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Protests during the 2021 Climate Conference in Glasgow exemplified our dilemma. The establishment perpetuates old thinking, while young people demand a new approach to mitigate the impact of climate change. The authors agree with the young people, and as a solution we propose to replace the current fragmentary approach with a new holistic one. The passive house approach that was conceptualized by the University of Illinois and built in Canada in 1977 showed us that energy consumption can be reduced about half of that used in the traditional design. Seventeen years later, a European passive house was built in Darmstadt. In 2008, a demonstration house in Syracuse, NY, showed that integrated passive measures produced energy use by about half of the NY state code for 2004. At the same time, some advanced houses in the USA showed total energy use of about 70 kWh/(m2∙y). In 2008, at the first Building Enclosure Science and Technology Conference, two equally important objectives for 2030 were proposed by the Lawrence Berkeley National Laboratory: (1) a 90% reduction of energy use in new buildings and (2) 50% for the retrofitting of existing buildings, i.e., to the level achieved in the 1980s. The first objective has recently been achieved in small buildings while the large residential buildings remain on the level obtained in the 2000s. Yet, the retrofitting of existing buildings (the second objective) has been a dismal failure. This paper acknowledges progress in hydronic heating and cooling involving electric heat pumps and hybrid solar panels, building automatics used for operation of HVAC, and modification of air distribution systems that comes from experience with the SARS-CoV-2 pandemic. Furthermore, it highlights that to accelerate energy efficiency and carbon emission reductions, there must be broad public-private educational programs with demonstrations of a new generation of retrofitting. Economically and ecologically retrofitted buildings will create a new approach to real estate investment. [ABSTRACT FROM AUTHOR]

The grid plays a prominent role in architecture, aiding in space organization and influencing all aspects of planning, ranging from urban design to intricate building details. This paper posits that the grid receives heightened emphasis in Brutalism, particularly in constructivist Brutalism, where materials and construction are intentionally exposed. A question arises regarding the grid's characteristics—despite its subtle appearance, the grid can sometimes be deceptive, ambiguous, and manipulative. The paper analyzes the merits and drawbacks of employing the grid in architecture, shedding light on its contributions to both structural and perceptual comprehensibility, as well as its role in increasing usefulness. To illustrate the application and perception of the grid, the paper examines two primary planning levels: urban planning and building design. The case studies focus on examples from New York City housing developments, specifically those constructed between the 1950s and the 1970s, and projects by architect I. M. Pei, which offer valuable insights into practical implementation. The paper concludes that while the grid can establish order, it may also engender an "uncanny" feeling. [ABSTRACT FROM AUTHOR]

Harvesting usable and meaningful disaster-related, spatio-temporal data at a highly granular level poses major challenges in its cleaning and aggregation. This paper presents a strategy related to those challenges with respect to individual behavior near COVID-19 laden healthcare facilities. This is done to enable the visualizing of egress behavior data as interactive, three-dimensional (3D) scenes to investigate human behavior patterns regarding touch-based, disease transmission. Therefore, the aim is to demonstrate how this concept of 3D epidemiology may provide new mechanisms to understand the relative risk and exposure prevalence for data analysis. This paper demonstrates 3D enablement of disaster-related field data through use of first-hand observations of 1,936 individuals egressing New York City healthcare facilities during the onset of COVID-19 in the Spring of 2020. The observations capture egress behavior in terms of where people go (e.g. coffee shop, Subway) and how they physically interact with the surroundings (i.e. what they touch and how long they remain). This paper introduces a mechanism for automated extraction and 3D visualization of such data in Potree, an open-source Web Graphics Library (WebGL) point cloud viewer. Distinctive vertex shaders are used to distinguish specific destination selection and behavioral patterns (e.g. personal protective equipment usage). Two-dimensional heatmaps are paired with 3D scenes to demonstrate the potential of using 3D visualization of spatio-temporal patterns for visualizing disease transmission potential. [ABSTRACT FROM AUTHOR]

Given that Ecuadorians are one of the largest groups of Hispanics living in New York, they have become a tight community that they now call little Ecuador. Although Ecuadorians living in the diaspora in NYC come from different parts of the country (mostly from the Andean region), they share the same cultural practices they performed in Ecuador that give them the sense of being in their country without bearing the instability and turmoil their country experiences. This shows how the group has fostered a sense of a multifaceted, multidimensional simultaneity between the host country and the motherland. The goal of this paper is to analyze the strategies Ecuadorian migrants use to validate their language and cultural practices to negotiate their identity as a group. Data for this paper come from ethnographic observations, semi-spontaneous conversations, oral interviews with members of the group, along with pictures taken while walking the community and participating in some of their events. Our study reveals that participants hold varying perceptions regarding their linguistic and cultural practices. However, it is noteworthy that they recognize these practices as a manifestation of Ecuadorianness, signifying a sense of solidarity among community members. [ABSTRACT FROM AUTHOR]

The traditional air-assisted side-deep fertilization device has some problems, such as inaccurate control system parameters and poor precision in variable fertilization. It seriously affects the application and popularization of the device. Aiming at the above problems, this paper wanted to realize the precise fertilizer discharge control of an air-assisted side-deep fertilization device. This paper designs an electronically controlled fertilization system based on a PID controller from the past. The system model was constructed in MATLAB, and the mathematical model and transfer function model of a stepper motor, the mathematical model of fertilizer discharge, and the stepper motor rotational speed were established too. In order to improve the accuracy of precise fertilizer discharge control system parameters, the system parameters were optimized based on the particle swarm optimization algorithm and the control system tuner toolbox. We had established a validation test platform to test the performance of a precise fertilizer discharge control system. In the actual experiment, the maximum stability coefficient of variation was 0.91% at the target fertilizer discharge mass level of 350 g/min, and the maximum error of fertilizer discharge was 4.14% at 550 g/min of the target fertilizer discharge mass level. By analyzing the test results of the precise fertilizer discharge control system, the new precise fertilizer discharge control system had good fertilizer discharge stability and could also meet the technical specification for quality evaluation of fertilization machinery (NY/T 1003-2006). This research can improve the fertilizer discharge accuracy of the air-assisted side-deep fertilization control system. [ABSTRACT FROM AUTHOR]

