Search

The acceleration of urban imagery data analysis, driven by computer vision (CV), has created noteworthy opportunities for urban studies and planning. Data on street environments with high granularity derived from geo-tagged street views allow urban researchers to obtain geospatial data on greenery, pavement materials, and dimensions, building facades, urban furniture, lighting, vehicle presence, etc. However, how such attributes have been classified and used to address urban studies, planning, or mobility questions remains relatively poorly understood among non-technical researchers. Targeting urban planning and design researchers who do not have a background in CV, this paper reviews planning-relevant attributes that CV approaches of urban streetscapes have delivered to date and examines some of their research applications. We present a systematic analysis of 146 papers scrutinizing 104 street attributes in four groups. By exploring a subcollection of 24 papers, we discuss the effectiveness of those attributes being incorporated into current quantitative urban studies. This study's primary contribution lies in providing a comprehensive summary of CV-driven street attributes, their applications, and the algorithms used, serving as a valuable resource for future urban research. Additionally, we identify key challenges in this field, such as unclear definitions of attributes, a disproportionate emphasis on selecting models and features, and the absence of standardized measurement and definition methods. Furthermore, we offer recommendations for future research directions in this area. • We review 104 street attributes collected with computer vision in urban studies • We summarize 23 chosen attributes' correlations with known urban design measures • We provide a table of CV-driven street attributes with model details, references, and impacts • We highlight challenges in attribute definitions, research focus, and impact clarity • We provide recommendations for standardizing data and future research [ABSTRACT FROM AUTHOR]

City governments and planners alike commonly seek to increase pedestrian activity on city streets as part of broader sustainability, community building, and economic development strategies. Though walkability has received ample attention in planning literature, most planners still lack practical methods for predicting how development proposals could affect pedestrian activity on specific streets or public spaces at different times of the day. Cities typically require traffic impact assessments (TIAs) but not pedestrian impact assessments. In this study I present a methodology for estimating pedestrian trip generation and distribution between detailed origins and destinations in both existing and proposed built environments. Using the betweenness index from network analysis, I introduce a number of methodological improvements that allow the index to model pedestrian trips with parameters and constraints to account for pedestrian behavior in different settings. I demonstrate its application in the Kendall Square area of Cambridge (MA), where estimated foot traffic is compared during lunch and evening peak periods with observed pedestrian counts. The proposed approach can be particularly useful for TIAs, neighborhood plans, and large-scale development projects, where pedestrian flow estimates can be used to guide pedestrian infrastructure and safety improvements and public space investments or for locating pedestrian priority streets during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Cities are increasingly promoting walkability to tackle climate change, improve urban quality of life, and address socioeconomic inequities that auto-oriented development tends to exacerbate, prompting a need for predictive pedestrian flow models. This paper implements a novel network-based pedestrian flow model at a property-level resolution in the City of Melbourne. Data on Melbourne's urban form, land-uses, amenities, and pedestrian walkways as well as weather conditions are used to predict pedestrian flows between different land-use pairs, which are subsequently calibrated against hourly observed pedestrian counts from automated sensors. Calibration allows the model extrapolate pedestrian flows on all streets throughout the city center based on reliable baseline observations, and to forecast how new development projects will change existing pedestrian flows. Longitudinal data availability also allows us to validate how accurate such predictions are by comparing model results to actual pedestrian counts observed in following years. Updating the built-environment data annually, we (1) test the accuracy of different calibration techniques for predicting foot-traffic on the city's streets in subsequent years; (2) assess how changes in the built environment affect changes in foot-traffic; (3) analyze which pedestrian origin-destination flows explain observed foot-traffic during three peak weekday periods; and (4) assess the stability of model predictions over time. We find that annual changes in the built environment have a significant and measurable impact on the spatial distribution of Melbourne's pedestrian flows. We hope this novel framework can be used by planners to implement "pedestrian impact assessments" for newly planned developments, which can complement traditional vehicular "traffic impact assessments". [ABSTRACT FROM AUTHOR]

We examine differences in mobility outcomes between residents of highest and lowest socio-economic index (SEI) at the Census block group (CBG) level in nine major US cities prior to and during the COVID-19 pandemic. While low-SEI groups generally traveled shorter distances but visited more city-wide CBGs before the pandemic, high-SEI residents universally reduced their mobility to a greater extent during the pandemic. Although high-SEI residents were making more trips to parks and health-care providers, and fewer subsistence trips to retail stores already before the pandemic, COVID-19 significantly widened these differences thereby exacerbating "mobility gaps" between low-SEI and high-SEI groups. We further examine how such "mobility gaps" can be mitigated by spatial advantages of home locations, controlling for political inclination. We find that living in better transit-served or more walkable neighborhoods generally benefited high-SEI residents more than low-SEI residents, with some variation across cities. This suggests that built environments not only impact mobility outcomes during "normal" times, but also influence how different socio-economic groups are able to adapt during times of crisis. • We analyze pre- and during COVID-19 mobility behavior by socio-economic status based on over 139 million individual trips in nine U.S. cities. • High-SEI residents universally reduced their mobility to a greater extent during the pandemic. • We find a broad shift to automobile travel in transit-oriented areas during the pandemic. • Neighborhoods characteristics such as walkability and transit accessibility impact travel outcomes beyond income or socio-economic status. • Unaffordability of neighborhood amenities in walkable urban districts could push low SEI residents to travel further to more affordable destinations. [ABSTRACT FROM AUTHOR]

• Study of how socioeconomic status (SES) modifies neighbourhoods' effect on BMI. • Based in Singapore, which has less structural confounding from spatial segregation. • More park connectors associated with BMI decrease for higher SES moms; inverse for lower SES. • Increased access to bus stops associated with BMIz increase of lower SES children; inverse for higher SES. • Effect of urban interventions might be modified by socioeconomic status. Research on how socioeconomic status interacts with neighbourhood characteristics to influence disparities in obesity outcomes is currently limited by residential segregation-induced structural confounding, a lack of empirical studies outside the U.S. and other 'Western' contexts, and an over-reliance on cross-sectional analyses. This study addresses these challenges by examining how socioeconomic status modifies the effect of accumulated exposures to obesogenic neighbourhood environments on children and mothers' BMI, drawing from a longitudinal mother-child birth cohort study in Singapore, an Asian city-state with relatively little residential segregation. We find that increased access to park connectors was associated with a decrease in BMI outcomes for mothers with higher socioeconomic status, but an increase for those with lower socioeconomic status. We also find that increased access to bus stops was associated with an increase in BMIz of children with lower socioeconomic status, but with a decrease in BMIz of children with higher socioeconomic status, while increased access to rail stations was associated with a decrease in BMIz of children with lower socioeconomic status only. Our results suggest that urban interventions might have heterogeneous effects by socioeconomic status. [ABSTRACT FROM AUTHOR]