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This paper analyses how Vision Zero (VZ) efforts in New York City (NYC) account for equity and social justice implications of road safety work. VZ policy documents, research literature, popular science and opinion articles on road safety work in the city were studied with a prime focus on equity and social justice. Twelve semi-structured interviews with stakeholders involved in road safety and transport planning in the city and at national level were conducted to gain an in-depth understanding of policy design, the adoption process, and the role of equity considerations in the city's road safety work. The results show that major equity and social justice issues arise in the adoption and implementation of VZ. These issues are primarily related to equity and fairness in the distribution of life saving interventions, the socio-economic impacts of road safety strategies, and the nature of community engagement in policy design and implementation. The findings point to a need for VZ practitioners to give due considerations to equity and social justice implications of VZ policies and strategies. Among others, it supports the need for understanding the nature of past equity and social justice problems in road safety and transport planning in the VZ policy design process. Moreover, the findings suggest the need for empirical studies on the socio-economic implications of VZ strategies and interventions. • There were major road safety related inequities and injustices prior to the adoption of Vision Zero in New York City. • The promotion of Vision Zero also gives rise novel equity and social justice concerns related to its adoption process and the choice of road safety strategies and interventions. • A successful and morally acceptable implementation of Vision Zero demands recognizing and proactively accounting for ethically problematic aspects of road safety work, including those related to equity and social justice. • Vision Zero should integrate equity and social justice into road safety work, for example, by adopting a broader conceptualization of a data driven approach that includes socioeconomic and community data in addition to quantitative crash data. • The promotion of Vision Zero should allow meaningful inclusion and participation of different road user and socioeconomic groups in the goal setting and implementation of Vision Zero policies. [ABSTRACT FROM AUTHOR]

This paper aims to examine the association between asthma severity and one-year lagged fitness in New York City Public school youth by neighborhood opportunity. Using the Child Opportunity Index 2.0 and individual-level repeated measures NYC Office of School Health (OSH) fitness surveillance data (2010–2018), we ran multilevel mixed models stratified by neighborhood opportunity, adjusting for sex, race/ethnicity, grade level, poverty status, and time. Asthma severity was based on a physician-completed Asthma Medication Administration Form (MAF) from each school year and drawn from the Automated Student Health Record (ASHR). Across all youth in grades 4–12 (n = 939,598; 51.7 % male; 29.9 % non-Hispanic Black, 39.3 % Hispanic; 70.0 % high poverty), lower neighborhood opportunity was associated with lower subsequent fitness. Youth with severe asthma and very low and low neighborhood opportunity had the lowest 1-year lagged fitness z-scores − 0.24 (95 % CI, −0.34 to −0.14) and − 0.26 (95 % CI, −0.32 to −0.20), respectively, relative to youth with no asthma and very high opportunity. An inverse longitudinal relationship between asthma severity and subsequent fitness was observed. Study findings have implications for public health practitioners to promote physical activity and improved health equity for youth with asthma, taking neighborhood factors into account. [ABSTRACT FROM AUTHOR]

As resilience planning gains prominence in urban sustainable development, green infrastructures (GIs) are widely recognized as effective solutions to address climate risks and environmental challenges. While urban greening initiatives have multiple benefits, they can also lead to unintended consequences such as environmental injustice, increasing property values, and the displacement and gentrification of vulnerable residents in the affected areas. While gentrification remains a significant concern for urban sustainability initiatives that aim to promote environmental justice, a consensus upon approaches and methods for precisely identifying communities undergoing gentrification is still lacking. While most of the existing research studies focused on examining the relationship between GIs and gentrification, little-to-no research was conducted to quantify the green gentrification vulnerability of different communities. To this end, and to bridge this knowledge gap, this paper proposes and develops a novel index called the green gentrification vulnerability index (GGVI). First, an extensive list of 35 indicators that contribute to green gentrification was identified and grouped into 7 categories: economic, social, housing, household, demographic, amenities, and GIs. Second, data were gathered on the 35 indicators for all the 2325 census tracts across New York City (NYC) in the US. Third, the data were preprocessed to ensure their suitability for implementing Principal Component Analysis (PCA). Fourth, PCA was implemented for each of the 7 categories separately to weight their respective green gentrification indicators and calculate vulnerability scores. Finally, these vulnerability scores were used to develop the GGVI. The research findings provided insights on the importance of the 35 indicators as well as the 7 green gentrification-related vulnerability categories in relation to their contribution to the GGVI. The 7 categories were ranked as follows: amenities, GIs, economic, demographic, social, housing, and households. In addition, proximity to parks, frequency of parks, proximity to GIs, and total area of parks were found to be the most important GIs-related indicators that could lead to green gentrification. Overall, this paper contributes to the growing body of research that addresses green gentrification by being the first study that proposes and develops a GGVI that can be used to identify at-risk communities from green gentrification and ultimately help practitioners, urban planners, decision-makers, and researchers in developing new policies and strategies aimed to mitigate the unintended consequences of green gentrification (i.e., displacement of vulnerable communities) and promote environmental justice and urban sustainability. • Green gentrification vulnerability was studied based on 35 indicators. • The importance of the different indicators was determined. • Principal Component Analysis was used to develop a GGVI. • The proposed index allows census-track green gentrification vulnerability analysis. [ABSTRACT FROM AUTHOR]

Recent research on police misconduct has adopted a network perspective, while recent work on correctional officer (CO) use of force has similarly framed it as an organizational behavior, learned through socialization and deployed with considerable discretion. This paper leverages the network paradigm in police misconduct research to study the use of force by COs. Using data on lawsuits involving the New York City Department of Correction from 2013 to 2022, this paper forms a co-offending network of those COs who have been sued for excessive force. This paper then uses descriptive social network analysis and key player selection and community detection methods. After describing the basic network structure of COs who use force together, this paper identifies brokers who connect disparate parts of the CO network; it also uses a community detection algorithm to identify clusters of COs involved in force incidents. Finally, the paper compares this network to an analogous network of police use of force, finding structural similarities. This analysis bridges disparate work on correctional officer use of force and police misconduct; for policymakers who seek to curtail COs' excessive use of force, this paper might provide a blueprint for identifying key players and clusters. • Correction officer use of force is an organizational, socialized behavior. • Network methods can be used to study both police and CO use of force. • CO use of force has a networked structure; I identify network brokers and clusters. • These results can guide the targeted disruption of excessive force in jails. [ABSTRACT FROM AUTHOR]

This paper critically examines the concept of "green resilience" – the mobilization of gardens and urban green spaces for enhancing the resilience of communities and cities in the face of increasing uncertainty and risk. Drawing inspiration from seminal work in human-plant geographies, it explores the governmental functions and political implications of green resilience, arguing that these spaces serve as key forms of state rule and biopolitical power. The study employs the notion of green resilience as a "composition," emphasizing the assembling of diverse human and nonhuman actors in resilience projects. The developed compositional ontology highlights the importance of aesthetic and visual practices in green resilience projects and reveals how these are continuously decomposed and recomposed by the involved actors. The paper develops its arguments through a "paradigmatic case study" of various green resilience projects in New York City – including survivor trees, memorial groves, and community gardens. The study illuminates the multiplicity of green resilience initiatives and the underlying governmental logic by comparing these projects. Through its analysis, the paper contributes to the literature on resilience as an emerging governance paradigm. It reveals how green resilience projects embody a multiplicity of practices, uncovers the first comprehensive comparative analysis of various forms of green resilience, and offers a critique of resilience from within. The study concludes by identifying three tensions or contradictions of (green) resilience as a form of biopolitical government: visibility and invisibility, knowledge and ignorance, and control and emergence. [ABSTRACT FROM AUTHOR]

Service trips, a frequently overlooked segment of urban traffic, represent a disproportionately large share of the negative impacts associated with commercial activity. The reason is that although service trips are less frequent than freight trips, they often take longer and thus occupy a significant share of commercial parking. Demand management programs seeking to move service trips off-hours can potentially reduce congestion and emissions but they have been understudied. This paper describes research conducted to investigate receivers' willingness to participate in off-hour programs for planned service activity. The research reported in this paper analyzes results from a survey of 189 business establishments in New York City and the Capital Region in New York State using descriptive analyses of the data, and by estimating a discrete choice model to gain insight into how business characteristics influence willingness to participate in off-hour services. To assess the impacts of a hypothetical off-hour services program, the authors applied the discrete choice model, together with service trip attraction models, to quantify the hours of daytime parking that could be eliminated by the proposed program in certain ZIP Codes in New York City and the Albany area. The research conducted led to the identification of numerous policy implications that will help policymakers understand and maximize the potential benefits of implementing off-hour service programs. [ABSTRACT FROM AUTHOR]

