Assessing the Effect of Urban Expansion and Deforestation on Temperature Rise in Cajazeiras, Brazil: A Data-Driven Approach

Climate change is a task of profound academic and societal relevance that promises to shape our actions for the planet’s sustainable future. Understanding the complex relationship between land transformations and temperature changes is crucial for effective decision-making in the face of climate change. This study uses a data-driven approach to assess the effect of urban expansion and deforestation on temperature rise in Cajazeiras, Brazil. By integrating statistical modeling and machine learning techniques, we investigate the influence of urban areas and other factors on temperature trends. The statistical model (ARIMA) reveals a strong association between urbanization and temperature increase, emphasizing the need to address urban development and land use changes to mitigate rising temperatures. The SHAP values technique applied to a Random Forest model also uncovers additional drivers of temperature rise beyond urban expansion, such as the absence of savanna formation near the city. This comprehensive analysis provides valuable insights for policymakers and urban planners in mitigating the impact of climate change.