Big Data

The Big Data group applies deep learning methods to various big data sources, especially high resolution satellite data and street imagery, to characterise urban environmental features and exposures, including housing, the social environment, air quality, noise and the transportation environment (mode, street safety, etc.) and to evaluate how urban land use and service delivery policies could impact health.  The group also advances methodologies for the use of big data, such as how best to combine satellite and street level images, transferability of prediction models trained in one city to others, and the use of images to evaluate temporal change.


Related Publications

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Hong KY, Tsin PK, van den Bosch M, Brauer M, Henderson SB

Urban greenness extracted from pedestrian video and its relationship with surrounding air temperatures

Urban Forestry & Urban Greening, 38 (1), pp. 280-285, 2019.

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Weichenthal S, Hatzopoulou M, Brauer M

A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology

Environment International, 122 (1), pp. 3-10, 2019.

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Middel A, Lukasczyk J, Zakrzewski S, Arnold M, Maciejewski R

Urban form and composition of street canyons: A human-centric big data and deep learning approach

Landscape and Urban Planning, 183 , pp. 122-132, 2018.

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Suel E, Boulleau M, Ezzati M, Flaxman S

Combining street imagery and spatial information for measuring socioeconomic status

NIPS 2018 Workshop Spatiotemporal 2018.

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Middel A, Lukasczyk J, Maciejewski R, Demuzere M, Rothe M

Sky View Factor footprints for urban climate modeling

Urban Climate, 25 , pp. 120-134, 2018.

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10 entries « 2 of 2 »