The paper presents a basic spatial discrete choice modeling framework obtained by applying random utility theory to discrete choices made by heterogeneous spatially dependent individuals. The newly developed framework has two main advantages over existing approaches. First, individual decision-makers are no longer assumed to be independent and non-interacting but spatially interdependent in their preferences facilitating the development of applied discrete choice models using a wide range of spatial data. Second, pseudo maximum likelihood estimator is developed for this model that is consistent and computationally feasible for large datasets. The performance of the pseudo maximum likelihood estimator for the spatial discrete choice model is illustrated using simulated data.
