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.