Big-But-Biased Data Analytics for Air Quality
Use este enlace para citar
http://hdl.handle.net/2183/26744
A non ser que se indique outra cousa, a licenza do ítem descríbese como Atribución 4.0 Internacional
Coleccións
- GI-MODES - Artigos [114]
Metadatos
Mostrar o rexistro completo do ítemTítulo
Big-But-Biased Data Analytics for Air QualityData
2020-09-22Cita bibliográfica
Borrajo, L.; Cao, R. Big-But-Biased Data Analytics for Air Quality. Electronics 2020, 9, 1551. https://doi.org/10.3390/electronics9091551
Resumo
[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.
Palabras chave
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
Versión do editor
Dereitos
Atribución 4.0 Internacional
ISSN
2079-9292