OGC Catalog Services: a key element for the development of Spatial Data Infrastructures

Spatial data infrastructures for cities in developing countries

This paper elaborates research prospects in the recent scenario, which identifies the use of Spatial Data Infrastructure (SDI) in the design of interdisciplinary geo-information systems for decision support systems to handle energy linked disasters at different scales for all user groups. The realization of the SDI potential as subsidiary for resolving sustainable local & global energy complications depends on multiple factors including reliable access to well-harmonized information. The work also introduces to an emerging digital geo-information infrastructure and policy framework at global, regional, national and local levels. The concept of SDI as a tool presents the notion for networked SDIs, designed and implemented to serve various specific applications at the national, regional or municipal level. Handling energy linked disasters could be such an application sector. The work illuminates the necessity of the legal and regulatory changes to be done in order to achieve the SDI framework. The work wishes at comprehending role of SDI in providing policy solutions for complex problems embedded with energy linked disaster caused due to conflict over energy issues with a scientific challenge. The work also examines the relationship between competition for energy resources and the inclination for conflict in the coming future taking the argument well beyond issues of energy politics and the significance of oil and gas to the global market, the work offers significant new findings concerning the impact of energy wealth on life and the economies. It also explores the ways attempting to uphold the energy interests in the newly independent states, and the impact of competition on regional security. Believe it or not, conflict is an untapped source for creativity and energy. The paper proposes a prototype with a set of recommended practices in the development of SDI on clear political accountability to its integrity to address the role as a framework for facilitating local and global energy linked disaster. It is discussed that the design and implementation of an SDI model framework can assist the managing agencies to improve the quality of their decision-makings at all level of the activities. This includes the development of a prototype web-based system which can facilitate sharing, access and use of data in energy linked disaster management and especially disaster response.

Abstract. Geo-information techniques have proven their usefulness for the purposes of early warning and emergency response. These techniques enable us to generate extensive geo-information to make informed decisions in response to natural disasters that lead to better protection of citizens, reduce damage to property, improve the monitoring of these disasters, and facilitate estimates of the damages and losses resulting from them. The maintenance and accessibility of spatial information has improved enormously with the development of spatial data infrastructures (SDIs), especially with second-generation SDIs, in which the original product-based SDI was improved to a process-based SDI. Through the use of SDIs, geo-information is made available to local, national and international organisations in regions affected by natural disasters as well as to volunteers serving in these areas. Volunteer-based systems for information collection (e.g., Ushahidi) have been created worldwide. However, the use of 3D maps is still limited. This paper discusses the applicability of 3D geo-information to disaster management. We discuss some important aspects of maps for disaster management, such as user-centred maps, the necessary components for 3D maps, symbols, and colour schemas. In addition, digital representations are evaluated with respect to their visual controls, i.e., their usefulness for the navigation and exploration of the information. Our recommendations are based on responses from a variety of users of these technologies, including children, geospecialists and disaster managers from different countries.

The development of a spatial data infrastructure to support marine spatial planning in Greece

In the wake of the so-called information technology revolution, many stakeholders from the public and private sectors (including citizens) have indeed grown accustomed to the promise and usability of spatial data infrastructures (SDI) for data access, use, and sharing. Analyzing the obstacles as well as the processes and mechanisms of integration and implementation, Spatial Data Infrastructures in Context: North and South investigates the technological and the non-technological aspects of the widespread adoption of spatial data infrastructures. Supporting theoretical issues with empirical studies, the editors pay particular attention to the non-technological aspects of organizational, financial, and legal issues including owner rights, liability, copyrights, and compatibility with precedent and supercedent laws. The authors also highlight the importance of understanding the local environment and circumstances in the process of tailoring the approaches to the conditions that characterize societies of different cultural, institutional, and economic settings. Designed to improve the accessibility, interoperability, and affordability of spatial data, the book focuses on the increasing challenges associated with integrating individuals and organizations into a network to support (1) public authorities and administrations at various levels, (2) thematic user communities, (3) enterprises, and (4) citizen-oriented society as a whole. It addresses the implementation and development of spatial data infrastructures for a wide range of themes, applicable technical standards and protocols, and specific organizational issues unique to data policy. Highlighting the potential for profound changes to the access, use, and exchange of spatial data for citizens, organizations, and geographically related applications, and therefore to the role and interaction of the stakeholders from the public and private sectors, this timely contribution provides new insights into improving our understanding of the increasing relevance, applicability, and value of spatial data infrastructures.