In the mid-twentieth century, growing North American textile and ready-to-wear industries vigorously appropriated Native American aesthetics to cultivate a commercial and design identity apart from Europe. Most studies of the circulation of Indigenous idioms in these industries focus on Southwestern or South Pacific regionalisms, and scholarship on studio and commercial fabric and fashion design from the Northwest Coast in the twentieth century is limited. This paper contributes by raising Indigenous and non-Indigenous use of Northwest Coast design forms during the politically turbulent 1940s–1960s and analyzing the impact of this aesthetic vocabulary within broader North American textiles and fashion. Throughout, I engage with the approaches of critical fashion theory and multiple modernisms, considering the frictions of property and power relations within settler-colonial states, then and now. Drawing from study of objects, periodicals, and archival materials as well as first-person perspectives, I contextualize these representations within entangled art, museum, and design worlds in the Northwest Coast, New York City, and the Southwest. My examination illustrates that Northwest Coast artists and art ideas asserted a peripheral but locatable role in mid-century textiles and fashion, facilitating the development of today's robust Indigenous fashion network on the Northwest Coast and its cultural politics. [ABSTRACT FROM AUTHOR]

The 2022 ASMC, our 33rd, returned to Saratoga Springs, NY as an in-person conference after 2 years as a virtual conference. While we are all grateful for the digital world’s enhancements that allowed this conference to be held remotely, attendees were happy to return to an in-person event where networking is much easier. [ABSTRACT FROM AUTHOR]

Open Educational Resources (OER) have continued to gain traction in higher education, assisting with ever-rising costs of publisher textbooks and supporting pedagogical innovation. Starting in 2017, NY state legislature has allocated funds annually to be split between its two public university systems to support and incentivize the adoption of OER. The author's institution, an included public college, has secured portions of this funding each year. This paper will focus on the initial five years of the statefunded OER initiative at the City University of New York (CUNY) College of Staten Island, with reflections on the successes of the program, the overall process of the annual grant cycle on campus, challenges that arose in the burgeoning OER program, and suggestions for future considerations. The main aim of this paper is to highlight the impact of state funding on the establishment of a library-facilitated OER initiative, to serve as a example for other institutions who are seeking to build an OER program. [ABSTRACT FROM AUTHOR]

Hierarchical Temporal Memory (HTM) is an unsupervised algorithm in machine learning. It models several fundamental neocortical computational principles. Spatial Pooler (SP) is one of the main components of the HTM, which continuously encodes streams of binary input from various layers and regions into sparse distributed representations. In this paper, the goal is to evaluate the sparsification in the SP algorithm from the perspective of information theory by the information bottleneck (IB), Cramer-Rao lower bound, and Fisher information matrix. This paper makes two main contributions. First, we introduce a new upper bound for the standard information bottleneck relation, which we refer to as modified-IB in this paper. This measure is used to evaluate the performance of the SP algorithm in different sparsity levels and various amounts of noise. The MNIST, Fashion-MNIST and NYC-Taxi datasets were fed to the SP algorithm separately. The SP algorithm with learning was found to be resistant to noise. Adding up to 40% noise to the input resulted in no discernible change in the output. Using the probabilistic mapping method and Hidden Markov Model, the sparse SP output representation was reconstructed in the input space. In the modified-IB relation, it is numerically calculated that a lower noise level and a higher sparsity level in the SP algorithm lead to a more effective reconstruction and SP with 2% sparsity produces the best results. Our second contribution is to prove mathematically that more sparsity leads to better performance of the SP algorithm. The data distribution was considered the Cauchy distribution, and the Cramer--Rao lower bound was analyzed to estimate SP's output at different sparsity levels. [ABSTRACT FROM AUTHOR]

Lateral roots, including adventitious roots, are the main component of rapeseed roots with support, absorb, and synthesis functions and their morphological parameters directly affecting the plant's aboveground growth and yield. Root biomass, as a material base for lateral root growth, can be used as a link between plant phenotypes and their physiological processes, as well as to enhance root 3D growth model mechanisms and accuracy. To quantify the relationships between lateral root morphological indices and the corresponding organ biomass for rapeseed, we used two cultivars, NY 22 (conventional) and NZ 1818 (hybrid), and conducted cultivar and fertilizing cylindrical tube experiments during the 2016–2019, with two fertilizer levels, no fertilizer, and 180 kg N ha−1 fertilizer. The lateral root biomass and morphological parameters were determined during the whole growth period. The biomass‐based lateral root morphological parameter models were developed by analyzing the quantitative relationship between the lateral root morphological indices and their corresponding biomass, and the descriptive models were verified with independent experimental data. The results showed that the correlation (r) of simulated and observed values for the lateral root morphological parameters are all greater than 0.9 with significant levels at p < 0.001. The absolute values of the average absolute difference (da) of simulated and observed values for the lateral root length (LLR), lateral root average diameter (ADLR), lateral root surface area (SALR), and lateral root volume (VLR) are −30.408 cm, −0.003 mm, 12.902 cm2, and 0.039 cm3, respectively. The RMSE values are 175.183 cm, 0.010 mm, 59.710 cm2, and 1.513 cm3, respectively. The ratio of da to the average observed values (dap) for the LLR and VLR are all less than 5%, and the ADLR and SALR are all <6%. The models developed in this paper have good performance and reliability for predicting lateral root morphological parameters of rapeseed. The study provides a mechanistic method for linking the rapeseed growth model with the morphological model using corresponding organic biomass and laying a good foundation for establishing a 3D morphological model for rapeseed root system based on biomass. [ABSTRACT FROM AUTHOR]

This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years. [ABSTRACT FROM AUTHOR]

