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Distressed areas are updated annually using the most recently available American Community Survey five-year estimates. Distressed areas for 2012 are presented at the 2000 census tract boundaries, and 2013-2022 areas are presented at the 2010 boundaries, 2023 areas are presented at the 2020 boundaries, and 2024-2025 areas are presented at the 2022 boundaries.
Percent changes are calculated by PolicyMap.
Personal income consists of income received in return for provision of labor, land, and capital used in current production, as well as other income such as personal current transfer receipts. It is the sum of wages and salaries, supplements to wages and salaries, proprietors’ income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and personal current transfer receipts, less contributions for government social insurance plus the adjustment for residence.
The annual data contains percent change calculations on the number of people in the labor force and employed, as well as a change in percent calculation of the unemployment rate. These were calculated by PolicyMap.
The Bureau of Labor Statistics’ gathers data on employment and wages from state workforce agencies and compiles it under the Quarterly Census of Employment and Wages (QCEW) program. The data are derived from quarterly tax reports submitted by employers to State workforce agencies under State Unemployment Insurance (UI) laws; and from Federal agencies under the Unemployment Compensation for Federal Employees (UCFE) program. PolicyMap displays only private sector employment and wage data.
PolicyMap displays QCEW annual location quotient data for both private and public sector employment. BLS uses location quotients, or LQs, to see where occupations or industries are concentrated in the United States. LQs measure how concentrated jobs in a particular industry is in a specific area compared to the national average. If an LQ is equal to 1, then the industry has the same share of its area employment as it does in the nation. An LQ greater than 1 indicates an industry with a greater share of the local area employment than is the case nationwide. For example, in Philadelphia County, the LQ for employment for Educational Services in the private sector is roughly 4. Educational Services makes up 8% of jobs in Philadelphia, compared to about 2% nationally. The concentration of employment in Philadelphia County in Private Educational Services is about 4 times that of the concentration of employment for Educational Services in the U.S. This industry makes up a larger share of the Philadelphia County employment total than it does for the nation as a whole. For more information on QCEW LQ calculations, visit https://www.bls.gov/cew/about-data/location-quotients-explained.htm
BDS data is available at the state and CBSA level. To view CBSA data, select “Metro Area” from the “Shaded by” menu in the map legend.
For clarification of Census variable definitions please refer to this list of subject definitions: https://www.census.gov/programs-surveys/cps/technical-documentation/subject-definitions.html.
PolicyMap ZIP codes are licensed from Precisely. See: Precisely ZIP Code Boundaries.
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LODES data are released at the 2020 Census tabulation block geographies and PolicyMap aggregated up to the larger geographies using Census provided relationship tables. PolicyMap displays all LEHD data at the TIGER 2010 boundary geographies, except CBSAs, which are shown at 2019 geographies, and congressional districts, which are shown at the 116 th congressional districts.
The Census Bureau releases an anonymized sample of the American Community Survey person and household survey responses as “microdata.” This microdata can be used to calculate estimates not available in the published American Community Survey data. To preserve anonymity, the Public Use Microdata Sample (PUMS) data is only available at a distinct large geography called a Public Use Microdata Area (PUMA), as well as the state level. PolicyMap calculates estimates for custom client reports using PUMS 5-year data. Any estimates at geographies other than the PUMA- or State-level were arrived at by combining survey results from PUMAs that overlap a given area. The PUMAs used in each calculation appear in footnotes.
PolicyMap calculated predominant tenure using Census’ American Community Survey estimates to identify whether most occupied housing units in a given geography are owner-occupied or renter-occupied.
For race and ethnicity breakdowns, the measures are calculated separately for occupied housing units with householders who identify as Hispanic or Latino, and for the following seven Census racial groups reported alone: White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander, Some Other Race, and Two or More Races (Tables B25003A–I). For example, “Predominant Tenure – Asian” indicates whether owner-occupied or renter-occupied units are more common among occupied housing units with a householder who is Asian.
Predominant tenure demographics rankings, calculated across all occupied housing units, show the racial and ethnic composition within the overall predominant tenure category for each geography.
Data are sourced from the U.S. Census Bureau’s American Community Survey 5-Year Estimates (Tables B25003 and B25003A–I). At PolicyMap, predominance is determined by ranking the percentage of owner-occupied and renter-occupied households within a given geography; the category with the higher percentage is reported as predominant.
Percent change compares the count of occupied housing units in the predominant tenure category between two ACS 5-year periods. It is only calculated when the same tenure type remains predominant across both periods, since counts of owner-occupied and renter-occupied units cannot be directly compared when the predominant category shifts. The measure calculates the growth or decline in the number of owner-occupied or renter-occupied units within a stable tenure category over time. It also indicates if the predominant housing tenure type has flipped, when owner- and renter-occupied units are tied, or when a racial or ethnic group is newly measurable or falls below the minimum threshold. A suppression threshold of 50 occupied housing units is applied to reduce noise and ensure more reliable comparisons across geographies and racial and ethnic groups. Migration for race and ethnicity groups is only flagged when there were at least 50 occupied housing units with householders in a given or previous time frame, to avoid overestimating and to account for margins of error in the data.
PolicyMap downloaded the 2000, 2010, 2008-2012, 2015-2019 data at the 2010 tract boundaries from the LTDB . The LTDB, developed by a research team including John Logan (Brown University), Zengwang Xu (University of Wisconsin, Milwaukee), and Brian Stults (Florida State University), provides public-use tools to create estimates within 2010 tract boundaries for tract-level Census data that are available as far back as 1970. For a detailed explanation of the LTDB team’s methodology for harmonizing data over this period, please see their website . Further explanation of the methodology can also be found in the Professional Geographer’s article entitled “ Interpolating US Decennial Census Tract Data from as Early as 1970 to 2010: A Longitudinal Tract Database ,” which was authored by Loan, Xu, and Stults.
On PolicyMap, the data is also given as an “estimate”. For each industry, at every geography, (including Zip code) CBP provides values for the number of establishments that fall in various ranges of number of employees. These ranges are 1-4 employees, 5-9, 10-19, 20-49, 50-99, 100-249, 250-499, 500-999, and 1000 or more. There are additional higher ranges at the county and CBSA level. PolicyMap takes the midpoint of each range (so, for 10-19 it would be 14.5) and multiplies that by the number of establishments. Establishments with 1,000 or more employees are given a value of 1,750. This number for each range is added together to get the estimate. Though this number is less precise, its advantages are that there are no longer suppressions (so there is better coverage on the map), and ZIP code data is included.
