What question did this study set out to answer?

This research aims to develop models for estimating spatiotemporal Poisson processes despite missing location data.

April 27, 2026Scientific Reports0 citationsOpen Access

Estimation of spatiotemporal poisson processes with missing data

VGVincent GuiguesUniversidade Regional de Blumenau AKAnton KleywegtGeorgia Institute of Technology VNVictor Hugo NascimentoFaculdades Guarulhos

Key Points

This research aims to develop models for estimating spatiotemporal Poisson processes despite missing location data.
Tested models on emergency call arrivals to an emergency medical service with missing location data.
Discussed four specific models that address missing location data and their estimation.
Provided corresponding code on GitHub for model implementation.
Demonstrated significant differences when using models that account for missing location data.
Developed models that effectively handle missing timestamp data.

Abstract

We consider models for spatiotemporal Poisson processes with some missing location data. We discuss four models that make provision for missing location data, and their estimation. The corresponding code is available on GitHub as an extension of LASPATED at https: //github. com/vguigues/LASPATED in the MissingData subdirectory. We tested our models using the process of emergency call arrivals to an emergency medical service, where the emergency reports often omit the location of the emergency. We show the difference made by using models that make provision for missing location data. Finally, we show how to develop models that deal with missing data of time stamp.

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Cite This Study

Guigues et al. (Sat,) studied this question.

synapsesocial.com/papers/69eefc6dfede9185760d3758 https://doi.org/https://doi.org/10.1038/s41598-026-46520-y

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