Drones are becoming indispensable in emergency search and rescue (SAR), particularly in intricate urban areas where rapid and accurate response is crucial. This study addresses the pressing need for enhancing drone navigation in such complex, dynamic urban environments, where obstacles like building layouts and varying wind conditions create unique challenges. Particularly, the need for adapting drone autonomous navigation in correspondence with dynamic wind conditions in urban settings is emphasized because it is important for drones to avoid loss of control or crashes during SAR. This paper introduces a pioneering method integrating multiobjective reinforcement learning (MORL) with a convolutional autoencoder to train autonomous drones in comprehending and reacting to aerodynamic features in urban SAR. MORL enables the drone to optimize multiple goals, whereas the convolutional autoencoder generates synthetic wind simulations with a substantially lower computation cost compared to traditional computational fluid dynamics (CFD) simulations. A unique data transfer structure is also proposed, which fosters a seamless integration of perception and decision-making between machine learning (ML) and reinforcement learning (RL) components. This approach uses imagery data, specific to building layouts, allowing the drone to autonomously formulate policies, prioritize navigation decisions, optimize paths, and mitigate the impact of wind, all while negating the necessity for conventional aerodynamic force sensors. The method was validated with a model of New York City, offering substantial implications for enhancing automation algorithms in urban SAR. This innovation enables the possibility of more efficient, precise, and timely drone SAR operations within intricate urban landscapes. [ABSTRACT FROM AUTHOR]

[Objective] Tiger nut is a cash crop in southern Xinjiang and its production relies on irrigation. This paper presents the results of an experimental study on the effect of irrigation amounts on physiological traits, yield, and water use efficiency of different varieties of tiger nuts. [Method] The experiment studied two varieties: the Henan Round Grain (HY) and the Inner Mongolia Round Grain (NY), each irrigated by 18 mm (W1), 27 mm (W2), 36 mm (W3), and 45 mm (W4) of water, respectively. In each treatment, we measured the changes in soil water storage, plant height, tiller numbers, photosynthetic traits, yield, water consumption, and water use efficiency of the plants at different growing stages. [Result] Soil water storage and water consumption of both varieties increased with irrigation amount. Compared to W1, W4 increased average soil water storage and water consumption of the plants by 24.84% and 60.57% for the HY, and 18.46% and 55.61% for the NY, respectively. The plant height, tiller numbers, net photosynthesis (Pn), stomatal conductance (Gs), transpiration rate (Tr) and the yield of HY increased with irrigation amount (P<0.05), peaking in W2 with increases of 24.60%, 15.11%, 28.14%, 34.86%, 40.49% and 13.92% compared to W1. Intercellular carbon content (Ci) decreased with increasing irrigation amount. With the increase in irrigation amount, the plant height, tiller numbers, Pn, Gs, Tr, and the yield of NY all increased first followed by a decline; their associated values in W2 were 26.07%, 20.26%, 19.69%, 38.38%, 44.92% and 20.83% higher than those in W1. On average, Ci decreased with irrigation amount, being lowest in W2. Water use efficiency and irrigation water use efficiency of both varieties decreased with irrigation amount. Principal component analysis showed the net photosynthetic rate and water use efficiency can be used as indicators for variety selection. [Conclusion] The optimal irrigation amounts for the varieties HY and NY are 45 mm and 27 mm respectively, which can promote healthy growth, improve yields, and enhance water use efficiency of the plants. [ABSTRACT FROM AUTHOR]

Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as "sensors", which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur. [ABSTRACT FROM AUTHOR]

More than 30 years after its premiere, Seinfeld continues its run as a seminally popular television show. On October 1, 2021, five‐year streaming rights to the show were purchased by Netflix for $500 million. Set in New York City, where rent control laws have a long history, several episodes of the show consider the trials of apartment living, including shortages, tastes for discrimination by sellers, bribery, search costs, and quality degradation. Seinfeld also illustrates the informal process through which rent‐controlled apartments are advertised (e.g., less advertising under rent control shortage). This paper argues that popular media can be used as an effective pedagogical tool in learning. This paper analyzes four episodes of Seinfeld to help students identify and differentiate the very real costs of rent control. The paper also guides students to appreciate the difficulty in crafting a policy that is free of unintended consequences. [ABSTRACT FROM AUTHOR]

Researchers have addressed uncertainty in multicriteria decision making from the perspective of the performance values of the alternatives, weighting of the evaluation criteria, and the evaluation methods. Still, they are yet to address the uncertainty caused by the normalization approach. In this paper, we show that different normalization methods, namely sum normalization, min–max normalization, vector normalization, and maximization normalization, can result in different rankings of the alternatives while the performance values and weights are unchanged. We applied the grey system theory to address the problem of uncertainty in this study from three aspects: alternative performance values measurement, criteria weighting, and decision matrix/table normalization within a period. The grey hybrid normalization method is proposed as the main contribution in this paper. Then, we present the rankings of 48 cities under uncertainty to decide the location of a branch office of a Chinese electric vehicle manufacturer as a practical example based on the grey entropy weighting method and grey relational analysis with positive and negative references (GRA-PNR) within the period from the year 2019 to 2021. The research results using this approach ranked New York City the best, with a stock market capitalization of economy validity as the top contributor in terms of weighting. Finally, we used simple additive weighting with grey value and the technique for order of preference by similarity to ideal solution with grey value methods to validate the study results. [ABSTRACT FROM AUTHOR]

Background: Healthy NYC is an innovative survey panel created by the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) that offers a cost-effective mechanism for collecting priority and timely health information. Between November 2020 and June 2021, invitations for six different surveys were sent to Healthy NYC panelists by postal mail, email, and text messages. Panelists had the option to complete surveys online or via paper survey. Methods: We analyzed whether panelists varied by sociodemographic characteristics based on the contact mode they provided and the type of invitation that led to their response using logistic regression models. Poisson regression models were used to determine whether the number of invitations received before participating in a survey was associated with sociodemographic characteristics. Results: Younger age and higher education were positively associated with providing an email or text contact. Furthermore, age, race, and income were significant predictors for invitation modes that led to a survey response. Black panelists had 72% greater odds (OR 1.72 95% CI: 1.11–2.68) of responding to a mail invite and 33% lesser odds (OR 0.67, 95% CI: 0.54–0.83) of responding to an email invite compared with White panelists. Additionally, in five of the six surveys, more than half of the respondents completed surveys after two invites. Email invitations garnered the highest participation rates. Conclusions: We recommend using targeted invitation modes as an additional strategy to improve participation in panels. For lower-income panelists who do not provide an email address, it may be reasonable to offer additional response options that do not require internet access. Our study's findings provide insight into how panels can tailor outreach to panelists, especially among underrepresented groups, in the most economical and efficient ways. [ABSTRACT FROM AUTHOR]

