Abstract
This paper summarizes the research conducted to improve the automatic generalization of man-made water networks for topographic maps by context-dependent pruning (Altena, 2014). The aim of this study was to improve existing thinning methods for map generalization by accounting for landscape types. The results show that it is possible to improve the thinning of water networks by taking into account separate landscape types. On a more abstract level, the study delivers a methodology for the pruning of man-made networks with regard to landscape typology. In addition, it provides a method for evaluating the quality of generalization results for networks. First, previous research on both thinning and evaluation of thinning results is described. Secondly, a selection of existing algorithms are implemented and evaluated by several experiments: identification of landscape variation based on feature morphology and humidity; selection of representative test areas; and geometric network improvement. Results show that the connectivity of the network can be significantly increased. This is important to obtain better generalization results. The final experiments investigated the effectiveness on various landscape types of three different thinning algorithms. The results are evaluated in terms of the amount of thinning, the resemblance of the results to the input data, and the deviation in connectivity. The findings of this research can be used to improve the thinning of artificial networks by applying a customized thinning method to each unique landscape type. In addition, the proposed metrics to measure the effectivity of thinning algorithms – reduction, resemblance and connectivity – have been proved to be appropriate criteria for the comparison of results of alternative thinning approaches.
Highlights
The issue of automated generalization has resounded for decades in the cartographic and academic worlds and has been seen as the 'holy grail' of cartography (Anderson-Tarver et al, 2011)
One area where further development is needed is the pruning of man-made water networks, because prevailing water-thinning algorithms do not deliver satisfying results for man-made water networks
Research aim and questions This research aims to “deliver a methodology for pruning of artificial manmade networks with regard to landscape typology and to research methods to evaluate the quality of generalization results” (Altena, 2014, p. 20), in order to overcome the issues identified
Summary
The issue of automated generalization has resounded for decades in the cartographic and academic worlds and has been seen as the 'holy grail' of cartography (Anderson-Tarver et al, 2011). Altena & Stoter (Revell et al, 2005), and the replacement of the manual generalization production line by a fully automated workflow, at the Dutch Kadaster, of the 1:50k map series (Stoter et al, 2014). Despite these successes, it is acknowledged that further development is necessary. Foundational to this research is the concept of generalization. This concept is best explained by visual examples
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