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Inferring latent patterns in air quality from urban big data

De, Suparna, Jassat, Usamah, Wang, Wei, Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 and Moessner, Klaus 2021. Inferring latent patterns in air quality from urban big data. IEEE Internet of Things Magazine 4 (1) , 20 -27. 10.1109/IOTM.0011.2000071

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Abstract

The emerging paradigm of urban computing aims to infer latent patterns from various aspects of a city's environment and possibly identify their hidden correlations by analyzing urban big data. This article provides a fine-grained analysis of air quality from diverse sensor data streams retrieved from regions in the city of London. The analysis derives spatio-temporal patterns, that is, across different location categories and time spans, and also reveals the interplay between urban phenomena such as human commuting behavior and the built environment, with the observed air quality patterns. The findings have important implications for the health of ordinary citizens and for city authorities who may formulate policies for a better environment.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Publisher: IEEE
ISSN: 2576-3180
Date of First Compliant Deposit: 21 September 2020
Date of Acceptance: 18 September 2020
Last Modified: 08 Nov 2022 05:22
URI: https://orca.cardiff.ac.uk/id/eprint/134941

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