Inferring Latent Patterns in Air Quality from Urban Big Data

Suparna De, , , ,

Research output: Contribution to journalArticlepeer-review


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 paper 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, i.e. 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
JournalIEEE Internet of Things Magazine
Issue number1
Publication statusPublished - 30 Mar 2021


  • air pollution
  • pollution pattern
  • Nonnegative Matrix Factorization

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