Cartography and spatial data infrastructures (SDIs) are mutually dependent on each other: SDIs provide geographic information for maps; and maps help to understand geographic information provided by an SDI. SDIs emerged in the 1990s when paper maps were replaced by map production from digital geographic information. They evolved from government-funded initiatives within the geographic information science (GISc) community to SDIs with a variety of data producers and an audience much beyond the relatively small GISc community. SDIs continue to evolve in response to new technologies and developments. This chapter traces SDI evolution over the last three decades and considers how SDI technical developments have impacted map production and how they are likely to impact map production in the future.

The need to create multi-participant, decision-supported environments to address the issues of sustainable development and improving the quality of life creates a growing need to organise data across disciplines and organisations through different forms of spatial data infrastructure (SDI). This infrastructure is fundamentally a concept about facilitation and coordination of the exchange and sharing of spatial data between stakeholders from different jurisdictional levels in the spatial data community. The concept is well explained as an integrated, multi-levelled hierarchy of interconnected SDIs based on partnerships at corporate, local, state/provincial, national, regional (multi-national) and global (GSDI) levels. The creation of such an infrastructure and understanding the role of dynamic partnerships within an SDI hierarchy are essential to develop any SDI initiative. This paper reviews the nature and concept of SDIs, including the SDI hierarchy, which has helped to build understanding about the impo...

Ensuring effective Ambient Intelligence based upon the wide range of static as well as real-time datasets required to manage a transport infrastructure such as a motorway, is a daunting task. This paper presents how this challenge has been faced through the development of a service-based networked infrastructure capable to make a wide range of Geographical Information (GI) available to the operators of a major Italian motorway. In doing so, the system has used open standards from ISO and Open Geospatial Consortium. Unlike current Spatial Data Infrastructures (SDIs), which address relatively static data in time slices, the work presented allows, similarly to traditional GIS, to manage true dynamic data available through geo-referenced sensors along the motorway. The main objective of the project described within this paper was to link existing systems and networks to deliver a comprehensive, coordinated and sustained intelligent infrastructure which can be used to improve management of the motorway as a system. This approach follows the vision of Ambient Spatial Intelligence, which explores how to better embed intelligent features to respond to spatio-temporal queries and to better monitor geographical events within built and natural environments. The combination of information available from different sources (GI, sensor networks, etc.) with information on traffic and vehicle situation is essential to provide context awareness to operators. The networked platform developed has extended the traditional concept of SDIs with an unprecedented volume of real-time geo-referenced sensor data, deployed along the motorway path, all characterized by high spatial and temporal resolution.

Nowadays, Spatial Data Infrastructures (SDIs) play an important role in government agencies, at different levels: global, national, and local. They aim to improve the management and sharing of geospatial data. Nonetheless, these SDIs have been developed as information islands, in which a user's query is compared to metadata described only in their own catalog services. The lack of interaction among SDIs limits the potential of these infrastructures in providing geospatial data to a larger audience. This article presents a distributed architecture, based on a federation of SDIs which interact among themselves, using query propagation. This propagation facilitates data discovery and sharing. We also describe a distributed query processing service used to enable the resource discovery in distributed infrastructures.

This research presents an operational framework to assess organizational Spatial Data Infrastructures (SDIs) from a risk perspective to develop a stable SDI. The core of the framework is constructed based on a survey, fuzzy inference system and cluster analysis, providing quantitative indicators to measure and prioritize the risks to SDI. This framework could mainly contribute to identifying, mitigating or avoiding the potential risks of different aspects of an SDI, such as spatial data and information, organizational and technological aspects. Additionally, it could be considered as an approach that supports multi-view SDI assessment framework toward a more comprehensive assessment of SDIs. A prototype implementation to assess and prioritize the risks of the spatial data and information demonstrates the framework merit, flexibility and usability for assessing the risks of SDI initiatives at different levels, such as organizational, local and national levels; however, the risks and SDIs change over time; thus, the development of stable SDI initiatives depends on a continuous process for coping with the risks.

With the dramatic development of spatial data infrastructure, CyberGIS has become significant for geospatial data sharing. Due to the large number of concurrent users and large volume of vector data, CyberGIS faces a great challenge in how to improve performance. The real-time visualization of vector maps is themost common function in CyberGIS applications, and it is time-consuming especially when the data volume becomes large. So, how to improve the efficiency of visualization of large vector maps is still a significant research direction for GIScience scientists. In this research, we review the existing three optimization strategies, and determine that the third category strategy (i.e., parallel optimization) is appropriate for the real-time visualization of large vector maps. One of the key issues of parallel optimization is how to decompose the real-time visualization tasks into balanced sub tasks while taking into consideration the spatial heterogeneous characteristics. We put forward some rules that the decomposition should conform to, and design a real-time visualization framework for large vector maps. We focus on a balanced decomposition approach that can assure efficiency and effectiveness. Considering the spatial heterogeneous characteristic of vector data, we use a "horizontal grid, verticalmultistage" approach to construct a spatial point distribution information grid. The load balancer analyzes the spatial characteristics of the map requests and decomposes the real-time viewshed into multiple balanced sub viewsheds. Then, all the sub viewsheds are distributed to multiple server nodes to be executed in parallel, so as to improve the realtime visualization efficiency of large vector maps. A group of experiments have been conducted by us. The analysis results demonstrate that the approach proposed in this research has the ability of balanced decomposition, and it is efficient and effective for all geometry types of vector data.

