Spatial Data Infrastructures

Abstract included in text

Assessment of spatial data infrastructures

A Spatial Data Infrastructure (SDI) is an evolving concept, essentially consisting of policies, institutional arrangements, Geographical Information Systems (GISs), data bases, networks, Web services and portals to facilitate and coordinate the availability, exchange and sharing of geospatial data and services between stakeholders from different levels. This article aims to provide some information on the role and value of SDIs and their potential relationship with, and contribution to other geospatial and evidence-based tools and technologies within the South African planning context and system. For this, it provides a brief overview and comparison of the key characteristics of the SDIs in South Africa, China, Brazil, Australia and India. The article highlights some of the complexities and use of an SDI and the value of, and need for an SDI to support the spatial and land development planning envisaged in the Spatial Planning and Land Use Management Act (SPLUMA)

Linked Data and Spatial Data Infrastructures

Spatial Data Infrastructures (SDIs) such as AuScope Grid and INSPIRE are being planned and built using discovery, access and processing components based on a services model. While the principle of distribution and delegation using the internet is a major step forward from traditional data warehouses and private collections, the query-oriented interaction paradigm is merely evolutionary, compared with traditional access systems designed for expert users. In contrast, the success and scalability of the world wide web has been based on hypertext, in which browsing is the key mode of interaction, supported by Universal Resource Identifiers (URIs). Linked Data has been proposed as the bridge from the browseable web to the deep web of technical data. Linked Data is still based on web-pages (usually HTML) for user interactions, but supported by Resource Description Framework (RDF) for richer link semantics. Links can resolve to datasets in legacy file formats which thus serve as leaf-nodes, but can also be part of the web of resources. Key standards used in SDI were designed on Linked Data principles, even before the name was coined. For instance, Geography Markup Language (GML) is essentially an RDF/XML application. Thus, in principle, SDIs should integrate seamlessly into the web of linked data. There are, however, a number of issues to consider or resolve in order to bring this about. These include: the definition of 'resource' in the context of databases that are accessed as projected subsets by query, accessed through web-service interfaces; multiple representations of the same feature from different services to support different applications; semantics embedded in structured representations expressed in non-RDF XML forms; standard vocabulary and identifier services.JRC.H.6-Digital Earth and Reference Dat

Methods to Improve and Evaluate Spatial Data Infrastructures

This thesis mainly focuses on methods for improving and evaluating Spatial Data Infrastructures (SDIs). The aim has been threefold: to develop a framework for the management and evaluation of an SDI, to improve the accessibility of spatial data in an SDI, and to improve the cartography in view services in an SDI. Spatial Data Infrastructure has been identified as an umbrella covering spatial data handling procedures. The long-term implementation of SDI increases the need for short/middle term feedbacks from different perspectives. Thus, a precise strategic plan and accurate objectives have to be defined for the implementation of an efficient environment for spatial data collection and exchange in a region. In this thesis, a comprehensive study was conducted to review the current methods in the business management literature to approach to an integrated framework for the implementation and evaluation of SDIs. In this context, four techniques were described and the usability of each technique in several aspects of SDI implementation was discussed. SDI evaluation has been considered as one of the main challenges in recent years. Lack of a general goal oriented framework to assess an SDI from different perspectives was one of the main concerns of this thesis. Among a number of the current methods in this research area, we focused on the Balanced Scorecard (BSC) as a general evaluation framework covering all perspectives in an SDI. The assessment study opened a window to a number of important issues that ranged from the technical to the cartographic aspects of spatial data exchange in an SDI. To access the required datasets in an SDI, clearinghouse networks have been developed as a gateway to the data repositories. However, traditional clearinghouse networks do not satisfy the end user requirements. By adding a number of functionalities, we proposed a methodology to increase the percentage of accessing required data. These methods were based on predefined rules and additional procedures within web processing services and service composition subjects to develop an expert system based clearinghouses. From the cartography viewpoint, current methods for spatial data presentation do not satisfy the user requirements in an SDI environment. The main presentation problem occurs when spatial data are integrated from different sources. For appropriate cartography, we propose a number of methods, such as the polygon overlay method, which is an icon placement approach, to emphasize the more important layers and the color saturation method to decrease the color saturation of the unimportant layers and emphasize the foreground layer according to the visual hierarchy concept. Another cartographic challenge is the geometrical and topological conflicts in data shown in view services. The geometrical inconsistency is due to the artificial discrepancy that occurs when displaying connected information from different sources, which is caused by inaccuracies and different levels of details in the datasets. The semantic conflict is related to the definition of the related features, i.e., to the information models of the datasets. To overcome these conflicts and to fix the topological and geometric conflicts we use a semantic based expert system by utilizing an automatic cartography core containing a semantic rule based component. We proposed a system architecture that has an OWL (Web Ontology Language) based expert system to improve the cartography by adjusting and resolving topological and geometrical conflicts in geoportals

Hierarchical Spatial Reasoning Applied to Spatial Data Infrastructures

Many countries throughout the world believe they can benefit both economically and environmentally from better management of their spatial data assets, enabling them to access and retrieve complete and consistent datasets in an easy and secure way. This has resulted in the development of the Spatial Data Infrastructure (SDI) concept at various political and/or administrative levels. The SDI concept has been represented by different descriptions of its nature, however, currently these demonstrate an overly-simplistic understanding of the concept. The simplicity in existing definitions has been slow to incorporate the concept of an integrated, multilevelled SDI formed from a hierarchy of inter-connected SDIs at corporate, local, state/provincial, national, regional (multi-national) and global (GSDI) levels. Failure to incorporate this multidimensionality, and the dynamic mechanistic and functional roles of the SDI have rendered many descriptions of SDI inadequate to describe the complexity and the dynamics of SDI as it develops,and thus ultimately constrain SDI achieving developmental potential in the future. As a result, the objective of this paper is to demonstrate the fitness and applicability of Hierarchical Spatial Reasoning (HSR) as a theoretical framework to demonstrate the multi-dimensional nature of SDIs. It is argued that by better understanding and demonstrating the nature of an SDI hierarchy, any SDI development can gain support from a wider community of both government and nongovernment data users and providers. The findings presented in this paper build on the authors experiences in Regional SDI (multi-national) development and HSR

Spatial data infrastructures for cities in developing countries: lessons from the Bangkok experience

The cities of the developing world face major problems in managing growth and their urban infrastructure. The experiences of cities in the developed world have usually proved inappropriate in developing countries and consequently new and innovative solutions are continually being explored for these cities. Geographic information systems and the underlying spatial data infrastructures appear to offer significant potential to assist in managing human settlements in developing countries. In examining the status of spatial data infrastructures in developing countries, the first part of the paper compares cities in developing countries to those in the developed world. It then highlights issues facing developing countries in establishing spatial data infrastructures to support efficient urban land management. The paper describes the Bangkok Land Information System as an example of a city attempting to use spatial information technologies to manage the urban environment. This case study, together with the experience of the authors, are used to describe generic issues involved in the development of spatial data infrastructures for cities in similar socio-economic conditions. The paper provides a technical and institutional framework for the development of spatial data infrastructures for cities in developing countries. The paper concludes by suggesting that simple, low cost, project oriented, easily maintained and user-friendly spatial information technologies have the best chance of success