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

IF 1.5 3区环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES

Environmetrics Pub Date : 2024-05-03 DOI:10.1002/env.2853

Serena Arima, Crescenza Calculli, Alessio Pollice

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引用次数: 0

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.

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意大利南部城市野火发生率的零膨胀泊松空间模型与误报问题

我们为受测量误差污染的零膨胀空间计数提出了一个泊松模型：我们考虑了计数中过多的零，考虑了可能存在的反应不足/过多的报告，并考虑了空间区域单位的邻近结构。贝叶斯推论是通过 R 软件包 NIMBLE 的 MCMC 实现的。为了评估模型的性能，在允许结构化和非结构化空间随机效应的配置下进行了模拟研究。提出的模型被用于研究 2021 年夏季意大利两个相邻大区市镇地区野火发生次数的分布情况。火灾次数是通过处理 MODIS 卫星数据获得的，同时，在建立模型时还考虑了一些由社会经济和环境驱动的潜在风险因素。对来自不同空间支持的多个来源的数据进行了处理，以符合市政单位的要求。结果表明该方法是适当的，并对野火发生的特点提供了一些启示。

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来源期刊

Environmetrics 环境科学-环境科学

CiteScore

2.90

自引率

17.60%

发文量

审稿时长

18-36 weeks

期刊介绍： Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences. The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.