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2011•Unpublished venueRequires access

A GWR Model for Local Analysis of Demographic Relationships

Massimo Mucciardi, Pietro Bertuccelli

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Abstract

In this paper, we apply the geographically weighted regression (GWR) to examine provincial variation of the total fertility rate (TFR) in Italy. Such spatial analysis is important because the GWR model has the potential to reveal local patterns in the spatial distribution of parameter which would be ignored by ordinary least square regression (OLS). We conclude that the analysis of local TFR shows a significant improvement in model performance of GWR over OLS.

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What this paper is about

In this paper, we apply the geographically weighted regression (GWR) to examine provincial variation of the total fertility rate (TFR) in Italy. Such spatial analysis is important because the GWR model has the potential to reveal local patterns in the spatial distribution of parameter which would be ignored by ordinary least square regression (OLS). We conclude that the analysis of local TFR shows a significant improvement in model performance of GWR over OLS.

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Available abstract

In this paper, we apply the geographically weighted regression (GWR) to examine provincial variation of the total fertility rate (TFR) in Italy. Such spatial analysis is important because the GWR model has the potential to reveal local patterns in the spatial distribution of parameter which would be ignored by ordinary least square regression (OLS). We conclude that the analysis of local TFR shows a significant improvement in model performance of GWR over OLS.

Key concepts: Geographically Weighted Regression, Ordinary least squares, Regression analysis, Econometrics, Spatial variability, Statistics, Distribution (mathematics), Regression

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