Cartographic knowledge-based generalisation of spatial features

Abstract

This study has developed a framework for generalisation of geographical features using a knowledge-based solution. The proposed method consists of three major components: • Generalisation Framework - a detailed generalisation framework for deriving multi-scale spatial data has been developed based on an assessment of existing generalisation systems. Generalisation techniques were applied over a test area in the Australian Capital Territory of Australia in order to generalise 1:250,000 national topographic data to produce small scale maps through derivative mapping. Also, a framework for segmentation and generalisation of raster data was developed. Test results shows that all objects derived from the generalisation of land use data over Canberra, Australia, were well classified and mapped with a classification accuracy of 85.5%. • Cartographic Knowledge-based Generalisation – this was achieved through an international cartographic generalisation survey that collected inputs of several national mapping agencies, state mapping agencies and a number of software vendors. The findings from the survey are formulated as a series of cartographic rules to propose and implement a knowledge-based generalisation solution. • Generalisation Expert System - acquired knowledge is utilised to build a knowledge-based solution: a ‘Generalisation Expert System’ (GES) developed in the Java-Python-C programming environments for the delivery of generalised geographical features. Its capabilities are demonstrated in a case study through generalising several line and polyline databases over the study area. The tests demonstrated that the algorithms implemented in GES are able to extract characteristic vertices on the original entity lines and polylines (e.g. for roads) while excluding non-characteristic ones so as to reduce insignificant computation. This study has demonstrated reasonable improvements in Vertex Reduction, Classification and Merge, Enhanced Douglas-Peucker and Douglas-Peucker-Peschier algorithms. The test results also demonstrated that the developed methodology improves the efficiency of line and polyline generalisation. This thesis aims to contribute to generalisation system design and the production of a clear framework for users. Experiments described in the study can be applied to real world problems such as the generalisation of road networks and area features using GES. Future research should be directed towards developing web mapping platforms with generalisation functionality at varying scales.