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This paper introduces a new algorithm (BUNTUS—Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method combines estimates of land cover, nighttime lights, and travel times to classify contiguous urban areas. The method is automatic, global and uses data sets with enough duration to establish trends. Validation using ground truth from Landsat-8 OLI images revealed an overall accuracy ranging from 60% to 95%. Thus, this approach is capable of describing spatial distributions and giving detailed information of urban extents. We demonstrate the method with examples from Brisbane, Australia, Melbourne, Australia, and Beijing, China. The new method meets the criteria for studying overall trends in urban emissions. [ABSTRACT FROM AUTHOR]

• Spatiotemporal usage patterns of station-free shared bikes are explored. • A topic-based two-stage algorithm is proposed to discover city functional regions. • Region functions are labeled by static POI data and dynamic mobility patterns. Station-free shared bike (SFSB) is a new travel mode that shared bikes are allowed to park in any proper places. It implies that the users are more likely to park the SFSB as close as their destinations. This advantage makes the SFSB data to be an ideal source to understand the land-use distribution. Inspired by the idea in text mining, this paper proposes a topic-based two-stage SFSB data mining algorithm to understand the SFSB user's travel behavior and to discover city functional regions. A region, a function and human mobility patterns are treated as a document, a topic and words, respectively. Then, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. The point-of-interest data is combined to annotate the clustered regions to discover the real functions. At last, the proposed method is tested using 14-day SFSB data in Beijing and the results are estimated by the Satellite Map data. The proposed methods and the results can be applied to infer the individual's travel purpose through functional regions and to improve land-use planning, etc. [ABSTRACT FROM AUTHOR]

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns are crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5-related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components—feature selection, clustering, and integrated prediction—the model first employs the non-dominated sorting genetic algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the AdaBoost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model's results were compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants. [ABSTRACT FROM AUTHOR]