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  • 1. Applications of spatial smoothing to survey data Archived
    Articles and reports: 12-001-X19960022984
    Description:

    In this paper we present two applications of spatial smoothing using data collected in a large scale economic survey of Australian farms: one a small area and the other a large area application. In the small area application, we describe how the sample weigths can be spatially smoothed in order to improve small area estimates. In the large area application, we give a method for spatially smoothing and then mapping the survey data. The standard method of weighting in the survey is a variant of linear regression weighting. For the small area application, this method is modified by introducing a constraint on the spatial variability of the weights. Results from a small scale empirical study indicate that this decreases the variance of the small area estimators as expected, but at the cost of an increase in their bias. In the large area application, we describe the nonparametric regression method used to spatially smooth the survey data as well as techniques for mapping this smoothed data using a Geographic Information System (GIS) package. We also present the results of a simulation study conducted to determine the most appropriate method and level of smoothing for use in the maps.

    Release date: 1997-01-30
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  • 1. Applications of spatial smoothing to survey data Archived
    Articles and reports: 12-001-X19960022984
    Description:

    In this paper we present two applications of spatial smoothing using data collected in a large scale economic survey of Australian farms: one a small area and the other a large area application. In the small area application, we describe how the sample weigths can be spatially smoothed in order to improve small area estimates. In the large area application, we give a method for spatially smoothing and then mapping the survey data. The standard method of weighting in the survey is a variant of linear regression weighting. For the small area application, this method is modified by introducing a constraint on the spatial variability of the weights. Results from a small scale empirical study indicate that this decreases the variance of the small area estimators as expected, but at the cost of an increase in their bias. In the large area application, we describe the nonparametric regression method used to spatially smooth the survey data as well as techniques for mapping this smoothed data using a Geographic Information System (GIS) package. We also present the results of a simulation study conducted to determine the most appropriate method and level of smoothing for use in the maps.

    Release date: 1997-01-30
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