Point Process applied to fire outbreaks in São Paulo

This study applied spatial analysis techniques for point processes to investigate the distribution of fire outbreaks in the state of São Paulo during the months of June to September 2024. Descriptive and inferential analyses were conducted, including Ripley's K Function and the Kolmogorov-Smirnov Test, to test the hypothesis of Complete Spatial Randomness (CSR). The results indicated a spatial clustering pattern at short distances, rejecting the randomness hypothesis. Predictive models based on Poisson processes were fitted, highlighting the most vulnerable areas, particularly in the Atlantic Forest and Cerrado biomes.

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Publication Details

Journal
Socioeconomic Analytics
Published
2024-12-20
DOI
https://doi.org/10.51359/2965-4661.2024.265082
Primary Topic
Spatial and Panel Data Analysis
Type
article
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article

Point Process applied to fire outbreaks in São Paulo

Ricardo Alves de Olinda, Lucas Cardoso Pereira, V. Monteiro, M Oliveira
Socioeconomic Analytics
Spatial and Panel Data Analysis
article

Point Process applied to fire outbreaks in São Paulo

Ricardo Alves de Olinda, Lucas Cardoso Pereira, V. Monteiro, M Oliveira
article en

Abstract

This study applied spatial analysis techniques for point processes to investigate the distribution of fire outbreaks in the state of São Paulo during the months of June to September 2024. Descriptive and inferential analyses were conducted, including Ripley's K Function and the Kolmogorov-Smirnov Test, to test the hypothesis of Complete Spatial Randomness (CSR). The results indicated a spatial clustering pattern at short distances, rejecting the randomness hypothesis. Predictive models based on Poisson processes were fitted, highlighting the most vulnerable areas, particularly in the Atlantic Forest and Cerrado biomes.

Socioeconomic AnalyticsVol. 2(1)
Universidade Estadual da Paraíba (BR)
Life in Land
Openalex Percentile: Top 70%
Spatial and Panel Data Analysis
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