A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study
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 fo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Geographies |
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| Online Access: | https://www.mdpi.com/2673-7086/3/2/16 |
| _version_ | 1797594571031445504 |
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| author | Fuyu Xu Kate Beard |
| author_facet | Fuyu Xu Kate Beard |
| author_sort | Fuyu Xu |
| collection | DOAJ |
| description | 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 incorporate 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, the developed similarity measures have wide application beyond the case study to other disciplinary contexts and geographical settings. They offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences. |
| first_indexed | 2024-03-11T02:25:09Z |
| format | Article |
| id | doaj.art-29187ceaeaae4a6aae5ea9d1d3ee67e9 |
| institution | Directory Open Access Journal |
| issn | 2673-7086 |
| language | English |
| last_indexed | 2024-03-11T02:25:09Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geographies |
| spelling | doaj.art-29187ceaeaae4a6aae5ea9d1d3ee67e92023-11-18T10:36:03ZengMDPI AGGeographies2673-70862023-04-013230332010.3390/geographies3020016A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case StudyFuyu Xu0Kate Beard1School of Computing and Information Science, University of Maine, Orono, ME 04469, USASchool of Computing and Information Science, University of Maine, Orono, ME 04469, USAExamining 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 incorporate 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, the developed similarity measures have wide application beyond the case study to other disciplinary contexts and geographical settings. They offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences.https://www.mdpi.com/2673-7086/3/2/16event sequencespatial contextsimilarity measureJaccard indexcluster analysis |
| spellingShingle | Fuyu Xu Kate Beard A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study Geographies event sequence spatial context similarity measure Jaccard index cluster analysis |
| title | A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study |
| title_full | A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study |
| title_fullStr | A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study |
| title_full_unstemmed | A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study |
| title_short | A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study |
| title_sort | novel similarity measure of spatiotemporal event setting sequences method development and case study |
| topic | event sequence spatial context similarity measure Jaccard index cluster analysis |
| url | https://www.mdpi.com/2673-7086/3/2/16 |
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