DTM Generalisation: Handling Large Volumes of Data for Multi-Scale Mapping

Map generalisation is a modelling process in which it is typical that detailed, high dimensional geographic phenomena are reduced down to a set of 'higher order', yet more generalized set of phenomena (for example, a large cluster of buildings is reduced to 'city'). This process of generalisation necessarily requires us to handle large volumes of data which results in high processing overheads. One way of managing this is to partition the data. When geographically partitioning data, we need to partition in such a way that each partition can be generalized without having to consider regions outside any given partition. The focus of this paper is to explore partitioning and generalisation methodologies that can be applied to digital elevation data - the ambition being to derive generalized descriptions of morphology at the National Scale (for the UK). The paper describes and compares two solutions to this problem, and demonstrates how it is possible to apply generalisation algorithms to national coverages.