Cadastre of Known Accuracy

Developing an algorithm to measure the quality of the NSW cadastre

The Challenge

A “cadastre” is an official register of property showing boundaries. The Digital Cadastral Database (DCDB) is a state-wide digital representation of the cadastre of New South Wales (NSW) and a component of the foundation datasets within the NSW Spatial Data Infrastructure. The initial capture of DCDB data began in 1988 by digitising the best available cadastral mapping at a range of scales and accuracies. The DCDB upgrade program commenced in 2007 and is ongoing, improving the spatial accuracy of different feature classes. Upgraded positional accuracy varies across the state and generally ranges from less than 5m from true position in rural areas to less than 0.2m from true position in urban areas, dependent on the Survey control available. NSW Spatial Services wanted to develop a set of evidence-based information services to support the proposed NSW Cadastre of Known Accuracy. This is to manage perceptions of the quality of the DCDB and provide users with transparent, easily accessible quality measures to help them determine the fitness-for-purpose of the DCDB.


The project partners were the NSW Department of Finance, Services and Innovation and Symbolix.

The Solution 

The vision for the Cadastre of Known Accuracy was to have a simple approach, such as ±0.75m, to convey the quality of measurements of a parcel anywhere in NSW. The research project aimed to:

  1. To develop an algorithm to measure the quality of the NSW Digital Cadastre Database (DCDB) using sample data from West Sydney
  2. To make the prototype quality measures for sample area available for visualisation on SLIM as hosted by Spatial Services
  3. To extend the prototypes to whole of state, subject to approval of Project Leader

It was identified that the two measures, Harmonic Mean Shift (a simple bias measure) and Dispersion (measuring variability) were designed to match the envisioned approach while complementing each other. Together with a third measure, Cadpoint Density (indicating pertinence), they provided a holistic picture of quality and were deemed appropriate measures of quality.

By examining historical measurements linked to each individual cadpoints of the DCDB, it was noted that different types of measurements contributed differently to the quality measures. In view of this observation, the agile approach undertaken by the collaborating organisations allowed the third objective to be pivoted to generating a Hybrid Harmonic Mean Shift as an alternative and acceptable quality measure for NSW DCDB. This used historical measurements based on reliable survey methods and quality image offset vector data while excluding unreliable measurements and measurements derived as part of the DCDB maintenance process.


Feedback from NSW staff was very positive, with results identified as being valuable in providing nuanced understanding the quality issues of the NSW DCDB. This value was seen through new understanding and visibility of how various types of measurements affect the quality measures, as well as the importance of input data filtering. The Hybrid Harmonic Mean Shift layer indicates that current image offset vectors can be comparable to the good quality measurements of the DCDB. Closer analyses of this layer and its generation process may suggest a way forward in the future management of the DCDB using image offset vectors, which are now more easily available and of lower cost and higher quality. In general, the methods developed by Symbolix and demonstrated through the prototype quality measure layers provide a mathematically robust, consistent and repeatable way of generating reliable quality measures based on statistical theory.

The team identified three recommendations, outlined below, and an additional three medium- to long-term recommendations exploring various options to improve quality measures.


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