• Dominant narratives situate worker cooperatives as solutions to poverty in New York City. • Such narratives ignore other forms of anti-capitalist organizing. • Solidarity Economy narratives propagate in 'informal' spaces like the home. • Recognizing radical narratives means recognizing these informal, radical spaces. The solidarity economy in North America has received growing attention at multiple scales in the past ten years. As worker cooperatives in New York City enjoy newfound municipal support, narrative struggles emerge between actors within the solidarity economy space. The solidarity economy may be theorized as capitalism's feminized 'other': malleable, unfixed, local, and difficult to quantify. This feminization extends to both its workforce, a majority of whom are women, and the labor it produces, primarily domestic work. It is for these reasons that solidarity economy work is often overlooked as a political economy capable of structural transformation, and the discomfort with its breadth has lead movement leaders to uphold and advocate for more 'formal' models like cooperatives and deploy 'poverty alleviation' and entrepreneurship narratives to stabilize the fluid field. I argue that this project blanches the radical edges of a movement, minimizing not only those whose labor accounts for the majority of solidarity economy work, but ignoring the transformative potential of sites where such work happens. Based on a series of qualitative interviews with solidarity economy practitioners, this paper argues that the dominant narrative of solidarity economy work in New York City ignores where most of this work occurs and by whom, erasing and undermining those efforts. Counter-narratives emerge in those 'forgotten' spaces and thus transform them into sites of radical, anti-capitalist organizing. In so doing, this paper poses a question for geographers about how movements may continue to challenge our assumptions about space. [ABSTRACT FROM AUTHOR]

This paper summarizes the research conducted by the authors to investigate time-dependent patterns in Freight Trip Generation (FTG). As part of the research, the authors estimated econometric FTG models as a function of establishment attributes and temporal variables, using multi-year establishment-level data collected during the period 2005–2014. These unique data—encompassing the period of economic turmoil around the 2008 collapse of Wall Street, and the period of recovery that followed—were collected in New York City from commercial establishments in various industry sectors. The comprehensive modeling effort undertaken considered four different functional forms, two combinations of establishment characteristics, two different metrics of time, and eleven different industry sectors defined by two-digits North-America Industry Classification System (NAICS). The resulting models were analyzed to assess the extent and magnitude of the time-dependent effects on FTG for the industry sectors in the sample. In its final sections, the paper discusses the implications for transportation planning and policy and summarizes the chief findings of the effort. [ABSTRACT FROM AUTHOR]

In this paper I bring together scholarship from the 'reparative turn' in the social sciences with the long tradition of critiquing schooling in the sociology of education. Most recently in this tradition, Ball and Collet-Sabé (2021) argue convincingly that the modern school is an 'intolerable institution' bound in its very epistemology to reproduce conditions of future uncertainty and inequity. I draw on Hong's (2014) framing of neoliberalism as a process of erasure and disavowal in order to link Ball and Collet-Sabé 's contention with the concept of reparative futures (Sriprakash et al. 2020). I do so through a consideration of my 2014–15 ethnography of Bronx High, a public high school in New York City. Through the story of seniors at Bronx High, I demonstrate how schooling is profoundly oriented towards particular anticipatory practices - namely, a modernist, neoliberal reckoning of the future that is both illusory in its promises and unsustainable in its ambitions. Bronx High exemplifies the deleterious impacts of living in a future-oriented present that appears wilfully detached from injustices of the past. However, this is not the whole story. The day-to-day actions of teachers and students reveal complex, multiple engagements with future-reckoning, including moments, often at the ambivalent margins of practice, where reparative futures-thinking offers a different way of engaging with the past, the present, and life after high school. • This paper explores reparative futures thinking for reimagining education beyond the modern school. • I explore this through my 2014–15 ethnography of a US public high school. • Through the story of seniors at Bronx High, I demonstrate how schooling is profoundly oriented towards particular anticipatory practices - namely, a modernist reckoning of the future. • However, the day-to-day actions reveal complexengagements with future-reckoning, including examples of reparative futures-thinking. [ABSTRACT FROM AUTHOR]

In the governance of the shared mobility market of a city or of a metropolitan area, there are two conflicting principles: (1) the healthy competition between multiple platforms, such as between Uber and Lyft in the United States, and (2) economies of network scale, which leads to higher chances for trips to be matched, and thus higher operation efficiency, but which also implies monopoly. The current shared mobility markets, as observed in different cities in the world, are either monopolistic, or largely segmented by multiple platforms, the latter with significant efficiency loss. How to keep the competition between platforms, but to reduce the efficiency loss due to segmentation with new market designs is the focus of this paper. We first proposed a theoretical framework of shared mobility market segmentation and then proposed four market structure designs thereupon. The framework and four designs were first discussed as an abstract model, without losing generality, thus not constrained to any specific city. High-level perspectives and detailed mechanisms for each proposed market structure were both examined. Then, to assess the real-world performance of these market structure designs, we used a ride-sharing simulator with real-world ride-hailing trip data from New York City to simulate. The proposed market designs can reduce the total vehicle-miles traveled (VMT) by 6% while serving 2.9% more customers with 8.4% fewer total number of trips. In the meantime, customers receive better services with on-average 5.4% shorter waiting time. At the end of the paper, the feasibility of implementation for each proposed market structure was discussed. • Proposed a unified framework for describing the shared mobility market structure. • Described two status-quo shared mobility markets with the proposed unified framework. • Designed four shared mobility market structures to dissolve market segmentation. • Evaluated the performance of proposed shared mobility markets using real-world data. [ABSTRACT FROM AUTHOR]

The built environment accounts for approximately 38% of global emissions. The evaluation of returns linked to investments in building decarbonization is impeded when confronted with uncertainties surrounding future energy prices, building emission regulations, and real estate market conditions. This paper develops an integrated framework that combines energy modeling with investment analysis to support the adoption of decarbonizing technologies under a context of uncertainty. The integrated framework includes projections of costs and benefits associated with the adoption of technology (i.e., changes in rental cashflows, vacancy rates, maintenance and energy costs) under a wide range of potential pathways of the energy markets and regulatory environment, and local real estate market demand for green buildings using Monte Carlo simulations. Additionally, this paper develops the approach to investigate the potential value of flexibility in designing for building electrification (i.e., the ability to fully electrify gas heating systems at a later year). We illustrate the use of this approach in a case study of a new office building in New York City, faced with design choices between natural gas and electrified heating systems. We consider 10,000 scenarios changing future greenhouse emission penalties, investments in the local electric grid, local real estate market conditions and energy prices. In approximately 60 percent of simulations, the most profitable decision for the building owner is to adopt a natural gas-powered heating system. However, adopting a building design that provides a building the flexibility to fully electrify at a later date is more profitable than a natural gas-heating building in 96 percent of scenarios. A sensitivity analysis shows that the most influential parameter in the valuation of a design option is the size of the green premiums associated with improved energy efficiency. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is an important indicator of urban heat environments and can be largely influenced by the morphology factors of cities. However, previous studies mainly focus on large-scale and coarse-grained forecast modeling, making it hard to inform architects and urban designers without the advantage of quick, fine-grained prediction and visualization. The paper uses Generative Adversarial Networks (GAN) to address this gap by proposing a prediction model of city plans and corresponding LST heat maps. Taking New York City as an example, we use the Light Detection and Ranging (LiDAR) data, Landsat Surface Temperature data, and other relevant data to build seven hundred image pairs as the training set to train the model of predicting LST distribution. Using untrained pairs as the test set, the model can generate LST maps relatively quickly and accurately with the input of city plans. Then after accuracy analysis, different scenarios are simulated to test the application of the model in predicting the environmental impacts of plan modifications on land surface temperature. Eventually, the principles proposed in this paper can be applied to the development of relevant interactive design and planning tools in the future. • Machine learning translates LiDAR city maps into LST heatmaps. • A Generative Adversarial Network (GAN) model is trained and tested. • AI-based urban design strategies are proposed to reduce LST hotspots. • Fine-scale prediction and fast interaction can contribute to design practices. [ABSTRACT FROM AUTHOR]

