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A Geographically Weighted Regression Method to Spatially Disaggregate Regional Employment Forecasts for South East Queensland

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Abstract

In this paper we present a new methodology by which regional employment forecasts can be spatially disaggregated to smaller administrative units. We develop a statistical model for disaggregating spatial data based upon related employment determinants (for example, the proximity of an area to a shopping centre), demonstrating there is a degree of spatial dependence and spatial heterogeneity in relationships. Applying an advanced statistical procedure, Geographically Weighted Regression (GWR), to account for these spatial effects this method utilises the locally fitted relationships to estimate employment numbers at the smaller geography whilst being constrained by the regional forecast. Results demonstrate that our GWR method generates superior estimates over a global regression model for spatially disaggregating regional employment forecasts.

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Acknowledgements

This paper is based on research conducted on a project funded by the Australian Research Council Linkage program grant #LP0453563 with additional support from the industry partner, the Office of Economic and Statistical research in the Queensland Treasury. The authors are solely responsible for the preparation of the text of the paper and the figures and tables. The views expressed in the paper are not necessarily to be taken as reflecting Queensland government policy.

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  1. University of Queensland Social Research Centre (UQSRC), Building 31B, St Lucia, University of Queensland, , Brisbane, Queensland, 4072, Australia

    Tiebei Li, Jonathan Corcoran, David Pullar, Alistair Robson & Robert Stimson

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  1. Tiebei Li
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Correspondence to Tiebei Li.

Additional information

Dr. Martin Charlton, Dr. Chris Brunsdon, and Dr. Stewart Fotheringham, University of Newcastle, UK are thanked for the provision of software GWR 3.0.1 used in this research, The Moran’I values were calculated using GEODA 0.9.5 developed at the Spatial Analysis Lab, University of Illinois, US.

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Li, T., Corcoran, J., Pullar, D. et al. A Geographically Weighted Regression Method to Spatially Disaggregate Regional Employment Forecasts for South East Queensland. Appl. Spatial Analysis 2, 147–175 (2009). https://doi.org/10.1007/s12061-008-9015-3

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