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This project is an Australian Research Council Linkage Project involving researchers from the Centre for Spatial Data Infrastructure and Land Administration at the University of Melbourne and three industry partners, as listed below. The project also involves collaboration with Working Group 3 (Cadastre) of the UN supported Permanent Committee for GIS Infrastructure for Asia Pacific (PCGIAP), which helps to involve seven different countries in the project.

Project Partners
Project Duration
Personnel
Project Overview
Project Background


Project Partners

DSE, Victoria
Department of Lands, NSW
Geoscience Australia

Project Duration

2005-2007


Personnel

Chief Investigator: Prof Ian Williamson
Senior Research Fellow and Project Coordinator: Dr. Abbas Rjabifard
Research Fellow: Mr Andrew Binns
PhD Student: Mr Hossein Mohammadi


Project Overview

Sustainable development and meeting "the triple bottom line" (economic, social and environmental objectives) requires an understanding of the natural and built landscape in order to observe and monitor change and to create realistic simulations of the evolving environment. This requires access to both built and natural environmental datasets. Over the last decade these needs are being addressed by establishing spatial data infrastructures (SDI) where one of the key objectives is the integration of these datasets, and specifically cadastral (built) and topographic (natural) spatial data.

The problem in Australia is that the states are the custodians of large to medium built and natural datasets while the Federal Government is the custodian of small scale natural datasets. Merging of these datasets at a local level has been achieved to some degree, however, attempts to integrate the datasets at a national level, even where SDIs are well developed, has been hampered by jurisdictional, institutional, administrative and legal issues, in both Australia and internationally. This research will investigate the differences in these forms of data and the justification and policy framework to integrate them in a NSDI.

A flowchart outlining the major areas of research can be downloaded here .pdf

Background Information


Sustainable Development
Spatial Data Infrastructures (SDI)
Integration of Datasets


Sustainable Development

Sustainable development and meeting "the triple bottom line" (economic, social and environmental objectives) requires an understanding of the natural and built landscape in order to observe and monitor change and to create realistic simulations of the evolving environment. This requires access to both built and natural environmental datasets. Over the last decade these needs are being addressed by establishing spatial data infrastructures (SDI) where one of the key objectives is the integration of these datasets, and specifically cadastral (built) and topographic (natural) spatial data (Figure 1). The drive to establish SDIs is also driven by a need for governments and businesses to improve their decision-making and increase efficiency (Gore, 1998), as well as the advent of accessible, powerful information and communications technologies.


Development of SDIs

In simple terms the concept of a Spatial Data Infrastructure (SDI) was developed throughout the world to deliver easier access to spatial data. An SDI facilitates and coordinates the exchange and sharing of spatial data between stakeholders in the spatial data community (Figure 2). SDI is an evolving concept. It is much more than data and goes far beyond surveying and mapping. An SDI comprises data, standards, access network, institutional arrangements and policies, and human resources, and comprises dynamic partnerships between inter- and intra-jurisdictional stakeholders. A fundamental part of any SDI is the spatial referencing system that ensures all positions conform to well defined horizontal and vertical datum’s and to a known quality.

Figure 2 - Components of an SDI

SDIs must be focused and coordinated to maximize investment in data collection, integration and maintenance. Existing SDIs evolved to facilitate cooperation between users and producers of spatial data. If well built, they promote economic development, stimulate better government, and foster environmental sustainability.

Amongst spatial data, cadastral and topographic datasets are the most important for describing the built and natural environment. These datasets are the ‘foundation data’ (Groot and MacLaughlin, 2000) in modern market economies. Cadastral datasets are the accumulation of individual property boundary surveys undertaken by land surveyors. By nature, cadastral data is very different to topographic data which is produced at medium to small scales over large regions using various techniques.


Integration of Built and Natural Environmental Datasets

Amongst spatial data, cadastral and topographic datasets are the most important for describing the built and natural environment. These datasets are the ‘foundation data’ (Groot and MacLaughlin, 2000) in modern market economies. Cadastral datasets are the accumulation of individual property boundary surveys undertaken by land surveyors. By nature, cadastral data is very different to topographic data which is produced at medium to small scales over large regions using various techniques.

Cadastral data is usually large to medium scale (1:500-1:10,000) and focuses primarily on boundaries of land parcels and properties shown within cadastral maps. It usually includes details of size, location and nature of land parcels, and in developed systems, a geo-referenced description of the land. Topographic data primarily represents physical features found on the surface of the earth including rivers and lakes, vegetation, landmark features, and hydrology. Topographic data is generally available at various precisions and scales, and can be represented in both two- and three-dimensional form. The nominal scale of these datasets is normally smaller than cadastral data and ranges from medium to small scale mapping.

In all countries, the two foundation datasets were developed to serve different purposes and are usually managed separately, creating inconsistency. This separation is recognised as a barrier to implementation of sustainable development. Duplication imposes unjustifiable costs on data collection and maintenance. The datasets should adopt the same overarching philosophy and data model to achieve multi-purpose data integration, both vertically and horizontally (Ryttersgaard, 2001). Merging of these datasets at a local level has been achieved to some degree, however, attempts to integrate the datasets at a national level, even here SDIs are well developed, has been difficult and problematic, in both Australia and internationally.

Within Australia, separation of the datasets is further institutionalised by law and jurisdictional competencies. National SDI initiatives for better coordination cannot overcome the institutional or data incompatibility barriers despite needs to maximise benefits from investment in data collection and to better inform land management decisions. Technological opportunities for data sharing alone cannot facilitate holistic comprehension of land as a composite of its built and natural components.

References

Groot, R. and McLaughlin, J. (2000), Geospatial Data Infrastructure: concepts, cases and good practice, Oxford University Press, New York.
Gore, A. (1998), The Digital Earth: understanding our planet in the 21st century, The Australian Surveyor 43(2): 89-91.
Ryttersgaard, J. (2001), Spatial Data Infrastructure – Developing Trends and Challenges, International Conference on Spatial Information for Sustainable Development, 2-5 October 2001, Nairobi, Kenya



Last modified: 09-November-06
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