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Sökning: id:"swepub:oai:gup.ub.gu.se/326467" > Regularised Semi-pa...

Regularised Semi-parametric Composite Likelihood Intensity Modelling of a Swedish Spatial Ambulance Call Point Pattern

Bayisa, Fekadu (författare)
Umeå universitet,Institutionen för matematik och matematisk statistik,Department of Mathematics and Statistics, Auburn University, AL, Auburn, United States
Ådahl, Markus, Universitetslektor (författare)
Umeå universitet,Institutionen för matematik och matematisk statistik
Rydén, Patrik (författare)
Umeå universitet,Institutionen för matematik och matematisk statistik
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Cronie, Ottmar, 1979 (författare)
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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 (creator_code:org_t)
Springer, 2023
2023
Engelska.
Ingår i: Journal of Agricultural Biological and Environmental Statistics. - : Springer. - 1085-7117 .- 1537-2693. ; 28, s. 664-83
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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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.

Ämnesord

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)

Nyckelord

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

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