Building permit data will not accurately reflect construction activity in those areas where building permits are not issued. Nationally, only roughly 2 percent of housing starts are issued in areas not requiring permits, however this varies greatly state to state and region to region.
The United States Census Bureau’s Planning Database (PDB) was designed as an aid for survey and census planning purposes. It contains information on the mail return rate for the 2010 census, self-response rate for the 2013-2017 ACS, and Low Response Score, all of which may be helpful in targeting areas for a higher response rate in future counts and surveys. In the 2010 census, households that did not return their census form were contacted through a non-response follow-up (NRFU), which utilized various methods to count these households. Non-response follow-ups require more resources than mail returns, so this data can be used to strategically increase mail returns in certain areas.
The researchers used demographic data from the 2000 and 2010 Census short forms, combined with data from the 2000 Census long form and 2015 American Community Survey. They linked this Census data with tax returns from 1989, 1994, 1995, and 1998 to 2015. By combining all this data at the person level, they were able to match people who were born from 1978 to 1983 with the census tracts where they were born and raised, and with the household earnings of their parents. They used this longitudinal dataset to calculate the incarceration rate (per 100 people) for people raised in households with incomes less than the 25th percentile based on whether they were in jail or prison on April 1, 2010, the reference date of the 2010 Census for two genders—men and women—and three racial and ethnic groups—Black, Hispanic, and White. Average household income was also calculated for this age cohort and parental income group using the income data from the tax returns. Incarceration rates and average incomes were prorated based on how much time the person spent in a given tract or county in their youth, and information was suppressed for areas with fewer than 20 children. Some noise was also infused into the source data to preserve privacy.
The U.S. Census provides annual survey data on public school finances. Data is available at school district geographies. It includes the following indicators: student enrollment, total elementary-secondary revenue, total revenue from federal sources, total revenue for Title I, total revenue for children with disabilities, total revenue for child nutrition act, total revenue from state sources, total revenue from local sources, total elementary-secondary expenditures. Rates calculated by PolicyMap (such as revenue per student) were suppressed for school districts with 0 students in the given year.
PolicyMap calculated the diversity index and predominant race/ethnicity data layers using Census’ American Community Survey estimates. For both the diversity index and predominant race/ethnicity, PolicyMap used a total of 8 non-overlapping racial and ethnic categories provided by the US Census Bureau. These included the ethnic category Hispanic and the following 7 Non-Hispanic racial categories: White, African American, American Indian or Alaska native, Asian, Native Hawaiian or Pacific Islander, some other race, and two or more races. For predominant race, PolicyMap used 7 non-overlapping racial categories: White, African American, American Indian or Alaska native, Native Hawaiian or Pacific Islander, some other race, and two or more races.
The diversity index reflects the probability that any two people chosen at random from a given study area (e.g., block group) are of different races or ethnicities. An index value of 0 indicates complete homogeneity (i.e., an area’s entire population belonging to one racial or ethnic group), while the maximum index value represents complete heterogeneity (i.e., each racial or ethnic group constituting an equal proportion of an area’s population). The maximum value is calculated as one minus the reciprocal of the number of racial or ethnic groups. For example, with 3 racial or ethnic groups, the index value reflecting complete diversity would be 1-(1/3) or 67%. Articulated further, with 3 racial groups of equal proportions (i.e., complete diversity), the index equation becomes 1 – (0.33^2 + 0.33^2 + 0.33^2) = 67%. Given 8 racial or ethnic categories, the maximum value of the index displayed on PolicyMap is 87.5%.
With the diversity index data layer, lower index values between 0 and 20 suggest more homogeneity and higher index values above 50 suggest more heterogeneity. Racial and ethnic diversity can be indicative of economic and behavioral patterns. For example, racially and ethnically homogenous areas are sometimes representative of concentrated poverty or concentrated wealth. They could also be indicative of discriminatory housing policies or other related barriers.
The predominant racial or ethnic group is calculated as the racial or ethnic group constituting the highest proportion of the population in a given geography. A “tie” was noted in cases where the more than one racial or ethnic group shared the highest percent of population, rounded to the nearest 0.1 percent.
Geographies for which no data or limited data were provided or for which the population was less than 10 are represented as having “Insufficient Data.”
The Census’ Small Area Income & Poverty Estimates (SAIPE) dataset provides more current estimates of selected income and poverty statistics than the most recent decennial census. Estimates are created for states, counties, and school districts, depending on the data. This dataset mainly serves administrators of federal programs who need current statistics on the demonstrated need of places.
The Census Bureau identifies urban areas (UAs) following every decennial census. For the 2020 decennial census, UAs are defined as a densely settled core of census blocks that encompasses at least 2,000 housing units or has a population of at least 5,000. UAs also include adjacent territory containing non-residential urban land uses. The Rural designation encompasses all areas not included within an UA. The Urban and Rural Classifications from the Census Bureau are released at the Census Block level. The indicators in PolicyMap are represented at the Census Block Group and Census Tract Level. Areas are identified as urban if a specified threshold of land area sits within the Census designated UA – 50 percent threshold or 80 percent threshold. These indicators were developed by PolicyMap through a spatial join of Block Group and Census Tract boundaries to Census UA boundaries.
Occasionally, states will overreport or underreport vaccine administration and distribution causing “Insufficient Data” to appear on the map for Change in number of people who are fully COVID-19 vaccinated, and Percent change in people who are fully COVID-19 vaccinated indicators.
This data differs from the data shown in the July 2017 issue of CDC Vital Signs, which featured different facets of opioid prescribing from 2006 to 2015. For more information visit https://www.cdc.gov/vitalsigns/opioids/index.html .
The CDC’s PLACES program is an expansion of the small area health estimates that they created for their 500 Cities program starting in 2015. The 500 Cities program used statistical techniques to produce estimates for various health outcomes or risk factors at small areas for the largest 500 Cities in the United States. The PLACES program expands these estimates across the country, and makes the data available for Census tracts, ZCTAs, Census places (called Cities on PolicyMap), and Counties. These estimates were created using the CDC’s Behavioral Risk Factor Surveillance System (BRFSS), the Decennial Census, and estimates from the American Community Survey.
