Using cluster analysis methods for multivariate mapping of traffic accidents
Many factors affect the occurrence of traffic accidents. The classification and mapping of the different attributes of the resulting accident are important for the prevention of accidents. Multivariate mapping is the visual exploration of multiple attributes using a map or data reduction technique....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2018-12-01
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| Series: | Open Geosciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/geo-2018-0060 |
| _version_ | 1818670713960136704 |
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| author | Selvi Huseyin Zahit Caglar Burak |
| author_facet | Selvi Huseyin Zahit Caglar Burak |
| author_sort | Selvi Huseyin Zahit |
| collection | DOAJ |
| description | Many factors affect the occurrence of traffic accidents. The classification and mapping of the different attributes of the resulting accident are important for the prevention of accidents. Multivariate mapping is the visual exploration of multiple attributes using a map or data reduction technique. More than one attribute can be visually explored and symbolized using numerous statistical classification systems or data reduction techniques. In this sense, clustering analysis methods can be used for multivariate mapping. This study aims to compare the multivariate maps produced by the K-means method, K-medoids method, and Agglomerative and Divisive Hierarchical Clustering (AGNES) method, which among clustering analysis methods, with real data. The results from the study will suggest which clustering methods should be preferred in terms of multivariate mapping. The results show that the K-medoids method is more appropriate in terms of clustering success. Moreover, the aim is to reveal spatial similarities in traffic accidents according to the results of traffic accidents that occur in different years. For this aim, multivariate maps created from traffic accident data of two different years in Turkey are used. The methods are compared, and the use of the maps produced with these methods for risk management and planning is discussed. Analysis of the maps reveals significant similarities for both years. |
| first_indexed | 2024-12-17T07:12:29Z |
| format | Article |
| id | doaj.art-11876854a612452ba7ca0f6f1865cab2 |
| institution | Directory Open Access Journal |
| issn | 2391-5447 |
| language | English |
| last_indexed | 2024-12-17T07:12:29Z |
| publishDate | 2018-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Geosciences |
| spelling | doaj.art-11876854a612452ba7ca0f6f1865cab22022-12-21T21:58:59ZengDe GruyterOpen Geosciences2391-54472018-12-0110177278110.1515/geo-2018-0060geo-2018-0060Using cluster analysis methods for multivariate mapping of traffic accidentsSelvi Huseyin Zahit0Caglar Burak1Necmettin Erbakan University Konya, Konya, TurkeyNecmettin Erbakan University Konya, Konya, TurkeyMany factors affect the occurrence of traffic accidents. The classification and mapping of the different attributes of the resulting accident are important for the prevention of accidents. Multivariate mapping is the visual exploration of multiple attributes using a map or data reduction technique. More than one attribute can be visually explored and symbolized using numerous statistical classification systems or data reduction techniques. In this sense, clustering analysis methods can be used for multivariate mapping. This study aims to compare the multivariate maps produced by the K-means method, K-medoids method, and Agglomerative and Divisive Hierarchical Clustering (AGNES) method, which among clustering analysis methods, with real data. The results from the study will suggest which clustering methods should be preferred in terms of multivariate mapping. The results show that the K-medoids method is more appropriate in terms of clustering success. Moreover, the aim is to reveal spatial similarities in traffic accidents according to the results of traffic accidents that occur in different years. For this aim, multivariate maps created from traffic accident data of two different years in Turkey are used. The methods are compared, and the use of the maps produced with these methods for risk management and planning is discussed. Analysis of the maps reveals significant similarities for both years.https://doi.org/10.1515/geo-2018-0060traffic accidentsmultivariate mappingdata miningcluster analysisvisualization |
| spellingShingle | Selvi Huseyin Zahit Caglar Burak Using cluster analysis methods for multivariate mapping of traffic accidents Open Geosciences traffic accidents multivariate mapping data mining cluster analysis visualization |
| title | Using cluster analysis methods for multivariate mapping of traffic accidents |
| title_full | Using cluster analysis methods for multivariate mapping of traffic accidents |
| title_fullStr | Using cluster analysis methods for multivariate mapping of traffic accidents |
| title_full_unstemmed | Using cluster analysis methods for multivariate mapping of traffic accidents |
| title_short | Using cluster analysis methods for multivariate mapping of traffic accidents |
| title_sort | using cluster analysis methods for multivariate mapping of traffic accidents |
| topic | traffic accidents multivariate mapping data mining cluster analysis visualization |
| url | https://doi.org/10.1515/geo-2018-0060 |
| work_keys_str_mv | AT selvihuseyinzahit usingclusteranalysismethodsformultivariatemappingoftrafficaccidents AT caglarburak usingclusteranalysismethodsformultivariatemappingoftrafficaccidents |