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LUISA: A Land-Use Interaction with Social Accounting Model; Presentation and Enhanced Calibration Method

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  • Marcial H Echenique

    (The Martin Centre for Architectural and Urban Studies, Department of Architecture, University of Cambridge, 1-5 Scroope Terrace, Cambridge CB2 1PX, England)

  • Vadim Grinevich

    (Southampton Management School, University of Southampton, Highfield, Southampton SO17 1BJ, England)

  • Anthony J Hargreaves
  • Vassilis Zachariadis

Abstract

Random utility modelling has been established as one of the main paradigms for the implementation of land-use spatial interaction (LUSI) models. We present a detailed formal description of a LUSI model that adheres to the random utility paradigm through the explicit distinction between utility and cost across all processes that represent the behaviour of agents. The model is rooted in a social accounting matrix, with the workforce and households accounts being disaggregated by socioeconomic type. Similarly, the land account is broken down by domestic and nondomestic land-use types. The model is developed around two processes. Firstly, the generation of demand for inputs required by established production; when appropriate the implicit production functions are assumed to depend on costs of inputs, which give rise to price-elastic demands. And, secondly, the spatial assignment of demanded inputs to locations of their production; here sequences of decisions are used to distribute demand both spatially and aspatially, and to propagate costs and utilities of production and consumption that emerge from imbalances between supply and demand. The implementation of this generic model is discussed in relation to the case of the UK. The model has been developed for testing the sustainability of integrated economic, spatial development policies, and output information for estimating urban form and the potential for decentralised technologies. The inputs include area-wide socioeconomic forecasts and the allocation policy of urban land. The outputs include the spatial allocation of activities and prices of labour, goods and services, land, and floorspace. They are combined with the land inputs to estimate the changes in the density of urban form and activities. These outputs can then be used to estimate the demands for infrastructure services and the potential for decentralised infrastructure supply. We focus primarily on the calibration process and its methodological implications, including a method of refining the calibration and demonstrate how this improves the spatial representation of the utility of land.

Suggested Citation

  • Marcial H Echenique & Vadim Grinevich & Anthony J Hargreaves & Vassilis Zachariadis, 2013. "LUISA: A Land-Use Interaction with Social Accounting Model; Presentation and Enhanced Calibration Method," Environment and Planning B, , vol. 40(6), pages 1003-1026, December.
    • Handle: RePEc:sae:envirb:v:40:y:2013:i:6:p:1003-1026
    DOI: 10.1068/b38202
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    References listed on IDEAS

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    2. Anas, Alex, 1983. "Discrete choice theory, information theory and the multinomial logit and gravity models," Transportation Research Part B: Methodological, Elsevier, vol. 17(1), pages 13-23, February.
    3. Glaeser, Edward L., 2008. "Cities, Agglomeration, and Spatial Equilibrium," OUP Catalogue, Oxford University Press, number 9780199290444.
    4. Geoff Payne, 1987. "Mobility and Social Class," Palgrave Macmillan Books, in: Employment and Opportunity, chapter 8, pages 189-192, Palgrave Macmillan.
    5. Marcial Echenique & Anthony Hargreaves & Gordon Mitchell & Anil Namdeo, 2012. "Growing Cities Sustainably," Journal of the American Planning Association, Taylor & Francis Journals, vol. 78(2), pages 121-137.
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    1. Athanasios Votsis, 2017. "Exploring the spatiotemporal behavior of Helsinki’s housing prices with fractal geometry and co-integration," Journal of Geographical Systems, Springer, vol. 19(2), pages 133-155, April.

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