Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA

Lotfata, Aynaz; Georganos, Stefanos; Kalogirou, Stamatis; Helbich, Marco

doi:10.3390/ijgi11110550

Cited by 6 publications

(4 citation statements)

References 75 publications

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“…A GWRF has a set of hyperparameters that need to be tuned. Following in the footsteps of others [ 37 , 38 ], we used Random Grid Search (RGS) on the RF model to optimize the hyperparameters of GWRF ('number of variables randomly sampled' and 'the number of trees') (using CARET library in R). The proportion of randomly sampled features at each node ranged from 1 to 7, and the number of trees ranged from 200 to 1000.…”

Section: Methodsmentioning

confidence: 99%

“…This practice is problematic, especially when the study area is large, because global models assume that the estimated coefficients are spatially stationary (i.e., they do not change across space regardless of the location) [ 36 ]. While there is no plausible reason for such a simplification, the novel geographically weighted random forest (GWRF) model [ 37 ] relaxes this constrain, as demonstrated in a few studies [ 37 , 38 ]. Razavi-Termeh et al [ 17 ] used GWRF to predict asthma associated with a wide variety of environmental data, such as PM 2.5 , ozone (O 3 ), and humidity in Tehran, Iran.…”

Section: Introductionmentioning

confidence: 99%

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Socioeconomic and environmental determinants of asthma prevalence: a cross-sectional study at the U.S. County level using geographically weighted random forests

et al. 2023

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Background Some studies have established associations between the prevalence of new-onset asthma and asthma exacerbation and socioeconomic and environmental determinants. However, research remains limited concerning the shape of these associations, the importance of the risk factors, and how these factors vary geographically. Objective We aimed (1) to examine ecological associations between asthma prevalence and multiple socio-physical determinants in the United States; and (2) to assess geographic variations in their relative importance. Methods Our study design is cross sectional based on county-level data for 2020 across the United States. We obtained self-reported asthma prevalence data of adults aged 18 years or older for each county. We applied conventional and geographically weighted random forest (GWRF) to investigate the associations between asthma prevalence and socioeconomic (e.g., poverty) and environmental determinants (e.g., air pollution and green space). To enhance the interpretability of the GWRF, we (1) assessed the shape of the associations through partial dependence plots, (2) ranked the determinants according to their global importance scores, and (3) mapped the local variable importance spatially. Results Of the 3059 counties, the average asthma prevalence was 9.9 (standard deviation ± 0.99). The GWRF outperformed the conventional random forest. We found an indication, for example, that temperature was inversely associated with asthma prevalence, while poverty showed positive associations. The partial dependence plots showed that these associations had a non-linear shape. Ranking the socio-physical environmental factors concerning their global importance showed that smoking prevalence and depression prevalence were most relevant, while green space and limited language were of minor relevance. The local variable importance measures showed striking geographical differences. Conclusion Our findings strengthen the evidence that socio-physical environments play a role in explaining asthma prevalence, but their relevance seems to vary geographically. The results are vital for implementing future asthma prevention programs that should be tailor-made for specific areas.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Socioeconomic and environmental determinants of asthma prevalence: a cross-sectional study at the U.S. County level using geographically weighted random forests

et al. 2023

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“…The potential of merging scan statistics with machine learning models is an expanding area of research [ 19 ], with exciting applications in obesity [ 20 ], HIV, and mobile data [ 21 ]. It can also be pivotal in identifying clusters with unexpectedly high incidence rates based on local characteristics.…”

Section: Introductionmentioning

confidence: 99%

Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach

Teixeira¹,

Toporcov²,

Chiaravalloti-Neto³

et al. 2023

Int J Public Health

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Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM).Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types.Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R2 = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%–96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers.Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality.

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“…When selecting an appropriate spatial scale to model the data, the GRF model is superior to other models [24][25][26]. Moreover, GRF could effectively demonstrate the regional differences in the relationship between dependent and independent variables [27,28]. Therefore, GRF provides a new opportunity to further accurately map the spatiotemporal distribution of GDP and identify the regional differences in the importance of related environmental variables.…”

Section: Introductionmentioning

confidence: 99%

Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model

Fu-liang

Cao

et al. 2023

Sustainability

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Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China’s multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China’s GDP moved 128.80 km to the southwest. These results indicate China’s achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy.

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Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA

Cited by 6 publications

References 75 publications

Socioeconomic and environmental determinants of asthma prevalence: a cross-sectional study at the U.S. County level using geographically weighted random forests

Socioeconomic and environmental determinants of asthma prevalence: a cross-sectional study at the U.S. County level using geographically weighted random forests

Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach

Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model

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