Inferring air quality for station location recommendation based on urban big data
Journal
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Journal Volume
2015-August
Pages
437-446
Date Issued
2015
Author(s)
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. © 2015 ACM.
SDGs
Other Subjects
Air quality; Data mining; Entropy; Location; Monitoring; Entropy minimization; Environmental data; Monitoring locations; Monitoring stations; Point of interest; Semi-supervised; Sensor placement; Station location; Big data
Type
conference paper