The CDC publishes both crude and age-adjusted estimates. Many of the chronic health conditions and risk factors estimated in this dataset have a strong correlation with age. This means that areas with much older populations may have deceptively high crude rates of illness. Age-adjusted estimates correct for the different age profiles of different geographies, which makes it easier to understand whether certain health conditions are truly worse in certain areas. Crude rates are available at the Census tract, ZCTA, City, and County levels, and Age-adjusted rates are available at the City and County levels.
PLACES measures include the following chronic conditions: arthritis, asthma, heart disease, high blood pressure, cancer, high cholesterol, kidney disease, COPD (lung disease), depression, diabetes, obesity, all teeth lost, and stroke. Prevention: lack of health insurance, routine medical checkups, dental visits, high blood pressure medication, cholesterol screening, mammography, cervical cancer screening, colonoscopy, core clinical preventive services for male and female older adults. Risk behaviors: binge drinking, smoking, no physical activity, limited sleep. Health-Related Social Needs (only available for 2022-):social isolation, food stamps, food insecurity, housing insecurity, utility services threat, transportation barriers, and lack of social and emotional support. Health status: poor mental health, poor physical health, poor self-rated health. Disability Prevalence (only available for 2021-): cognition, mobility, self-care, independent living, vision, and any disability.For more details on measures:
Beginning in 2007, data are reported from the 2003 U.S. standard Certificate of Live Birth, with additions of information on birth anomalies and several less variables related to maternal risk factors than the previous 2003 revision.
New York City reports flu data to CDC separately from New York State; as such, New York State flu activity and geographic spread measures do not take New York City into account.
Data in the HHI come from the Centers for Disease Control and Prevention (CDC), the National Emergency Medical Services Information System (NEMSIS), the United States Census Bureau, the Multi-Resolution Land Characteristics Consortium (MRLC), and the Environmental Protection Agency (EPA).
For more information on the data visit https://wonder.cdc.gov/mcd-icd10.html .
CDC provides estimates on new HIV diagnoses for a given year. HIV data for a given year presented on PolicyMap represent the number of confirmed diagnoses of HIV infection or infection classified as stage 3 (AIDS) confirmed by laboratory analysis as of December 31 of that year and reported to the CDC by June 30 of the following year. These data may not be limited to new infections (i.e., incidence); rather, these data represent new diagnoses, as the date of infection may vary by individual. Estimates are statistically-adjusted values based upon actual case counts reported to CDC by state and local health departments. The CDC suppressed values in areas with fewer than 5 reported cases and/or population less than 100 as well as in the county with the lowest population in states where only one other county’s data were suppressed.
Data on the number of new cases of chlamydia, gonorrhea, and syphilis reported each year, and the rate of new STD cases reported for every 100,000 residents, by state and county are available from CDC. Data are based on cases of STDs reported to state and local health departments. Data is reported by both public and private agencies, such as STD clinics, counseling/testing sites, drug treatment clinics, family planning clinics, and private physicians. The CDC collects data from regional jurisdictions, and publishes the data in an annual report, which can be downloaded here: https://www.cdc.gov/std/stats/default.htm .
Syphilis is presented as a combined sum of cases classified in either primary or secondary stages of the disease. Other categories of syphilis – not included in the data – are latent (without symptoms), tertiary (late stage), and congenital (transferred from mother to child). Primary and secondary forms of the disease are the most infectious and therefore important when considering the risk of transfer and spread of disease.
Some variability in the amount of in the amount of reporting may exist across the country. Chlamydia, gonorrhea, and syphilis are considered Nationally Notifiable , which means that regional jurisdictions provide information to the CDC on a voluntary basis. A nationally notifiable disease is not necessarily reportable by law within a given state. Because of incomplete diagnosis and reporting, the number of STD cases reported is less than the actual number of cases occurring. The level of consistency may vary between local jurisdictions, reporting agencies, and reporting years. In some areas, reporting from public sources is thought to be more complete than reporting from private sources.
Incidence rates were calculated by the CDC using total population as the denominator. The population denominators used to compute these rates for the 50 states and the District of Columbia were based on the U.S. Census Bureau population estimates utilizing the OMB compliant race categories. Each rate was calculated by dividing the number of cases for the calendar year by the population for that calendar year and then multiplying the number by 100,000 Ten-year percent change variables for incidence of STDs and five-year percent change for HIV incidence were calculated by PolicyMap.
Starting with 2019 data, STI data presented in the AtlasPlus follows the 2017 Council of State and Territorial Epidemiologists (CSTE) data re-release rules. DSTDP suppresses data at any geographic level based on the following 2 conditions:
The current SVI uses 16 U.S. Census variables from the 5-year American Community Survey (ACS) to identify communities that may need support during or after disasters. These variables are grouped into four themes representing major areas of social vulnerability, which are then combined into a single measure. The GRASP program assigned each geography a percentile ranking for each variable and calculated an overall score for each category by summing these percentiles. Each of the four themes is then assigned a percentile ranking, which contributes to the overall Social Vulnerability Index. Based on the overall SVI score, communities are classified into four vulnerability levels: Low, Low to Moderate, Moderate to High, and High, dividing all tracts or counties into quantiles. A percentile ranking represents the proportion of tracts (or counties) with equal or lower vulnerability than a given tract or county. For example, a ranking of 0.85 signifies that 85% of tracts (or counties) in the state or nation are less vulnerable, while 15% are more vulnerable. Find more information on the CDC’s Social Vulnerability Index at https://svi.cdc.gov/ .
The predominant chronic conditons was calculated from CDC’s PLACES data. The same twelves chronic conditions that went into the chronic condition prevalence index were also used. The predominant chronic condition in a given geographic area is the one that accounts for the highest percentage of individuals that experience that chronic condition.
For 2017-2021 data, Indiana did not meet publication criteria and was excluded from the analysis. County data are not available from Kansas because state legislation and regulations prohibit the release of county-level data to outside entities.
For 2016-2020 data, Indiana and Nevada did not meet publication criteria and was excluded from the analysis. County data are not available from Kansas and Minnesota because state legislation and regulations prohibit the release of county-level data to outside entities. County data from Virginia are suppressed due to incomplete data. Due to a coding issue reported by North Dakota and Wisconsin, state- and county-specific counts and rates by race and ethnicity are not presented for North Dakota. For Wisconsin, state- and county-specific counts and rates are not presented for Hispanic persons. These data for all races and ethnicities are included in national rates.
