A nonseparable first-order spatiotemporal intensity for events on linear networks: An application to ambulance interventions Article Swipe
Andrea Gilardi
,
Riccardo Borgoni
,
Jorge Mateu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1214/23-aoas1800
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1214/23-aoas1800
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 non-separability 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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1214/23-aoas1800
- https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-1/A-nonseparable-first-order-spatiotemporal-intensity-for-events-on-linear/10.1214/23-AOAS1800.pdf
- OA Status
- bronze
- Cited By
- 4
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3170258155
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3170258155Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1214/23-aoas1800Digital Object Identifier
- Title
-
A nonseparable first-order spatiotemporal intensity for events on linear networks: An application to ambulance interventionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-31Full publication date if available
- Authors
-
Andrea Gilardi, Riccardo Borgoni, Jorge MateuList of authors in order
- Landing page
-
https://doi.org/10.1214/23-aoas1800Publisher landing page
- PDF URL
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https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-1/A-nonseparable-first-order-spatiotemporal-intensity-for-events-on-linear/10.1214/23-AOAS1800.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-1/A-nonseparable-first-order-spatiotemporal-intensity-for-events-on-linear/10.1214/23-AOAS1800.pdfDirect OA link when available
- Concepts
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Psychological intervention, Intensity (physics), Order (exchange), Computer science, Medical emergency, Medicine, Business, Physics, Nursing, Optics, FinanceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2043560035, https://openalex.org/W28026382, https://openalex.org/W1978135292, https://openalex.org/W2554699473, https://openalex.org/W2969270714, https://openalex.org/W2124814626, https://openalex.org/W1981890344, https://openalex.org/W2967899556, https://openalex.org/W2966168789, https://openalex.org/W3037085755, https://openalex.org/W2000359198, https://openalex.org/W2119160928, https://openalex.org/W2582743722, https://openalex.org/W4250310729, https://openalex.org/W3010677300, https://openalex.org/W2744637967, https://openalex.org/W3091012624, https://openalex.org/W2117188044, https://openalex.org/W2143944501, https://openalex.org/W4399638219, https://openalex.org/W2548087597, https://openalex.org/W2144836668, https://openalex.org/W4206420516, https://openalex.org/W4234404834, https://openalex.org/W1965843983, https://openalex.org/W4214738127, https://openalex.org/W1971541813, https://openalex.org/W4299708757, https://openalex.org/W204885769, https://openalex.org/W2800501357, https://openalex.org/W1993277105, https://openalex.org/W1978293581, https://openalex.org/W2995255014, https://openalex.org/W2082633923, https://openalex.org/W2023708446, https://openalex.org/W2296650473, https://openalex.org/W1535106372, https://openalex.org/W1519516901, https://openalex.org/W2891613568, https://openalex.org/W2480176673, https://openalex.org/W2427277061 |
| referenced_works_count | 41 |
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