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Large-scale modelli...
Large-scale modelling and forecasting of ambulance calls in northern Sweden using spatio-temporal log-Gaussian Cox processes
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- Bayisa, Fekadu (författare)
- Umeå universitet,Institutionen för matematik och matematisk statistik,Umeå University
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- Ådahl, Markus, Universitetslektor (författare)
- Umeå universitet,Institutionen för matematik och matematisk statistik,Umeå University
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- Rydén, Patrik (författare)
- Umeå universitet,Institutionen för matematik och matematisk statistik,Umeå University
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- Cronie, Ottmar, 1979 (författare)
- Umeå universitet,Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för samhällsmedicin och folkhälsa,Institute of Medicine, School of Public Health and Community Medicine,Institutionen för matematik och matematisk statistik,Umeå University,University of Gothenburg
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- Elsevier BV, 2020
- 2020
- Engelska.
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Ingår i: Spatial Statistics. - : Elsevier BV. - 2211-6753. ; 39
- Relaterad länk:
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https://doi.org/10.1...
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https://umu.diva-por... (primary) (Raw object)
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Although ambulance call data typically come in the form of spatio-temporal point patterns, point process-based modelling approaches presented in the literature are scarce. In this paper, we study a unique set of Swedish spatio-temporal ambulance call data, which consist of the spatial (GPS) locations of the calls (within the four northernmost regions of Sweden) and the associated days of occurrence of the calls (January 1, 2014-December 31, 2018). Motivated by the nature of the data, we here employ log-Gaussian Cox processes (LGCPs) for the spatiotemporal modelling and forecasting of the calls. To this end, we propose a K-means clustering based bandwidth selection method for the kernel estimation of the spatial component of the separable spatio-temporal intensity function. The temporal component of the intensity function is modelled by means of Poisson regression, using different calendar covariates, and the spatiotemporal random field component of the random intensity of the LGCP is fitted using the Metropolis-adjusted Langevin algorithm. Spatial hot-spots have been found in the south-eastern part of the study region, where most people in the region live and our fitted model/forecasts manage to capture this behaviour quite well. Also, there is a significant association between the expected number of calls and the day-of-the-week, and the season-ofthe-year. A non-parametric second-order analysis indicates that LGCPs seem to be reasonable models for the data. Finally, we find that the fitted forecasts generate simulated future spatial event patterns which quite well resemble the actual future data. (C) 2020 The Author(s). Published by Elsevier B.V.
Ämnesord
- 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)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Health Care Service and Management, Health Policy and Services and Health Economy (hsv//eng)
Nyckelord
- K-means clustering based bandwidth selection
- Metropolis-adjusted
- Langevin algorithm
- Minimum contrast estimation
- Poisson regression
- Spatio-temporal point process statistics
- emergency medical-services
- edge effect correction
- point process
- 2nd-order analysis
- pattern-analysis
- response-times
- k-function
- intensity
- statistics
- survival
- Geology
- Mathematics
- Remote Sensing
- Ambulance call data
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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