For 2015-2019 data, Nevada did not meet publication criteria and was excluded from the analysis. County data are not available from Kansas and Minnesota because of state legislation and regulations which prohibit the release of county-level data to outside entities. In addition, Kansas opted not to present state- and county-specific Asian and Pacific Islander counts and rates for these years. The national rates presented include data for Kansas. Please visit U.S. Cancer Statistics data website for detailed technical documentation.
The Environmental Justice Index uses data from the U.S. Census Bureau, the U.S. Environmental Protection Agency, the U.S. Mine Safety and Health Administration, the U.S. Geological Survey, OpenStreetMap, the U.S. Department of Transportation, and the U.S. Centers for Disease Control and Prevention to rank the cumulative impacts of environmental injustice on health for every census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. The EJI ranks each tract on 36 environmental, social, and health factors and groups them into four overarching modules and ten different domains. The overall EJI score is calculated by summing the ranked scores of three modules: the Social Vulnerability Module, the Environmental Burden Module, and the Health Vulnerability Module. The EJI ranking is produced using this cumulative score. Newly released in 2024, the EJI + Climate Burden rank is calcualted using an equation that combines percentile ranks for the Social Vulnerability Module, the Environmental Burden Module, the Health Vulnerability Module, and the Climate Burden Module.
Overall EJI Scores are percentile ranked to produce a final EJI Ranking with a range of between 0 – 1. A percentile ranking represents the proportion of tracts (or counties) that are equal to or lower than a tract of interest in environmental burden. For example, a EJI ranking of .85 signifies that 85% of tracts in the nation likely experience less severe cumulative impacts from environmental burden than the tract of interest, and that 15% of tracts in the nation likely experience more severe cumulative impacts from environmental burden. Due to a lack of scientific evidence supporting a specific weighting scheme, all modules are weighted equally in calculating the Overall EJI Score. This method of equal weighting for all modules aligns with that used by the Environmental Justice Screening Method (Sadd et al.,2011). EJI can be used for identifying areas that may need additional attention or resources to improve health and equity. This tool can help characterize local factors contributing to cumulative health impacts to inform policy and decision-making. Note that the legend for EJI indicators are displayed using non-standard quartile-like breaks where
EJI does not include measures for Alaska, Hawaii, or U.S. territories and dependencies due to a lack of data for these states/territories. The census tract boundaries used are based on the 2020 decennial census.
Chronic health condition data is based on CMS administrative enrollment and claims data for Medicare fee-for-service beneficiaries. A Medicare beneficiary is considered to have a chronic condition if there is a CMS claim indicating that the beneficiary received a service or treatment for that specific condition. Beneficiaries may have more than one of the chronic conditions listed.
In March 2022, the CMS Chronic Conditions Warehouse released an updated algorithm for their Chronic Condition indicators. Due to this algorithm update, some Chronic Conditions indicators may experience significant changes to prevalence rates (i.e., percentages). This update applies to 2021 data and forward. For more information, please visit CMS Mapping Medicare Disparities Tool Technical Documentation. Please note that Alcohol Use Disorder and Drug Use Disorder conditions are available for years 2007-2018, and 2021 due to CMS’s data availability.
CMS demographics, spending, and service utilization data available on PolicyMap comes from CMS’s Fee-for-Service Geographic Variation Public Use File (FFS GV PUF). This public use file is based primarily on information from CMS’s Chronic Conditions Data Warehouse.
In May 2024, CMS released 2022 data for the FFS GV PUF and updated historic data for years 2014-2021. This is due to CMS using different classification systems to define physician services between the 2014-2022 and 2007-2013 data years. For more information on methodology changes, please visit the FFS GV PUF Technical Documentation May 2024 Update.
All dollar amounts in this data set are standardized by CMS to adjust for factors that result in different payment rates for the same service, including local variations in wages and payments Medicare makes to hospitals to advance program goals (including training doctors). The standardized values represent what Medicare would have paid in the absence of those adjustments. Because the state of Maryland is exempt from reporting special payments to Medicare, costs in Maryland were standardized using different factors than the nationwide model.
This dataset is available on PolicyMap as point data based on hospital location, and can be viewed upon clicking each respective point. The CMS Hospital Compare data was joined by PolicyMap to hospital locations using data from HRSA. HRSA hospital location data can be found here .
As of the 2025 data release, Medicaid opioid claims are available at the zip, county, and state level. However, data may be sparse for smaller geographies due to CMS data processing methods. County-and zip-level measures presented in this tool were aggregated from the Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF). In order to be included in this tool, state-level TAF data had to meet quality checks for each data year. Due to known data quality issues in prior years, TAF data is presented only for data years 2016 and onward. In order to protect the privacy of beneficiaries, suppression is applied to the data. At each aggregated geographic level, any derived opioid claim counts between 1 and 10 are suppressed (replaced with missing a.k.a. Insufficient Data values). Secondary suppression is applied in cases where a single subgroup category is primary suppressed.For example, if the Fee-for-Service opioid claim count for a given state is primary suppressed but the Managed Care opioid claim count is not primary suppressed, then the Managed Care opioid claim count for that state must be secondary suppressed to prevent disclosure of the Fee-for-Service opioid claim count suppressed value. Secondary suppression prevents backing into a primary suppressed value by using the values from the other opioid claim counts such as total opioid claim count. These claims include all types of opioids.
In addition, PolicyMap has incorporated data from CMS’ PY 2022 Clinician Public Reporting: Overall MIPS Performance dataset to include facility-level information on the MIPS scores of providers at a location. CMS calculates MIPS scores at both the individual and group levels; PolicyMap uses only group-level scores to better reflect the collective performance of providers at a facility. When multiple groups practice at the same location, their scores are averaged.
Each point on the map is labeled with either the name of the facility where the providers are located or, when there is only a single provider, the provider’s name. When providers of different sub-specialties practice at the same facility, multiple points may appear at the same location.
AHRQ defines a health system as follows: “A health system includes at least one hospital and at least one group of physicians that provides comprehensive care (including primary and specialty care) who are connected with each other and with the hospital through common ownership or joint management.” Additionally, a health system must include at least one non-federal acute care hospital, at least 50 physicians, and at least 10 primary care physicians.
Organizations in the source are aggregated to their highest level of ownership, meaning subsidiary systems of larger national systems are not listed separately. As a result, AHRQ notes that not all health systems operating in certain states or local markets may appear in the source. For more information on these files and AHRQ’s methodology for identifying health systems, please visit: https://www.ahrq.gov/chsp/data-resources/compendium.html .