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction. Predictable ride-hailing demand can facilitate more reasonable vehicle scheduling and online car-hailing platform dispatch. Conventional deep learning methods with no external structured data can be accomplished via hybrid models of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) by meshing plentiful pixel-level labeled data, but data sparsity of high grid granularity in spatial perspective and limited learning capabilities of long-term dependencies in temporal perspective are still two striking bottlenecks. To address these problems, we propose a novel virtual graph modeling approach to focus on significant demand regions and a novel Deep Multi-View Spatio-temporal Virtual Graph Neural Network (DMVST-VGNN) to strengthen the learning capabilities of spatial dynamics and long-term temporal dependencies. Specifically, DMVST-VGNN integrates structures of 1D CNN, Multi-Graph Attention Neural Network and Transformer Network, which correspond to short-term temporal dynamics view, spatial dynamics view and long-term temporal dynamics view respectively. In this paper, multiple experiments are conducted on two large-scale New York City datasets in higher granularity prediction scenes. And the experimental results demonstrate the effectiveness of DMVST-VGNN framework in ride-hailing demand prediction, no matter in spatial scale or the temporal scale. [ABSTRACT FROM AUTHOR]

Bayesian optimization (BO) is an attractive method for tackling transportation optimization problems due to its ability to balance exploitation and exploration. However, scaling BO to solve high-dimensional problems is a major challenge which has remained unsolved. Since transportation problems can be high-dimensional, the use of BO to solve transportation problems has been limited. This paper explores the use of BO with Gaussian process (GP) models to tackle high-dimensional transportation problems. It proposes formulations of the prior mean function and covariance function of the GP that enable BO to incorporate problem-specific transportation information, while remaining computationally tractable. This is done through the use of an analytical surrogate model. We validate the method with the 1-D and 100-D Griewank functions. The impact of different forms of bias in the surrogate model was also evaluated using the 100-D Griewank function example. We then apply the method to a high-dimensional traffic signal control problem in New York City. The results indicate the added value of using the problem-specific information in the prior mean and/or the covariance function. Importantly, for surrogate models that are not accurate approximations of, but have significant (anti-)correlation with the true objective function, the better approach is to embed the information in the covariance function, rather than in the prior mean function. More generally, the use of problem-specific information in the covariance function is robust to the accuracy of the surrogate model. This opens the way for a variety of low-resolution analytical transportation models to be used to tackle high-dimensional simulation-based optimization problems. • Problem-specific information can be used for exploration and exploitation in BO. • Using the surrogate model in the GP prior mean function improves exploitation. • Using the surrogate model in the GP covariance function improves exploration. • The surrogate model-based covariance function is robust to biases in the model. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 caused unprecedented disruptions to public transport services. As such, this paper proposes a methodology for analyzing COVID-19 impact on public transport mobility. The proposed methodology includes: (1) a new unsupervised machine learning (UML) method, which utilizes a decoder-encoder architecture and a flow property-based learning objective function, to estimate the origin-destination (OD) flows of public transport systems from boarding-alighting data; and (2) a temporal-spatial analysis method to analyze OD flow change before and during COVID-19 to unveil its impact on mobility across time and space. The validation of the UML method showed that it achieved a coefficient of determination of 0.836 when estimating OD flows using boarding-alighting data. Upon the successful validation, the proposed methodology was implemented to analyze the impact of COVID-19 on the mobility of the New York City subway system. The implementation results indicate that (1) the rise in the number of weekly new COVID-19 cases intensified the impact on the public transport mobility, but not as strongly as public health interventions; and (2) the inflows to and outflows from the center of the city were more sensitive to the impact of COVID-19. • A new methodology for analyzing COVID-19 impact on public transport mobility. • It includes a proposed unsupervised ML method to estimate public transport system OD flows from boarding-alighting data. • It includes a temporal-spatial analysis method to analyze OD flow changes before and during COVID-19. • The proposed methodology was implemented in analyzing COVID-19 impact in the New York City area. • The methodology could support analyzing the impact of natural disasters on public transport mobility across time and space. [ABSTRACT FROM AUTHOR]

Spine surgery accounts for a large proportion of neurosurgical procedures, with approximately 313 million spine surgeries conducted annually worldwide. Considering delayed recovery and postoperative complications that are commonly reported, there has been a recent shift toward minimally invasive spine procedures conducted under local anesthesia. Despite proven success, there exists a limited body of literature on the use of awake surgery in spinal procedures. A bibliometric analysis was conducted to map the current landscape of work in this field. 190 articles were identified from the Web of Science (Clarivate, NY) database. A comprehensive bibliometric analysis was performed on a narrowed list of the most relevant articles using Bibliometrix, an R-based programming tool. There has been a rise in academic papers published on the topic of awake spine surgery since 2016, with an increase in publication count by approximately 18% annually and each article cited approximately ten times on average to date. The year 2022 saw an uptick in publications, with 9 throughout the entire year. The most impactful article, with a total of 95 citations, was published by Sairyo et al.1 Thematic analysis revealed that the terms "lumbar spine" and "stenosis" are well-developed topics in the literature, whereas the topics of "complications," "fusion," and "cost-analysis" are less well-developed topics. This study provides a comprehensive overview of the most-cited articles in the field of awake spine surgery. Specifically, it identifies areas that are well represented in the literature and those which are underrepresented and should be areas of continued future research. [ABSTRACT FROM AUTHOR]

Objectives: Multiple barriers limit access to the full range of contraceptive options. The purpose of this quality improvement initiative was to increase single-visit access to the full range of contraceptive methods in primary care, postabortion, and immediate postpartum settings in New York City (NYC).Study Design: From 2015 to 2018 we convened 2 learning collaboratives, named the Quality Improvement Network for Contraceptive Access, with 17 teams (representing 40 sites) from New York City-based hospitals and health centers using an adaptation of the Institute for Healthcare Improvement's Breakthrough Series Learning Collaborative model. Participating teams sought to implement evidence-informed recommendations to increase access. The goal was to increase the patient-centeredness of services by reducing barriers. In the absence of a way to directly measure access, we measured progress toward implementation of the 4 recommendations and contraceptive care utilization measures as proxies for access, and asked teams to describe facilitating factors.Results: Learning collaborative teams successfully implemented all 4 of the recommendations in 95% of the participating sites. Patients who chose and received a most or moderately effective method increased from 22% to 38% in primary care, and from 0% to 17% in the immediate postpartum period. Patients who chose and received a long-acting-reversible contraceptive increased from 5% to 11% in primary care, and from 0% to 3% in immediate postpartum. Facilitating factors included the involvement of interdisciplinary teams, consideration of costs, utilization of peers to demonstrate change, and champions to drive change.Conclusions: The application of evidence-informed recommendations using a structured quality improvement initiative increases contraceptive access.Implications: This paper identifies key facilitators and factors that influenced the successful implementation of evidence-based recommendations for access to the full range of contraceptive methods in primary care, postabortion, and immediate postpartum settings. Findings can inform future initiatives that seek to increase contraceptive access at the service delivery level, as a component of reproductive autonomy, and contraceptive equity. [ABSTRACT FROM AUTHOR]

A form of building-integrated photovoltaics, luminescent solar concentrators are devices used to enhance solar cell output per unit area through red-shifted, concentrated internal reflection of sunlight. Luminescent solar concentrators have shown promise, but they have yet to achieve commercial success. At present, it is prohibitively difficult for architects and solar engineers to assess their annual energy production and subsequently conceptualize their economic potential. To bridge this gap, this paper provides an approach for annual performance evaluation of luminescent solar concentrators that parallels conventional processes used internationally for existing photovoltaic projects and solar development proposals. The approach starts by following conventional processes to evaluate the plane-of-array irradiance. Then, two metrics are introduced to evaluate the effective irradiance incident on solar cells within an LSC: 1) LSC optical efficiency is employed to determine the proportion of light reaching solar cells relative to the exposed surface; 2) LSC spectral mismatch factor is defined to represent the impact of spectral differences due to absorption and luminescent emission within an LSC. These metrics have never been employed in such a context before. With their addition, the process to produce LSC energy estimates becomes analogous to the conventional process to produce estimates for solar panels. The proposed approach is first validated against the commercial software, PVsyst. Then, case studies in Albany, NY and Phoenix, AZ are conducted to present energy estimates (at the maximum power point) for solar panels, and both planar and wedge-shaped luminescent solar concentrators when mounted vertically on a southern facing surface. [ABSTRACT FROM AUTHOR]

