Quantifying traffic congestion is a critical task for transportation planning and research. Numerous metrics have been developed, mainly focusing on changes in vehicle speeds, their extents, and travel time. In this study, new metrics are presented using the Hägerstrand’s space-time cube that has been studied from time geography perspectives since the 1960s. Particularly, the product of distance and time, i.e., distanceTime, is proposed as a base metric to measure traffic congestion amounts. Using the base metric such as mileHours, metrics of weighted congestion and normalized congestion amounts were also developed. New metrics were applied to six metropolitan areas and their vicinities in the United States (Atlanta, Chicago, Washington, D.C. and Baltimore, Dallas and Fort Worth, Los Angeles, and New York), and congestion amounts were calculated and compared. The Google Traffic Layer API was used to obtain traffic congestion datasets for six months (April–September 2022), and GIS (geographic information systems) was used for delineating road features and traffic intensity levels. Among the six areas, New York and its vicinity showed the largest congestion when only heavy congestion was used. Los Angeles and its vicinity showed the largest congestion when all congestion levels were considered. This study shows that the proposed metrics are very effective in summarizing traffic amounts and broadly applicable for further analyses of traffic congestion phenomena by associating various other factors, such as weekdays, months, or gas prices. The new metrics developed in this research may help transportation researchers and practitioners by providing them with a set of metrics applicable to summarizing congestion amounts by synthesizing congestion intensity, extent, and duration.

The use of telehealth has significantly increased in recent years, providing patients with remote access to healthcare services. To measure accessibility of telehealth services, two-step virtual catchment area (2SVCA) methods have been developed as a broadband-aware extension of the traditional two-step floating catchment area (2SFCA) framework. Although telehealth utilization varies across demographic groups, existing 2SVCA approaches do not account for such variation and typically treat population demand as uniform. To address the knowledge gap, this study proposes the demographic-weighted 2SVCA (DW-2SVCA), which enhances the 2SVCA framework by adjusting telehealth demand through a weight matrix based on demographic characteristics, including age, gender, race, and ethnicity. In a case study of primary healthcare in Mecklenburg County, North Carolina, we applied the proposed method to measure telehealth accessibility and compared it with the existing 2SVCA approach. While the overall statistical and spatial patterns were similar, DW-2SVCA exhibited greater variance in accessibility values. To further evaluate whether the proposed method more effectively captures demographic variations in telehealth utilization, we classified the study area into four groups based on demographic profiles by using hierarchical clustering and conducted statistical tests. The results revealed that the DW-2SVCA method indicates more pronounced and statistically significant differences, with higher accessibility in regions characterized by white and middle-aged populations and lower accessibility in more racially/ethnically diverse areas. This study offers a more comprehensive tool for evaluating telehealth accessibility, highlighting the importance of incorporating demographic variations in telehealth utilization.

The COVID-19 pandemic has profoundly disrupted urban systems, reshaping human mobility and activity patterns. While previous studies have documented macro-level declines using aggregated mobility indicators, the evolution of localized activity patterns throughout the pandemic phases remains unclear. This study integrates geotagged social media imagery with a geospatial AI-based analytical framework to examine long-term shifts in urban activity in Seoul, South Korea. We collected 14,875 Flickr photographs from 2018 to 2023 and applied HDBSCAN to delineate urban areas of interest. We classified image content using a ResNet-50 model fine-tuned on the Places365 dataset to capture semantic activity patterns. The results revealed sharp contractions of indoor-oriented categories—such as shopping malls and coffee shops—during 2020–2021, alongside a rise in outdoor categories, including parks and plazas, reflecting both formal restrictions and heightened risk perceptions. By 2022–2023, indoor activities gradually reappeared while outdoor activities remained significant, suggesting a rebalancing of urban behavior rather than a simple return to pre-pandemic conditions. The analyses highlight differentiated adaptation pathways: cultural resilience in the central district, rapid commercial recovery in Gangnam–Songpa, and balanced leisure–work restoration in Mapo–Yeouido. Interpreted through the stimulus–organism–response framework, these findings provide empirical evidence of how external shocks reshaped perceptions and behaviors, mediated by district-specific functions. The study demonstrates the value of linking behavioral framework with geospatial AI-based spatial analysis to monitor place-specific adaptation. By offering a 6-year longitudinal perspective, it contributes to both conceptual integration and empirical insights for adaptive and resilient urban planning.