The Medicare chronic condition prevalence index rating was created by following the CDC’s guidelines for creating estimates of high prevalence of multiple chronic diseases. Nineteen CMS Medicare chronic diseases were included: Alzheimer’s disease, arthritis, asthma, atrial fibrillation, autism spectrum disorder, cancer (excluding skin), COPD, depression, diabetes, heart disease, heart failure, hepatitis (chronic viral B & C), high cholesterol, hypertension (high blood pressure), HIV/AIDS, chronic kidney disease, osteoporosis, schizophrenia, and stroke. These county-level chronic disease prevalence estimates come from the CMS medicare chronic conditions data set. A composite score was calculated by ordering each disease estimate and assigning a score of 0 to counties in the bottom 25th percentile, 1 for counties in the middle (25th to 75th percentiles) or 2 to those in the top 25th percentile. These scores were summed so each county has a score range of 0-24. This data was then rated as “lowest” if it was in the bottom 25th percentile, “moderate” if in the middle percentile (25-75 percentile) and “highest” if in the top 25th percentile.
Equitable Recovery Program: The Community Development Financial Institutions Fund awards Community Development Financial Institutions (CDFIs) grants for economic recovery from the COVID-19 pandemic. The CDFI Fund designates census tracts as eligible for these grants based on the following criteria: (a) are census tracts that (i) demonstrate “severe impact” of the COVID-19 pandemic, and (ii) have a median income at or below 120% of the Area Median Income, and (iii) are CDFI Investment Areas; or (b) are Native Areas.
Severe COVID Impact/Low Community Resilience designation is given to census tracts that demonstrate any of the following: severe mortality (being in the highest tercile of the number of deaths per 100,000 people, according to reported cumulative mortality for the period from April 1, 2020 to March 31, 2021), severe change in unemployment or low community resilience (based on data from the US Census Bureau Community Resilience Estimates Program). CDFI Investment Areas are geographies which have a population poverty rate of at least 20% (including Persistent Poverty Counties, which are counties that have had 20% of more of their populations living in poverty over the past 30 years),or have unemployment rates of at least 1.5 times the national or other criteria listed by CDFI Fund.
The Community Development Financial Institution (CDFI) Fund, a division of the US Department of the Treasury, administers the New Markets Tax Credit (NMTC) and Bank Enterprise Award (BEA) programs, and supports and invests in Community Development Financial Institutions. For information about the NMTC, please see entry, below. The CDFI Fund maintains a list of Census Tracts and their program eligibility or designation, based on income, poverty and unemployment data provided by the Census Bureau’s 2016-2020 American Community Survey (ACS) for 2020 census tracts. For more on these programs users should consult the CDFI Fund website directly: www.cdfifund.gov .
CDFI Fund New Markets Tax Credit NMTC Eligibility NMTC Eligible Census tracts include those that have either (1) Median Family Income at or below 80% of Area Median Income (AMI) in the period of 2016-2020 or (2) Poverty Rate of 20% or greater in the period of 2016-2020. PolicyMap provides a map of those eligible Census tracts (“Eligible Tracts”), as well as the underlying data used to create that map in the (“Eligibility Criteria”). PolicyMap also provides the underlying data without the NMTC thresholds (“Tract Family Income as % of AMI” and “Poverty”).
Severe Distress, Deep Distress, High Migration Rural County, and Non-Metropolitan Meeting the NMTC Severe Distress, Deep Distress, High Migration Rural County, or Non-Metropolitan criteria is based on whether or not a given Census tract meets basic NMTC Eligibility, plus one of the following factors:
Severe Distress : having a median family income at or below 60% of AMI in the period of 2016-2020; having a poverty rate at or above 30% in the period of 2016-2020; having an unemployment rate of at least 1.5 times the national unemployment rate in the period of 2016-2020;
Deep Distress : having a median family income at or below 40% of AMI in the period of 2016-2020; having a poverty rate at or above 40% in the period of 2016-2020; having an unemployment rate of at least 2.5 times the national unemployment rate in the period of 2016-2020;
High Migration Rural County : having median family income at or below 85 percent of the applicable AMI in the period of 2016-2020; being in any county during the 20-year period ending with the year in which the most recent census was conducted, with a net out-migration of inhabitants from the county of at least 10 percent of the population of the county at the beginning of such period; high migration rural counties qualify as low-income communities;
Non-Metropolitan : being in a county that is not part of a metropolitan statistical area.
*The median family income threshold for NMTC, more specifically, is: Census tracts with, if located within a non-Metropolitan Area, median family income at or below 60% of statewide median family income or, if located within a Metropolitan Area, median family income at or below 60% of the greater of the statewide median family income or the Metropolitan Area median family income.
Other Criteria for NMTC: Other criteria can include two of the following: meeting NMTC Heavy Distress requirements; being located within: an SBA Designated HUB Zone, a Medically Underserved Area (MUA), a Census tract within which a Brownfield is located, a HOPE VI Redevelopment Area, a Federal Native Area, an Appalachian Regional Commission or Delta Regional Authority Area, a Colonias Area, a State or Local Economic Zone (such as TIF or KOZ), a FEMA Disaster Area, or a ERS/USDA Food Desert. Please note that the data on PolicyMap do not take into account the following, due to unavailability of data: HOPE VI Redevelopment Areas, Federal Native Areas, Colonias Areas, State or Local Economic Zones, and FEMA Disaster Areas. Included in this submenu are the data for each of the available factors that constitute the Secondary Criteria for NMTC Severely Distressed.
Because any of these data sources may have been updated since the production of these calculations, users should verify eligibility directly with the CDFI Fund. Information in PolicyMap does not include HOPE VI Redevelopment Areas, Federal Native Areas, Colonias Areas, or State or Local Economic Zones. FEMA Disaster Areas are accessible under Federal Guidelines.
NMTC Eligibility and Qualified Opportunity Zones A joint dataset that includes both NMTC eligibility and designated Qualified Opportunity Zones. Census tracts labeled as “Designated OZ” are census tracts that have been nominated and designated as a Qualified Opportunity Zone (QOZ), according to the CDFI Fund and census tracts labeled as “NMTC Eligible” are census tracts that meet the CDFI Fund’s New Markets Tax Credit (NMTC) eligibility for CY 2018. For more information see the directory entry for Qualified Opportunity Zones .