• Streaming data are characterised as evolution of Stochastic Differential Equations. • Gaussian process regression used for nonparametric estimation of SDE coefficients. • Stochastic process theory applied to automatically compare SDE models. • Bootstrapping methods allow for control of the false alarm rate of detector. • Evaluation on datasets demonstrates detector's high discriminative power. This paper characterises streaming data as the evolution of a stochastic differential equation, with the aim of extracting information that can be used to detect anomalies in the stream. Gaussian process regression provides a flexible approach to approximating components of the stochastic differential equation, allowing for complex modelling of underlying data generation dynamics. The proposed algorithm displays superior discriminative power over different time-series anomaly detection methods for both synthetic and NYC taxi datasets, whilst the introduced bootstrapping method for setting the detection threshold provides control over the false alarm rate of the anomaly detector. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had significant effects on urban and regional food systems. Local administrations worldwide have been challenged to design and implement policies to mitigate immediate food system disruptions while planning for longer-term equity and resilience. The fast pace and high degree of uncertainty of the pandemic have made systematic tracking and assessment of food system change and related policy responses arduous. To address this gap, this paper applies the multilevel perspective on sociotechnical transitions and the multiple streams framework on policy change to 16 months of food policy (March 2020 through June 2021) during the New York State-issued COVID-19 state of emergency, comprising more than 300 food policies advanced by New York City and State legislators and administrators. Content analysis of these policies revealed the most salient policy areas during this period, the status of legislation, and key programs and budget allocations, as well as local food governance and the organizational spaces within which food policy operates. The paper shows that food policy domains that gained prominence focused on support for food businesses and food workers and on ensuring and expanding food access through food security and nutrition policies. Most COVID-19 food policies were incremental and were limited to the duration of the emergency, yet the crisis allowed for enactment of novel policies that deviated from the common policy issues or the typical scale of changes proposed pre-pandemic. Taken together, and viewed through a multilevel and policy streams framework, the findings provide insight into the trajectory of food policymaking in New York during the pandemic and the areas that food justice activists, researchers, and policy makers should focus on as the COVID-19 pandemic is abated. • NY City and State advanced more than 300 food policies during the COVID emergency. • Policies focusing on food businesses, food workers, and food access were prominent. • Most policies were incremental and limited to the duration of the emergency. • The crisis propelled the enactment of several breakthrough food policies. • Pandemic food policymaking continues beyond the emergency. [ABSTRACT FROM AUTHOR]

On March 22, 2020, the State of New York issued a "stay-at-home" policy, wherein all non-essential businesses were on pause until June 8, 2020. The bike-sharing system (BSS) and yellow taxi system (YTS) in Manhattan were substantially affected. This sudden drop in demand can impact not only short and long-term mobility but also the sustainability of transport network. Given that few empirical studies are focusing on the impacts of the "stay-at-home" policy on the BSS and YTS, this further substantiates the importance of analyzing how the policy affects the overall transportation system in New York City (NYC). This paper aims to fill this gap by quantifying the impacts of the "stay-at-home" policy on the two aforementioned transportation systems. Specifically, the following three research gaps are summarized in this study: I) The hidden biases in current "stay-at-home" policy estimation methods were not properly addressed; II) The policy impacts on BSS and YTS during different periods of the effective day were unclear; III) The sensitivity of uncontrolled confounders in long-term policy impact estimations was poorly discussed. We addressed these important research gaps by introducing robust statistical approaches like regression discontinuity design (RDD) and propensity score matching (PSM) methods, which can overcome methodological challenges such as counterfactual restoration, spatiotemporal heterogeneities, and unmeasured confounders. The BSS and YTS were studied at the aggregated neighborhood levels. Results demonstrate that the impacts on BSS have higher variations than YTS usage. The monthly average treatment effects on the treated (ATT) for BSS ranged from −72% to −28% respectively in March and June, while YTS ranged from −96% to −94%. Evidence suggests that demand for BSS surged on weekends in May and June. Understanding the impact of this short-term yet significant policy change on travel behavior will help optimize supply and demand management strategies, thereby improving the long-term sustainability should similar situations arise in the future. • Quantifying the short-term and long-term impacts of the "stay-at-home" policy. • Data-driven approaches using regression discontinuity design and propensity score matching method. • The bike-sharing system and yellow taxi system have different sensitivities to the policy. • Policy implications are derived from the robust analyses. [ABSTRACT FROM AUTHOR]

The increasing utilization of distributed energy resources such as behind-the-meter solar has given rise to a grid-supplied load profile conditional on solar conditions that are difficult to forecast using conventional methods. As a result, the accuracy of the load forecasts is reduced, representing a possible operational challenge to system operators. This paper presents a method to resolve this challenge using New York City electricity zone data. The analysis presented in this paper demonstrates that the load forecasting challenges associated with behind-the-meter solar can be successfully addressed using econometric time-series methods and better forecast information. • Distributed energy resources have the potential to reduce the accuracy of the system operator's load forecasts. • Inaccurate load forecasts may adversely affect the power grid's resiliency. • A method is presented to predict near-real-time grid-supplied load accurately. [ABSTRACT FROM AUTHOR]

With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing companies have become an important element of the urban mobility system. There are two critical components in the operations of ride-hailing companies: driver–customer matching and vehicle rebalancing. In most previous literature, each component is considered separately, and performances of vehicle rebalancing models rely on the accuracy of future demand predictions. To better immunize rebalancing decisions against demand uncertainty, a novel approach, the matching-integrated vehicle rebalancing (MIVR) model, is proposed in this paper to incorporate driver–customer matching into vehicle rebalancing problems to produce better rebalancing strategies. The MIVR model treats the driver–customer matching component at an aggregate level and minimizes a generalized cost including the total vehicle miles traveled (VMT) and the number of unsatisfied requests. For further protection against uncertainty, robust optimization (RO) techniques are introduced to construct a robust version of the MIVR model. Problem-specific uncertainty sets are designed for the robust MIVR model. The proposed MIVR model is tested against two benchmark vehicle rebalancing models using real ride-hailing demand and travel time data from New York City (NYC). The MIVR model is shown to have better performances by reducing customer wait times compared to benchmark models under most scenarios. In addition, the robust MIVR model produces better solutions by planning for demand uncertainty compared to the non-robust (nominal) MIVR model. • Introduce a matching-integrated vehicle rebalancing (MIVR) model for generating better rebalancing decisions. • A robust MIVR model incorporates inherent demand uncertainty in the ride-hailing system. • Real-world simulations show the performance improvements over two benchmark models. • Compare the robust and normal MIVR models under multiple demand scenarios. [ABSTRACT FROM AUTHOR]

• 'Infrastructure-first' approach is inadequate in understanding local vulnerabilities and needs. • Technocratic approaches to resilience planning exacerbate local tensions. • Adverse outcomes of climate plans impact residents' sense of security and wellbeing. This paper argues that the rise of a global mainstream resilience narrative is advancing a strategically simplified concept of vulnerability that is being exploited to open up lucrative new opportunities for profit. In particular, it presents three ways in which mainstream narratives are currently masking--if not exacerbating--the vulnerability of residents in New York City and Copenhagen. First, it explores how a technocratic orientation to community engagement is affecting local perceptions of participatory processes such as planning consultations and visioning exercises. Next, it investigates how the pursuit of an 'infrastructure-first' approach to interventions and a reputation for eco-innovation is creating tensions between institutional and local experiences of resilience. Lastly, it discusses some of the ways in which simplistic understandings of vulnerability are leading to adverse outcomes--such as eco-gentrification and displacement--that are making local communities more, not less, vulnerable to the impacts of climate change. It concludes by arguing that the meaningful integration of diverse perspectives and values is integral to the process of giving rise to more critical and expansive narratives of resilience. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been unprecedented in its scale and speed, impacting the entire world, and having an impact on metropolitan transportation systems. New York City (NYC) was especially hard hit in March and April 2020. A mandatory stay-at-home order was instituted, with all but essential businesses ordered closed. In this paper we examine the impact on the Citi Bike system and the NYC subway. Usage patterns during the lockdown are compared to corresponding days in 2019. Controlling for weather patterns we examine the effect of the lockdown and subsequent reopening of economic activity up through the end of September 2020. The results show that both subway ridership and bikeshare usage plummeted initially; bikeshare usage has nearly returned to normal while subway ridership remains substantially below pre-COVID levels. Implications for policy suggest that the bikeshare system provides resilience to the overall transportation system during disasters when public transit is considered dangerous or is disrupted. • Bikeshare and Subway use in New York City dropped at the onset of the COVID-19 pandemic. • We estimate changes in bikeshare use before and during the pandemic. • We control for weather effects and examine casual rider and subscriber usage. • Bikeshare use returned to pre-pandemic levels while subway use remained low. • Results suggest that bikeshare provides a resilient transport option for cities. [ABSTRACT FROM AUTHOR]

