A mixed spatially correlated logit model: formulation and application to residential choice modeling

Bhat, Chandra R.; Guo, Jessica Y

doi:10.1016/s0191-2615(03)00005-5

Cited by 261 publications

(188 citation statements)

References 31 publications

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“…On the other hand, the parameter of the variable referring to home-work journey times was clearly significant in all the models at least a level of confidence of 90% with no significant differences between the overall group of households and the group with higher incomes. This result can therefore be considered to be compatible with the results obtained in previous research such as Guo and Bhat (2001) and Bhat and Guo (2004). The ''Residence/work'' variable referring to the location of work place and residence in the same zone also had a positive sign using all the models even if it was only significant at a confidence level of between 71% and 87%.…”

Section: Discussionsupporting

confidence: 91%

“…Nevertheless, in order to limit the number of parameters to be estimated in the models, a technique that had been previously used in other studies (Bhat and Guo, 2004) was applied to establish the condition that a zone could only belong to those nests which presented common borders. Only alternatives 5 and 6 continued to hold direct connected with the root nest.…”

Section: Models Considering Spatial Correlation Between Alternatives:mentioning

confidence: 99%

“…However, the NL model still requires the analyst to specify a priori, a correlation structure (Hunt et al, 2004;Pellegrini and Fotheringham, 2002). Bhat and Guo (2004) proposed a model derived from the GEV family which they called Mixed Spatially Correlated Logit (MSCL) for the case of spatially correlated alternatives. This model is based on a special case of the Generalized Nested Logit (GNL) model as originally formulated by Wen and Koppelman (2001) with the incorporation of a distribution of mixes to capture the heterogeneity of tastes between individuals.…”

Section: Introduction and Objectivesmentioning

confidence: 99%

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Modelling the spatial interactions between workplace and residential location

Ibeas

Cordera²,

dell’Olio

et al. 2013

Transportation Research Part A: Policy and Practice

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a b s t r a c tThe use of Multinomial Logit (MNL) models specification for the simulation of residential location have been often criticised due to the Independence of Irrelevant Alternatives hypothesis (IIA) which does not allow for the existence of spatial correlation between residential zones. Moreover, it is not clear when and to what extent the influence of the workplace zone and accessibility to employment affect the residential location choices made by households; in other word, whether the residing choice is conditional to the workplace, or vice versa; or if such choices (residence and work place) are joint.In this paper, Nested Logit (NL) and Cross-Nested Logit models of residential location choice are specified and compared to MNL, to investigate the existence of spatial correlation between different locations. Furthermore, different assumptions are tested, considering the choice of residential zone and the joint choice of residential zone and work place zone.The models were estimated for the urban area of Santander (Spain). The results indicate that the inclusion in the model specification, of the spatial correlation between zones fit the data significantly better. Home-work journey times were a statistically significant factor in household location choice, whereas accessibility to employment had the correct sign but it was not statistically significant.

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Section: Discussionsupporting

confidence: 91%

Section: Models Considering Spatial Correlation Between Alternatives:mentioning

confidence: 99%

Section: Introduction and Objectivesmentioning

confidence: 99%

See 1 more Smart Citation

Modelling the spatial interactions between workplace and residential location

Ibeas

Cordera²,

dell’Olio

et al. 2013

Transportation Research Part A: Policy and Practice

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“…This type of spatial correlation has been examined primarily in the transportation and geography literatures (see Hunt et al, 2004). The common model structures to accommodate such inter-alternative spatial correlation include the mixed logit model (see Bolduc et al, 1996 andMiyamoto et al, 2004), the multinomial probit model (see Garrido andMahmassani, 2000 andBolduc et al, 1997), and a GEV-based spatially correlated logit (SCL) model (see Bhat and Guo, 2004). While spatial correlation across alternatives is an important component of modeling choice among multinomial spatial units, there are several choice occasions where the alternatives themselves are not spatial units.…”

Section: Discrete Choice Models With Spatial Error Autocorrelationmentioning

confidence: 99%

A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units

Bhat

Sener

2009

J Geogr Syst

Self Cite

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This study focuses on accommodating spatial dependency in data indexed by geographic location. In particular, the emphasis is on accommodating spatial error correlation across observational units in binary discrete choice models. We propose a copula-based approach to spatial dependence modeling based on a spatial logit structure rather than a spatial probit structure. In this approach, the dependence between the logistic error terms of different observational units is directly accommodated using a multivariate logistic distribution based on the Farlie-Gumbel-Morgenstein (FGM) copula. The approach represents a simple and powerful technique that results in a closedform analytic expression for the joint probability of choice across observational units, and is straightforward to apply using a standard and direct maximum likelihood inference procedure. There is no simulation machinery involved, leading to substantial computation gains relative to current methods to address spatial correlation. The approach is applied to teenagers' physical activity participation levels, a subject of considerable interest in the public health, transportation, sociology, and adolescence development fields. The results indicate that failing to accommodate heteroscedasticity and spatial correlation can lead to inconsistent and inefficient parameter estimates, as well as incorrect conclusions regarding the elasticity effects of exogenous variables.

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“…If the data on dwelling's renovation is not available, observations of choices toward the dwelling with the higher price would lead to the erroneous conclusion that the sensitivity to price is smaller than it really is. Numerous empirical applications in residential location choice modeling have shown estimated coefficients of dwelling price that are non-significant or even positive when endogeneity is not taken into account (Guevara and Ben-Akiva, 2006;Bhat and Guo, 2004;Sermonss and Koppelman, 2001; Levine, 1998;Waddell, 1992;Quigley, 1976).…”

Section: Introductionmentioning

confidence: 99%

Change of Scale and Forecasting with the Control-Function Method in Logit Models

Guevara

Ben‐Akiva

2012

Transportation Science

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The control-function method is the most suitable tool to address endogeneity for several discrete choice models that are relevant in transportation research. The estimators obtained with the control-function method are consistent only up to a scale. In this paper we first depict the determinants of this change of scale by adapting an existing result for omitted orthogonal attributes in Logit models. Then, we study the problem of forecasting under these circumstances. We show that a procedure proposed in previous literature may lead to significant biases, and we suggest novel alternatives to be used with synthetic populations. We also discuss potential extensions of these results to other non-Logit discrete choice models. We use Monte Carlo experimentation and real data on residential location choice to demonstrate these results. The paper finishes summarizing the findings of this research and suggesting future lines of research in this area.

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A mixed spatially correlated logit model: formulation and application to residential choice modeling

Cited by 261 publications

References 31 publications

Modelling the spatial interactions between workplace and residential location

Modelling the spatial interactions between workplace and residential location

A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units

Change of Scale and Forecasting with the Control-Function Method in Logit Models

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