CMF grantees track the use of funds though periodic financial and project reports submitted to the CDFI Fund.
For more information about the Capital Magnet Fund data, see the CDFI Fund’s website here .
For CDFI transactions that span multiple census tracts or counties, medians are calculated using the total project cost while aggregations are calculated by dividing the total transaction cost by the number of census tracts or counties involved. Transaction or project counts at smaller geographies may not match larger geography counts given the double counting of split transactions and projects across census tracts and counties. Subcategories of transactions, such as CDFI Investments by Borrower Type, may not sum to the total amount of CDFI investments in a given area.
CDE Locations are no longer updated by the source. PolicyMap will remove this data in 2020 unless the dataset is updated or another source is found.
The Community Development Financial Institutions (CDFI) Fund is a division of the U.S. Department of Treasury. Per the Consolidated Appropriations Act of 2012, funding was provided for several CDFI Fund programs (Bank Enterprise Award Program, CDFI Program, Healthy Food Financing Initiative, and Native American CDFI Assistance Program) on condition that a minimum of 10% of the projects served must be in persistent poverty counties. The legislation defines a persistent poverty county as any county that has had 20 percent or more of its population living in poverty for the past 30 years as measured by the U.S. Census Bureau. Based on this criteria, the CDFI Fund used data from the 1990 and 2000 decennial censuses, and the 2016-2020 American Community Survey to determine qualifying counties.
The score is tabulated using various distress indicators, which are also mapped on PolicyMap. These indicators are: poverty rates, median household income, unemployment rates, home foreclosures and high-cost mortgages.
Up to five percent of tracts that are nominated for the QOZ can be qualified non-LIC as long as they are contiguous to a LIC and have a median family income that is not greater than 125 percent of the adjacent LIC. Eligible contiguous non-LIC tracts that are contiguous to a LIC in a different state are also eligible for nomination if the adjacent state nominates the LIC QOZ.
Claritas PRIZM® Premier classifies every U.S. household into one of 68 consumer segments based on household preferences for a broad range of products and behaviors. PRIZM Premier offers an extensive set of supplementary market research databases and links to third party data. This allows marketers access to a wealth of research, which can be used to pinpoint the products and services that their best customers are most likely to use. It is the wide scope of PRIZM Premier external links that allows marketers to construct a portrait of their customers.
Claritas uses industry standard modeling practices and a minimum number of demographic factors to assign households to a segment. PRIZM Premier was designed to classify households based on consumer purchasing behaviors. Thus, we use data that describe overall life stage such as presence of children and household size. The 14 Social Groups of PRIZM Premier are based on urbanicity and affluence, two important variables used in the creation of PRIZM Premier. Social Groups are fixed by urbanicity measures, but demographics such as income, education, and home value were used to group by affluence. While Social Groups are based on both affluence and the Claritas urbanicity measure, Lifestage Groups account for affluence and a combination of householder age and presence of children. Within three Lifestage classes, the 68 PRIZM Premier segments are divided into 11 Lifestage Groups.
PolicyMap aggregated the 68 consumer segments based on key demographic groupings provided by Claritas, including household income, household composition (e.g., presence of children), and age of householder. These demographic categories are not mutually exclusive, meaning they describe the predominant characteristics of each segment rather than strict definitions. To find the overall median income for each category, PolicyMap calculated the median of all the segment-level medians within each group.
For the full list and definitions of Claritas PRIZM® Premier segments, read more at
Percent and rate indicators have all been derived by PolicyMap. School locations, names, district names, level, type, and charter school status are all from the NCES CCD .
The Escrow Requirements under the Truth in Lending Act rule (known as the Escrows Rule) requires that certain creditors create escrow accounts for a minimum of five years for higher-priced mortgage loans (HPMLs), except HPMLs made by certain small creditors that operate predominantly in rural or underserved counties. Rural counties are defined by using the USDA Economic Research Service’s urban influence codes, and underserved counties are defined by reference to data collected under the Home Mortgage Disclosure Act (HMDA).
To identify tracts that are “designated disaster areas” consult the Federal Emergency Management Agency (FEMA) website: http://wwww.fema.gov . Disaster designations are also mapped on PolicyMap and can be found in the Federal Guidelines menu under FEMA Disaster Declarations.
Data obtained from the Convenient Care Association on March 31, 2017. Includes only members of the Convenient Care Association.
Data on racial disparities in COVID-19 cases and deaths was collected as part of a collaboration between The COVID Tracking Project and the Boston University Center for Anti-Racism. The researchers collected data on race and ethnicity of people diagnosed with COVID-19 and those who died of COVID-19 directly from state departments of public health. Because of variations across states in racial and ethnic categories, it is not always advisable to compare case or death rates of a given race or ethnicity with their prevalence within the local population. PolicyMap suppressed racial and ethnic population data for states flagged by the source as incomparable. Population data is from ACS 2014-2018 5-year estimates. Race and ethnicity categories are not mutually exclusive.
Census tracts are considered disadvantaged if they meet the thresholds for at least one of the CEJST categories of burden OR if they are on land within the boundaries of Federally Recognized Tribes. Meeting one of the CEJST categories of burden requires that a tract be at or above specified thresholds for one or more environmental, climate, housing, health or other burdens AND be at or above the threshold for an associated socioeconomic burden (eg. low income or low educational attainment). Additionally, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50th percentile for low income is also considered disadvantaged. Guam, the U.S. Virgin Islands, American Samoa, and the Northern Mariana Islands: For these U.S. territories, the tool uses the following data: unemployment, poverty, low median income, and high school education. These burdens are in the workforce development category. The CEJST uses a slightly different methodology to calculate the relevant percentiles for Guam, the U.S. Virgin Islands, American Samoa and the Northern Mariana Islands because the relevant data are from the 2010 American Community Survey, which is not used for the other regions. CEJST uses data from the U.S. Census’s American Community Survey (2015-2019) for all U.S. states, the District of Columbia, and Puerto Rico. For more information, please access the CEJST Technical Support Document.
HRR and HSA boundaries were created by the Dartmouth Atlas based on contemporary hospital and Medicare data. Hospital Service Area boundaries are available only for the contiguous United States. Hospital Referral Region boundaries include Alaska and Hawaii.