The popularity of Uber and Lyft and advances in autonomous vehicle technology have spurred public interest in the potential of shared ride services to reduce traffic congestion, vehicle emissions and space devoted to parking. However, research has shown that long waiting times, circuitous routes and privacy concerns might lead most patrons to choose exclusive-ride services over shared services (ride-hail or autonomous), thus increasing rather than decreasing vehicle mileage. This paper uses Uber and Lyft experience from 2014 to 2020 to examine the effectiveness of shared (or "pooled") services in reducing vehicle miles traveled (VMT) in four cities with large concentrations of ride-hail trips and suburban areas of California. Taking into account three key inputs -- pooling rates, modal shifts and deadhead miles – results show that pre-pandemic levels of pooling led to at least a doubling of VMT when comparing ride-hail trips with patrons' previous mode, with increases of 97% in Chicago, 114% in New York City, 118% in San Francisco, 157% in Boston and 118% in California suburbs. These large VMT increases are driven by the addition of dead-head miles before each pick-up and the absence of offsetting VMT reductions among travelers who switch to ride-hail from public transportation, biking and walking. VMT increases are only modestly mitigated by the use of ride-hail for "first mile/last mile" trips to connect to public transportation or by reduced cruising for parking by drivers in their personal vehicles. In sum, ride-hail adds to vehicle mileage for trips associated with ride-hail even taking into account pooling. This pattern is likely to endure in a world of autonomous vehicles given that auto users tend to switch to solo services due to considerations of travel time, reliability, comfort and privacy, while pooled options mainly draw patrons from sustainable modes like public transportation. The implication for public policy is that in dense urban areas, it remains important for policy-makers to prioritize space-efficient modes of public transportation, walking and biking. At the same time, ride-hail can clearly be valuable to meet specific needs such as providing paratransit services to people with disabilities, providing first and last mile connections to transit services and connecting late-night workers to jobs. These results will be important as cities emerge from the coronavirus pandemic and navigate a path to economic recovery, social equity, and environmental sustainability. Image 1 • Ride-hail leads to doubling or more of vehicle mileage in 4 major U.S. cities and a suburban environment. • Increases are due to ride-hail patrons shifting from public transit and other sustainable modes, and from added deadhead miles. • Changes in travel behavior underscore core distinction between shared and non-shared urban travel markets. • Autonomous vehicles are unlikely to change resistance of auto users to shared services. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to find the optimal design or rehabilitation of water distribution system (WDS) under both steady-state and transient conditions. Then, assessing the obtained optimal solution and studying its stability, reliability, due to uncertainty in pipe roughness coefficients (C HW). The WDS optimization objective is to minimize the rehabilitation cost by installing new pipes parallel to the existing ones achieving all hydraulic constraints. For this purpose, particle swarm optimization (PSO) is used as an optimization tool and Monte Carlo simulation (MCS) is applied to generate multiple realizations of C HW for different coefficient of variation (COV CHW). A comparison between crisp shape and continuous shape (S-shape) fuzzy membership functions is carried out to study the hydraulic availability indices to nodal pressure head values resulted from pipe roughness uncertainty. Both steady-state nodal and system reliabilities are estimated while, only the transient system reliability is calculated for the obtained optimal solution. The proposed methodology is applied on the New York City tunnel network (NYCTN) and the corresponding reliability is analyzed. Results show that as pipe roughness uncertainty increases, WDS reliability decreases. [ABSTRACT FROM AUTHOR]

Drones can be used for tasks such as data collection and logistics in civil engineering. Current research on autonomous drones mainly focuses on planning a safe path and avoiding obstacles in a static environment. However, navigating a drone in complex environments like urban areas involves many dynamic constraints, such as building layout, winds, and signal coverage, which are interdependent. The wind factor is the most important among these environmental factors, which may cause a drone to lose control or even crash. This paper presents a multi-objective navigation reinforcement learning algorithm (MONRL) for the drone to navigate and avoid obstacles in an unknown environment when dynamic wind zones present, with only imagery data about the building layouts. Based on a deep reinforcement learning and memory architecture, the drone develops policies to prioritize navigation decisions, optimizing the path while minimizing negative impact of winds with only sparse sensor data, in our case, camera inputs. By leveraging the advantages of the proposed method in estimating the environmental factors from previous trials, no aerodynamic force sensors are needed for the drone to develop effective strategies to navigate to target while counteracting to milder winds and dodging aways from stronger winds. The proposed method was tested in a virtual environment, and a real model of New York City. The method is expected to contribute to new automation algorithms for urban aerial logistics and future automated civil infrastructure inspection. • A multi-objective reinforcement learning method was proposed for autonomous drones to avoid dynamic winds while navigating. • By leveraging a memory RL architecture, no aerodynamic force sensors are needed to sense and react to winds. • A framework for representing complex aerodynamic forces with mathematical formulation was proposed to supportRL trials. • Experiments performed in a VR simulator with data from New York City to confirm the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

• Deep stochastic reinforcement learning based approach to address this issue of epistemic uncertainty in a midsize fuel cell hybrid electric vehicle. • The performance of the proposed approach is benchmarked against Double Deep Q-Network (DDQN), Power Follower Controller (PFC) and Fuzzy Logic Controller (FLC). • Using New York City cycle as a validation drive cycle, the approach improves fuel economy by 7.68%, 13.53%, and 10% compared to DDQN, PFC, and FLC, respectively. • The deep REINFORCE approach improves fuel economy by 5.31 %,9.78 %, and 9.93 % compared to DDQN, PFC, and FLC, respectively under another validation cycle, Amman cycle. • The proposed method has 38% less training time when compared to the DDQN approach. Fuel cell hybrid electric vehicles offer a promising solution for sustainable and environment friendly transportation, but they necessitate efficient energy management strategies (EMSs) to optimize their fuel economy. However, designing an optimal leaning-based EMS becomes challenging in the presence of limited training data. This paper presents a deep stochastic reinforcement learning based approach to address this issue of epistemic uncertainty in a midsize fuel cell hybrid electric vehicle. The approach introduces a deep REINFORCE framework with a deep neural network baseline and entropy regularization to develop a stochastic policy for EMS. The performance of the proposed approach is benchmarked against three EMSs: i) a state-of- art deep deterministic reinforcement learning technique called Double Deep Q-Network (DDQN), Power Follower Controller (PFC) and Fuzzy Logic Controller (FLC). Using New York City cycle as a validation drive cycle, the deep REINFORCE approach improves fuel economy by 7.68%, 13.53%, and 10% compared to DDQN, PFC, and FLC, respectively. The deep REINFORCE approach improves fuel economy by 5.31 %,9.78 %, and 9.93 % compared to DDQN, PFC, and FLC, respectively under another validation cycle, Amman cycle. Moreover, the training results show that the proposed algorithm reduces training time by 38% compared to the DDQN approach. The proposed deep REINFORCE-based EMS shows superiority not only in terms of fuel economy, but also in terms of dealing with epistemic uncertainty. [ABSTRACT FROM AUTHOR]

