No abstract

As a leading cause of morbidity and premature mortality, obesity has become a major global public health problem. It is therefore important to investigate the spatial variation of obesity prevalence and its associations with environmental and behavioral factors (e.g., food environment, physical activity), to optimize the targeting of scarce public health resources. In this study, the geographic clustering of obesity in the Netherlands was explored by analyzing the local spatial autocorrelation of municipal-level prevalence rates of adulthood obesity (aged ≥19 years) in 2016. The potential influential factors that may be associated with obesity prevalence were first selected from five categories of healthrelated factors through binary and Least Absolute Shrinkage and Selection Operator (LASSO) regressions. Geographically Weighted Regression (GWR) was then used to investigate the spatial variations of the associations between those selected factors and obesity prevalence. The results revealed marked geographic variations in obesity prevalence, with four clusters of high prevalence in the north, south, east, and west, and three clusters of low prevalence in the north and south of the Netherlands. Lack of sports participation, risk of anxiety, falling short of physical activity guidelines, and the number of restaurants around homes were found to be associated with obesity prevalence across municipalities. Our findings show that effective, region-specific strategies are needed to tackle the increasing obesity in the Netherlands.

Applications of machine-learning-based approaches in the geosciences have witnessed a substantial increase over the past few years. Here we present an approach that accounts for spatial autocorrelation by introducing spatial features to the models. In particular, we explore two types of spatial features, namely spatial lag and eigenvector spatial filtering (ESF). These features are used within the widely used random forest (RF) method, and their effect is illustrated on two public datasets of varying sizes (Meuse and California housing datasets). The least absolute shrinkage and selection operator (LASSO) is used to determine the best subset of spatial features, and nested cross-validation is used for hyper-parameter tuning and performance evaluation. We utilize Moran’s I and local indicators of spatial association (LISA) to assess how spatial autocorrelation is captured at both global and local scales. Our results show that RF models combined with either spatial lag or ESF features yield lower errors (up to 33% different) and reduce the global spatial autocorrelation of the residuals (up to 95% decrease in Moran’s I) compared to the RF model with no spatial features. The local autocorrelation patterns of the residuals are weakened as well. Compared to benchmark geographically weighted regression (GWR) models, the RF models with spatial features yielded more accurate models with similar levels of global and local autocorrelation in the prediction residuals. This study reveals the effectiveness of spatial features in capturing spatial autocorrelation and provides a generic machine-learning modelling workflow for spatial prediction.