Spatio-temporal clustering in application
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Date
20/05/2022Author
Gieschen, Antonia
Metadata
Abstract
The importance of machine learning methods in the data analysis of both academic research and
industry applications has advanced rapidly in recent years. This thesis will investigate how a
method of unsupervised machine learning known as clustering can be employed to analyse spatial
and spatio-temporal data from different fields of application. Spatio-temporal data present
a particular challenge. In spatial contexts, the notion of dependency among geographically close
elements needs to be considered when analysing the geographic distance as well as other spatial
components. The temporal dimension of the data makes traditional dissimilarity metrics unsuitable
due to the sequential ordering of data points. For this reason, this thesis will present ways
of overcoming the shortcomings in existing methodologies when applied to these data types. By
doing so, it will contribute to the literature on clustering through innovative extensions, adaptations,
and considerations. The flexibility of clustering will be demonstrated in three different
application contexts in health, finance, and marketing. As such, this thesis will also contribute to
the academic literature in these areas and offer valuable insights into applicable machine learning
methodology for practitioners.