Arianna Salazar-Miranda

At a moment when housing shortages dominate policy discussions, missing middle housing has emerged as a central topic in U.S. policy debates. Cities and towns across the United States are trying to...

We analyze changes in pedestrian behavior over a 30-y period in four urban public spaces located in New York, Boston, and Philadelphia. Building on William Whyte’s observational work, which involved manual video analysis of pedestrian behaviors, we employ computer vision and deep learning techniques to examine video footage from 1979–80 and 2008–10. Our analysis measures changes in walking speed, lingering behavior, group sizes, and group formation. We find that the average walking speed has increased by 15%, while the time spent lingering in these spaces has halved across all locations. Although the percentage of pedestrians walking alone remained relatively stable (from 67% to 68%), the frequency of group encounters declined, indicating fewer interactions in public spaces. This shift suggests that urban residents are using streets as thoroughfares rather than as social spaces, which has important implications for the role of public spaces in fostering social engagement.

Recent advancements in data collection have expanded the tools available for urban science, economic, and real estate research. The integration of diverse data sources has supported the development of ‘smart city’ initiatives in city governance and driven the adoption of data-centric strategies in business applications. This chapter presents an overview of big data sources used in urban science and urban economics, with the goal of directing and enriching future research in these, and other, fields. We structure our discussion around data origins and analytical methods, discussing geographic information maps, GPS and phone records, textual repositories, social media, credit card transactions, street imagery, sensor readings, volumetric data, street patterns, transportation metrics, public records, historical data, business analytics, real estate transactions, and crowdsourced inputs. While aiming to provide an overarching perspective, we also touch upon common challenges in urban big data research, especially those unique to data collection, analysis, and inference.

The lived experience of urban life is shaped by personal mobility through dynamic relationships and resources, marked not only by access and opportunity, but also inequality and segregation. The recent availability of fine-grained mobility data and context attributes ranging from venue type to demographic mixture offer researchers a deeper understanding of experienced inequalities at scale, and pose many new questions. Here we review emerging uses of urban mobility behaviour data, and propose an analytic framework to represent mobility patterns as a temporal bipartite network between people and places. As this network reconfigures over time, analysts can track experienced inequality along three critical dimensions: social mixing with others from specific demographic backgrounds, access to different types of facilities, and spontaneous adaptation to unexpected events, such as epidemics, conflicts or disasters. This framework traces the dynamic, lived experiences of urban inequality and complements prior work on static inequalities experience at home and work.

Images have played a crucial and enduring role in the study of cities, providing valuable insights into the physical environment and influencing urban design theories. This historical importance ha...

Amid rising congestion and transport emissions, policymakers are embracing the ‘15-minute city’ model, which envisions neighbourhoods where basic needs can be met within a short walk from home. Prior research has primarily examined amenity access without exploring its relationship to behaviour. We introduce a measure of local trip behaviour using GPS data from 40 million US mobile devices, defining ‘15-minute usage’ as the proportion of consumption-related trips made within a 15-minute walk from home. Our findings show that the median resident makes only 14% of daily consumption trips locally. Differences in access to local amenities can explain 84% and 74% of the variation in 15-minute usage across and within urban areas, respectively. Historical data from New York zoning policies suggest a causal relationship between local access and 15-minute usage. However, we find a trade-off: increased local usage correlates with higher experienced segregation for low-income residents, signalling potential socio-economic challenges in achieving local living.