Improving cartographic generialization focusing on point clustering in interactive maps (PhD project) 

Aggregation techniques used in interactive maps often lead to maps where the spatial context of the original data is drastically changed and distributions are not apparent anymore. The resulting maps are used in search, pattern recognition and other cognitive tasks that benefit from or even require a representative visualisation of the original data.

We aim to improve upon the status quo developing a new, tailored approach. With a focus on point display in web maps we plan to supply pre-computed seed points to aid cluster initialisation and definition via semantic knowledge. Leveraging correlations of many datasets to a shared "parent", such as the distribution of many VGI datasets closely resembling population distribution (at least down to a certain scale) should allow to cluster that parent dataset once and use the resulting parameters to cluster the many correlating datasets for low cost. A tight coupling between the desired visualisation technique and the clustering approach will allow to tune both aspects in unison.

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