Development of spatiotemporal high-resolution temperature data for São Paulo, Brazil; a valuable resource for epidemiologists

Abstract

Background and aim Most studies investigating the temperature-mortality association in cities rely on temperature recordings from few meteorological stations or models at coarse spatial resolution, implicitly assuming an homogenous spatial distribution across. Evidence from urban heat island studies, remote sensing data, and atmospheric and physical sciences suggest this assumption does not hold. Overlooking the spatial variability of temperature exposure in cities may lead to biased epidemiological findings and limit our ability to identify areas of high risk. Herein, we aim to develop an open-access dataset of daily ambient temperature at 500 meters resolution for the municipality of São Paulo (2015-2020). Methods We obtained daily mean temperature data from 60 ground stations within the city and used spatiotemporal regression kriging to predict temperature at unmeasured locations. This technique uses multiple linear regression to model the spatiotemporal trend in temperature, and spatiotemporal kriging to model the regression residuals spatiotemporal autocorrelation. For the multilinear regression, we used several earth observation products including data relating to topography, the built environment and atmospheric processes, as explanatory covariates. The regression residuals were modelled by fitting a sum-metric spatiotemporal variogram model. We validated the model using a leave-one-out and 5-fold cross-validation. Results Of the 60 monitoring stations, 36 had data >75% valid data. Temperature records showed some spatial heterogeneity (interstation standard deviation: 1.4 ̊ C), supporting the need for spatially resolved temperature data. Of all covariates used in the multilinear regression model, remotely sensed land surface temperature was the best predictor. Any residual variability was modelled through spatiotemporal kriging. Validation using R2 and root-mean-square-error indicators is ongoing. Conclusions This dataset provides epidemiologists with a unique opportunity to investigate exposure to daily mean temperature in São Paulo at high spatio-temporal resolution, and to identify areas of high risk for certain health outcomes, e.g., mortality, over time and space.