This paper explores the ways in which Mexican immigrants experience, narrate, and describe stress and the extent to which it impacts their efforts at engaging in physical activity using a biocitizenship framework. Data were derived from a mixed-method study among Mexicans living in New York City recruited from a large Catholic church. The qualitative sample of 25 participated in quantitative and qualitative components of the study and as such we include some of these quantitative indicators as descriptors. Our main qualitative findings reveal that study participants experience stress and time constraint as factors that contribute to the waning of their physical and mental well-being. As such, time constraints for many of our participants were among the factors that contributed to high perceived levels of stress. They attributed this to their difficulty maintaining a physically active lifestyle due to factors like the fast-paced lifestyle in New York, working long hours, and not having enough time to exercise, though some important differences in narratives were noted across gender. Findings have implications for interventions aimed at improving the health of immigrants in general and Mexican immigrants in New York City specifically. [ABSTRACT FROM AUTHOR]

Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from the conventional heuristic-based methods commonly used to achieve timely solutions in large-scale ride-matching. Instead, we propose a novel approach that provides snapshot-optimal solutions for various forms of one-to-one matching while ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant in which the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset. [ABSTRACT FROM AUTHOR]

This paper presents methods to proactively choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. We show that by a smart integration of exploration/exploitation balancing, contact tracing, and location-based sampling, one can effectively mitigate the disease spread and significantly reduce the infection rates and death rates. Under different vaccination policies and under different compliance levels to quarantine order and/or testing requests, our smart testing algorithm can bring down the death rate significantly by 20% to 30%, as well as the percentage of infected drops by approximately 30%. The load on hospitals at peak times, a crucial aspect of managing COVID-19, drops, by 50% when implementing smart testing. We also show how procedural fairness can be incorporated into our method and present results that show that this can be done without hurting the effectiveness of the mitigation that can be achieved. This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with quarantine policies, can handle different objectives, and is dynamic and adaptive in the sense that it continually adapts to changes in real-time data. The bandit algorithm uses contact tracing, location-based sampling and random sampling in order to select specific individuals to test. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. Experiments also suggest that smart-testing can significantly reduce the death rates as compared with current methods, with or without vaccination. While smart testing strategies can help mitigate disease spread, there could be unintended consequences with fairness or bias when deployed in real-world settings. To this end we show how procedural fairness can be incorporated into our method and present results that show that this can be done without hurting the effectiveness of the mitigation that can be achieved. History: Ahmed Abbasi, Senior Editor; Maytal Saar-Tsechansky, Associate Editor. Funding: W. Yahav is supported by the Jeremy Coller Foundation and the Henry Crown Institute of Business Research in Israel. Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2023.1215. [ABSTRACT FROM AUTHOR]

With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user's work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user's decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average. [ABSTRACT FROM AUTHOR]

Given the need to reduce fuel emissions from transport many research studies have been devoted to the development of technologies and identification of new policies to tackle this problem. The majority of these strategies either increase the costs (e.g., new technology), are more time-consuming (i.e., choosing a more sustainable mode of transport), or encourage consumers to forgo travel plans (i.e., flights to holiday destinations). Implementing any of these changes is challenging for a society where cost, quality and time are the key motivators. The paper differs from previous research, given that the focus is not to slow down global warming, through the development of new technologies, or through inconveniences to lifestyle. Instead, the focus is to improve the sustainability of transport using current technology without increasing the cost or time factor. By using the concept of effective speed, this paper estimates the possible reduction in emissions, external costs and land use if people can be persuaded to choose the most cost-effective mode of transport. The effective speed is calculated by dividing the distance travelled by the time spent (i.e., travelling to work and earning the money to pay for the commute). This case study uses data from a survey of residents in New York City (NYC) and incorporates supporting data about commuting patterns in Germany. If people use their most cost-effective mode of transport in NYC, it is possible to have emission reductions of up to 14.7%, external cost reduction of 11.6% and a reduction in the time–area requirements of 16.5%. The results of this paper highlight that people do not always need to spend more time or money on their transport activity to travel in a more sustainable way. Indeed, encouraging people to use a mode of transport with a faster effective speed may even reduce the external effects for some. [ABSTRACT FROM AUTHOR]

This paper examines a potentially fatal type of pathogen transmission, namely, the spillover of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from COVID-19-positive humans to nonhuman animals. This neglected direction of pathogen transmission ("anthroponosis") was first publicized in March 2020, when eight large felids at a zoo in New York City were infected with SARS-CoV-2 by a COVID-19-positive employee. The paper gathers and problematizes the as-yet sparse evidence of anthroponotic transmissions of SARS-CoV-2 at sites in the animal-industrial complex where animals are held captive in zoos; appointed as human companions; used in scientific experiments; and raised and slaughtered in industrialized agriculture. The great fear is that animals infected with SARS-CoV-2 by COVID-19-positive humans will develop mutant strains of the virus, that these variants will be transmitted back to humans, and that the variants will be immune to the vaccines currently in use or in development. When we harm animals, we harm ourselves. Never has the need for a nonspeciesist approach to public health and safety been more urgent. [ABSTRACT FROM AUTHOR]

Shared bicycles provide a green, environmentally friendly, and healthy mode of transportation that effectively addresses the "final mile" problem in urban travel. However, the uneven distribution of bicycles and the imbalance of user demand can significantly impact user experience and bicycle usage efficiency, which makes it necessary to predict bicycle demand. In this paper, we propose a novel shared-bicycle demand prediction method based on station clustering. First, to address the challenge of capturing patterns in station-level bicycle demand, which exhibits significant fluctuations, we employ a clustering method that combines graph information from the bicycle transfer graph and potential energy. This method aggregates closely related stations into corresponding prediction regions. Second, we use the GCN-CRU-AM (Graph Convolutional Network-Gated Recurrent Unit-Attention Mechanism) model to predict bicycle demand in each region. This model extracts the spatial information and correlation between regions, integrates time feature data and local weather data, and assigns weights to the input features. Finally, experimental results based on the data from Citi Bike System in New York City demonstrate that the proposed model achieves a more accurate demand prediction. [ABSTRACT FROM AUTHOR]

