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Background: Following China's official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China. Methods: The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software. Results: A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model's reliability in predicting risk levels. Conclusions: Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China. [ABSTRACT FROM AUTHOR]

While many studies examine the correlates of car ownership, only a few have quantified the relative contributions of individuals' socio-economic characteristics, alternative transportation choices, and built environment attributes to car ownership in developing cities. By employing gradient boosting decision trees to the 2020 regional household travel survey data from Wuhan, a megacity in China, we estimated the respective impacts of these factors on car ownership. The results of this study highlight household wealth as the dominant factor in predicting car ownership. Additionally, the ownership of Ebikes, an alternative travel option, emerges as an important mitigator of car ownership. However, most transit and built environment variables contribute marginally to the prediction of car ownership – though an exception is to direct future development into inner city neighborhoods. The findings suggest that Ebikes not only serve as a crucial alternative to cars but also complement limited transit services. Therefore, in developing cities with growing economies, banning Ebikes would not be conducive to curbing car growth. [ABSTRACT FROM AUTHOR]

Long-time-series, high-resolution datasets of the column-averaged dry-air mole fraction of carbon dioxide (XCO 2) have great practical importance for mitigating the greenhouse effect, assessing carbon emissions and implementing a low-carbon cycle. However, the mainstream XCO 2 datasets obtained from satellite observations have coarse spatial resolutions and are inadequate for supporting research applications with different precision requirements. Here, we developed a new spatial machine learning model by fusing spatial information with CatBoost, called SCatBoost, to fill the above gap based on existing global land-mapped 1° XCO 2 data (GLM-XCO 2). The 1-km-spatial-resolution dataset containing XCO 2 values in China from 2012 to 2019 reconstructed by SCatBoost has stronger and more stable predictive power (confirmed with a cross-validation (R2 = 0.88 and RSME = 0.20 ppm)) than other traditional models. According to the estimated dataset, the overall national XCO 2 showed an increasing trend, with the annual mean concentration rising from 392.65 ppm to 410.36 ppm. In addition, the spatial distribution of XCO 2 concentrations in China reflects significantly higher concentrations in the eastern coastal areas than in the western inland areas. The contributions of this study can be summarized as follows: (1) It proposes SCatBoost, integrating the advantages of machine learning methods and spatial characteristics with a high prediction accuracy; (2) It presents a dataset of fine-scale and high resolution XCO 2 over China from 2012 to 2019 by the model of SCatBoost; (3) Based on the generated data, we identify the spatiotemporal trends of XCO 2 in the scale of nation and city agglomeration. These long-term and high resolution XCO 2 data help understand the spatiotemporal variations in XCO 2 , thereby improving policy decisions and planning about carbon reduction. • We propose SCatBoost as a new model to reconstruct XCO 2 with 1 km resolution. • Validation indicates high accuracy of the novel reconstruction model. • The spatiotemporal distributions of XCO 2 in China from 2012 to 2019 is studied. [ABSTRACT FROM AUTHOR]