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

Chapter
Part of the Advances in Spatial Science book series (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.

Keywords

Housing Price Census Tract Multilevel Model Floor Area Noise Pollution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.CRESEUniversité de Franche-ComtéBesançon CedexFrance
  2. 2.Facultad de Economomicas, Departamento de Economía Aplicada (módulo 14)Universidad Autonoma de MadridMadridSpain

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