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The rise in COVID-19 cases has not only generated anxiety and uncertainty but has also caused an upsurge in anti-Asian racism all over the world. The racially motivated attacks have forced us to rethink the concept of migration, integration, and racism in a global context. In this paper, I look back at Meena Alexander's Manhattan Music and relying on Arjun Appadurai's critical theory in Fear of Small Numbers: An Essay on the Geography of Anger I investigate the dynamics that go into making the dominant culture hostile towards immigrants and ethnic populations. Drawing a connection between my reading of the novel and the COVID-fueled racism, I debunk the myth of assimilation as a means of integration and attest that race and ethnicity still play crucial role in the politics of power. That is, despite the ethnic protagonists' negotiation of their subjectivity, their ethnic body will continue to be victims of racial politics and used as scapegoats as long as white supremacy prevails and is considered normative. Revisiting Manhattan Music and rendering the connection between race, ethnicity and racism opens up avenues for critical rethinking and compels us to ever more consider the intersection of ethnic identity and race today in an era of global pandemic that has targeted Asian migrants in and beyond America. [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 COVID-19 pandemic has severely impacted human activities in a way never documented in modern history. The prevention policies and measures have abruptly changed well-established urban mobility patterns. In this context, we exploit different sources of urban mobility data to gain insights into the effects of restrictive policies on the daily mobility and exhaust emissions in pandemic and post-pandemic periods. Manhattan, the most densely populated borough in New York City, is chosen as the study area. We collect data generated by taxis, sharing bikes, and road detectors between 2019 and 2021, and estimate exhaust emissions using the COPERT (Computer Programme to calculate Emissions from Road Transport) model. A comparative analysis is conducted to identify important changes in urban mobility and emission patterns, with a particular focus on the lockdown period in 2020 and its counterparts in 2019 and 2021. The results of the paper fuel the discussion on urban resilience and policy-making in a post pandemic world. • A comparative analysis is conducted to identify important changes in urban mobility and emission patterns due to the COVID-19. • Remarkable reductions in taxi trips and exhaust emissions are witnessed during the lockdown period. • The need for social distancing has encouraged the utilization of bike-sharing services. • Evidence on the resilience of bike sharing systems during this pandemic has been found. [ABSTRACT FROM AUTHOR]