The constrained computing and application for intelligent generalization of settlements

Abstract

With the expanding and further development of GIS application, automated mapping has attracted wide attention both in multi-scale expression of spatial data and map automation production. Basing on least squares parameter estimation, we do much intensive research on constrained models and algorithms in generalization of settlements. This paper perfects the constrained conditions for intelligence generalization of building groups and establishes least squares algorithm models for simplification, exaggeration, collapse, merging and displacement of buildings. We realize the generalization of settlement through the constrained solving of building groups all at once. The available and correction of generalization result has been proved through MATLAB programming. © 2011 Springer-Verlag.