@article{Monteiro2024,
abstract = {This study applied spatial analysis techniques to point processes to investigate the distribution of fire outbreaks in the state of S{\~{a}}o Paulo during the months of June to September 2024. Using data from the Burning Program of the National Institute for Space Research (INPE), descriptive and inferential analyses were performed, including Ripley's K Function and the Kolmogorov-Smirnov Test, with the aim of testing the hypothesis of Complete Spatial Randomness (CSR). The results indicated a pattern of spatial clustering in short distances, rejecting the hypothesis of randomness. Predictive models based on Poisson processes were adjusted, highlighting the most vulnerable areas, especially in the Atlantic Forest and Cerrado biomes. This work reinforces the importance of spatial statistics as an essential tool for identifying patterns and planning mitigation strategies, contributing to environmental preservation and combating fire outbreaks.},
author = {Monteiro, Vinicius Crispim Tavares and de Oliveira, Manoel Alves and de Olinda, Ricardo Alves and Pereira, Lucas Cardoso},
doi = {https://doi.org/10.51359/2965-4661.2024.265082},
journal = {Socioeconomic Analytics},
keywords = {Fires,Hypothesis Tests,Point Process Analysis,Spatial Statistics,S{\~{a}}o Paulo},
number = {1},
pages = {164--174},
title = {{Point Process Analysis applied to fire outbreaks in S{\~{a}}o Paulo}},
url = {https://doi.org/10.51359/2965-4661.2024.265082},
volume = {2},
year = {2024}
}