High-Speed Rail (HSR) has increasingly become an important mode of inter-city transportation between large cities. Inter-city interaction facilitated by HSR tends to play a more prominent role in promoting urban and regional economic integration and development. Quantifying the impact of HSR’s interaction on cities and people is therefore crucial for long-term urban and regional development planning and policy making. We develop an evaluation framework using toponym information from social media as a proxy to estimate the dynamics of such impact. This paper adopts two types of spatial information: toponyms from social media posts, and the geographical location information embedded in social media posts. The framework highlights the asymmetric nature of social interaction among cities, and proposes a series of metrics to quantify such impact from multiple perspectives – including interaction strength, spatial decay, and channel effect. The results show that HSRs not only greatly expand the uneven distribution of inter-city connections, but also significantly reshape the interactions that occur along HSR routes through the channel effect.

Spatiotemporal prediction of crime is crucial for public safety and smart cities operation. As crime incidents are distributed sparsely across space and time, existing deep-learning methods constrained by coarse spatial scale offer only limited values in prediction of crime density. This paper proposes the use of deep inception-residual networks (DIRNet) to conduct fine-grained, theft-related crime prediction based on non-emergency service request data (311 events). Specifically, it outlines the employment of inception units comprising asymmetrical convolution layers to draw low-level spatiotemporal dependencies hidden in crime events and complaint records in the 311 dataset. Afterward, this paper details how residual units can be applied to capture high-level spatiotemporal features from low-level spatiotemporal dependencies for the final prediction. The effectiveness of the proposed DIRNet is evaluated based on theft-related crime data and 311 data in New York City from 2010 to 2015. The results confirm that the DIRNet obtains an average F1 of 71%, which is better than other prediction models.

This paper reviews a large number of scholarly articles in the housing field spanning the last thirty years, from 1993 to 2022 by implementing bibliometric analysis method. We examine scientific outputs, identify influential articles, journals, international collaboration and evolution of research trends. Keywords such as “Housing price,” “Housing policy,” “Affordable housing,” “Homeownership,” “Housing market,” “Urban planning,” and “Neighborhood” have been identified as the most prevalently cited terms during this period. Furthermore, the prominence of terms such as “China,” “Gentrification,” “Public housing,” “Social housing,” “Homelessness,” “Migration,” “Urbanization,” “Energy,” “Inequality,” “Land use,” “Gender,” and “Foreclosure” have grown in importance, pointing to future research trends. The analysis also reveals that articles pertaining to the COVID-19 pandemic predominantly address the comprehensive effects of the virus on aspects of mental and physical health, consumer behavior, and economic and societal challenges.

This paper examines the effects of an investment in light rail transit on single-family property values in Montgomery County, Maryland, with a focus on when and where such capitalization takes place and with intent to inform efforts to promote equitable transit-oriented development. We employ both hedonic price and repeat-sales estimation techniques to assure the results are robust. The findings indicate that housing prices, after the light rail transit went into the engineering phase, rose with proximity to anticipated new rail stations but not with proximity to existing metro stations. For planners and policymakers and advocates in Montgomery County, the results provide critical information about where and when value capture and anti-displacement policies are most needed and potentially effective.