Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)

The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the un...

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Bibliographic Details
Published in:Journal of physics. Conference series 2018-05, Vol.1025 (1), p.12096
Main Authors: Warsito, Budi, Yasin, Hasbi, Ispriyanti, Dwi, Hoyyi, Abdul
Format: Article
Language:English
Subjects:
Air pollution
Air temperature
Modelling
Outliers (statistics)
Physics
Pollution levels
Population density
Regression models
Robustness (mathematics)
Wind speed
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Summary:The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1025/1/012096