| NSF Org: |
CBET Div Of Chem, Bioeng, Env, & Transp Sys |
| Recipient: |
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| Initial Amendment Date: | April 20, 2020 |
| Latest Amendment Date: | April 20, 2020 |
| Award Number: | 2029918 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Bruce Hamilton
bhamilto@nsf.gov (703)292-0000 CBET Div Of Chem, Bioeng, Env, & Transp Sys ENG Directorate For Engineering |
| Start Date: | May 1, 2020 |
| End Date: | April 30, 2021 (Estimated) |
| Total Intended Award Amount: | $197,475.00 |
| Total Awarded Amount to Date: | $197,475.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
66 W 12TH ST NEW YORK NY US 10011-8603 (212)229-5600 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NY US 10011-8603 |
| Primary Place of Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | COVID-19 Research |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
The study will integrate survey, social media, building infrastructure, energy demand and use, and social- demographic data with simulations of potential emerging weather-related extremes to examine interdependent social vulnerability to COVID-19 and weather in New York City (NYC). The research will leverage cutting-edge simulations, modeling, and visualizations of urban social and infrastructure systems to understand how human behavior changes in response to shelter-in-place policies may expose potential interdependent and cascading social vulnerability to COVID and weather extremes. The primary research question is: How will existing vulnerabilities to health, weather, and economic hazards be affected by new guidelines designed to reduce COVID-19 transmission rates in NYC? The primary outcome will be to advance knowledge for understanding COVID-19 impacts in the national epicenter of the virus outbreak and where solutions will be needed for many months as the pandemic begins to interact with weather dynamics and drive interdependent vulnerability over time.
As the COVID-19 pandemic evolves rapidly in NYC, there is an urgent need to collect data on social and economic impacts as they emerge, and join these with existing local, regional, and national datasets to anticipate potential interdependent impacts of COVID-19 as weather dynamics shift over coming months. Social survey and social media data are especially critical to collect now as perspectives on location specific experiences and perspective of green space as critical infrastructure can change over time. Additionally, social media data can only be accessed cost-effectively via Twitter in weekly intervals and analyses are needed now to understand policy impacts in time to plan responses and strategies for resilience to interdependent COVID-weather extremes impacts. A convergent scientific approach is critical for examining how vulnerable populations may be further impacted as spring turns to summer with potential heat waves and extreme rainfall events. The analysis will examine how overlapping vulnerabilities interact with availability and usage of urban green spaces for physical and mental health during COVID-19 shelter-in-place policies. For example, data will include weekly geo-located tweets overlaid with buildings and green space spatial data to explore dominant locations of social media activity in NYC to understand which parks and open space are most used, and which will require additional resources to meet public need for physical and mental health. This data will provide input to real-time decision-making in NYC to impact current emergency responses, planning and policies that consider direct and indirect impacts of COVID-19, weather extremes, and interdependent vulnerabilities. There remains limited systemic understanding of what forms resilience to COVID-19 should take, especially when considering interactions with additional drivers of social vulnerability. Thus, the broader impacts of this research lie primarily in direct engagement with local practitioners?governmental officials, non-governmental organizations, community organizations?to improve their ability to conduct integrative planning and improve real-time decision-making to reduce social vulnerability and plan emergency response in the novel context of ongoing COVID-19 transmission that may be combined with weather-related extremes. Further, research will be provided to current NSF Growing Convergence Research (GCR) collaborators in Atlanta, Phoenix, San Juan (PR), and across the cities in the UREx Sustainability Research Network. (SRN), seeding opportunities to replicate methods and findings. The project PIs will train interdisciplinary graduate students and postdoctoral scholars in this convergent science approach and provide an important mechanism to bring scholars with advanced data science skills to gather important emerging data and advance novel research to understand the potential of interdependent COVID-19 and weather-related impacts on vulnerable populations in NYC.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project enabled the Urban Systems Lab team to achieve three main objectives: (1) to advance knowledge and understanding of COVID-19 impacts in New York City, (2) examine how overlapping vulnerabilities interact with availability and usage of urban green spaces, and (3) to improve decision making for addressing weather, climate, and COVID-19 interdependencies in NYC.
The findings from our analysis of social and spatial distributions of COVID-19 and their relationships with indicators of social vulnerability show that immediate impacts of COVID-19 largely fall along lines of race and class. Indicators of poverty, race, disability, language isolation, rent burden, unemployment, lack of health insurance, and housing crowding all significantly drive spatial patterns in prevalence of COVID-19 testing, confirmed cases, death rates, and severity. Income in particular has a consistent negative relationship with rates of death and disease severity. The largest differences in social vulnerability indicators are also driven by populations of people of color, poverty, housing crowding, and rates of disability.
The results of a social survey to better understand the perception and use of urban green spaces (UGS) show New Yorkers continued to use UGS during the pandemic and considered them to be more important for mental and physical health than before the health crisis began. However, the study revealed a pattern of concerns residents have related to perceived accessibility and safety which prevented some individuals from using open space during the pandemic. Finally, the results of an analysis of vulnerability to both extreme heat and the pandemic highlight areas with significant overlaps between heat and COVID-19 cases as a function of those most vulnerable to them (particularly for parts of the Bronx). Moreover, results of our social and spatial analysis show that geographical proximity variables can predict a number of the perceived access variables, particularly those related to COVID-19 measures. Although lower-income zip codes were found to have higher spatial access to UGS, many of the same communities, including people living in crowded and multi-unit buildings, on average only have access to smaller green spaces, suggesting an uneven distribution of larger quality parks.
Our research highlights the need for targeted responses to address injustice of COVID-19 cases and deaths, importance of recovery strategies that account for differential vulnerability, and provide an analytical approach for advancing research to examine potential similar injustice of COVID-19 in other U.S. cities.
Last Modified: 08/17/2021
Modified by: P. Timon Mcphearson
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In our analysis, we consider the average of the daily maximum temperatures in June-July-August and use COVID-19 case rates as an indicator of the virus hazard. Zip codes with high overlapping risk (upper 25th quartile) are shown red, with low risk areas in blue (lower 25th quartile)Luis Ortiz and Timon McPhearsonCreative CommonsP. Timon McphearsonModeling and Visualizing Multi-Hazard Risk: Heat Vulnerability and COVID-19 in NYC -
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(Left) Urban Green Spaces (UGS) clusters: Zip codes with low UGS per resident (low area) and high COVID-19 severity rates that adopted from McPhearson et al. (in press), and Zip codes with consistent groupings as low-low, low-high, high-low, and high-high for UGS per resident and geographical accesAhmed MustafaCreative CommonsP. Timon McphearsonRelationship between urban green space availability, and COVID-19 severity in New York City -
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Zip codes with consistent groupings as high and low impact clusters for COVID-19 cases, deaths, and estimated severity.Maps developed by Pablo Herreros-Cantis, Ahmed Mustafa, Zbigniew Grabowski, Luis Ortiz, Timon McPhearsonCreative CommonsP. Timon McphearsonCOVID-19 groupings as low and high impact clusters
clusters--rgov-214x142.jpg)
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