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Land use and transport interactions (LUTI) models are widely employed by planners, to forecast future urban patterns and to test the influence of land use or transport policies. This thesis examines their sensitivity to two types of spatial bias, the choice of the study area ("spatial extent") and of the areal units (“spatial resolution”).
The first part of this thesis focuses on econometric components inside LUTI models. We propose two extensions to the literature on the Modifiable Areal Unit Problem (MAUP). First, the sensitivity of regression models to spatial extent is examined. Secondly, the influence of the spatial resolution on discrete choice models, used by most state-of-the-art LUTI models to forecast location choices of agents, is assessed. Significant variations are found in parameter estimates, leading to changes in the
behaviour of the model.
The second part of the thesis assesses the influence of these spatial biases on LUTI models' outputs. Two applications of the UrbanSim model are developed. We rely on a synthetic case study to examine the sensitivity of the final situation predicted by the model. Then, a model of the urban region of Brussels is used to explore the implications of this sensitivity for policy evaluation. The results show that variations induced by spatial bias on LUTI models’ outputs are larger than those due to transport
or land use scenarios, and that they affect the estimation of the sustainability of these scenarios.
The last chapter offers recommendations to reduce the sensitivity of LUTI models to these spatial biases, consisting in the adoption of “best spatial practices” and of potential technical development, together with alternative approach of increasing the applicability of LUTI models
Land use and transport interactions (LUTI) models are widely employed by planners, to forecast future urban patterns and to test the influence of land use or transport policies. This thesis examines their sensitivity to two types of spatial bias, the choice of the study area ("spatial extent") and of the areal units (“spatial resolution”).
The first part of this thesis focuses on econometric components inside LUTI models. We propose two extensions to the literature on the Modifiable Areal Unit Problem (MAUP). First, the sensitivity of regression models to spatial extent is examined. Secondly, the influence of the spatial resolution on discrete choice models, used by most state-of-the-art LUTI models to forecast location choices of agents, is assessed. Significant variations are found in parameter estimates, leading to changes in the
behaviour of the model.
The second part of the thesis assesses the influence of these spatial biases on LUTI models' outputs. Two applications of the UrbanSim model are developed. We rely on a synthetic case study to examine the sensitivity of the final situation predicted by the model. Then, a model of the urban region of Brussels is used to explore the implications of this sensitivity for policy evaluation. The results show that variations induced by spatial bias on LUTI models’ outputs are larger than those due to transport
or land use scenarios, and that they affect the estimation of the sustainability of these scenarios.
The last chapter offers recommendations to reduce the sensitivity of LUTI models to these spatial biases, consisting in the adoption of “best spatial practices” and of potential technical development, together with alternative approach of increasing the applicability of LUTI models
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