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Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and A Case Study on Monitoring Coastal Fecal Coliform Pollution Events
Xu, F.; Beard, K. A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study. Geographies2023, 3, 303-320.
Xu, F.; Beard, K. A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study. Geographies 2023, 3, 303-320.
Xu, F.; Beard, K. A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study. Geographies2023, 3, 303-320.
Xu, F.; Beard, K. A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study. Geographies 2023, 3, 303-320.
Abstract
Examining the similarity of event environments or surroundings, more precisely settings, provides additional insight in analyzing event sequences as it provides information about the context and potential common factors that may have influenced them. This article proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been studied. While modeling event setting sequences we consider spatial and temporal scales to define the bounds of the setting and incorporates dynamic variables alongside static variables. Using a matrix-based representation and an extended Jaccard index we developed new similarity measures that allow for the use of all variable data types. We successfully used these similarity measures coupled with other multivariate statistical analysis approaches in a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, which validate the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. In conclusion, these similarity measures have many potential real-world applications, and offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