[Display omitted] • NY could selectively detect Cu2+ in aqueous solutions. • NY had a detection limit for Cu2+ as low as 13 nM. • NY had mechanochrotic properties by exhibiting orange-red fluorescence when subjected to mechanical grinding. • NY was successfully applied to the imaging of human HeLa cells and targeting mitochondria. In this paper, a novel coumarin-derived fluorescent probe NY was designed and synthesized. NY displayed a significant ratiometric fluorescence response towards Cu2+ in PBS buffer (10 mM, pH = 7.4), with the emission wavelength blue-shifted from 580 to 495 nm, and a fluorescence change from orange to green was evident under a 365 nm UV light. Meanwhile, NY had the advantages of high selectivity, short response time (5 min), low detection limit (1.3 × 10-8 M) and large binding constant (1.45 × 105 M-1) towards Cu2+. The binding mechanism between NY and Cu2+ was elucidated by FT-IR, 1H NMR titration, TOF-MS and Job's plot analysis. In addition, NY was successfully employed in the detection of Cu2+ within environmental water and vegetable samples with satisfactory results. Laser confocal microscopy imaging results showed that NY could easily penetrate HeLa cells membrane to target mitochondria and image Cu2+ in living cells. Furthermore, NY demonstrated mechanochromic properties by exhibiting orange-red fluorescence when subjected to mechanical grinding. [ABSTRACT FROM AUTHOR]

Freight transportation is critical to metropolitan regions' economic competitiveness and long-term viability. However, it is one of the activities that produce the most negative externalities, including greenhouse gas emissions. The study of the relationship between freight transportation, land use, and demand management is critical for promoting enlightened policies that increase the social benefits of supply chains while avoiding their undesirable effects, such as climate change. Nonetheless, the tools available to public planners for incorporating freight transportation into their analyses are limited. In this context, this paper aims to highlight the impact that freight demand management strategies and freight-efficient land uses can have on increasing urban freight accessibility. In this study, the authors suggest a freight accessibility framework based on two measures of accessibility (an isochrone-based model and a gravity-based model) to analyze the spatiotemporal variation of freight accessibility in the New York City metropolitan statistical area for four sectors—retail trade, accommodation and food services, transportation and warehousing, and e-commerce. The results indicate that accessibility measures provide valuable insights towards policymaking, particularly to policies that target land use (e.g., location of warehouses), and policies that aim to shift the traffic of freight vehicles to periods with less traffic congestion. [ABSTRACT FROM AUTHOR]

Bike-Sharing Systems (BSSs) have exploded in popularity worldwide because of their beneficial impacts on traffic, pollution levels, and public health, which has resulted in moving toward a green city. The rebalancing problem, as one of the most important operational problems of such systems, deals with planning bike distribution at different stations. Regarding conducted studies, simulation models are the most common tool for analyzing BSSs and decision-making. This popularity is based on simulation's capabilities in modeling complexities of systems and uncertainty. Despite their advantages, lack of quickness is a significant drawback of simulation-based methods, making them inefficient for real-time decision-making processes, especially in large-scale and complex systems. In this regard, this paper introduces a Supervised Learning-Based Simulation (SLBS) method as an alternative to the conventional simulation-based methods dealing with rebalancing problems. SLBS is a huge step toward developing a real-time Decision Support System (DSS) for BSSs. For developing SLBS, firstly, we have developed a simulation model based on real-world assumptions of station-based BSSs and big data analysis of CitiBike, a well-known BSS located in New York City. The simulation model is able to calculate the number of unsatisfied demands (either number of failed pick-ups (FPs) or failed drop-offs (FDs)) as a result of different rebalancing plans. Then, the developed simulation model was used to generate quality and quantity training datasets to train Machine Learning (ML) algorithms involved in SLBS. While these ML models are trained once, SLBS will be capable of predicting the number of unsatisfied demands without running highly time-intensive simulations replications. The results obtained from a wide range of conducted experiments indicate that SLBS, up to 300 times faster than simulation models, can provide predictions with over 90% of R 2 Score. • A Decision Support Tool to address the rebalancing problem in the Bike-Sharing System. • A two-stage Supervised Learning-based Simulation (SLBS) method has been developed. • Substituting ML for simulation, SLBS aims to predict two objective functions. • Ensemble ML algorithms are applied to be compared in terms of accuracy and execution time. • The results support that SLBS performs much more time-saving with remarkable. predictions. [ABSTRACT FROM AUTHOR]

Urban transportation systems account for tremendous population movements, which can also trigger epidemic outbreaks. This paper investigates the coupling of epidemic spreading and human mobility via taxi-trip data analysis and the Markov chain approach, and proposes targeted epidemic prevention measures. Significant variations in travel patterns are observed across different taxi zones within the city, and these disparities have a substantial impact on epidemic dynamics. First, the probability of taxi drivers and passengers traveling among taxi zones is obtained from empirical data. Abstracting human travel pattern as Markov process, a traffic-driven epidemic spreading model is established. Considering the impact of trip probability on disease spreading, the model can effectively reproduce the outbreak of COVID-19 in New York City, with correct features in different boroughs. Quantitative parameters are derived to indicate the influence of taxi zones and origin-destination trips on epidemic transmission. Applying prevention measures to a small number of important zones or key origin-destination trips in the early stage of spreading, the scale of epidemic outbreaks can be significantly reduced. This research offers insights for suppressing epidemic spread in densely populated metropolitan areas, with the potential to benefit policy efforts. • Human mobility pattern is modelled and verified as a Markov process. • Efficacy of traffic-driven epidemic model is assessed by real COVID-19 case data. • Importance of taxi zones and OD trips in epidemic transmission is quantified. • Specific measures are proposed to prevent the spread of epidemics. [ABSTRACT FROM AUTHOR]

Sharing economy platforms hold a promise of more sustainable uses of resources. However, their legitimacy remains "debated" as various ecosystem stakeholders hold different opinions, challenging platform's values and activities. One key contested aspect is their capacity to contribute to sustainability beyond a search for economic performance. Despite these criticisms, sharing economy platforms have managed to grow and spread worldwide. Nevertheless, little is known about how a sudden market crisis affects an online platform characterized by a debated legitimacy. Studying the relationships between Airbnb and its main stakeholders during the first wave of COVID-19 pandemic in New York City, our paper identifies three relational strategies - identity, professionalization, and selection - implemented by the platform to reinforce its legitimacy within its local ecosystem. Based on secondary and primary data analyses, we identify and describe the arbitrations and opportunistic choices made by Airbnb to manage various stakeholder groups, to maintain its legitimacy during the COVID-19 crisis, and their impact on sustainability. [ABSTRACT FROM AUTHOR]

• Embed a non-local grouping (clustering) process into tensor train decomposition (NLG-TTD). • Characterize higher-order and nonlinear relationship between categorical variables and travel demand. • Discover non-Euclidean and non-linear patterns between categorical/discrete levels. • Validate NLG-TTD with bike-sharing usage data and metro demand data in New York City. • Apply NLG-TTD to analyze and predict the impact of land use shifts on travel demand. With the booming of transportation big data, plenty of spatial and temporal features become available for developing travel demand analysis models. However, existing machine learning based travel demand fitting models could face significant model complexity issues once there are multiple discrete/categorical independent variables. This paper embeds a non-local grouping (clustering) process into the tensor train decomposition (NLG-TTD) to characterize the higher-order and nonlinear relationship between categorical variables and travel demand. That is, the transportation dataset with multiple discrete/categorical independent variables and travel demand is treated as a multi-mode tensor, wherein levels of a discrete/categorical variable are regarded as indexes of a mode in the tensor, leading to a "regression on index". The proposed NLG-TTD model divides the full tensor into non-local sub-tensors, clusters sub-tensors with similarity, conducts tensor train decomposition (TTD) within each cluster, and aggregates cluster-specific learning results. Compared with a conventional TTD that generally works on image completions, NLG-TTD can reveal non-Euclidean and non-linear patterns between categorical/discrete levels in independent variables. It is expected to discover similar travel demand patterns among categorical levels and provide high-level goodness-of-fit. For numerical studies, we use bike-sharing usage data and metro demand data in New York City (NYC), in which several widely-used machine learning models are trained for comparison. The results demonstrate the good performance of the proposed model. Moreover, we apply the NLG-TTD model to analyze and predict the impact of land use shifts on travel demand. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel approach to control CO 2 and H 2 O composition in an enclosed environment. • A cyclic CO 2 adsorption process and dehumidication units are considered. • The mathematical model shows superior performance of the proposed system. • Carbon footprint of the system is discussed with scale-up study. • Influence of parametric uncertainties in the model is discussed. Rapid growth in global energy consumption has raised concern on the environmental impacts such as ozone layer depletion and climate change. Enclosed space, such as commercial buildings, accounts for about 40% of global energy consumption and the demand is constantly increasing due to increasing population, urbanization, and economic development. The energy demands in the building sector calls for strategic measures to develop energy efficient technologies. This paper presents a strategy to decrease energy demands inside buildings by proposing a ventilation system which regulates the enclosed air quality resulting in reduced air conditioning. The system consists of multiple adsorption beds with zeolite 13X monoliths for CO 2 removal, and silica gel for humidity control, inside the enclosed space. The air conditioning system results in decrease in energy requirement and improvement in economics by 55% as compared with conventional ventilation system. The model is scaled up to the size comparable with total office inventory of New York City, and the reduction in carbon emissions by introducing the air composition control system for New York City is equivalent to replacing 57 million incandescent light bulbs by LEDs. This paper concludes that the air conditioning system proposed in this study results in the improvement in performance as compared to a conventional ventilation system and could reduce energy consumption inside commercial buildings. [ABSTRACT FROM AUTHOR]