This paper aims to leverage Twitter data to understand travel mode choices during the pandemic. Tweets related to different travel modes in New York City (NYC) are fetched from Twitter in the two most recent years (January 2020–January 2022). Building on these data, we develop travel mode classifiers, adapted from natural language processing (NLP) models, to determine whether individual tweets are related to some travel mode (subway, bus, bike, taxi/Uber, and private vehicle). Sentiment analysis is performed to understand people's attitudinal changes about mode choices during the pandemic. Results show that a majority of people had a positive attitude toward buses, bikes, and private vehicles, which is consistent with the phenomenon of many commuters shifting away from subways to buses, bikes and private vehicles during the pandemic. We analyze negative tweets related to travel modes and find that people were worried about those who did not wear masks on subways and buses. Based on users' demographic information, we conduct regression analysis to analyze what factors affected people's attitude toward public transit. We find that the attitude of users in the service industry was more easily affected by MTA subway service during the pandemic. [ABSTRACT FROM AUTHOR]

Grace Darling Wetherbee Coolidge's 1917 book Teepee Neighbors is a little-known collection of twenty-nine sketches of Indian life on the Wind River Reservation, Wyoming, where she worked as a missionary from 1902 to 1910. Only recently have Coolidge's personal papers been made publicly available by the Pioneers Museum in Colorado Springs, Colorado, allowing scholars to investigate the true contours of her life for the first time. These primary source materials shed new light on a woman who--though born to great privilege in New York City--rejected a life of leisure and wealth in favor of a subsistence existence on a remote Indian reservation, devoted to charitable acts. This paper offers the first accurate essay-length biographical treatment of Grace Coolidge. It as well analyzes selections of her Teepee Neighbors as an attempt to generate sympathy among white readers for a colonized people, the Arapaho, and to offer a critique of Euro-American society from the standpoint of the communal Indian values she encountered at Wind River. Coolidge's project, however, is ultimately hampered by her own ethnocentrism and admitted inability to understand Indian cultures. [ABSTRACT FROM AUTHOR]

While data on human behavior in COVID-19 rich environments have been captured and publicly released, spatial components of such data are recorded in two-dimensions. Thus, the complete roles of the built and natural environment cannot be readily ascertained. This paper introduces a mechanism for the three-dimensional (3D) visualization of egress behaviors of individuals leaving a COVID-19 exposed healthcare facility in Spring 2020 in New York City. Behavioral data were extracted and projected onto a 3D aerial laser scanning point cloud of the surrounding area rendered with Potree, a readily available open-source Web Graphics Library (WebGL) point cloud viewer. The outcomes were 3D heatmap visualizations of the built environment that indicated the event locations of individuals exhibiting specific characteristics (e.g., men vs. women; public transit users vs. private vehicle users). These visualizations enabled interactive navigation through the space accessible through any modern web browser supporting WebGL. Visualizing egress behavior in this manner may highlight patterns indicative of correlations between the environment, human behavior, and transmissible diseases. Findings using such tools have the potential to identify high-exposure areas and surfaces such as doors, railings, and other physical features. Providing flexible visualization capabilities with 3D spatial context can enable analysts to quickly advise and communicate vital information across a broad range of use cases. This paper presents such an application to extract the public health information necessary to form localized responses to reduce COVID-19 infection and transmission rates in urban areas. [ABSTRACT FROM AUTHOR]

The construction and expansion of subway systems represents an important step towards better livability conditions in a rapidly urbanizing world. However, underground construction has not benefited from well-established ontologies of semantic and geometric representation, such as Building Information Modelling (which is used for standalone structures) and City Geography Markup Language (which is designed for continuous urban elements). To bridge that gap, this paper proposes a novel and highly flexible means to underpin a relevant ontology. The approach uses the ontology log, or olog, a model of knowledge representation based on Category Theory. In an olog, dependencies between objects are restricted to functional relationships (for every object there is a unique correspondence). This robust mathematical formulation allows for a more flexible, yet also informative and user-readable model of the studied entities. In this paper, the olog's usability is demonstrated through the ontological representation of common items in the fare-control areas of two New York City metro stations. Ologs are shown to capture similar underlying structures both across different stations and within the same station. Importantly, the olog allows for further generalization to incorporate pre-existing data, as well as being a transferable framework for conceptualizations of other metro systems. [ABSTRACT FROM AUTHOR]

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Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking. Measurement(s) Particulate matter, gaseous pollutants Technology Type(s) Low-cost environmental sensing platform (City Scanner) Sample Characteristic - Environment City Sample Characteristic - Location Global [ABSTRACT FROM AUTHOR]

The paper examines how U.S.-American movies stage and convey intercultural encounters. Drawing from the case study of cinematic New York City, it tackles intercultural encounters and spaces emerging from interactions of protagonists that are staged as being 'culturally different'. Theoretical ideas on urban encounters, interculturality, and boundaries are intertwined. A comparative analysis 17 movies reveals three key dimensions of intercultural spaces: boundary drawing, boundary crossing, and boundary commuting. As polysemous staging strategies these provide insight into how movies display everyday intercultural encounters in an urban context. The paper concludes that New York City is imagined as a place in which intercultural encounters are consciously reflected as cultural coexistence—the cinematic city serves as canvas for a culturally separated society. This finding disenchants the medially widespread urban myth of New York City being a harmonious intercultural metropolis. [ABSTRACT FROM AUTHOR]

