Research on automatic generalization methods of geographical spatial data

Abstract

Different geographical phenomena and processes take on diverse laws, this is to say, specific laws of geographical phenomena and processes should be researched on a given spatial scale. In addition, geographical phenomena and processes don't linearly change with spatial scale, which can't obtain by linear interpolation or extrapolation. As a result, we must resolve the problem of multi-scale representation of geographical spatial data, which is also the key technique of Multi-Scale GIS or Scale-Free GIS. At the present time people have to adopt multi-ply representation for the sake of satisfying people's research and production needs on different spatial scales. However, this method has such shortcomings as a good many data redundancies, ensuring no the consistency of the same spatial entity on different spatial scales, low efficiency of updating spatial database and bad real-time characteristic of spatial database. Ideal aim of multi-scale representation of geographical spatial data is deriving desired spatial database of various scales or detail degrees from dominant cartographic database, which is automatic generalization of geographical spatial data. Research fields of automatic generalization of geographical spatial data include theory foundation, concept framework, solution and algorithms of automatic generalization. Therein algorithms of automatic generalization are the technique basis of automatic generalization of geographical spatial data. Since the seventies of the last century people have already put continuously forward a large number of algorithms of automatic generalization. Those algorithms have indeed resolved a good many important problems, but they have some obvious shortcomings and application confines. This article makes a research on those algorithms of automatic generalization, and research contents comprise classification, principle, application confines, merits and drawbacks of algorithms of automatic generalization. This paper also brings forward the research orientation of algorithms of automatic generalization for the future.