Journal article
Use of mixed-type data clustering algorithm for characterizing temporal and spatial distribution of biosecurity border detections of terrestrial non-indigenous species
PLoS ONE, Vol.17(8), Art. e0272413
2022
Abstract
Appropriate inspection protocols and mitigation strategies are a critical component of effective biosecurity measures, enabling implementation of sound management decisions. Statistical models to analyze biosecurity surveillance data are integral to this decision-making process. Our research focuses on analyzing border interception biosecurity data collected from a Class A Nature Reserve, Barrow Island, in Western Australia and the associated covariates describing both spatial and temporal interception patterns. A clustering analysis approach was adopted using a generalization of the popular k-means algorithm appropriate for mixed-type data. The analysis approach compared the efficiency of clustering using only the numerical data, then subsequently including covariates to the clustering. Based on numerical data only, three clusters gave an acceptable fit and provided information about the underlying data characteristics. Incorporation of covariates into the model suggested four distinct clusters dominated by physical location and type of detection. Clustering increases interpretability of complex models and is useful in data mining to highlight patterns to describe underlying processes in biosecurity and other research areas. Availability of more relevant data would greatly improve the model. Based on outcomes from our research we recommend broader use of cluster models in biosecurity data, with testing of these models on more datasets to validate the model choice and identify important explanatory variables.
Details
- Title
- Use of mixed-type data clustering algorithm for characterizing temporal and spatial distribution of biosecurity border detections of terrestrial non-indigenous species
- Authors/Creators
- B. Kachigunda (Author/Creator)K. Mengersen (Author/Creator)D.I. Perera (Author/Creator)G.T. Coupland (Author/Creator)J. van der Merwe (Author/Creator)S. McKirdy (Author/Creator)
- Publication Details
- PLoS ONE, Vol.17(8), Art. e0272413
- Publisher
- Public Library of Science
- Identifiers
- 991005543795007891
- Copyright
- © 2022 Kachigunda et al.
- Murdoch Affiliation
- Harry Butler Institute
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.61 Artificial Intelligence & Machine Learning
- 4.61.869 Clustering Algorithms
- Web Of Science research areas
- Ecology
- ESI research areas
- Environment/Ecology