Inferring air quality for station location recommendation based on urban big data

Hsun Ping Hsieh, Shou De Lin, Yu Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

90 Citations (Scopus)

Abstract

This paper tries to answer two questions. First, how to infer realtime air quality of any arbitrary location given environmental data and historical air quality data from very sparse monitoring locations. Second, if one needs to establish few new monitoring stations to improve the inference quality, how to determine the best locations for such purpose? The problems are challenging since for most of the locations (>99%) in a city we do not have any air quality data to train a model from. We design a semi-supervised inference model utilizing existing monitoring data together with heterogeneous city dynamics, including meteorology, human mobility, structure of road networks, and point of interests (POIs). We also propose an entropy-minimization model to suggest the best locations to establish new monitoring stations. We evaluate the proposed approach using Beijing air quality data, resulting in clear advantages over a series of state-of-the-art and commonly used methods.

Original languageEnglish
Title of host publicationKDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages437-446
Number of pages10
ISBN (Electronic)9781450336642
DOIs
Publication statusPublished - 2015 Aug 10
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: 2015 Aug 102015 Aug 13

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume2015-August

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period15-08-1015-08-13

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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