This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are from the 2011-2015 American Community Survey (ACS). To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low to Moderate Income Populations by Block GroupDate of Coverage: ACS 2020-2016

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are derived from the 2011-2015 American Community Survey (ACS) and based on Census 2010 geography.

To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low to Moderate Income Populations by Tract

Map for question/section 1: "Who lives there?"Containing Rural areas as defined by US Census 2013 urban/rural defined areas, http://nmcdc.maps.arcgis.com/home/item.html?id=fbd1e91ec0a54c58b6fcca8a5138c1fc. Filtered to include: 'RURAL' in 2 category designation, Population of LESS THAN 5001 persons, AND % Low access low-income at 20 miles to AT LEAST 5%. Map displaying by Esri 2019 Age Dependency Ratios.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

The feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States.

According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments.

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings.

Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:

‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) 
‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) 
‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) 
‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) 
‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) 
 ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) 
‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) 
Null - Could not be geocoded (does not appear on the map) 

For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10.

HCD downloaded the HUD data in April 2021. They sourced the data from https://hub.arcgis.com/datasets/fedmaps::public-housing-buildings.

To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/.

Discounts for Internet service through the Affordable Connectivity Program (ACP) ended June 1, 2024 due to lack of additional funding. Whether the program will receive additional funding in the future is uncertain. Please see ACP program information from the FCC for more details.The Affordable Connectivity Program (ACP) claims data set summarizes reimbursement claims submitted by participating Internet service providers for households in Detroit zip codes by month and zip code. The Affordable Connectivity Program (ACP) is a U.S. government program to help low-income households pay for Internet services and connected devices. Households that participate in ACP receive discounts on qualifying broadband Internet services of up to $30 per month and can also receive a one-time discount of up to $100 to purchase a laptop, desktop computer, or tablet. Households can qualify for ACP based on participation in Lifeline or other service provider programs for low-income households, income at or below 200% of the federal poverty guidelines, participation in other Lifeline-qualifying programs such as SNAP or Medicaid, or participation in free and reduced-price school lunch and breakfast programs. Additionally, service providers can also ask the FCC to approve an alternative verification process and use that approved process to check consumer eligibility. ACP program discounts first became available to eligible enrolled households on January 1, 2022. The ACP claims process is built on the Lifeline Claims System and this data set is derived from snapshots of all subscribers entered in the National Lifeline Accountability Database (NLAD) as of the first of each month. The ACP was created under the Infrastructure Investment and Jobs Act, also known as the Bipartisan Infrastructure Law, and is administered by the independent not-for-profit Universal Service Access Co. under the direction of the Federal Communications Commission (FCC). Eligible beneficiaries who participated in the Emergency Broadband Benefit (EBB) program that was funded by the Coronavirus Aid, Relief, and Economic Security (CARES) Act, were transitioned to ACP between January 1 and March 1, 2022. EBB was ACP's predecessor program and ran from May 12, 2021 until it was phased out on February 28, 2022. Due to the granularity of available data, claims for households located in communities adjacent to Detroit that share a zip code such as Hamtramck and Highland Park are included in this data set.Fieldsogc_fid - Zip code id.zipcode - Zip code where the enrolled household is located.postalcity - City associated with the zip code.data_month - Data month is associated with the subscriber snapshot for each claim month. If data month is listed as '5/1/2022', then the subscriber snapshot was captured on June 1, and the data represents the number of households in ACP on June 1. This is the universe of subscribers that providers can claim for the May 2022 data month.total_claimed_subscribers - Total number of enrolled households claimed for reimbursement as of the data month snapshot.total_claimed_devices - Total number of devices (laptops, desktop computers, or tablets) claimed for reimbursement as of the data month snapshot.service_support - Amount program providers claimed for reimbursement under the ACP program in the given month, in dollars. Reimbursement claims are for discounts provided to enrolled households to reduce the standard rate of an eligible broadband service and associated equipment rentals. For households that receive both Lifeline and ACP discounts and apply both benefits to their qualifying broadband service, the Lifeline discount ($9.25) is applied first and the ACP discount is then applied to the remaining amount.device_support - Amount discounted to households for purchasing a device (laptop, desktop computer, or tablet) in the given month, in dollars. Each household is eligible for a one-time reimbursement payment of up to $100 for one connected device.total_support - Sum of service support and device support in the given month, in dollars.

Created in the Tax Cuts and Jobs Act of 2017, Opportunity Zones is a new federal program that provides tax incentives for investments in new businesses and commercial projects in low-income communities. On April 2018, Mayor Bowser nominated 25 census tracts to be Opportunity Zones. The U.S. Department of Treasury certified these tracts on May 18, 2018.

The Food Access Research Atlas presents a spatial overview of food access indicators for low-income and other census tracts using different measures of supermarket accessibility, provides food access data for populations within census tracts, and offers census-tract-level data on food access that can be downloaded for community planning or research purposes.