This paper presents a trajectory clustering method to discover spatial and temporal travel patterns in a traffic network. The study focuses on identifying spatially distinct traffic flow groups using trajectory clustering and investigating temporal traffic patterns of each spatial group. The main contribution of this paper is the development of a systematic framework for clustering and classifying vehicle trajectory data, which does not require a pre-processing step known as map-matching and directly applies to trajectory data without requiring the information on the underlying road network. The framework consists of four steps: similarity measurement, trajectory clustering, generation of cluster representative subsequences, and trajectory classification. First, we propose the use of the Longest Common Subsequence (LCS) between two vehicle trajectories as their similarity measure, assuming that the extent to which vehicles’ routes overlap indicates the level of closeness and relatedness as well as potential interactions between these vehicles. We then extend a density-based clustering algorithm, DBSCAN, to incorporate the LCS-based distance in our trajectory clustering problem. The output of the proposed clustering approach is a few spatially distinct traffic stream clusters, which together provide an informative and succinct representation of major network traffic streams. Next, we introduce the notion of Cluster Representative Subsequence (CRS), which reflects dense road segments shared by trajectories belonging to a given traffic stream cluster, and present the procedure of generating a set of CRSs by merging the pairwise LCSs via hierarchical agglomerative clustering. The CRSs are then used in the trajectory classification step to measure the similarity between a new trajectory and a cluster. The proposed framework is demonstrated using actual vehicle trajectory data collected from New York City, USA. A simple experiment was performed to illustrate the use of the proposed spatial traffic stream clustering in application areas such as network-level traffic flow pattern analysis and travel time reliability analysis. [ABSTRACT FROM AUTHOR]

• Cross-sectional studies of infection rates are critical to understand the drivers behind the COVID epidemic. • This study sought to identify demographic and socio-economic indicators that drive the incidence rate of COVID-19. • The paper assesses these factors at zip-code granularity. • The average household size is the single most important explanatory variable; the percentage of the population 65 or older, and that below the poverty line are also strongly positively associated. • Population density, per se, does not have a significantly positive impact on incidence rates. With an eye toward possible public policy implications, our objective is to identify the socio-economic and demographic factors that drive the large variation in COVID-19 incidence rates observed within relatively compact geographic regions, and to quantify the relative impact of each of these factors. We use international comparisons as a starting point. New York City, consisting of some 175 zip codes, is an ideal arena to pursue the above study given the large variation in case incidence rates across zip codes. We conducted systematic regression studies employing data with zip code granularity. Our model specifications are based on a well-established epidemiologic model that explains the effects of household sizes on R0. Average household size emerges as the single most important driver behind the large variation in COVID-19 incidence rates. It independently explains 62% of the variation. The percentage of the population above the age of 65 and the percentage below the poverty line are also strongly positively associated with zip code incidence rates. As to ethnic/racial characteristics, the percentages of African Americans, Hispanics and Asians within the population are significantly associated, but the magnitude of the impact is smaller. (The proportion of Asians within a zip code has a negative association.) Contrary to common belief, population density, by itself, does not have a significantly positive impact (other than when a high population is driven by large household sizes). Our findings support implemented and proposed policies to quarantine patients and separate infected individuals from families or dormitories; they also support newly revised nursing home admission policies. [ABSTRACT FROM AUTHOR]

• Highlights how historic context shapes the development of displacement patterns. • Ties displacement patterns to racial capitalism and settler colonialism. • Relies on historic materials to investigate long-range displacement patterns. • This research is organized as a micro-scale analyses of residential displacement. Researchers have often called for micro-scale analyses of residential displacement, and more recently, for work that acknowledges the importance of temporal and spatial relationships that influence current iterations of residential displacement. Relying on grounding in urban political ecology, and work in gentrification, racial capitalism, and settler colonialism, this paper highlights the historic relationships between development interests and resident groups in Gowanus, Brooklyn to examine how historic context shapes the development of displacement patterns. Relying on historic texts and images found in New York City archives, this work documents the developmental history of Gowanus, which has held a prominent place in New York City history since before the 1600s, when European settler colonists began to create farms and mills based on existing local knowledge of the landscape held by the Canarsee, a Munsee speaking Indigenous group of Lenape people who lived in the area now known as Gowanus. Over time, several groups of developers and industrialists dominated this space by morphing the tidal wetland into an industrial waterway, capitalizing on the waterfront as a commodity. Most recently, residents who can afford luxury rentals with views of waterfront have moved into the area. At the same time, city plans and the United States Environmental Protection Agency are addressing the legacy of contamination in the area. The varying access and attitude toward the canal over time continues to impact the development in the area, with some people being forced out, while others are moving in. This work builds on the growing literature on residential displacement to argue that even with embedded silence in archival materials, investigating the long-range historic patterns of development can be a useful method for understanding current iterations of development and displacement, including the most recent shift towards high-end waterfront living. [ABSTRACT FROM AUTHOR]

• Galleries in New York had always played important roles in NY art world. • The boundaries between NY museums and other organizations gradually collapsed. • Boundaries had been formed between the university and other organizations. • NY galleries and museums in other American cities had strong connections in the 1940s. • The connection between NY galleries and NY museums became more apparent in the 1960s. This paper examines the composition and transition of symbolic boundaries in the New York art world of painting and sculpture from 1940 to 1969 through social network analysis of inter-organizational ties between art venues. As culture-led redevelopments become more controversial in the age of neoliberal urban management, recent studies make it clear that symbolic boundaries and boundary works of urban cultures become more important topics for urban studies. So far, existing research on symbolic boundaries in urban culture has focused on social class and urban organizations, such as amenities and business establishments, and have pointed to the loss of the symbolic boundary between the museum and the gallery, this paper will show that more diverse changes were occurring in addition to such changes while following the critical methods of the previous studies. Utilizing the exhibition histories of 43 prominent American avant-garde artists based in New York, I obtain affiliation network data of artists-by-art venues. Based on the affiliation data, this paper examines the social networks among art venues, such as museums, galleries and universities, using block modeling and other methods of social network analysis. [ABSTRACT FROM AUTHOR]

This paper utilizes the 2009/2010/2011 American Community Survey to examine spatial patterns of later-life relocation from the state of New York into Florida. Given that the first-wave of the Baby Boom generation reached the retirement age of 65 years in January 2011 and many more will continue to do so, examining the mobility/subsequent residential choices of this group is a worthwhile undertaking. This research paper is also a noteworthy contribution because it offers an interdisciplinary study of spatial statistics and population geography. Exploratory spatial analysis and multinomial regressions suggest older adults from New York leave select origins, such as Capital District, Mid-Hudson, and Lower Hudson. In addition, these older adults select preferred destinations in Florida, such as Fort Myers, Fort Pierce–Stuart, and West Palm Beach. This finding can inform planners, policy analysts, and social workers about how to best address issues related to health and community services since not all older adult migrants seeking coastal and recreational areas in Florida maintain greater wealth and better health. [ABSTRACT FROM AUTHOR]

