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Large-scale modelling and forecasting of ambulance calls in northern Sweden using spatio-temporal log-Gaussian Cox processes
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2020 (English)In: Spatial Statistics, E-ISSN 2211-6753, Vol. 39, article id 100471Article in journal (Refereed) Published
Abstract [en]

In order to optimally utilise the resources of a country’s prehospital care system, i.e. ambulance service(s),it is crucial that one is able to spatio-temporally forecast hot-spots, i.e. spatial regions and periods with anincreased risk of seeing a call to the emergency number 112 which results in the dispatch of an ambulance.Such forecasts allow the dispatcher to make strategic decisions regarding e.g. the fleet size and where todirect unoccupied ambulances. In addition, simulations based on forecasts may serve as the startingpoint for different optimal routing strategies. Although the associated data typically comes in the form ofspatio-temporal point patterns, point process based modelling attempts in the literature has been scarce.In this paper, we study a unique set of Swedish spatio-temporal ambulance call data, which consists ofthe spatial (gps) locations of the dispatch addresses and the associated days of occurrence of the calls.The spatial study region is given by the four northernmost regions of Sweden and the study period isJanuary 1, 2014 to December 31, 2018. Motivated by the non-infectious disease nature of the data, wehere employ log-Gaussian Cox processes (LGCPs) for the spatio-temporal modelling and forecasting ofthe calls. To this end, we propose a K-means based bandwidth selection method for the kernel estimationof the spatial component of the separable spatio-temporal intensity function. The temporal componentof the intensity function is modelled by means of Poisson regression, using different calendar covariates,and the spatio-temporal random field component of the random intensity of the LGCP is fitted usingsimulation via the Metropolis-adjusted Langevin algorithm. A study of the spatio-temporal dynamics ofthe data shows that a hot-spot can be found in the south eastern part of the study region, where mostpeople in the region live and our fitted model/forecasts manage to capture this behaviour quite well. Thefitted temporal component of the intensity functions reveals that there is a significant association betweenthe expected number of calls and the day of the week as well as the season of the year. In addition,non-parametric second-order spatio-temporal summary statistic estimates indicate that LGCPs seem tobe reasonable models for the data. Finally, we find that the fitted forecasts generate simulated futurespatial event patterns which quite well resemble the actual future data.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 39, article id 100471
Keywords [en]
Ambulance call data, Forecasting/prediction, K-means clustering based bandwidth selection, Metropolis-adjusted Langevin Markov chain Monte Carlo, Minimum contrast estimation, Spatio-temporal point process modelling
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-169719DOI: 10.1016/j.spasta.2020.100471ISI: 000580942000004Scopus ID: 2-s2.0-85091965093OAI: oai:DiVA.org:umu-169719DiVA, id: diva2:1424603
Available from: 2020-04-17 Created: 2020-04-17 Last updated: 2024-04-05Bibliographically approved

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Bayisa, FekaduÅdahl, MarkusRydén, PatrikCronie, Ottmar

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CiteExportLink to record
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Cite
Citation style
  • apa
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Output format
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