A vector field model to handle the displacement of multiple conflicts in building generalization

Abstract

In map generalization, the displacement operation attempts to resolve proximity conflicts to guarantee map legibility. Owing to the limited representation space, conflicts may occur between both the same and different features under different contexts. A successful displacement should settle multiple conflicts, suppress the generation of secondary conflicts after moving some objects, and preserve the distribution patterns. The effect of displacement can be understood as a force that pushes related objects away with properties of propagation and distance decay. This study borrows the idea of vector fields from physics discipline and establishes a vector field model to handle the displacement of multiple conflicts in building generalization. A scalar field is first constructed based on a Delaunay triangulation skeleton to partition the buildings being examined (e.g., a street block). Then, we build a vector field to conduct displacement measurements through the detection of conflicts from multiple sources. The direction and magnitude of the displacement force are computed based on an iso-line model of vector field. The experiment shows that this global method can settle multiple conflicts and preserve the spatial relations and important building patterns.

A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales

“…Zhou and Li () have adopted artificial neural networks to acquire quantity and qualification knowledge from existing road networks at different scales and then used this knowledge to determine which and how many roads should be updated in an older, smaller‐scale dataset based on a newer, larger‐scale dataset. In addition, some studies have investigated the problem of maintaining consistency after updating, such as eliminating inconsistencies between river and contour data (Ai, Yang, Zhang, & Tian, ) and conflicting results from street widening changes (Ai, Zhang, Zhou, & Yang, ).…”

Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm

“…Zhang [22] developed a method to recognize center and side pattern alignments. Ai [23] proposed the idea that a building's displacement vector can be considered as a vector in a vector field, introduced a movement vector recession function to describe movement propagations, and incorporated the building pattern recognition results by following Zhang's work.…”

Contextual Building Selection Based on a Genetic Algorithm in Map Generalization

“…Other studies have focused mainly on developing new algorithms for specific generalization operators. These algorithms can be roughly categorized into two types: non-contextual operators for an individual building like simplification [24,36], and contextual operators for building groups such as displacement, selection or typification [4][5][6][7][8][9][22][23][24].…”

“…To solve such conflicts, cartographers have conducted a great deal of research [4][5][6][7][8][9][10][11][12]. In these approaches, displacement has been the major generalization operator [4][5][6][7][8][9].…”