Geographically weighted regression (GWR) enjoys wide application in regional science, thanks to its relatively straightforward formulation and explicit treatment of spatial effects. The application of GWR to discrete-response data sets and land use change at the level of urban parcels has remained a novelty, however. This paper describes work that combined logit specifications with GWR techniques to anticipate five categories of land use change in Austin, Texas, and controlled for parcel geometry, slope, regional accessibility, local population density, and distances to Austin's downtown and various roadway types. Results of this multinomial logit GWR model suggested spatial variations in—and significant influence of—these covariates, especially roadway vicinity and regional access. A 1% increase in the distance of an undeveloped parcel to the nearest freeway, for example, was estimated, on average, to increase the probability of residential development by 1.2%, while the same increase in distance to a major arterial was estimated to increase the probability by 1.8%. Conversely, proximity to roads (through reductions in such distances) was estimated to boost the likelihood of nonresidential development (e.g., 9.0% in the case of commercial development in response to a 1% decrease in distance to such arterials). The logsum accessibility index was estimated to exert an average positive influence on commercial, office, and industrial development tendencies, while it dampened land use transitions from an undeveloped state to residential uses. Comparisons of results with a spatial autoregressive binary probit (with the use of all developed land use categories as a single response) and GWR binary probit also provided some insights. The latter seemed to surpass the former in its account for spatial effects, as reflected by a lower Akaike information criterion value.

No abstract

No abstract

The COVID-19 pandemic has caused worldwide lockdowns and similar containment measures aiming to curb the spread of the virus. Lockdown measures have been implemented in cities amid the COVID-19 outbreak. After the pandemic is under control, cities will be gradually reopened. This study aims to investigate the variations in urban travel behavior during the lockdown and reopening phases. On the basis of long-term traffic congestion index data and subway ridership data in eight typical cities of China, this study carried out comparisons on urban travel behaviors with and without the pandemic. Changes in the multimodal travel behaviors in different times of day and days of week are analyzed during the lockdown and reopening phases. Multivariate and one-way analyses of variance are conducted to show the statistical significance of the changes. This study further investigates the relationship between the returned-to-work (RTW) rate and travel behaviors in the reopening phase. A stepwise multiple regression is conducted to quantify the impacts of influencing factors (i.e., population migration index, RTW rate, socioeconomic indices, and pandemic statistical indicators) on vehicular traffic after reopening. Results show that the lockdown measure has a significant impact on reducing the traffic congestion during the peak hours on workdays, and the subway ridership dropped to below 10% of the prepandemic level during the lockdown phase. Travel demands tended to switch from subways to private vehicular travel modes during the reopening phase, leading to a rapid recovery of vehicular traffic and a slow recovery of subway ridership. The recovery of vehicular traffic is proved to be related to the RTW rate, certain city characteristics, and new COVID-19 cases after city reopening.

This paper develops two new models and evaluates the impact of using different weight matrices on parameter estimates and inference in three distinct spatial specifications for discrete response. These specifications rely on a conventional, sparse, inverse-distance weight matrix for a spatial autoregressive probit (SARP) model, a spatial autoregressive approach where the weight matrix includes an endogenous distance-decay parameter (SARPα), and a matrix exponential spatial specification for probit (MESSP). These are applied in a binary choice setting using both simulated data and parcel-level land-use data. Parameters of all models are estimated using Bayesian methods.In simulated tests, adding a distance-decay parameter term to the spatial weight matrix improved the quality of estimation and inference, as reflected by a lower deviance information criteriaon (DIC) value, but the added sampling loop required to estimate the distance-decay parameter substantially increased computing times. In contrast, the MESSP model's obvious advantage is its fast computing time, thanks to elimination of a log-determinant calculation for the weight matrix. In the model tests using actual land-use data, the MESSP approach emerged as the clear winner, in terms of fit and computing times. Results from all three models offer consistent interpretation of parameter estimates, with locations farther away from the regional central business district (CBD) and closer to roadways being more prone to (mostly residential) development (as expected). Again, the MESSP model offered the greatest computing-time savings benefits, but all three specifications yielded similar marginal effects estimates, showing how a focus on the spatial interactions and net (direct plus indirect) effects across observational units is more important than a focus on slope-parameter estimates when properly analyzing spatial data.a yiyi