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Detection of hierarchical crowd activity structures in geographic point data
The pervasive adoption of GPS-enabled sensors has lead to an explosion on the amount of geolocated data that captures a wide range of social interactions. Part of this data can be conceptualized as event data, characterized by a single point signal at a given location and time. Event data has been u...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138037/ https://www.ncbi.nlm.nih.gov/pubmed/35634120 http://dx.doi.org/10.7717/peerj-cs.978 |
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author | Salazar, J. Miguel López-Ramírez, Pablo S. Siordia, Oscar |
author_facet | Salazar, J. Miguel López-Ramírez, Pablo S. Siordia, Oscar |
author_sort | Salazar, J. Miguel |
collection | PubMed |
description | The pervasive adoption of GPS-enabled sensors has lead to an explosion on the amount of geolocated data that captures a wide range of social interactions. Part of this data can be conceptualized as event data, characterized by a single point signal at a given location and time. Event data has been used for several purposes such as anomaly detection and land use extraction, among others. To unlock the potential offered by the granularity of this new sources of data it is necessary to develop new analytical tools stemming from the intersection of computational science and geographical analysis. Our approach is to link the geographical concept of hierarchical scale structures with density based clustering in databases with noise to establish a common framework for the detection of crowd activity hierarchical structures in geographic point data. Our contribution is threefold: first, we develop a tool to generate synthetic data according to a distribution commonly found on geographic event data sets; second, we propose an improvement of the available methods for automatic parameter selection in density-based spatial clustering of applications with noise (DBSCAN) algorithm that allows its iterative application to uncover hierarchical scale structures on event databases and, lastly, we propose a framework for the evaluation of different algorithms to extract hierarchical scale structures. Our results show that our approach is successful both as a general framework for the comparison of crowd activity detection algorithms and, in the case of our automatic DBSCAN parameter selection algorithm, as a novel approach to uncover hierarchical structures in geographic point data sets. |
format | Online Article Text |
id | pubmed-9138037 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91380372022-05-28 Detection of hierarchical crowd activity structures in geographic point data Salazar, J. Miguel López-Ramírez, Pablo S. Siordia, Oscar PeerJ Comput Sci Algorithms and Analysis of Algorithms The pervasive adoption of GPS-enabled sensors has lead to an explosion on the amount of geolocated data that captures a wide range of social interactions. Part of this data can be conceptualized as event data, characterized by a single point signal at a given location and time. Event data has been used for several purposes such as anomaly detection and land use extraction, among others. To unlock the potential offered by the granularity of this new sources of data it is necessary to develop new analytical tools stemming from the intersection of computational science and geographical analysis. Our approach is to link the geographical concept of hierarchical scale structures with density based clustering in databases with noise to establish a common framework for the detection of crowd activity hierarchical structures in geographic point data. Our contribution is threefold: first, we develop a tool to generate synthetic data according to a distribution commonly found on geographic event data sets; second, we propose an improvement of the available methods for automatic parameter selection in density-based spatial clustering of applications with noise (DBSCAN) algorithm that allows its iterative application to uncover hierarchical scale structures on event databases and, lastly, we propose a framework for the evaluation of different algorithms to extract hierarchical scale structures. Our results show that our approach is successful both as a general framework for the comparison of crowd activity detection algorithms and, in the case of our automatic DBSCAN parameter selection algorithm, as a novel approach to uncover hierarchical structures in geographic point data sets. PeerJ Inc. 2022-05-19 /pmc/articles/PMC9138037/ /pubmed/35634120 http://dx.doi.org/10.7717/peerj-cs.978 Text en ©2022 Salazar et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Salazar, J. Miguel López-Ramírez, Pablo S. Siordia, Oscar Detection of hierarchical crowd activity structures in geographic point data |
title | Detection of hierarchical crowd activity structures in geographic point data |
title_full | Detection of hierarchical crowd activity structures in geographic point data |
title_fullStr | Detection of hierarchical crowd activity structures in geographic point data |
title_full_unstemmed | Detection of hierarchical crowd activity structures in geographic point data |
title_short | Detection of hierarchical crowd activity structures in geographic point data |
title_sort | detection of hierarchical crowd activity structures in geographic point data |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138037/ https://www.ncbi.nlm.nih.gov/pubmed/35634120 http://dx.doi.org/10.7717/peerj-cs.978 |
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