Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2017
DOI: 10.1145/3139958.3140013
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Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution

Abstract: Air quality models are important for studying the impact of air pollutant on health conditions at a fine spatiotemporal scale. Existing work typically relies on area-specific, expert-selected attributes of pollution emissions (e,g., transportation) and dispersion (e.g., meteorology) for building the model for each combination of study areas, pollutant types, and spatiotemporal scales. In this paper, we present a data mining approach that utilizes publicly available OpenStreetMap (OSM) data to automatically gen… Show more

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Cited by 27 publications
(33 citation statements)
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“…The Tobler's First Law of Geography reported that "Everything is related to everything else, but near things are more related than distant things" [36]. This phenomenon forms the basis for the study of Geographical Influences, which has seen wide adoption in the literature: from POI recommendation [42], [44], [45] to air quality prediction [27]. In our work, the geographical context is constituted of categories (e.g., restaurants, apartments, hospitals) of the points of interest in the local vicinity of a stay point.…”
Section: Detectmentioning
confidence: 99%
“…The Tobler's First Law of Geography reported that "Everything is related to everything else, but near things are more related than distant things" [36]. This phenomenon forms the basis for the study of Geographical Influences, which has seen wide adoption in the literature: from POI recommendation [42], [44], [45] to air quality prediction [27]. In our work, the geographical context is constituted of categories (e.g., restaurants, apartments, hospitals) of the points of interest in the local vicinity of a stay point.…”
Section: Detectmentioning
confidence: 99%
“…land use and roads, derived from OpenStreeMap, to predict PM2.5 concentrations. When generating relative importance measures for the different risk factors, MAUP effects reduced when applying a random forest model that was trained with the distances between the features and the monitoring PM2.5 stations, (Lin et al, 2017). The rapid development of geoAI methods, their advantage to deal with big data, and their rapid computational time, makes them an attractive and advantageous tool to tackle limitations with modelling schistosomiasis and other diseases.…”
Section: Discussionmentioning
confidence: 99%
“…Innovations in this field aim to address real-world problems like the ones related to human health, in particular, spatial epidemiology (Baker and Nieuwenhuijsen, 2008). geoAI in spatial epidemiology looks to target issues related to inefficient computational processing and data constraints regarding coarse spatial and temporal supports, for exposure assessment (Lin et al, 2017;Vopham et al, 2018).…”
Section: Emerging Trends In Mapping Sch and Uncertainty Quantificationmentioning
confidence: 99%
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