ARC Linkage Project - the Integration
of Built and Natural Environmental Datasets in National Spatial
Data Infrastructure Initiatives
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.
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
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
Maintained by:Andrew Binns
Disclaimer: This page, its contents and style, are the responsibility
of the author and do not represent the views, policies or opinions
of The University of Melbourne.
