URD 2020

The research is developing an automated approach for quantifying human perception toward built environments regarding the sense of restorativeness in urban settings. Since the restorative value of urban design has direct effects on mental fatigue, stress indicators, and quality of life of urban dwellers, quantification of such physical environments' restorativeness capabilities plays an important role in terms of the overall well-being of the city residents. In this study, we are using 6,000 street-level imagery (360 panoramas) near the Washington Square Park and 15 MetroTech area and crowdsourcing methods to collect perception regarding restorativeness. With collected evaluated images, we analyze the impacts of the urban design features on the restorativeness capacities using computer vision and deep learning. As a result, we expect that the result will show the effects of each urban design feature and the current map of evaluated streets in New York City.

Our team examined the influence of housing -- including housing security and housing conditions -- on an important public health indicator: exclusive breastfeeding. Previous research has found that factors associated with urban environments can impact successful breastfeeding; the effects of housing, however, are relatively understudied. In this study, we examined the predictive associations between urban housing security (using a multidimensional measurement approach), as well as individual indicators of housing conditions (over-crowding and chaos in the home), on breastfeeding intentions and actual breastfeeding behaviors for a sample of Medicaid-eligible families in Pittsburgh and New York City. We found that housing insecurity predicts less-exclusive breastfeeding directly, as well as through changes to breastfeeding intention. Crowding, meanwhile, predicted more-exclusive breastfeeding intention, but did not directly predict breastfeeding behaviors. Understanding how features of urban living can act as barriers or facilitators to healthy infant feeding is crucial to promoting public health in America's cities.

Middle school is widely regarded as emotionally and socially challenging for many students, but it may be particularly difficult for students with disabilities (SWDs). Does it get better when these students move to high school? This paper examines the change in student perceptions of school environment for SWDs and their general education peers (GENs), as well as changes in the SWD-GEN gap, to shed light on whether (and how) high school climate improves for SWDs and their GEN peers. Our analysis uses longitudinal student-level data for five cohorts of 7th through 10th grade students enrolled in NYC public schools from 2006-07 through 2011-12. These rich administrative data provide sufficient power to analyze differences between SWDs and GENs in middle and high school and to explore heterogeneity in this relationship. Descriptive regression analyses provide systemic assessment of whether differences occur based on SWD status, either overall or for certain populations.

The urban façade inspection program is crucial to ensure public safety, especially in densely populated cities like New York City. This research aims to propose a computational approach that improves the building façade inspection with BIMs and LIDAR data. First, the information required for façade safety condition evaluation is identified by analyzing the submitted inspection reports. Second, based on the identified information, a data schema for information representation and a flexible reasoning mechanism will be proposed to enable the model-based façade inspection data management. Then, a change detection approach will be proposed to compare the point cloud data captured during the different façade inspection cycles. The initial results will be presented: the defects identified on the façade, the building elements that hold the defects, and the attributes of the defects, the most problematic building elements, and the most frequently happened defects identified for different types of façade.

I use data from the American Dental Association and IPEDS to investigate trends in dental school enrollment since the 1980s. Further, I use ADA data to assess the price sensitivity of first-year students at U.S. dental schools from 2011 - 2017. My research shows great stratifications across U.S. dental institutions: more expensive schools have higher rates of applications, admittances, and enrollment but lower yield rates. Across all U.S. dental schools, my research shows less student diversity at more expensive schools but no significant relationship between changes in tuition and student diversity over time. My results indicate fewer African American and Hispanic/Latino students enroll in expensive dental schools, across all schools and in the public and private sub-groups. On the contrary, Asian students are more likely to enroll in higher priced U.S. dental schools. Finally, my results suggest fewer female students enroll in higher priced schools, and fewer female students enroll as tuition increases.