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A zero‐inflated Poisson spatial model with misreporting for wildfire occurrences in southern Italian municipalities

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  • Serena Arima
  • Crescenza Calculli
  • Alessio Pollice

Abstract

We propose a Poisson model for zero‐inflated spatial counts contaminated by measurement error: we accommodate the excess of zeroes in the counts, consider the possible under/over reporting of the response and account for the neighboring structure of spatial areal units. Bayesian inferences are provided by MCMC implementation through the R package NIMBLE. To evaluate the model performance, a simulation study is carried out under configurations that allow for structured and unstructured spatial random effects. The proposed model is applied to investigate the distribution of the counts of wildfire occurrences in the municipal areas of two neighboring Italian regions for the summer season 2021. Fire counts are obtained by processing MODIS satellite data, while several socio‐economic and environmental‐driven potential risk factors are also considered in the model formulation. Data from multiple sources with different spatial support are processed in order to comply with the municipal units. Results suggest the appropriateness of the approach and provide some insights on the features of wildfire occurrences.

Suggested Citation

  • Serena Arima & Crescenza Calculli & Alessio Pollice, 2025. "A zero‐inflated Poisson spatial model with misreporting for wildfire occurrences in southern Italian municipalities," Environmetrics, John Wiley & Sons, Ltd., vol. 36(1), January.
    • Handle: RePEc:wly:envmet:v:36:y:2025:i:1:n:e2853
    DOI: 10.1002/env.2853
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    References listed on IDEAS

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    Cited by:

    1. Davide Berardi & Marta Galuppi & Angelo Libertà & Mara Lombardi, 2025. "Forecasting of Wildfire Probability Occurrence: Case Study of a Mediterranean Island of Italy," Land, MDPI, vol. 14(2), pages 1-28, January.

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