The Climate Leadership and Community Protection Act (CLCPA) directs the Climate Justice Working Group (CJWG) to establish criteria for defining disadvantaged communities. This dataset identifies areas throughout the State that meet the final disadvantaged community definition as voted on by the Climate Justice Working Group.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Abstract

ADI: An index of socioeconomic status for communities. Dataset ingested directly from BigQuery.

Documentation

The Area Deprivation Index (ADI) can show where areas of deprivation and affluence exist within a community. The ADI is calculated with 17 indicators from the American Community Survey (ACS) having been well-studied in the peer-reviewed literature since 2003, and used for 20 years by the Health Resources and Services Administration (HRSA). High levels of deprivation have been linked to health outcomes such as 30-day hospital readmission rates, cardiovascular disease deaths, cervical cancer incidence, cancer deaths, and all-cause mortality. The 17 indicators from the ADI encompass income, education, employment, and housing conditions at the Census Block Group level.

The ADI is available on BigQuery for release years 2018-2020 and is reported as a percentile that is 0-100% with 50% indicating a "middle of the nation" percentile. Data is provided at the county, ZIP, and Census Block Group levels. Neighborhood and racial disparities occur when some neighborhoods have high ADI scores and others have low scores. A low ADI score indicates affluence or prosperity. A high ADI score is indicative of high levels of deprivation. Raw ADI scores and additional statistics and dataviz can be seen in this ADI story with a BroadStreet free account.

Dataset source: https://help.broadstreet.io/article/adi/

The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted an online survey investigating people’s everyday financial practices, evolving perceptions of trust and risk, and their unmet financial needs. The general population survey data was collected between April 15 and April 22, 2022. The credit union data was collected between May 3 and July 18, 2022. This data set contains the responses of the survey participants after excluding any personally identifying data. All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839)...., Survey data was collected via the Qualtrics platform. The survey contains 52 questions. It was distributed to the general population in zip codes within the counties of Los Angeles and Orange. It was also distributed directly to members of a large credit union headquartered in Orange County (“large†according to NCUA asset classes). Participants were eligible to complete the survey if they live in Orange County or Los Angeles County, are older than 18, and have a combined household income of less than $100,000. Incomplete responses have been removed. The survey yielded 1,370 complete responses (1,213 from the general population participants and 157 from members of the large credit union)., Note that the files do not contain all the responses from the survey questions. Responses that provided potentially identifying information were removed. Survey participants’ gender, education status, employment status, and marital status were removed; data on these elements are provided in aggregate in the readme file. Responses are segmented into two files reflecting participants from the general population (“Gen Pop†) and from the credit union (“CU†).

The New York City Longitudinal Survey of Wellbeing (NYC-LSW) also known as the Poverty Tracker (PT) is a study of disadvantage in New York City. Launched in 2012, the Poverty Tracker surveys a representative sample of New Yorkers every three months collecting data on the dynamics of poverty and other forms of disadvantage. The Poverty Tracker covers two distinct panels. The first panel collected from 2012-2015 following 2,286 New Yorkers and the second panel which follows 3,908 New Yorkers. Collection of the second panel of data began in 2015 after respondents took the Community Health Survey with the NYC Department of Health and Mental Hygiene. In the first panel (n=2286) the majority of respondents were recruited by landline and mobile phone using random digit dialing (n=2002). Landline phone numbers from zip codes where more than 20% of residents live in poverty based on the 2000 US Census were oversampled. An additional sample (n=226) was recruited from 14 social service agencies randomly selected from a list of all agencies funded by the Robin Hood Foundation. The agency sample allowed the oversampling of low-income persons who utilize social services. An additional sample (n=58) of respondents randomly selected from homes in zip codes affected by Hurricane Sandy were also recruited. Respondents who joined the panel study were surveyed at baseline in late 2012 and early 2013. Follow-up interviews were conducted in English and Spanish every 3 months over a 2-year period. Surveys were 10-20 minutes in length. Persons recruited from social service agencies who did not have a stable telephone number were offered cell phones and paid phone service in lieu of monetary compensation. The second panel (n=3908), began collection in Spring 2015 after respondents participated in the Community Health Survey administered by the NYC Department of Health and Mental Hygiene, which was also sampled using random digit dialing (n=3403). Again, this sample contains an additional subsample (n=505) from 26 randomly selected Robin Hood-funded social service agencies designed to provide an oversample of New Yorkers engaged in social services. Follow-up interviews were conducted in English and Spanish every 3 months over a 6-year period. Surveys are 10-25 minutes in length. Persons recruited from social service agencies who did not have a stable telephone number were offered cell phones and paid phone service in lieu of monetary compensation.

