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Regularised semi-parametric composite likelihood intensity modelling of a Swedish spatial ambulance call point pattern
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. Department of Mathematics and Statistics, Auburn University, AL, Auburn, United States.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
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.
2023 (engelsk)Inngår i: Journal of Agricultural Biological and Environmental Statistics, ISSN 1085-7117, E-ISSN 1537-2693, Vol. 28, nr 4, s. 664-683Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skellefteå 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.

sted, utgiver, år, opplag, sider
Springer, 2023. Vol. 28, nr 4, s. 664-683
Emneord [en]
Bandwidth selection, Cyclic coordinate descent algorithm, Emergency alarm, Inhomogeneous Poisson process, Lasso-like elastic-net, Multivariate point process
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-206937DOI: 10.1007/s13253-023-00534-5ISI: 000981152000001Scopus ID: 2-s2.0-85152546695OAI: oai:DiVA.org:umu-206937DiVA, id: diva2:1753629
Forskningsfinansiär
Vinnova, 2018-00422Region VästerbottenNorrbotten County CouncilRegion VästernorrlandRegion Jämtland HärjedalenTilgjengelig fra: 2023-04-28 Laget: 2023-04-28 Sist oppdatert: 2024-01-05bibliografisk kontrollert

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Bayisa, FekaduÅdahl, MarkusRydén, Patrik

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