Three events occurred in the second half of 1946 in three adjoining US States (NJ, NY, and PA) which marked the birth of Hydrosilylation Technology. They occurred before the landmark 1957 JACS paper and the 1958 issued US patent by Speier et al. and before Chalk and Harrod named the reaction. First, on 27 June 1946, Mackenzie et al., of Montclair Research Corp., applied for a patent to prepare addition compounds of hydridosilanes and unsaturated organic compounds. Then, on 9 October 1946, Wagner and Strother of Union Carbide Corp. applied for a patent on a process to produce organic compounds of silicon with Si–C bonds by reacting a hydridosilane and an alkene or alkyne in the presence of a catalyst metal of the platinum group. Finally, Sommer et al., submitted a paper on peroxide-catalyzed hydrosilylation to JACS on 17 December 1946. It was published in January 1947. The landmark patent interference § and priority § case law associated with the Mackenzie et al. and Wagner et al., applications is well known to patent attorneys. This presentation will retrace the origins of hydrosilylation and report events (1946–1960) in the history of the reaction that are most probably unknown to most authors and presenters of hydrosilylation investigations. George Wagner's contribution to the birth of this technology is also highlighted. [ABSTRACT FROM AUTHOR]

This paper explores the role of social capital in contributing to the success of a new breed of organizations known as 'blank check companies' or special purpose acquisition companies (SPACs) that are set up solely to target and acquire listed companies as a fast-track route to gain listing status in the stock market. The paper is a case study of Pershing Square Holdings Ltd., St. Peter Port, UK (PSH), which launched SPAC IPOs (Initial Public Offerings), Pershing Square Tontine Holdings Ltd., New York, NY, USA (PSTH), which succeeded in raising the largest capital from influential investors in 2020. Social capital theory is employed to provide theoretical structure for the analysis. Using annual reports, publicly available information on the internet, as well as social media platforms related to the company and its strategy, the authors critically analyse and highlight how the Tontine's founder and his team utilized their structural, relational, and cognitive social capital to attract investors and gained recognition as the most successful SPAC IPO in the market in 2020. The authors found the ability to structure a SPAC IPO that departs from a typical SPAC, and the choice of timing to enter the SPAC market resulted in an over subscription and higher market valuation ratings of its IPO, as well as allowed the sponsor to be selective of its investors. This is the first study to address the significance of social capital at the individual and organizational level in creating value for SPAC IPOs. Potential investors can gain understanding and insights on the mechanics of SPAC IPOs and the importance of the founder's social capital in ensuring successful investment. Successful SPAC IPOs will create interest in the marketplace and enhance the value of investment for investors and helped private companies to get listed faster. [ABSTRACT FROM AUTHOR]

Assessment caps lead to inequities because the same growth limit is applied to high and low growth properties. High growth properties that fall under the cap have assessments artificially lowered relative to prices resulting in lower effective tax rates. This paper presents a method to transition out of a capped assessment system where instead of stipulating the percentage increase in assessment, this paper explores limiting the rate of value that can be captured, defined as the difference in the market value and the capped assessed value. Unlike the assessment cap approach which benefits high-growth properties, this approach conversely shifts the burden to high growth properties that have benefited from the cap. With the value recapture approach the burden is shifted to properties that have benefitted most from artificially low assessments, are furthest from market values, and therefore have more value to recapture. The inequities resulting from capped assessment are well known and can be of two forms - horizontal and vertical inequity. Using New York City data we use Gini measures of equity to show that the value recapture approach converges to an equitable state faster than when assessment increases are capped. The measure of vertical equity is the Modified Kakwani Index whereas the index for horizontal equity is introduced in this paper for the first time as the ratio of the withingroup Gini to the citywide Gini. The within-group Gini is the weighted average of the subregion Ginis. Horizontal equity exists when all the subregions have the same Gini as the citywide Gini, or that the ratio is one. With NYC data, we show that it is possible for the ratio to be at 1 within five years of transitioning out of capped assessments. [ABSTRACT FROM AUTHOR]

Violence involving firearms in the USA is a very important problem. As a consequence, a large number of crimes of this type are recorded every year. However, the solutions proposed have not managed to reduce the number of this type of crime. One of the cities with a large number of violent crimes is New York City. The number of crimes is not homogeneous and depends on the district where they occur. This paper proposes to study the information about the crimes in which firearms are involved with the aim of characterizing the factors on which the occurrence of this type of crime depends, such as the levels of poverty and culture. Since the districts are not homogeneous, the information has been analyzed at the district level. For this, data from the open data portal of the city of New York have been used and machine-learning techniques have been used. The results have shown that the variables on which they depend are different in each district. [ABSTRACT FROM AUTHOR]

Efficient vertical transportation is vital to a skyscraper's functional operation and the convenience and satisfaction of its tenants. This review complements the author's previously published research by updating the readers on innovative hardware and software-based solutions. It lays out, organizes, and combines extensive and scattered material on numerous aspects of elevator design in a straightforward and non-technical narrative. Rope-less elevators, the MULTI, artificial intelligence (AI), the Internet of Things (IoT), and extended reality technologies are some of the developments and advancements this article examines. The analysis also contextualizes current technical developments by reviewing how they are used in significant projects such as the One World Trade Center in New York City. Lastly, the paper examines innovative technologies, such as holographic elevator buttons and ultraviolet rays that disinfect elevators, in response to the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

