A nonseparable first-order spatiotemporal intensity for events on linear networks: An application to ambulance interventions
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Title
A nonseparable first-order spatiotemporal intensity for events on linear networks: An application to ambulance interventionsDate
2024-03Publisher
Institute of Mathematical StatisticsISSN
1932-6157; 1941-7330Bibliographic citation
Gilardi, A., Borgoni, R., Mateu, J. (2024). A nonseparable first-order spatiotemporal intensity for events on linear networks: An application to ambulance interventions. The Annals of Applied Statistics, 18(1), 529-554. DOI: 10.1214/23-AOAS1800Type
info:eu-repo/semantics/articlePublisher version
https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue- ...Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
The algorithms used for the optimal management of an ambulance fleet require an accurate description of the spatiotemporal evolution of the emergency events. In the last years, several authors have proposed sophisticated ... [+]
The algorithms used for the optimal management of an ambulance fleet require an accurate description of the spatiotemporal evolution of the emergency events. In the last years, several authors have proposed sophisticated statistical approaches to forecast ambulance dispatches, typically modelling the data as a point pattern occurring on a planar region. Nevertheless, ambulance interventions can be more appropriately modelled as a realisation of a point process occurring on a linear network. The constrained spatial domain raises specific challenges and unique methodological problems that cannot be ignored when developing a proper statistical approach. Hence, this paper proposes a spatiotemporal model to analyse ambulance dispatches focusing on the interventions that occurred in the road network of Milan (Italy) from 2015 to 2017. We adopt a nonseparable first-order intensity function with spatial and temporal terms. The temporal dimension is estimated semiparametrically using a Poisson regression model, while the spatial dimension is estimated nonparametrically using a network kernel function. A set of weights is included in the spatial term to capture space-time interactions, inducing nonseparability in the intensity function. A series of tests show that our approach successfully models the ambulance interventions and captures the space-time patterns more accurately than planar or separable point process models. [-]
Is part of
The Annals of Applied Statistics, 2024, vol. 18, no 1Funder Name
European Union Next-Generation EU | Ministerio de Ciencia, Innovación y Universidades
Funder ID
http://dx.doi.org/10.13039/501100011033
Project code
1033 17/06/2022, CN00000023 | MICIU/ICTI2017-2020/PID2019-107392RB-I00
Project title or grant
Análisis estadístico de eventos en espacio-tiempo sobre redes y trayectorias. características de segundo orden, modelos paramétricos, inferencia y análisis de marcas funcional
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Copyright © Institute of Mathematical Statistics
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info:eu-repo/semantics/openAccess
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