Physical disorder is associated with negative outcomes in economic performance, public health, and social stability, such as the depreciation of property, mental stress, fear, and crime. A limited but growing body of literature considers physical disorder in urban space, especially the topic of identifying physical disorder at a fine scale. There is currently no effective and replicable way of measuring physical disorder at a fine scale for a large area with low cost, however. To fill the gap, this article proposes an approach that takes advantage of the massive volume of street view images as input data for virtual audits and uses a deep learning model to quantitatively measure the physical disorder of urban street spaces. The results of implementing this approach with more than 700,000 streets in Chinese cities—which, to our knowledge, is the first attempt globally to quantify the physical disorder in such large urban areas—validate the effectiveness and efficiency of the approach. Through this large-scale empirical analysis in China, this article makes several theoretical contributions. First, we expand the factors of physical disorder, which were previously neglected in U.S. studies. Second, we find that urban physical disorder presents three typical spatial distributions—scattered, diffused, and linear concentrated patterns—which provide references for revealing the development trends of physical disorder and making spatial interventions. Finally, our regression analysis between physical disorder and street characteristics identified the factors that could affect physical disorder and thus enriched the theoretical underpinnings.

This article develops an analytical framework to connect the theories of market and planning failures with the reasoning of incorporating land use and pricing policies for reducing driving and car dependence. A multilevel multinomial logit model (MML) is applied to estimate the effects of neighbourhood land use characteristics and price variables on travel mode choice of non-work trips and their interaction effects in the city of Austin, Texas. Results reveal that pricing policies are more efficient for reducing auto travel while land use policies appear better for adjusting non-auto travel. The practical significance of land use policies for reducing car dependence are more reflected in the modified effects of land use on pricing elasticities of driving probability. Pricing policies are more effective for reducing driving in neighbourhoods with a higher sidewalk density and a better accessibility to mixed-use activity centres. These findings validate the theoretical analysis and demonstrate the importance of coordinating land use and pricing policies together in the transportation planning practice.

Studies in the land use–travel connection have long neglected the role of neighbourhood crime. This paper analyses such a role of crime and explores whether more compact land-use characteristics tend to cluster more crimes, thus having a negative impact on public transit use. A path analysis model is used to estimate the relationship between land use, crime, and bus ridership in the city of Austin, Texas. The results demonstrate that higher population density and more mixed land use may significantly stimulate more crimes near the bus stops while the level of crime may have a nonlinear effect on ridership. Once the number of reported crimes exceeds a threshold level, ridership is negatively affected. As a result, very dense residential and commercial development may trigger a rise in crimes, leading to a fall in ridership. These findings suggest that those who seek to implement land use policies to increase transit use and reduce auto dependence should recognise and evaluate land use impact on crime and how this impact affects travel behaviours.