Building an environmental quality index for a big city: a spatial interpolation approach combined with a distance indicator
One of the main tasks of regional and environmental economics is to construct Environmental Quality Indexes for big cities. A standard method is to generate a single measure as a linear combination of several contaminants by applying Principal Component Analysis. Spatial interpolation is then carried out to determine pollution levels across the city. We innovate on this method and propose an alternative approach. First, we combine a set of noise and air pollutants measured at a number of monitoring stations with data available for each census tract. This yields a mixed environmental index that is socioeconomically more complete. We then apply kriging to match the monitoring station records to the census data. Finally, we construct a composite pollution index using the Pena Distance method (DP2), which proves more robust than traditional approaches.
distance indicators noise air pollution environmental index kriging subjective expectations c21 c43 q53