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Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information. [ABSTRACT FROM AUTHOR]

Geographically and temporally weighted regression (GTWR) has been demonstrated as an effective tool for exploring spatiotemporal data under spatial and temporal heterogeneity. Exploiting the advantages of the two most popular GTWR methods, we propose an alternative GTWR with a good balance between complexity and interpretability via a unilateral temporal weighting scheme called unilateral GTWR (UGTWR). When compared to the other two popular GTWR methods, the simulation experiment shows that UGTWR has comparable estimation accuracy and model fit, but it is more efficient. Furthermore, we propose its multiscale extension, coined multiscale UGTWR (MUGTWR), to characterize the spatiotemporal dynamic regression relationships at multiple scales. The proposed MUGTWR was applied to the analysis of house prices in the period of 2014–2018 in Beijing as a case study. Our analysis reveals that MUGTWR can effectively capture different levels of spatiotemporal heterogeneity in selected factors affecting house prices at different scales. Therefore, this study is useful for the formulation of housing policy in which the spatiotemporal dynamics of house prices with respect to specific factors can be considered. [ABSTRACT FROM AUTHOR]

The growth of georeferenced data sources calls for advanced matching methods to improve the reliability of geospatial data processing, such as map conflation. Existing matching methods mainly focus on similarity measures at the entity scale or area scale. A measure that combines entity-scale and area-scale similarities can provide sound matching results under various circumstances. In this paper, we propose a georeferenced-graph model that integrates multiscale similarities for data matching. Specifically, a match of correspondent data objects is identified by the entity-scale measure under the constraint of the area-scale measure. Nodes in the proposed georeferenced graph model represent polygons by their centroids, whereas the links in the graph connect the nodes (i.e. centroids) according to pre-defined rules. Then, we develop an algorithm to identify many-to-many matches. We demonstrate the proposed graph model and algorithm in real-world experiments using OpenStreetMap data. The experimental results show that the proposed georeferenced-graph model can effectively integrate the context and the location-and-form distance of geospatial data matches across different datasets. [ABSTRACT FROM AUTHOR]