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Modelling Spatial Variations of Fertility Rate in Italy

  • Massimo Mucciardi
  • Pietro Bertuccelli
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Standard regression model parameters are assumed to apply globally over the entire territory where measured data have been taken, under the assumption of spatial stationarity in the relationship between the variables under study. In most cases this assumption is invalid. Instead, geographically weighted regression (GWR) explicitly deals with the spatial non-stationarity of empirical relationships. Considering a georeferenced dataset on provincial total fertility rate (TFR) in Italy, GWR technique shows a significant improvement in model performance over ordinary least squares (OLS). We also discuss about the test for spatial non-stationarity.

Keywords

Ordinary Little Square Total Fertility Rate Geographically Weighted Regression Ordinary Little Square Model Geographically Weighted Regression Model 
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.

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers who provided detailed feedback on the earlier version of this article.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Economics, Statistics, Mathematics e SociologyUniversity of MessinaMessinaItaly

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