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1. Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data

   https://doi.org/10.1145/3313831.3376399  

   Miranda, Fabio; Hosseini, Maryam; Lage, Marcos; Doraiswamy, Harish; Dove, Graham; Silva, Cláudio T. (April 2020, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   null (Ed.)

   Urban planning is increasingly data driven, yet the challenge of designing with data at a city scale and remaining sensitive to the impact at a human scale is as important today as it was for Jane Jacobs. We address this challenge with Urban Mosaic, a tool for exploring the urban fabric through a spatially and temporally dense data set of 7.7 million street-level images from New York City, captured over the period of a year. Work- ing in collaboration with professional practitioners, we use Urban Mosaic to investigate questions of accessibility and mobility, and preservation and retrofitting. In doing so, we demonstrate how tools such as this might provide a bridge between the city and the street, by supporting activities such as visual comparison of geographically distant neighborhoods, and temporal analysis of unfolding urban development. 

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2. Parks are Critical Urban Infrastructure: Perception and Use of Urban Green Spaces in NYC During COVID-19

   https://doi.org/10.20944/preprints202008.0620.v1  

   Lopez, B. (August 2020, Preprints)

   null (Ed.)

   Urban green spaces provide a range of environmental and health benefits, which may become even more critical during times of crisis such as the current COVID-19 pandemic. However, with a radical shift in mobility, additional concerns over safety, and access temporarily restricted during the implementation of social distancing policies, the experience and use of urban green spaces may be reduced. This is particularly concerning for densely populated cities like New York, considered the first U.S. epicenter or vanguard of the outbreak. To better understand the impact of COVID-19 on the perception and use of urban green spaces, we conducted a social survey during the early months of the Covid-19 pandemic in New York City (May 13 - June 15, 2020). The results of the survey show respondents continued to use urban green spaces during the pandemic and consider them to be more important for mental and physical health than before the pandemic began. However, the study revealed a pattern of concerns residents have about green space accessibility and safety, and found key differences between the concerns and needs of different populations, suggesting a crucial role for inclusive decision-making, support for additional management strategies, and urban ecosystem governance that reflect the differential values, needs and concerns of communities across the City. As urban centers face looming budget cuts and reduced capacity, this study provides some empirical evidence to illustrate the value of urban green spaces as critical urban infrastructure, and may have implications for funding, policy, and management, of urban green spaces in NYC, with potential applications to other cities, particularly during times of crisis. 

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3. Tradeoffs between safety and time: A routing view

   https://doi.org/10.1016/j.trc.2019.09.020  

   Carmody, Daniel R.; Sowers, Richard B. (November 2019, Transportation research Part C Emerging technologies)

   This article proposes a data-driven combination of travel times, distance, and collision counts in urban mobility datasets, with the goal of quantifying how intertwined traffic accidents are in the road network of a city. We devise a bi-attribute routing problem to capture the tradeoff between travel time and accidents. We apply this to a dataset from New York City. By visualizing the results of this computation in a normalized way, we provide a comparative tool for studies of urban traffic. 

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4. Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis

   https://doi.org/10.1007/s42421-021-00033-4  

   Karve, Vaibhav; Yager, Derrek; Abolhelm, Marzieh; Work, Daniel B.; Sowers, Richard B. (April 2021, Journal of Big Data Analytics in Transportation)

   null (Ed.)

   Abstract This article proposes several advances to sparse nonnegative matrix factorization (SNMF) as a way to identify large-scale patterns in urban traffic data. The input to our model is traffic counts organized by time and location. Nonnegative matrix factorization additively decomposes this information, organized as a matrix, into a linear sum of temporal signatures. Penalty terms encourage this factorization to concentrate on only a few temporal signatures, with weights which are not too large. Our interest here is to quantify and compare the regularity of traffic behavior, particularly across different broad temporal windows. In addition to the rank and error, we adapt a measure introduced by Hoyer to quantify sparsity in the representation. Combining these, we construct several curves which quantify error as a function of rank (the number of possible signatures) and sparsity; as rank goes up and sparsity goes down, the approximation can be better and the error should decreases. Plots of several such curves corresponding to different time windows leads to a way to compare disorder/order at different time scalewindows. In this paper, we apply our algorithms and procedures to study a taxi traffic dataset from New York City. In this dataset, we find weekly periodicity in the signatures, which allows us an extra framework for identifying outliers as significant deviations from weekly medians. We then apply our seasonal disorder analysis to the New York City traffic data and seasonal (spring, summer, winter, fall) time windows. We do find seasonal differences in traffic order. 

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5. The Curriculum and Community Enterprise for Restoration Science (STEM + C): Blending Social Justice Engagement with Academic Instruction to Help Pique Student Interest in Environmental Restoration and Urban Renewal

   Birney, Lauren B. (October 2023, Journal of curriculum and teaching)

   Ella Smith Editorial Assistant Journal of Curriculum and Teaching Sciedu Press (Ed.)

   Abstract A major blight of urban development has been the existence of environmental inequities which affect how and where low-income communities and communities of color live. Targeted communities are beginning to receive long-overdue analysis and civic action. New laws and amendments have been made to better the conditions of these historically underserved communities. Currently, in New York City, historic progress is being made by providing all residents of these affected communities with the tools to advocate for the best outcomes for their neighborhoods. It is the first time in the city’s history that the issue of environmental justice has reached such a milestone. Civic participatory action of this magnitude requires the development of alliances between all members of the community. Students from these marginalized neighborhoods can make a vital contribution in eliminating environmental racism and restoring their communities’ environmental urban footprint. To this end, the Curriculum and Community Enterprise for Restoration Science (STEM + C) Project has been working to provide long-term, hands-on, environmental restoration education and action research to the students in New York City. Restoration of the Eastern oyster in New York Harbor waterways surrounding many of these communities exposes the youth of the city to the community inequities. One of the objectives of the CCERS STEM + C Project is to study its effect on student awareness, motivation and engagement in community-based environmental restoration. Over 500 New York City school students were surveyed on self-reported factors including awareness, motivation and community engagement. Those students in the CCERS STEM + C Program had a significantly higher level of awareness and motivation in terms of community-engagement and social action. 

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