Tracking the Spatiotemporal Spread of the Ohio Overdose Epidemic with Topological Data Analysis
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916972869976064 |
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| author | Bermingham, Nicholas White, David Willey, Nathan |
| author_facet | Bermingham, Nicholas White, David Willey, Nathan |
| contents | In recent years, techniques from Topological Data Analysis (TDA) have proven effective at capturing spatial features of multidimensional data. However, applying TDA to spatiotemporal data remains relatively underexplored. In this work, we extend previous studies of disease spread by using the Mapper algorithm to analyze the Ohio drug overdose epidemic from 2007 to 2024. We introduce a novel method for constructing covers in Mapper graphs of spatiotemporal data that respects geographic structure and highlights the time-dependent variables. Finally, we generate a Mapper visualization of regional demographics to examine how these factors relate to overdose deaths. Our approach effectively reveals temporal trends, overdose hotspots, and time-lagged patterns in relation to both geography and community demographics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22705 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tracking the Spatiotemporal Spread of the Ohio Overdose Epidemic with Topological Data Analysis Bermingham, Nicholas White, David Willey, Nathan Applications Algebraic Topology Methodology In recent years, techniques from Topological Data Analysis (TDA) have proven effective at capturing spatial features of multidimensional data. However, applying TDA to spatiotemporal data remains relatively underexplored. In this work, we extend previous studies of disease spread by using the Mapper algorithm to analyze the Ohio drug overdose epidemic from 2007 to 2024. We introduce a novel method for constructing covers in Mapper graphs of spatiotemporal data that respects geographic structure and highlights the time-dependent variables. Finally, we generate a Mapper visualization of regional demographics to examine how these factors relate to overdose deaths. Our approach effectively reveals temporal trends, overdose hotspots, and time-lagged patterns in relation to both geography and community demographics. |
| title | Tracking the Spatiotemporal Spread of the Ohio Overdose Epidemic with Topological Data Analysis |
| topic | Applications Algebraic Topology Methodology |
| url | https://arxiv.org/abs/2509.22705 |