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The State-of-the-Art in Building Residential Location Models

In: Residential Location Choice

Author

Listed:
  • Francesca Pagliara

    (University of Naples Federico II)

  • Alan Wilson

    (University College London)

Abstract

This chapter provides an overview of the history of modelling residential location choice. Models of residential mobility typically have developed for illuminating the nature of location choice at different territorial scales or as part of an integrated model of land-use and transport. The latter tend to be more comprehensive in nature, though certain other investigations do consider interactions of location choice with other key decisions, such as work location. Models presented in this book are described here briefly and are presented here according to three dimensions: theory and method, i.e. the modelling approach at the root of the model; categorisation of residential decision makers; and treatment of space, i.e. continuous, zoning or cells.

Suggested Citation

  • Francesca Pagliara & Alan Wilson, 2010. "The State-of-the-Art in Building Residential Location Models," Advances in Spatial Science, in: Francesca Pagliara & John Preston & David Simmonds (ed.), Residential Location Choice, pages 1-20, Springer.
    • Handle: RePEc:spr:adspcp:978-3-642-12788-5_1
    DOI: 10.1007/978-3-642-12788-5_1
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    Citations

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    Cited by:

    1. Bhat, Chandra R., 2015. "A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 161-188.
    2. Ibeas, Ángel & Cordera, Ruben & dell’Olio, Luigi & Coppola, Pierluigi, 2013. "Modelling the spatial interactions between workplace and residential location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 110-122.
    3. Liying Yue & Morton E. O’Kelly, 2023. "Rents and wages derived from spatial interaction analysis in Shanghai," Journal of Geographical Systems, Springer, vol. 25(1), pages 59-75, January.
    4. Namazi-Rad, Mohammad-Reza & Mokhtarian, Payam & Shukla, Nagesh & Munoz, Albert, 2016. "A data-driven predictive model for residential mobility in Australia – A generalised linear mixed model for repeated measured binary data," Journal of choice modelling, Elsevier, vol. 20(C), pages 49-60.
    5. Henke, Ilaria & Moyano, Amparo & Pagliara, Francesca, 2023. "Influence of high-speed rail on the decentralisation of events from big metropolitan areas to smaller intermediate cities," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    6. Perez-Lopez, Jose-Benito & Novales, Margarita & Orro, Alfonso, 2022. "Spatially correlated nested logit model for spatial location choice," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 1-12.

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