Coastal typology development with heterogeneous data sets

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.