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Building an environmental quality index for a big city: a spatial interpolation approach combined with a distance indicator

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Abstract

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.

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Notes

  1. The use of satellite images is an alternative although complex way to overcome the scarcity of ground monitoring stations (Kanaroglou et al. 2002).

  2. Cokriging can also be a good option in a multivariate approach since it accounts not just for the spatial dependence of each variable but also for the inter-variable correlation (Montero et al. 2009). However, it is more complex than kriging and often yields no additional benefits. This is true, say, for the so-called ‘isotopic case’, i.e. when variables are measured at the same monitoring stations. Cokriging is tantamount to kriging in the specific case of autokrigeability (Subramanyam and Pandalai 2004). Besides, when using cokriging, not only are valid variograms needed to represent the structure of the spatial dependence of the variables but valid cross-variograms are too.

  3. Another option would be direct estimation of the environmental index including the correction factor and the conditions proposed by Matheron (1979), but in our opinion this is much more difficult to implement than our proposal.

  4. PCA and DP2 are complementary—not substitute—methods (see Zarzosa 1996, p. 194; Cancelo and Uriz 1994, pp. 177–178). PCA is capable of reducing the information of a group of variables and eliminating redundant information. DP2, though, also allows relative comparisons between different spatial units and/or time periods.

  5. As shown in Pena (1977) and Zarzosa (1996), DP2 fulfils all the properties of a good composite indicator; i.e. existence and determination, monotony, unicity, invariance, homogeneity, transitivity, exhaustivity, and additivity.

  6. Some indicators have clear reference values (e.g. those legally established by national or international organizations). This is the case of most air quality variables (SO2, CO, etc.), for which the EU has set limits for the protection of human health (Official Journal of the European Union 2008). However, we have opted not to use them owing to the complexity and diversity of the measurements, which do not match the average annual data available for Madrid.

  7. If all the partial indicators are uncorrelated, R 2 = 0 and DP2 = DF.

  8. Ivanovic (1963) proposed the I-Distance, which considered the partial coefficients as a correction factor. However, as stated in Pena (1977), this procedure cannot eliminate the redundant information of the DF.

  9. These data can be downloaded from the Municipality of Madrid’s web page (http://www.munimadrid.es).

  10. All the computations are available from the authors upon request.

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Acknowledgments

Previous versions of this paper were presented at the 3rd Jean Paelinck Seminar on Spatial Econometrics (Cartagena, Spain, October 10–11, 2008), at the 2nd World Conference of the Spatial Econometrics Association (New York City, November 18–19, 2008), and at the 56th North American Meeting of the Regional Science Association International (New York City, November 19–21, 2008). We would like to thank Toni Mora, Jan Mutl, Roberto Patuelli, Vicente Royuela, Richard Sellner, and other participants at those meetings for their valuable comments. The usual disclaimer applies. Coro Chasco acknowledges financial support from the Spanish Ministry of Education and Science SEJ2006-02328/ECON and the Comunidad de Madrid CCG08-UAM/HUM-4173. Beatriz Larraz acknowledges the financial support of the MICINN project CSO2009-11246.

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Authors and Affiliations

  1. Área de Estadística, Universidad de Castilla-La Mancha, Cobertizo de San Pedro Mártir, s/n, 45071, Toledo, Spain

    José-María Montero & Beatriz Larraz

  2. Dpto. Economía Aplicada, Avda. Tomás y Valiente 5, Universidad Autónoma de Madrid, 28049, Madrid, Spain

    Coro Chasco

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  1. José-María Montero
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Correspondence to Coro Chasco.

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Montero, JM., Chasco, C. & Larraz, B. Building an environmental quality index for a big city: a spatial interpolation approach combined with a distance indicator. J Geogr Syst 12, 435–459 (2010). https://doi.org/10.1007/s10109-010-0108-6

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