New York City faces accelerating inundation risk from sea level rise, subsidence, and increasing storm intensity from natural and anthropogenic causes. Here we calculate a previously unquantified contribution to subsidence from the cumulative mass and downward pressure exerted by the built environment of the city. We enforce that load distribution in a multiphysics finite element model to calculate expected subsidence. Complex surface geology requires multiple rheological soil models to be applied; clay rich soils and artificial fill are calculated to have the highest post‐construction subsidence as compared with more elastic soils. Minimum and maximum calculated building subsidence ranges from 0 to 600 mm depending on soil/rock physical parameters and foundation modes. We compare modeled subsidence and surface geology to observed subsidence rates from satellite data (Interferometric Synthetic Aperture Radar and Global Positioning System). The comparison is complicated because the urban load has accumulated across a much longer period than measured subsidence rates, and there are multiple causes of subsidence. Geodetic measurements show a mean subsidence rate of 1–2 mm/year across the city that is consistent with regional post‐glacial deformation, though we find some areas of significantly greater subsidence rates. Some of this deformation is consistent with internal consolidation of artificial fill and other soft sediment that may be exacerbated by recent building loads, though there are many possible causes. New York is emblematic of growing coastal cities all over the world that are observed to be subsiding (Wu et al., 2022, https://doi.org/10.1029/2022GL098477), meaning there is a shared global challenge of mitigation against a growing inundation hazard. Plain Language Summary: As coastal cities grow globally, the combination of construction densification and sea level rise imply increasing inundation hazard. The point of the paper is to raise awareness that every additional high‐rise building constructed at coastal, river, or lakefront settings could contribute to future flood risk, and that mitigation strategies may need to be included. The subsidence mapping concept helps to quantify the hazard and adds specificity to soil types and conditions. We present satellite data that show that the city is sinking 1–2 mm/tr with some areas subsiding much faster. Key Points: More than 8 million people live in New York City, which is observed to be sinking 1–2 mm/year, while sea level risesWe calculate the mass of all buildings in New York City and model the subsidence caused by the pressure they exert on the EarthWe show detailed images of observed subsidence in New York City from satellite data [ABSTRACT FROM AUTHOR]

The investigation of linguistic landscapes (LL) among the Arab community in downtown Brooklyn, New York City, is an underserved public space in the literature. This research focused on social and commercial or 'bottom-up signs' in LL to understand their purpose, origin and target audience. Drawing upon discourse analysis, the study was conceptualized according to the principles of border theory and geosemiotics. The latter was used to analyze the data, which consisted of random photographs of shopfronts in Brooklyn taken with a digital camera during the summer of 2016. The three semiotic aggregates used for analysis consisted of interaction order, visual and place semiotics. The data analysis showed the multi-layered nature of LL in this urban community and the subjectiveness of spatial borders through a combination of text and symbolic imagery. The paper highlights the importance of commercial signs in the LL among ethnic minority communities. [ABSTRACT FROM AUTHOR]

In this paper, I argue the benefits of incorporating a study of the life and archival works of scholar Arturo Schomburg into the canon of an antiracist, culturally responsive and sustaining education curriculum. Scholars traditionally study Schomburg in the context of the Latin American independence movements or in relation to his involvement with the Harlem Renaissance. In this paper, I discuss Schomburg's life and show how two of his works, Excerpt from Racial Integrity: A Plea for the Establishment of a Chair of Negro History in Our Schools and Colleges and "A Negro Digs Up His Past," mirror the rhetoric used by antiracist educators and activists today. Finally, I describe the pedagogical merit in Schomburg's archival work as a basis for a culturally responsive and sustaining education unit of study in a New York City high school English class. [ABSTRACT FROM AUTHOR]

Ride hailing services, such as Uber, Lyft, and Grab, have become a major transportation mode in the last decade. The number of current passenger requests is one of the important factors for such services routing and pricing algorithms. Therefore, predicting future passenger request for ride hailing services can boost the efficiency of the service for both drivers and riders by pre-planning the allocation of vehicles and avoiding traffic congestions. Demand forecasting for ride hailing services relies upon the spatial and temporal correlations of its features. The existing literatures mostly divide the target area into rectangular grids (based on the longitude and latitude), consider only adjacent grids for spatial correlation, and calculate demand for each grid independently. An individual grid can contain different regions with high and low demand or have a major part of it outside the land area, which obscures the granularity and precision of estimations and predictions. This paper attempts to mitigate the limitations of grid-based methods by estimating and predicting ride hailing service demand between geographic regions as pickup and destination zones. For predicting demand, a convolutional neural network is integrated with a recurrent autoencoder network to best capture the spatial–temporal correlations of features, including time of the day, month, year, weekend, holiday, pickup zone, destination, and demand. In our experiments, we forecast the demand for each pickup–destination pair for the next day at a certain hour by observing the demands over the past 2 weeks during the same hour in the New York City hire vehicle data set. Using the same model (CNN-biLSTM-AE) to predict demand for geographical regions, it achieved an R 2 of 0.984, while predicting demand for cells in the grid achieved an R 2 0.545. While using the geographical regions instead of grids for partitioning the space, we compared our deep learning model with LSTM, CNN, CNN-LSTM, and LSTM-AE models and observed an improvement in R 2 from 0.632 to 0.767 and an improvement in RMSE from 20.53 to 16.33 against CNN. [ABSTRACT FROM AUTHOR]

Existing studies suggest that platform access restriction may cause restricted complementors to switch to competing platforms, which will increase complement quantity on competing platforms. We re-examine this prediction by accounting for the impact of economies of scope on complementor responses to platform access restriction. We argue that restricting a complementor's access on a platform may prevent it from achieving economies of scope from multi-homing, thereby incentivizing it to abandon both the restricted and (unrestricted) competing platforms. Using rideshare data in New York City, we compare the numbers of trips made by Lyft and Uber drivers, respectively, before and after Lyft restricted drivers' access on its platform. We find that Lyft's access restriction reduced trip numbers not only on Lyft but also on Uber. In addition, both Lyft's and Uber's trip numbers decreased not only during the restricted low-demand periods (e.g., non-rush hours) but also during the unrestricted high-demand periods (e.g., rush hours). In contrast, after a substantial number of multi-homing drivers left both platforms following Lyft's access restriction, a subsequent access restriction by Uber led to an increase in trip numbers on Lyft. These results highlight the importance of accounting for interdependencies across complementor activities when designing platform governance policies. This paper was accepted by Alfonso Gambardella, business strategy. Funding: H. D. Chung acknowledges support from the Strategic Research Foundation's Dissertation Research [Grant SRF-2021-DRG-8363]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4706. [ABSTRACT FROM AUTHOR]

