Big-But-Biased Data Analytics for Air Quality
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Big-But-Biased Data Analytics for Air QualityDate
2020-09-22Citation
Borrajo, L.; Cao, R. Big-But-Biased Data Analytics for Air Quality. Electronics 2020, 9, 1551. https://doi.org/10.3390/electronics9091551
Abstract
[Abstract]
Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight function, a small-sized simple random sample of the real population is assumed to be additionally observed. The general parameter considered is the mean of a transformation of the random variable of interest. A new bootstrap algorithm is used to approximate the mean squared error of the new estimator. Its minimization leads to an automatic bandwidth selector. The method is applied to a real data set concerning the levels of different pollutants in the urban air of the city of A Coruña (Galicia, NW Spain). Estimations for the mean and the cumulative distribution function of the level of ozone and nitrogen dioxide when the temperature is greater than or equal to 30 ∘C based on 15 years of biased data are obtained.
Keywords
Air quality
Automatic bandwidth selection
Big data
Bootstrap
Kernel density estimation
Large sample size
Sampling bias
Smart city
Automatic bandwidth selection
Big data
Bootstrap
Kernel density estimation
Large sample size
Sampling bias
Smart city
Editor version
Rights
Atribución 4.0 Internacional
ISSN
2079-9292