The Department of Homeland Security’s Yearbook of Immigration Statistics is an annual publication on documented foreign nationals in the United States. PolicyMap contains state and CBSA-level data on the number of people granted Legal Permanent Resident (LPR) status by region of birth and by selected countries. If the volume of immigrants receiving green cards in any year was more than 15,000 people, the country was included.
References in the data to “renewable” energy sources refers to energy from biomass, hydropower, geothermal, wind, and solar. This is consistent with how the EIA classifies renewable energy sources as outlined on their renewable sources webpage .
The United States’ Environmental Protection Agency (US EPA) provides a Median Air Quality Index (AQI) at the county level. The median AQI is based on the value for which half of daily AQI values during the year were less than or equal to the median value, and half equaled or exceeded it. Air quality is defined by the EPA as follows: good air quality ranges from 0-50; moderate air quality ranges from 51-100; unhealthy air quality for sensitive groups ranges from 101-150; and unhealthy air quality is 151 or higher, which includes the AQI categories of unhealthy, very unhealthy and hazardous.
PolicyMap also stamps brownfield site locations to Census Tract boundaries to produce a data layer showing tracts with a presence of brownfields.
EPA EJScreen is EPA’s environmental justice mapping and screening tool that provides EPA with a nationally consistent dataset and approach to present three kinds of information: environmental burden indicators, socioeconomic indicators and EJ/Supplemental Indexes. EJScreen combines demographic and environmental indicators to highlight places that may have environmental quality issues, higher environmental burdens, and vulnerable populations.
The EPA’s Safe Drinking Water Information System provides information on public water systems, and their quantity and types of violations of drinking water regulations. Using EPA guidelines, PolicyMap categorizes each violation as a health violation or a monitoring and reporting violation. The source data comes at the agency-level and water systems are identified as serving particular counties. PolicyMap uses the underlying source data to produce county-level indicators. Only water systems that serve 3,300 people or more are included. Violation counts are weighted by the number of people served by each water system to produce population weighted average indicators for each area.
To create the indicators on job or workforce accessibility by auto travel, the EPA joined an origin-destination matrix to employment and demographic data from the 2010 Census. Although the transit accessibility indicators were analyzed the same way as the auto accessibility, it was analyzed for evening peak travel period only, as this is normally the period of relatively intense levels of transit service.
Human exposure and groundwater migration information are environmental indicators based on metrics set by the EPA. These indicators are used to measure progress made through site cleanup activities.
The Federal Bureau of Investigation’s Uniform Crime Reporting (UCR) Program compiles standardized incident reports from local law enforcement agencies in order to produce reliable, uniform, and national crime data. The UCR Program collects data on known offenses and persons arrested by law enforcement agencies, however, it does not record the findings of a court, coroner, jury, or the decision of a prosecutor. Previously, PolicyMap received county and city level data directly from the FBI that was used to publish incidents and rates of crime for counties, places, and states. Due to this source no longer being available, PolicyMap has transitioned to using crime estimates from the UCR Program’s Crime in the United States (CIUS) reports to publish state and MSA level data.
The CIUS report provides data on the volume and rate of violent and property crime offenses for the nation and by state using Summary Reporting System data and summarized data from the National Incident-Based Reporting System (NIBRS). As the UCR Program is voluntary, offenses may be estimated by the UCR if an agency does not provide twelve months of complete data. The estimation process considers factors such as the following: population size covered by the agency; type of jurisdiction, e.g., police department versus sheriff’s office; and geographic location. The CIUS report provides crime statistics for all fifty states, Metropolitan Statistical Areas (MSAs), and Metropolitan Divisions (MDs), which are subdivisions of MSAs that have a core population of at least 2.5 million people. More information on the CIUS estimation process can be found in the CIUS report’s methodology.
On January 1, 2021, the FBI’s UCR Program transitioned from the Summary Reporting System to the National Incident-Based Reporting System (NIBRS) for crime data collection. Due to this transition, participation in 2021 remained below a statistically acceptable level to be estimated at a national level. Therefore, the UCR program chose to publish a limited release of the CIUS report which did not include state and MSA estimates of crime data. PolicyMap is monitoring the UCR program page to see if a supplemental version of this report is published.
Limited data for 2022 were available for Florida, Illinois, Maryland, and Pennsylvania. Limited data for 2023 were available for Florida. Due to data availability, The City of New York, NY is not included in the 2022 New York-Newark-Jersey City, NY-NJ-PA Metro Area.
The Hate Crime Statistics Program within the FBI’s (UCR) Program collects data regarding criminal offenses that were motivated, in whole or in part, by the offender’s bias against a race, ethnicity, ancestry, gender, gender identity, religion, disability or sexual orientation and were committed against persons, property, or society. Hate crime data is captured by including the element of bias in offenses already being reported to the UCR Program. State hate crime counts reflect the sum of all reported offenses from agencies within the state that submitted data to the FBI. The state population count used in the rate calculations is the total population of the state as reported in the Census’s Population Estimates Program. The State of Hawaii does not participate in the Hate Crime Statistics Program. Due to variation in reporting and hate crime definitions changing over time, FBI hate crime statistics should not be compared across states, and should not be compared from one year to another. An agency can report up to four bias motivation types per offense. Multiple-bias offenses are not common, but when they occur, they are double-counted in the value of the total number of hate crimes.
Wireless data are also provided at the block level. However, because wireless signals often do not conform to these boundaries, the source data indicates how much of the block is covered by a particular provider. Separate data is provided for 4G LTE and 4G non-LTE service. Because these two technologies provide a similar level of service, they are shown as a single indicator on PolicyMap. The area covered by 4G service represents whichever one of the two technologies covers the most area, not necessarily the total area covered by the two technologies. Similarly, the area covered by all wireless broadband service represents whichever one of 3G, 4G, and 4G LTE covers the most area.
The FDIC’s Summary of Deposits (SOD) is an annual survey of branch offices for all FDIC-insured institutions, including U.S. branches of foreign banks. Data is updated by the FDIC annually.
Like with all estimates derived from survey data, the values published in these data layers are associated with some uncertainty. In order to help users understand the reliability of the estimates, PolicyMap published margins of error at the 90% confidence level associated with each estimate. This means that there is a 90% likelihood that if every household in the given geography was interviewed, the count or percent would be within the margin of error above or below the estimate derived from the survey sample. For example, 4.5% of households in Colorado were unbanked, with a margin of error of 1.6%. This may be expressed as 4.5% ± 1.6%, which means that there is a 90% likelihood that the true percentage of unbanked households in Colorado was between 2.9% and 6.1% (expressing the margin of error in this way is known as a “confidence interval”).
