list of practices, standards and tools that may facilitate open and reproducible research in the geospatial domain, contextualising them on Peng’s reproducibility spectrum. Among these resources, we focused on containerisation technologies and performed a shallow review to reflect on the level of adoption of these technologies in combination with OSGeo software. Finally, containerisation technologies proved to enhance the reproducibility and we used UML diagrams to describe representative work-flows deployed in our GIScience project

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

Building an Online Learning and Research Environment to Enhance Use of Geospatial Data

Geospatial data availability, interoperability, and integration remain a problem today. Current spatial data infrastructures (SDIs) are of limited use particularly to non-expert user communities. GeoBrain, a NASA funded project, has aimed to address those challenges, facilitate easy use of geospatial data and overcome some limitations of current SDIs through building a data-intensive, Service-oriented Architecture (SOA) based online learning and research environment. By adopting the latest developing Web services and knowledge management technologies, this online environment enables easy, open, seamless, and on-demand discovery, access, retrieval, visualization and analysis of distributed geospatial data, information, services, and models from any computer connected to the Internet. Such an online environment is able to serve the different needs of global Earth sciences research and higher education communities, bridge gaps between data user needs and provider capabilities, and greatly enhance use of geospatial data