Developing a line-of-sight based algorithm for urban street network generalization

Abstract

This paper presents a generalization algorithm focusing specifically on urban street networks. A street map that uses outlines of blocks to implicitly delineate the shape of streets is essentially a complex polygon with holes in computer graphics. Given such a street map, this algorithm first applies medial axis transformation to derive a rudimentary skeleton of the street network. Because the resulting medial axes tend to exaggeratedly reflect even a minor change in the street shapes, the rudimentary skeleton must undergo a generalization process to become a representation that does not simply resemble the street shapes but is actually informative for understanding its deeper structure. Instead of discarding the street shapes and using only cartographic generalization techniques to simplify the skeleton, the algorithm uses the medial axes as the guide to partition the shape of the streets into individual convex spaces. These initial convex spaces are then successively merged into larger convex spaces following the line-of-sight and least-angle-change principles until the least set of convex space has been achieved. Next, the algorithm uses the same line-of-sight and least-angle-change principles to successively group convex spaces into mutually exclusive convex-space sets until no further grouping can occur. Finally, original medial axes inside each final convex-space set are replaced with a straight-line segment. Collectively, these new line segments form a generalized street network representation that is more favorable in network theory. This new algorithm points out an improved and likely automatic solution of generating appropriate street network representations for spatial configuration analyses of the urban environment.