【摘要】This paper develops an estimation strategy for and then applies a spatial autoregressive multinomial probit model to account for both spatial clustering and cross-alternative correlation. Estimation is achieved using Bayesian techniques with Gibbs and the generalized direct sampling (GDS). The model is applied to analyze land development decisions for undeveloped parcels over a 6-year period in Austin; Texas. Results suggest that GDS is a useful method for uncovering parameters whose draws may otherwise fail to converge using standard Metropolis-Hastings algorithms. Estimation results suggest that residential and commercial/civic development tends to favor more regularly shaped and smaller parcels; which may be related to parcel conversion costs and aesthetics. Longer distances to Austin's central business district increase the likelihood of residential development; while reducing that of commercial/civic and office/industrial uses. Everything else constant; distances to a parcel's nearest minor; and major arterial roads are estimated to increase development likelihood of commercial/civic and office/industry uses; perhaps because such development is more common in less densely developed locations (as proxied by fewer arterials). As expected; added soil slope is estimated to be negatively associated with residential development; but positively associated with commercial/civic and office/industry uses (perhaps due to some steeper terrains offering view benefits). Estimates of the cross-alternative correlations suggest that a parcel's residential use 'utility' or attractiveness tends to be negatively correlated with that of commercial/civic; but positively associated with that of office/industrial uses; while the latter two land uses exhibit some negative correlation. Using an inverse-distance weight matrix for each parcel's closest 50 neighbors; the spatial autocorrelation coefficient is estimated to be 0.706; indicating a marked spatial clustering pattern for land development in the selected region.

【文献来源】Wang Y;Kockelman K M;Damien P.Annals of regional Science.2014（1）