This paper addresses the state of spatial data infrastructures within North America in late 1997. After providing some background underlying the philosophy and development of the SDI concept, the authors discuss effects of technology, institutions, and standardization that confront the cohesive implementation of a common infrastructure today. The paper concludes with a comparative framework and specific examples of elements and initiatives defining respective spatial data infrastructure initiatives in the United States and Canada.

Abstract. The Japanese government, local governments and businesses are working closely together to establish spatial data infrastructures in accordance with the Basic Act on the Advancement of Utilizing Geospatial Information (NSDI Act established in August 2007). Spatial data infrastructures are urgently required not only to accelerate computerization of the public administration, but also to help restoration and reconstruction of the areas struck by the East Japan Great Earthquake and future disaster prevention and reduction. For construction of a spatial data infrastructure, various guidelines have been formulated. But after an infrastructure is constructed, there is a problem of maintaining it. In one case, an organization updates its spatial data only once every several years because of budget problems. Departments and sections update the data on their own without careful consideration. That upsets the quality control of the entire data system and the system loses integrity, which is crucial to a spatial data infrastructure. To ensure quality, ideally, it is desirable to update data of the entire area every year. But, that is virtually impossible, considering the recent budget crunch. The method we suggest is to update spatial data items of higher importance only in order to maintain quality, not updating all the items across the board. We have explored a method of partially updating the data of these two geographical features while ensuring the accuracy of locations. Using this method, data on roads and buildings that greatly change with time can be updated almost in real time or at least within a year. The method will help increase the availability of a spatial data infrastructure. We have conducted an experiment on the spatial data infrastructure of a municipality using those data. As a result, we have found that it is possible to update data of both features almost in real time.

With the advancement of GIS technology since its inception in 1960’s, many educational institutions, government departments, public/ private sectors and individuals have started its use for the production and management of spatial data. Spatial Data Infrastructure (SDI) concept was introduced in the early1990’s and provides a set of technologies, standards, protocols, policies and guidelines on the whole cycle of geospatial data production and distributions, i.e., from data capture to storage and to sharing. SDI initiative at national level, termed as National Spatial Data Infrastructure (NSDI), has been taken by different countries including India. Geospatial community is facing various challenges like handling of large volumes of geospatial data, requirement of high computing resources to process geospatial data, scalability and interoperability. Therefore, need of advanced technologies in the form of SDI and cloud computing is realized to resolve the above challenges. Cloud computing has several characteristics like scalability, elasticity and self-provisioning that offers high-performance computing resources to perform geoprocessing efficiently. The main aim of the present paper is to study SDI and its components along with analysis and comparison of NSDI of various countries as well as to conceptualize and discuss service oriented architecture of cloud enabled SDI. Several challenges of the spatial data handling and processing that occurred due to the high intensity of data and lack of processing capability can be solved by adopting proposed cloud enabled SDI architecture. This research will help geospatial community and SDI developers in various perspectives including data sharing and management, interoperability, security and reliability, fault tolerance, mass market solution, upfront cost and global collaboration.

Abstract. Education on spatial data infrastructures is an important building block of study programs focusing on spatial data. The focus is on equipping students with essential skills for future contributions to spatial data infrastructure development and application. Despite a wide range of available teaching materials, their reuse is hindered by a lack of harmonization and integration into learning modules. This paper reports on the activities in the "Open Educational Resources for Spatial Data Infrastructures" (OER4SDI) project, which addresses this gap by developing easily reusable teaching materials for spatial data infrastructures. OER4SDI strives to create high-quality, findable, accessible, interoperable, and easily reusable (FAIR) educational resources, fostering efficient knowledge exchange in the field of spatial data infrastructures. The project puts a special focus on the modularity of open educational resources to enable reuse in a wide variety of settings. We describe the workflows and technical setup we use to enable collaborative development of open educational resources and their continuous revision, which is vital in this rapidly evolving field. Three example open educational resources for spatial data infrastructures are given, dealing with the "AAA Data Catalog", "OGC Open API Features", and "Knowledge Graphs". We share the lessons learned from the project to foster the development of more high-quality open educational resources in our community.