A spatial multinomial logit model for analysing urban expansion

TL;DR

This paper introduces a Bayesian spatial multinomial logit model to analyze urban expansion patterns, capturing regional spillovers and land use class interactions with improved computational efficiency.

Contribution

It presents a novel Bayesian spatial multinomial logit model that effectively models spatial dependence and spillovers in urban land use data.

Findings

Spatial dependence significantly influences land sealing of cropland and grassland.

Land sealing spillovers occur across multiple arable land classes.

Model outperforms competing specifications in Monte Carlo benchmarks.

Abstract

The paper proposes a Bayesian multinomial logit model to analyse spatial patterns of urban expansion. The specification assumes that the log-odds of each class follow a spatial autoregressive process. Using recent advances in Bayesian computing, our model allows for a computationally efficient treatment of the spatial multinomial logit model. This allows us to assess spillovers between regions and across land use classes. In a series of Monte Carlo studies, we benchmark our model against other competing specifications. The paper also showcases the performance of the proposed specification using European regional data. Our results indicate that spatial dependence plays a key role in land sealing process of cropland and grassland. Moreover, we uncover land sealing spillovers across multiple classes of arable land.