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Geographic Information Infrastructure
Applications | Technology | Right to Information National Geo-spatial Data Infrastructure: Theories and Technologies A. R. Das Gupta Indian Space Research Organisation Email: arup@ipdpg.gov.in Introduction The key to effective decision making and planning is information. When we address the issues of sustainable development we are looking at a multi dimensional scenario. There are the dimensions of space as represented by the earth and its features, both surface well as sub-surface and the atmosphere that shapes the climate and weather. Add to this the dimension of time which tracks changes which may be as short as a few hours for weather related phenomena to as long as years for phenomena such as forest regeneration. The dimension that is most critical is the human dimension. It represents the people who live and work in, influence and are in turn influenced by, the environment consisting of the earth and its atmosphere. To address such a complex problem the decision maker needs appropriate, accurate and timely information to help reduce uncertainties in the process of decision making. Without a modern operational information infrastructure there will be faulty decisions, trauma for the people and degradation of the environment and quality of life. These issues have been addressed from time to time and perhaps most comprehensively in recent times by Agenda 21. A quick perusal of this Agenda shows that a reliable information infrastructure is a prerequisite. How can such an infrastructure be put in place fast and yet be accurate and dependable? What would such an infrastructure consist of? What are the processes that need to be modified or evolved which will be needed to operate such an infrastructure? This paper will address a few of the key issues. Definitions of Spatial Data Infrastructure The first formal definition of the term ‘National Spatial Data Infrastructure’ was formulated in the US and published in the Federal Register on April 13, 1994. It states: “National Spatial Data Infrastructure (NSDI) means the technology, policies, standards, and human resources necessary to acquire, process, store, distribute, and improve utilisation of geospatial data”. The definition of ‘Global Spatial Data Infrastructure’ follows this closely. It states: “A co-ordinated approach to technology, policies, standards, and human resources necessary for the effective acquisition, management, storage, distribution, and improved utilisation of geo-spatial data in the development of the global community”. Yet another view is that the SDI is that of a system where the general community can expect the geo-spatial data to be available and accessible transparently with networking technology. In this view co-operation and collaboration between several disciplines and the emergence of a strategic plan for the maintenance of databases, which include spatial databases, is a key component of the SDI. A third definition states: “Spatial Data Infrastructure encompasses the data sources, systems, linkages, processes, standards and institutional arrangements involved in delivering spatially-related information (both commercially and publicly held) to the widest possible group of potential users” The Permanent Committee on GIS Infrastructure for Asia and the Pacific, PCGIAP, views the SDI as an infrastructure with the same rationale as roads and telecommunications networks. It states that the governments have a role “on behalf of the community, to provide a common and consistent infrastructure upon which a variety of government, private sector and community activities can take place”. “SDI is needed to support the regions economic growth and its social and environmental objectives, backed by international standards, guidelines and policies on the access of those data.” From these definitions, we can see that the SDI has to cover technology, policies, and processes. It has to deliver services to a very large community The SDI Community In most approaches to information systems, the end user is taken for granted. We would like to reverse the process and begin our quest by identifying the end users and their needs. At the organisational level, this would include both governmental and private agencies. In the area of development, Non-governmental agencies play an important role. Development of new and advanced technologies requires the involvement of institutions for education, training and research. Finally, we must address the community of citizens for whom information is a necessity for survival and development. We can organise these players into three groups. The first are those who generate the data like Survey of India or the National Remote Sensing Agency. The second are those who add value to the data by extracting information from a suite of data. In this category we have a very wide range of players ranging from government departments to commercial enterprises. The third category is the information users who reap the benefit of the availability of information by way of economic growth. In this category we can put administrators, managers, NGOs, farmers co-operatives, individuals and the general public. Each community has its own requirement to be met by the SDI. The generators would like to see an efficient management system for their data. The value addition group would like to have a wide choice of data suites customised to their needs and available at an attractive price. They would also look forward to a market for their products. The third group requires reliable and cheap information at the time and place where it is most needed. All would have to work within a technical and legal framework, which has to be put in, place by the government. As the title of this paper suggests, we will not look into all the ramifications of the SDI but look only at the technologies involved. However, we shall keep in mind the overall requirements so that the technologies are relevant to the needs of the community. Technological Components of the SDI The technological components of a SDI are illustrated in figure -1. We shall discuss these elements one by one. Geodata This forms the core of the SDI. There are multiple techniques for data creation ranging from Rapid Appraisals on the ground to Remotely Sensed Data acquired from space. The key characteristics of data are its accuracy, currency, consistency and uniqueness. Infrastructure data must be good enough to act as the base on which other data sets can be referenced. Prime examples of such data sets are satellite imagery, topographic and cadastral data sets. However, other data sets suffer from a variety of problems.  Fig 1: components of a SDI
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