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

The Heat Vulnerability Index (HVI) shows neighborhoods whose residents are more at risk for dying during and immediately following extreme heat. It uses a statistical model to summarize the most important social and environmental factors that contribute to neighborhood heat risk. The factors included in the HVI are surface temperature, green space, access to home air conditioning, and the percentage of residents who are low-income or non-Latinx Black. Differences in these risk factors across neighborhoods are rooted in past and present racism. Neighborhoods are scored from 1 (lowest risk) to 5 (highest risk) by summing the following factors and assigning them into 5 groups (quintiles):

Median Household Income (American Community Survey 5 year estimate, 2016-2020) Percent vegetative cover (trees, shrubs or grass) (2017 LiDAR, NYC DOITT) Percent of population reported as Non-Hispanic Black on Census 2020 Average surface temperature Fahrenheit from ECOSSTRESS thermal imaging, August 27,2020 Percent of households reporting Air Conditioning access, Housing ad Vacancy Survey, 2017

Background Rural U.S. communities are at risk from COVID-19 due to advanced age and limited access to acute care. Recognizing this, the Vashon Medical Reserve Corps (VMRC) in King County, Washington, implemented an all-volunteer, community-based COVID-19 response program. This program integrated public engagement, SARS-CoV-2 testing, contact tracing, vaccination, and material community support, and was associated with the lowest cumulative COVID-19 case rate in King County. This study aimed to investigate the contributions of demographics, geography and public health interventions to Vashon’s low COVID-19 rates. Methods This observational cross-sectional study compares cumulative COVID-19 rates and success of public health interventions from February 2020 through November 2021 for Vashon Island with King County (including metropolitan Seattle) and Whidbey Island, located ~50 km north of Vashon. To evaluate the role of demography, we developed multiple linear regression models of COVID-19 rates using metrics of age, race/ethnicity, wealth and educational attainment across 77 King County zip codes. To investigate the role of remote geography we expanded the regression models to include North, Central and South Whidbey, similarly remote island communities with varying demographic features. To evaluate the effectiveness of VMRC’s community-based public health measures, we directly compared Vashon’s success of vaccination and contact tracing with that of King County and South Whidbey, the Whidbey community most similar to Vashon. Results Vashon’s cumulative COVID-19 case rate was 29% that of King County overall (22.2 vs 76.8 cases/K). A multiple linear regression model based on King County demographics found educational attainment to be a major correlate of COVID-19 rates, and Vashon’s cumulative case rate was just 38% of predicted (p<.05), so demographics alone do not explain Vashon’s low COVID-19 case rate. Inclusion of Whidbey communities in the model identified a major effect of remote geography (-49 cases/K, p<.001), such that observed COVID-19 rates for all remote communities fell within the model’s 95% prediction interval. VMRC’s vaccination effort was highly effective, reaching a vaccination rate of 1500 doses/K four months before South Whidbey and King County and maintaining a cumulative vaccination rate 200 doses/K higher throughout the latter half of 2021 (p<.001). Including vaccination rates in the model reduced the effect of remote geography to -41 cases/K (p<.001). VMRC case investigation was also highly effective, interviewing 96% of referred cases in an average of 1.7 days compared with 69% in 3.7 days for Washington Department of Health investigating South Whidbey cases and 80% in 3.4 days for Public Health–Seattle & King County (both p<0.001). VMRC’s public health interventions were associated with a 30% lower case rate (p<0.001) and 55% lower hospitalization rate (p=0.056) than South Whidbey. Conclusion While the overall magnitude of the pre-Omicron COVID-19 pandemic in rural and urban U.S. communities was similar, we show that island communities in the Puget Sound region were substantially protected from COVID-19 by their geography. We further show that a volunteer community-based COVID-19 response program was highly effective in the Vashon community, augmenting the protective effect of geography. We suggest that Medical Reserve Corps should be an important element of future pandemic planning. Methods The study period extended from the pandemic onset in February 2020 through November 2021. Daily COVID-19 cases, hospitalizations, deaths and test numbers for King County as a whole and by zip code were downloaded from the King County COVID-19 dashboard (Feb 22, 2022 update). Population data for King County and Vashon are from the April 2020 US Census. Zip code level population data are the average of two zip code tabulation area estimates from the WA Office of Financial Management and Cubit (a commercial data vendor providing access to US Census information). The Asset Limited, Income Constrained, and Employed (ALICE) metric, a measure of the working poor, was obtained from United Way.

The New York City (NYC) Heat Vulnerability Index (HVI) is a measure of how the risk of heat-related illness or death differs across neighborhoods. Neighborhood risk factors that increase heat- vulnerability in NYC are: less home air conditioning less green space hotter surface temperatures and more residents who are low-income or non-Latinx Black. Differences in these risk factors across neighborhoods are rooted in past and present racism.HVI is calculated by summing the z scores of the following variables and then assigning the sum to quintile (1-5, with 5 being highest risk of death during heat events):• Median household income, (American Community Survey 2016-2020 5-year estimates)• Percent vegetative cover (trees, shrubs or grass) (2017 LiDAR, NYC DOITT)• Percent of population reported as Non-Hispanic Black on American Community Survey (2016-2020 5-year estimates)• Average surface temperature data from the NASA’s ECOSTRESS (2020)• Percent of households reporting Air Conditioning access, Housing ad Vacancy Survey, 2017This updated HVI is using the 2020 census boundaries.MODZCTA- A shapefile for mapping data by Modified Zip Code Tabulation Areas (MODZCTA) in NYC, based on the 2010 Census ZCTA shapefile. MODZCTA are being used by the NYC Department of Health & Mental Hygiene (DOHMH) for mapping COVID-19 Data.