Inferring Latent Patterns in Air Quality from Urban Big Data

Suparna De, Usamah Jassat, Wei Wang, Charith Perera, Klaus Moessner

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Article number9390469
Pages (from-to)20-27
Number of pages8
JournalIEEE Internet of Things Magazine
Issue number1
Publication statusPublished - Mar 2021


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