Community gardens have historically played an important role in the social–ecological resilience of New York City (NYC). These public-access communal gardens not only support flora and fauna to enhance food security and ecosystem services, but also foster communities of practice which nurture the restorative and communal aspects of this civic ecology practice. After NYC communities were devastated by Hurricane Sandy in 2012, the topic of resilience has surfaced to the top of the city's disaster planning and policy agenda. This paper explores the role of community gardens in coastal “red zones” of NYC by analyzing the meaning and relevance of community garden spaces in the resilience and recovery of local residents and community garden members post-Sandy. From April 2013 to February 2014, ethnographic analyses, including participant observation, exploratory and in-depth interviews, and archival research, was undertaken at five community gardens post-Sandy. Our findings indicate that community gardens functioned as multi-purpose community refuges which hosted meaningful and restorative greening practices, and developed supportive communities. This paper seeks to add to our knowledge of post-disaster greening, public spaces, and social–ecological resilience. [ABSTRACT FROM AUTHOR]

Two central issues of climate change have become increasingly evident: Climate change will significantly affect cities; and rapid global urbanization will increase dramatically the number of individuals, amount of critical infrastructure, and means of economic production that are exposed and vulnerable to dynamic climate risks. Simultaneously, cities in many settings have begun to emerge as early adopters of climate change action strategies including greenhouse gas mitigation and adaptation. The objective of this paper is to examine and analyze how officials of one city – the City of New York – have integrated a flexible adaptation pathways approach into the municipality's climate action strategy. This approach has been connected with the City's ongoing response to Hurricane Sandy, which struck in the October 2012 and resulted in damages worth more than US$19 billion. A case study narrative methodology utilizing the Wise et al. conceptual framework (see this volume) is used to evaluate the effectiveness of the flexible adaptation pathways approach in New York City. The paper finds that Hurricane Sandy serves as a “tipping point” leading to transformative adaptation due to the explicit inclusion of increasing climate change risks in the rebuilding effort. The potential for transferability of the approach to cities varying in size and development stage is discussed, with elements useful across cities including the overall concept of flexible adaptation pathways, the inclusion of the full metropolitan region in the planning process, and the co-generation of climate-risk information by stakeholders and scientists. [ABSTRACT FROM AUTHOR]

Demand for taxis varies over time. Finding the factors that affect demand and understanding the dynamics of taxi demand will benefit different groups. It helps planners to improve their transportation systems and also help drivers to reduce their empty vacant time. This paper focuses on modeling and analyzing the taxi demand, using the demand for other transportation modes and weather conditions. We started with the linear regression model, and then combined with a time series models. This combined model is called "linear regression with ARMA errors". (ARMA: Auto Regressive Moving Average). We considered the borough of Manhattan in New York City, and collected three months of data of yellow cabs, subway, Uber, bike, temperature, and precipitation. The observations for each variable, are aggregated hourly. We analysed these data sets and removed their seasonality, and then applied the models to those data sets. The power of time series model in explaining a variable is discussed and the results show that the developed model outperforms the linear regression, by improving the R-squared more than %30. [ABSTRACT FROM AUTHOR]

• Bike share usage follows an "increase-decrease-rebound" pattern from 2018 to 2022. • Spatiotemporal patterns of cycling in pre- and post-pandemic periods were similar. • Connectivity of bicycle network was improved after the pandemic outbreak. • People traveled a lot even when confirmed cases were at their peak in 2022. The bike-sharing system plays an important role during the COVID-19 pandemic due to its benefit of reducing the risk of infection. Previous studies have employed bike trip datasets to analyze the impact of COVID-19 on spatiotemporal cycling mobility, which was limited to a short pandemic period. An understanding of the long-term impact of the pandemic on human travel patterns is urgently needed. This paper presents a framework for investigating the long-term spatiotemporal change in cycling patterns in the pre-, during and post-pandemic periods. A spatiotemporal clustering algorithm is combined with spatiotemporal visualization, network analysis, and important location identification to explore latent mobility patterns. The strength of the proposed framework is that it can model a unified representation of cycling networks across different years, and provide insightful spatiotemporal patterns that allow easy interpretation. The experiments are conducted on five years of data collected from the 'Citibike' system in New York City. The results demonstrate the efficiency of the proposed approach in exploring the evolutionary cycling patterns of how people respond to the COVID-19 pandemic and are beneficial for various stakeholders. [ABSTRACT FROM AUTHOR]

Community gardens are a form of green infrastructure that provides social and environmental benefits for garden users and the surrounding area. However, little is known about who has walking access to community gardens in most cities today and how this may change in the future. To address this gap, we examine 15-minute walking access to community gardens in New York City (NYC), and how land-use and investment decisions will impact the future of NYC as a 15-minute garden city. Our results indicate that more than half of the city's residents have this access to a community garden, and that neighborhoods with lower income, lower proportions of white residents and homeowners, and higher rates of educational attainment have better access. Optimization modeling indicates that universal 15-minute access is unlikely, though more than 70% of NYC residents could have walking access with as few as 50 additional community gardens. Supporting new community gardens on vacant land and parking lots is the most efficient way to expand access, though siting community gardens on a range of land use types is required to maximize access. By mapping community gardens, analyzing their distribution, and modeling how to scale-up urban agriculture, this paper presents a scenario-based process for exploring how and where to expand urban amenities. • 50% of New Yorkers currently have 15-minute walking access to a community garden. • Access today is concentrated in low-income Communities of Color. • Vacant land and parking lots expand the 15-minute garden city most efficiently. • Universal 15-minute access is unlikely, and planning must attend to equity in access. [ABSTRACT FROM AUTHOR]

Empirical research is increasingly evaluating how municipal rezonings may affect the racial and socioeconomic characteristics of neighborhoods over time. There are also some zoning types that are not intended to affect development capacity and are based on the policy rationale of protecting urban character. Contextual rezonings are one such type and apply a layer of mandatory regulation on building height, setbacks from the street line, and yard requirements to create more restrictive but visually interesting building envelopes. In the American context, bundled with the notion of urban character are also discriminatory biases about the demographic characteristics of who should be allowed to live in urban neighborhoods and whose presence adds economic value. There is a need for additional studies that examine how character-based zoning controls may also be implicated in forms of neighborhood change, particularly exclusionary practices. This paper contributes to the literature by examining the neighborhood socioeconomic impacts of contextual rezonings implemented in New York City between 1986 and 2019 using a difference-in-difference method. The findings indicate that after implementation, contextually zoned neighborhoods tend to increase in socioeconomic status and become more predominately white and affluent. These changes represent a further entrenchment of white isolation that solidifies related privileges. • Contextually zoned neighborhoods in NYC increase in socioeconomic status and become more predominantly white and affluent. • Changes are likely not due to gentrification but are an entrenchment of white isolation that solidifies related privileges. • Ostensibly race-neutral zoning controls implemented after the 1968 Fair Housing Act likely produce exclusionary effects. [ABSTRACT FROM AUTHOR]

• Food-energy-water nexus policies that directly include UA are limited. • Regulatory policies were ranked as most effective at promoting resource-efficient UA. • Regulation-based policies dominate in case studies of Dortmund, Gorzów and Nantes. • Among the five case study cities, the most distinctive UA policies are in New York City. • Alignment between FEW nexus policies and UA indicates potential for future integration. The growth of urban agriculture (UA) has raised the awareness of city officials and civil society organizations of its potential effects on food systems. This has led to various policies to regulate and support UA. This research characterizes existing food, energy and water (FEW nexus) policies based on policy data from five case study cities in Europe and the U.S. (Dortmund, Gorzów Wielkopolski, London, Nantes, and New York City) to analyze their relationships to UA, and to identify policy types that support resource-efficient UA. The paper presents the results of an analysis of 78 policy documents related to UA and the FEW nexus, and the results of a Q-sort ranking by UA policy experts on the effectiveness of 16 generalized UA policies in promoting resource-efficient UA. The number, type, and degree of support for nexus policies vary among the five case studies. The results show that the majority of policies (36) are implemented at the local scale, that few policies (19) incorporate all elements of the nexus, yet many nexus policies include UA indirectly. Regulations are more prevalent and are considered more effective at ensuring resource-efficient UA than incentives or awareness-raising policies. The study offers guidance to policy makers who want to improve resource use in future UA pointing at the increasing importance of local food policies. [ABSTRACT FROM AUTHOR]