2015 InferringAirQualityforStationLo

• (Hsieh et al., 2015) ⇒ Hsun-Ping Hsieh, Shou-De Lin, and Yu Zheng. (2015). “Inferring Air Quality for Station Location Recommendation Based on Urban Big Data.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783344

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Inferring+Air+Quality+for+Station+Location+Recommendation+Based+on+Urban+Big+Data
• http://dl.acm.org/citation.cfm?id=2783258.2783344&preflayout=flat#citedby

Quotes

Author Keywords

• Air quality; city dynamics; data mining; location recommendation; monitoring station; semi-supervised inference; sensor placement; spatial databases and gis

Abstract

This paper tries to answer two questions. First, how to infer real-time 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.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 InferringAirQualityforStationLo	Yu Zheng Shou-De Lin Hsun-Ping Hsieh			Inferring Air Quality for Station Location Recommendation Based on Urban Big Data				10.1145/2783258.2783344		2015