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Defining the Spatial Scale in Modern Regional Analysis pp 281–309Cite as

Dealing with Data at Various Spatial Scales and Supports: An Application on Traffic Noise and Air Pollution Effects on Housing Prices with Multilevel Models

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Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

In empirical studies dealing with spatial data, researchers are frequently confronted with data available at different spatial scales. For instance, hedonic models on housing prices usually combine individual data pertaining to the price and structural characteristics of the dwelling and socio-economic neighbourhood characteristics that are available at some upper administrative levels. Another frequent issue is the change of support problem or misaligned regression problem (Gotway and Young 2002; Banerjee et al. 2004) when there is a spatial mismatch between the spatial supports of the variables. For instance, the measurement of air quality is based on regular sampling at a few stations in an area whereas socio-economic data are available for aggregate administrative.

This work has been carried out with the financial support of the Spanish Ministry of Education and Science ECO2009-10534. Coro Chasco also acknowledges financial support from Xunta de Galicia 2009/AX702.

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Notes

  1. 1.

    Although road tracks are the sum of census tracts, there are some census tracts, mainly in the track intersections, which are part of more than one track.

  2. 2.

    Arbia (1989) uses the term “spatial data transformations” to refer to situations in which the spatial process of interest is inherently of one form but the data observed are of another form, resulting in a “transformation” of the original process of interest. For example, sometimes the data are just not available at the desired scale of interest. Consider meteorologic processes and pollution that occur over a continuum, but only small areal-data aggregates or even point observations along such a surface can be recorded.

  3. 3.

    Since we use a log-linear model, this figure is the result of computing exp(12.96223).

  4. 4.

    They are computed respectively as follows:

    $$ \sigma_{\varepsilon}^2/(\sigma_{\varepsilon}^2 + \sigma_w^2 + \sigma_u^2);\sigma_w^2/(\sigma_{\varepsilon}^2 + \sigma_w^2 + \sigma_u^2){\rm{and}}\sigma_u^2/(\sigma_{\varepsilon}^2 + \sigma_w^2 + \sigma_u^2). $$

    The last two equations correspond respectively to the intra-class correlation for tracks and census tracts that are reported in Table 14.2.

  5. 5.

    This is why the deviance statistic has not been computed in as Model 1 is not nested in this model.

  6. 6.

    As stated in Bickel et al. (1999), noise costs are extremely variable since they depend on several factors and exhibit large non-linearities. This is why it is more difficult to find a generalization for marginal noise costs than for air pollution.

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Authors and Affiliations

  1. CRESE, Université de Franche-Comté, 45D, Avenue de l’Observatoire, Besançon Cedex, 25030, France

    Julie Le Gallo

  2. Facultad de Economomicas, Departamento de Economía Aplicada (módulo 14), Universidad Autonoma de Madrid, Avda. Francisco Tomás y Valiente 5, Madrid, 28049, Spain

    Coro Chasco

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  1. Julie Le Gallo
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  2. Coro Chasco
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Correspondence to Julie Le Gallo .

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  1. , Applied Economics (RegioLab), University of Oviedo, Campus del Cristo s/n, Oviedo, 33006, Spain

    Esteban Fernández Vázquez

  2. , Applied Economics (RegioLab), University of Oviedo, Campus del Cristo s/n, Oviedo, 33006, Spain

    Fernando Rubiera Morollón

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Le Gallo, J., Chasco, C. (2012). Dealing with Data at Various Spatial Scales and Supports: An Application on Traffic Noise and Air Pollution Effects on Housing Prices with Multilevel Models. In: Fernández Vázquez, E., Rubiera Morollón, F. (eds) Defining the Spatial Scale in Modern Regional Analysis. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31994-5_14

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