Coastal typology development with heterogeneous data sets

Abstract

This paper presents a data-driven expert-guided method of coastal typology development using a large, heterogeneous data set. The development of coastal typologies is driven by a desire to upscale detailed regional information to a global scale in order to study coastal zone function and the effects of global climate change. We demonstrate two methods of automatic typology generation – unsupervised clustering and region growing with agglomerative clustering – and a method of selecting an appropriate number of classes based on the concept of Minimum Description Length. We compare two methods of defining distance between data points with a large number of variables and potentially missing data – average scaled Euclidean distance and maximum scaled difference. To visualize the resulting typologies we use a novel algorithm for assigning colors to different classes of data based on class similarity in a high-dimensional space. This combination of techniques results in a methodology through which one or more experts can easily develop a useful coastline typology with results that are similar to preexisting expert typologies, but which makes the process more quantitative, objective, consistent, and applicable across space and time.