The abstractionist paintings of Puerto Rican artist Olga Albizu (1924–2005) gained prominence in the late 1950s when her work debuted in galleries across the Americas and entered the commercial music industry with RCA and Verve records. However, existing scholarship has failed to capture the complex relationship between Albizu's anti-commercial abstractionist aesthetic and its mass reproduction as cover art for vinyl records during the Cold War era. Returning to the canvas to explore the iconographic, formal, and aesthetic qualities of Albizu's work within its sociohistorical post-World War II context, this study reveals Albizu's devotion to formal borders, vivid color juxtapositions, and compositional tensions. I argue that Albizu's practice constitutes an ongoing concern with a Modernist dialectic and ideals about subjective transformation in a postmodern world of mass culture, a message she conveyed through the material and experiential borders of the canvas. As an avowed formalist and Modernist existing between the postcolonial and postmodern worlds of San Juan and New York City, her work merits formal scrutiny. This paper will add to the diverse histories of Abstract Expressionism and mid-century Modernisms across the Americas while shedding light on an important post-war historical moment and artistic impulse that held on to anti-commercial values in an all-encompassing consumerist world. [ABSTRACT FROM AUTHOR]

With progressive technological advancements, the time for electric vehicles (EVs) and unmanned aerial vehicles (UAVs) has finally arrived for the masses. However, intelligent transportation systems need to develop appropriate protocols that enable swift predictive communication among these battery-powered devices. In this paper, we highlight the challenges in message routing in a unified paradigm of electric and flying vehicles (EnFVs). We innovate over the existing routing scheme by considering multi-objective EnFVs message routing using a novel modified genetics algorithm. The proposed scheme identifies all possible solutions, outlines the Pareto-front, and considers the optimal solution for the best route. Moreover, the reliability, data rate, and residual energy of vehicles are considered to achieve high communication gains. An exhaustive evaluation of the proposed and three existing schemes using a New York City real geographical trace shows that the proposed scheme outperforms existing solutions and achieves a 90 % + packet delivery ratio, longer connectivity time, shortest average hop distance, and efficient energy consumption. [ABSTRACT FROM AUTHOR]

Taxi travel time estimation based on real-time traffic flow collection in IoT has been well explored; however, it becomes a challenge to use the limited taxi data to estimate the travel time. Most of the existing methods in this scenario rely on shallow feature engineering. Nevertheless, they have limited performance in learning complex moving patterns. Thus, a Latent Semantic Pulse Sequence-based Deep Neural Network (LSPS-DNN) is proposed in this paper to improve the taxi travel time estimation performance by constructing a latent semantic propagation graph representing the latent path sequence. It first extracts the shallow modal features of trips, such as the time period and spatial location at different granularities. The representation of the pulse propagation graph is then extracted from shallow spatial features using a Pulse Coupled Neural Network (PCNN). Further, the propagation graph is encoded with negative sampling to obtain the embedding of deep propagation features between ODs. Meanwhile, we conduct deep network learning based on the Chengdu and NYC taxi datasets; our experimental evaluation results show it has a better performance compared to traditional feature construction methods. [ABSTRACT FROM AUTHOR]

The objective of the Ground to Space CALibration Experiment (G-SCALE) is to demonstrate the use of convex mirrors as a radiometric and spatial calibration and validation technology for Earth Observation assets, operating at multiple altitudes and spatial scales. Specifically, point sources with NIST-traceable absolute radiance signal are evaluated for simultaneous vicarious calibration of multi- and hyperspectral sensors in the VNIR/SWIR range, aboard Unmanned Aerial Vehicles (UAVs), manned aircraft, and satellite platforms. We introduce the experimental process, field site, instrumentation, and preliminary results of the G-SCALE, providing context for forthcoming papers that will detail the results of intercomparison between sensor technologies and remote sensing applications utilizing the mirror-based calibration approach, which is scalable across a wide range of pixel sizes with appropriate facilities. The experiment was carried out at the Rochester Institute of Technology's Tait Preserve in Penfield, NY, USA on 23 July 2021. The G-SCALE represents a unique, international collaboration between commercial, academic, and government entities for the purpose of evaluating a novel method to improve vicarious calibration and validation for Earth Observation. [ABSTRACT FROM AUTHOR]

This paper explores the properties of a family of bivariate copulas based on a new approach using the counter-monotonic shock method. The resulting copula covers the full range of negative dependence induced by one parameter. Expressions for the copula and density are derived and many theoretical properties are examined thoroughly, including explicit expressions for prominent measures of dependence, namely Spearman's rho, Kendall's tau and Blomqvist's beta. The convexity properties of this copula are presented, together with explicit expressions of the mixed moments. Estimation of the dependence parameter using the method of moments is considered, then a simulation study is carried out to evaluate the performance of the suggested estimator. Finally, an application of the proposed copula is illustrated by means of a real data set on air quality in New York City. [ABSTRACT FROM AUTHOR]

Several observational studies from locations around the globe have documented a positive correlation between air pollution and the severity of COVID-19 disease. Observational studies cannot identify the causal link between air quality and the severity of COVID-19 outcomes, and these studies face three key identification challenges: 1) air pollution is not randomly distributed across geographies; 2) air-quality monitoring networks are sparse spatially; and 3) defensive behaviors to mediate exposure to air pollution and COVID-19 are not equally available to all, leading to large measurement error bias when using rate-based COVID-19 outcome measures (e.g., incidence rate or mortality rate). Using a quasi-experimental design, we explore whether traffic-related air pollutants cause people with COVID-19 to suffer more extreme health outcomes in New York City (NYC). When we address the previously overlooked challenges to identification, we do not detect causal impacts of increased chronic concentrations of traffic-related air pollutants on COVID-19 death or hospitalization counts in NYC census tracts. [ABSTRACT FROM AUTHOR]

The physical condition of an occupant's home represents a significant factor in determining the dweller's overall quality of life. This paper provides a statistical framework for measuring housing quality in an urban area through a standardized index. This index is constructed using principal component analysis, incorporating demographic, geographic, and economic factors from the New York City Housing and Vacancy Survey. This metric allows for investigating differences in housing quality based upon ownership status. Analysis of the index demonstrates that renters face more housing quality issues than owners. Several of the index's input variables driving these differences were found to exhibit varying effects on housing quality over time, possibly due to events such as the 2008 financial crisis. Implementing this novel statistical framework, housing quality indices can be constructed for other cities to examine housing disparities and inform policies aimed at improving quality of life for urban residents. [ABSTRACT FROM AUTHOR]