This chapter provides an overview of what has been achieved so far toward creating a comprehensive set of generalization operators. It contains discussions related to the classification of these operators, and how different classifications have been defined to suite different contexts; it proposes a generic list of generalization operators and a detailed list of implementation of these operators for different types of features. This chapter discusses a virtual toolbox that can be used when designing automatic generalization solutions. Most of the research in generalization assumes that the process can be broken down into a series of logical operations that can be classified according to the type of geometry of the feature, into generalization operators. For instance, a smoothing operator is designed for linear features, while an amalgamation operator works on areal features. Advances in hardware and modeling tools provide new opportunities to develop new types of algorithms. The list of different types of phenomena that need to be processed during the generalization is very large, and the list of representations that one may want to derive for each of them is larger still. Thus, there is always a need to develop new algorithms, particularly those that can take into account the context in which they operate. The chapter concludes by discussing the changing nature of algorithms and operators in response to technological developments and changing contexts of use.
|Original language||English (US)|
|Title of host publication||Generalisation of Geographic Information|
|Editors||William Mackaness, Ann Ruas, L T Sarjakoski|
|Place of Publication||Amsterdam|
|Number of pages||30|
|State||Published - 2007|