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Regularised Semi-pa...
Regularised Semi-parametric Composite Likelihood Intensity Modelling of a Swedish Spatial Ambulance Call Point Pattern
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- Bayisa, Fekadu (author)
- Umeå universitet,Institutionen för matematik och matematisk statistik,Department of Mathematics and Statistics, Auburn University, AL, Auburn, United States
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- Ådahl, Markus, Universitetslektor (author)
- Umeå universitet,Institutionen för matematik och matematisk statistik
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- Rydén, Patrik (author)
- Umeå universitet,Institutionen för matematik och matematisk statistik
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- Cronie, Ottmar, 1979 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences,University of Gothenburg,Chalmers tekniska högskola,Chalmers University of Technology,Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden; School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
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- Springer, 2023
- 2023
- English.
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In: Journal of Agricultural Biological and Environmental Statistics. - : Springer. - 1085-7117 .- 1537-2693. ; 28, s. 664-83
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Abstract
Subject headings
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- Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skelleftea during 2014-2018 in order to identify important spatial covariates and discern hotspot regions. Our large-scale multivariate data point pattern of call events consists of spatial locations and marks containing the associated priority levels and sex labels. The covariates used are related to road network coverage, population density, and socio-economic status. For each marginal point pattern, we model the associated intensity function by means of a log-linear function of the covariates and their interaction terms, in combination with lasso-like elastic-net regularized composite/Poisson process likelihood estimation. This enables variable selection and collinearity adjustment as well as reduction of variance inflation from overfitting and bias from underfitting. To incorporate mobility adjustment, reflecting people's movement patterns, we also include a nonparametric (kernel) intensity estimate as an additional covariate. The kernel intensity estimation performed here exploits a new heuristic bandwidth selection algorithm. We discover that hotspot regions occur along dense parts of the road network. A mean absolute error evaluation of the fitted model indicates that it is suitable for designing prehospital resource dispatching strategies. Supplementary materials accompanying this paper appear online.
Subject headings
- NATURVETENSKAP -- Matematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)
Keyword
- Bandwidth selection
- Cyclic coordinate descent algorithm
- Emergency
- alarm
- Inhomogeneous Poisson process
- Lasso-like elastic-net
- Multivariate point process
- variable selection
- regression shrinkage
- lasso
- Life Sciences & Biomedicine - Other Topics
- Mathematical & Computational
- Biology
- Mathematics
- Emergency alarm
Publication and Content Type
- ref (subject category)
- art (subject category)
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