Map Generalization

Abstract

Map generalization is concerned with the optimal display of geographic information in map form. It involves the careful selection of data and the use of a range of abstraction techniques that seek to give emphasis to what is important, while still including sufficient contextualizing information. If geographic phenomena are stored at fine levels of detail, the goal of automated map generalization is to derive maps at coarser (smaller) scales. Traditionally, the focus has been on paper maps, but increasingly map generalization services are available via the web, and accessible to users with relatively little cartographic knowledge who wish to integrate data from multiple sources (including their own). These high levels of automation require us to make explicit the relationships and behaviors among geographic phenomena, in order that we can reason about the complex decision‐making process that is cartographic design.

Automatic (Tactile) Map Generation—A Systematic Literature Review

“…We managed to extract many algorithms that can be used for the generalization of spatial data. Douglas-Peucker along with Visvalingam-Whyatt are well-known algorithms for simplifying line objects that appeared frequently across the studies [29,41]. The first one focuses on choosing line vertices to keep in the generalized version (bases on linear offset of each vertex), while the latter selects the ones to be deleted (uses area of displacement of each vertex).…”

“…The results might be interesting not only for practitioners of cartography, but also for everyone else willing to involve maps in their daily work for spatial data representation. Today, due to democratization of cartography, everyone is a cartographer and can make his or her own maps [29]. Researchers, on the other hand, might learn about the current state of technology and find inspiration for future research.…”

Finding Optimal Sequences for Area Aggregation—A ⋆ vs. Integer Linear Programming

“…If we do not require that each aggregation step must involve a smallest patch, then we only need constraints ( 26)- (40) and variables x t,p,r , y t,p,o,r , z t,p,q,r , and c t,p,o,r . If we insist on involving a smallest patch at each step, then we need more variables and more constraints.…”

“…A requirement of map generalization is to emphasize the essential while suppressing the unimportant, and at the same time maintain logical relationship between objects [76]. As manual generalization is labor-intensive [16], automating map generalization is a promising way to produce up-to-date maps at high speed and low cost [40].…”