Automated Generalization of Facility Points-of-Interest With Service Area Delimitation

Abstract

Point cluster generalization is a major concern in cartography. This problem is complicated by how to preserve the distribution patterns (e.g., density) of points during the process of deriving small-scale maps from a large-scale map. To address this problem, existing methods adopt geometric models such as planar Voronoi diagram, which considers the geographic space as an ideal Euclidean one. The associated operations determine the relationships of points based on the Euclidean straight-line distance. However, many map features are in different geographic environments. For example, as one of the most significant features on a map, urban facilities rely on the transport function of street networks to compete for service areas, and thus their distributions, in reality, are constrained to the spatial layout of street networks. To preserve such characteristic after generalization, this paper establishes a network-constrained Voronoi model for delimitating service areas of facility points-of-interest (POIs). With the help of Voronoi model, the proposed method further treats the generalization process of facility POIs as a compete and merge iterative process of service areas: a facility that has a relatively small service area faces more intense competition from its neighbors and by deleting it from the resulting small-scale map in the first priority its service area will be partitioned and merged to the neighbors. The experimental results demonstrate that our method considers the service patterns of facility POIs is particularly useful in applications related to navigation.

Keywords

• Point pattern
• cartography
• map generalization
• spatial database
• points-of-interest