Using geographically weighted choice models to account for spatial heterogeneity of preferences

In this paper we investigate the prospects of using geographically weighted choice models for modelling of spatially clustered preferences. The data used in this study comes from a discrete choice experiment survey regarding public preferences for the implementation of a new country-wide forest management and protection program in Poland. We combine it with high-resolution geographical information system data related to local forest characteristics. Using locally estimated discrete choice models we obtain location-specific estimates of willingness to pay (WTP). Variation in these estimates is explained by the socio-demographic characteristics of respondents and characteristics of the forests in their place of residence. The results are compared with those obtained from a more typical, two stage procedure which uses Bayesian posterior means of the mixed logit model random parameters to calculate individual-specific estimates of WTP. The latter approach, although easier to implement and more common in the literature, does not explicitly assume any spatial relationship between individuals. In contrast, the geographically weighted approach differs in this aspect and can provide additional insight on spatial patterns of individuals’ preferences. Our study shows that although the geographically weighted discrete choice models have some advantages, it is not without drawbacks, such as the difficulty and subjectivity in choosing an appropriate bandwidth. We find a number of notable differences in WTP estimates and their spatial distributions. At the current level of development of the two techniques, we find mixed evidence on which approach gives the better results.