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Title	Smart Beestricts: Improving The Spatial Resolution Of Air-Quality Data In Madrid Through Transfer Learning
ID_Doc	49184
Authors	Garrido-Hidalgo C.; Solmaz G.; Jacobs T.; Roda-Sanchez L.
Year	2025
Published	International Journal of Geographical Information Science
DOI	http://dx.doi.org/10.1080/13658816.2025.2491718
Abstract	Sensor infrastructures have become key enablers in collecting massive urban data. While representing an invaluable source of geographic information, high deployment costs of air quality stations relying on a chemical principle of operation leads to geographically imbalanced data. Typically, a minority of districts are equipped with such sensors or benefit from data collection campaigns, while others are data-poor. Moreover, the data available is highly heterogeneous, which adding on uncertainties. We present a methodology called ‘Smart Beestricts’ to improve the resolution of spatiotemporal data through Transfer Learning across districts of smart cities, based on: (i) aggregating spatiotemporal urban data into hexagonal grid representations, (ii) multi-variate clustering of the grid for discovering candidate regions for transfer, and (iii) transferring prediction models from data-rich sectors to data-poor ones. The transferred models are finally utilized to predict missing data across the city, thus improving spatiotemporal resolution. The methodology is validated in predicting missing air quality data in Madrid. The datasets generated are available through an open repository including meteorology, air pollution, and traffic data. Our methodology increased the spatial coverage of NO2 data from approximately 98 km2 to 253 km2, achieving coefficients of determination (R2) of up to 0.70 in the test regions. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Author Keywords	H3; Smart Beestricts; Smart Cities; Spatiotemporal ML; Urban Transfer Learning

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