Abstract
Effective settlements generalization for small-scale maps is a complex and challenging task. Developing a consistent methodology for generalizing small-scale maps has not gained enough attention, as most of the research conducted so far has concerned large scales. In the study reported here, we want to fill this gap and explore settlement characteristics, named variables that can be decisive in settlement selection for small-scale maps. We propose 33 variables, both thematic and topological, which may be of importance in the selection process. To find essential variables and assess their weights and correlations, we use machine learning (ML) models, especially decision trees (DT) and decision trees supported by genetic algorithms (DT-GA). With the use of ML models, we automatically classify settlements as selected and omitted. As a result, in each tested case, we achieve automatic settlement selection, an improvement in comparison with the selection based on official national mapping agency (NMA) guidelines and closer to the results obtained in manual map generalization conducted by experienced cartographers.
Highlights
The decision to remove or maintain an object while changing the level of detail requires many features of the object itself and its surroundings to be taken into account
The original road network from the Geographic Object Database (GGOD) database was used, without generalization, as we focused exclusively on settlement selection in this paper
While we looked at the map presenting the selection results for group 1, we noted that the density of the settlement network was better preserved on the map designed using machine learning (ML) than in the basic approach
Summary
The decision to remove or maintain an object while changing the level of detail requires many features of the object itself and its surroundings to be taken into account. This decision constitutes the essential element of cartographic generalization, defined by ICA (International Cartographic Association) as the selection and simplification of information appropriate to the scale and purpose of a map [1]. According to [2], selection, named as elimination, constitutes one of the generalization operators. Selection deals with one or more objects or object classes removal without replacement, it is used to reduce map or database content according to the target detail level. A large settlement located close to a larger one may be excluded, and a smaller settlement not in the neighborhood of any other larger one may be included because of its relative importance [3]
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