Social Vulnerability Assessment
MAS’s social vulnerability assessment sought to identify populations for whom protecting the public realm is particularly critical. To do this, MAS aggregated and scored citywide census tract data for a range of socioeconomic, health, and built environment indicators based on an extensive literature and peer review process. Census indicators such as age, race, median household income, and disability status were first selected to describe underlying social vulnerability. These were then combined with wellness-related health indicators like mental health, physical activity, and sleep. Finally, built environment indicators such as population density, public space acreage, and transit accessibility were layered to account for the disparate physical characteristics of New York City’s urban environment.
MAS’s literature review focused on social vulnerability assessments and best practices from New York City and elsewhere. These included, among others, the New York City Panel on Climate Change 2019 Report, New York City Department of Health and Mental Hygiene’s Heat Vulnerability Index, and Seattle’s Outside Citywide initiative.
MAS then inventoried a range of potential social and physical indicators, evaluating each in terms of data availability, commonality in other assessments, and relevance to our study’s particular focus. In total, MAS reviewed and analyzed more than 75 potential indicators, with decision-making input from outside peer reviewers. Ultimately, MAS chose 19 indicators, which were categorized and grouped into one of three broad factors: Socioeconomic, Health, and Built Environment. For the complete list of indicators and the rationale for including each, please refer to page 56 of A Framework for a City Built for Sunlight.
Socioeconomic data was downloaded from the 2018 American Community Survey 5-Year Estimates, while health data came from the CDC 500 Cities Project’s 2019 data release. All data was available at the census tract level. The built environment data was based primarily on tax lot level information from the New York City Department of City Planning’s 2020 MapPLUTO database as well as several open data layers. MAS cleaned, consolidated, and aggregated much of this data to the census tract level.
The data associated with each indicator was broken into five bins to generate scores for each census tract. Depending on the indicator, bins were determined based on either the quantile or equal interval statistical methods. Each indicator was assigned equal weight. Scores were then tallied for each indicator and aggregated to generate overall social vulnerability scores for each census tract.