PolicyMap has also published a data flag to help users interpret these margins of error. The data flags are based on the relative standard error, or the ratio of the standard error to the count or precent. The RSE is calculated by dividing the standard error of the estimate by the estimate itself, then multiplying the result by 100. Estimates considered “reliable” have standard errors that are 15% or less of the estimate. Estimates tagged “use with caution” have standard errors between 15% and 30% of the estimate. Esimates with standard errors greater than 30% of the estimate have been suppressed.
The annual indices for smaller geographies should be considered developmental. As with the standard FHFA HPIs, revisions to these indexes may reflect the impact of new data or technical adjustments. Indexes are calibrated using appraisal values and sales prices for mortgages bought or guaranteed by Fannie Mae and Freddie Mac. An index is not reported in cases where sample sizes on mortgage transactions are too small for a given geographic area. These indices were developed as part of FHFA Working Paper 16-01 (Bogin, Alexander N., Doerner, William M. and Larson, William D. (2019). “Local House Price Dynamics: New Indices and Stylized Facts”. Real Estate Economics, volume 47, issue 2, pages 365-398). The initial FHFA WP 16-01 is accessible at http://www.fhfa.gov/papers/wp1601.aspx .
The calculation for the Credit Insecurity Index involves adding the share of people who are not credit included to a value that represents the degree to which the population that is credit included is nonetheless credit constrained. In other words, the Credit Insecurity Index Score = (Not Credit Included) + (Credit Included x .25*(Credit Constraints)) where Credit Constraints = No Revolving Credit + Credit Over-Utilization + Deep Subprime Credit Score + Struggling or Consistently Delinquent Payment History.
On PolicyMap, the Credit Insecurity Index Score is available along with its two components: Not Credit Included and Credit Constrained. “Not Credit Included” is derived by subtracting the number of adults (18+) with credit files and credit scores from the total adult population in a given geography. The remainder is the estimated share of the adult population in the given geography that is not credit included. The second component, “Credit Constrained”, captures those adults who are credit included but also credit constrained due to the four sub-components described above. This value was calculated by finding the respective share of adults facing each of the credit-constraining components and taking the simple average, scaled by the share of people who are credit included in that geography. As explained by the Federal Reserve Bank of New York, “By breaking down the score into the two components, [we see] that the drivers of credit insecurity can vary considerably across states. For example, [in 2023] South Carolina and Hawaii have virtually identical scores overall. However, in Hawaii, around 13% of adults do not have a credit score or file, and the weighted share of the population that is credit constrained is 8%, whereas in South Carolina the numbers are flipped: just 5% of adults do not have a credit score or file and the average of the credit-constraining components is more than 16%. This has implications for what may be driving credit insecurity and what programs may ameliorate it (credit building versus credit repair programs, for example).”
Each credit constraint can be summarized below:
Lastly, the Credit Insecurity Index scores are assigned to tiers of relative severity: Credit Assured, Credit Likely, Mid-Tier, Credit At Risk, and Credit Insecure. These ranges are determined by using the quintiles of county-level Credit Insecurity Index scores. According to the Federal Reserve Bank of New York, 2018 is used as the base year of calculation, meaning the tiers are based on the county-level Index scores from that year. This allows the number of counties in each tier to change in subsequent years. The Tiers and the Index Score Ranges are defined as follows:
For detailed documentation, visit read the 2018-2023 report from the Federal Reserve Bank of New York.
Feeding America’s Map the Meal Gap analysis is published annually to assess food insecurity and food costs across the United States, with estimates modeled at national, state, county, and congressional district levels. The data can help illustrate how food insecurity varies across different communities and population groups.
Feeding America uses data from the Current Population Survey (CPS), American Community Survey (ACS), Bureau of Labor Statistics (BLS), and NielsenIQ. The relationship between food insecurity and factors such as poverty, unemployment, homeownership, and disability is modeled at the state level, then applied to local demographic and economic conditions.
More detail on Feeding America’s methodology can be found in the technical brief: https://www.feedingamerica.org/research/map-the-meal-gap/how-we-got-the-map-data
Source: Ribar, D.C., Harris, V., Dewey, A., Dawes, S., and Engelhard, E. (2025). Map the Meal Gap: An Analysis of Local Food Insecurity and Food Costs in the United States in 2023 . Feeding America National Organization.
PolicyMap receives updates annually from FEMA, and classifies areas based on the Designations of FEMA Flood Zone Designations . Not all counties are included in the NFHL. A coverage map can be found at FEMA’s website . Counties which show data available on the FEMA coverage map do not necessarily have complete coverage.
Federal disaster areas are places where the state or tribal government has requested and received federal assistance to protect the public’s health and safety in an emergency. After a governor seeks a presidential disaster declaration, FEMA conducts a preliminary damage assessment before recommending a decision to the president. Factors influencing the declaration of a federal disaster include the amount and type of damage, impact on infrastructure or critical facilities, imminent threats to health and public safety, impacts to essential government services and functions, unique capability of the Federal government to provide resources and available assistance from other sources, dispersion or concentration of damage, level of local insurance coverage, state and local resource commitments from previous events, and the frequency of recent disaster events.
FEMA provides disaster funding though four programs: Individuals and Households, Individual Assistance, Public Assistance, Hazard Mitigation. For more information on the programs, visit FEMA’s website . Each declaration area is assigned a sequential disaster number. Disaster numbers are unique to states. Disasters indicate both the dates the incident itself began and ended, as well as the date of the disaster declaration and the date all financial transactions for all programs are completed (the closeout date). Disaster areas are displayed as groups of counties and/or Indian areas.
Federal disaster declarations are only displayed on PolicyMap since 2012, and do not include disasters declared before January 1, 2012 or after August 22nd, 2025. Please visit www.fema.gov/disasters for a comprehensive list of current and/or historical disaster declarations.
Following a disaster declaration, two programs utilized by FEMA to provide funding are the Individuals and Households (IHP) and Public Assistance (PA) programs. The IHP program provides financial and direct services to eligible individuals and households affected by a disaster, who have uninsured or under-insured necessary expenses and serious needs. IHP assistance may include funds for temporary housing, repairing or replacing owner properties, or hazard mitigation assistance. The PA Program provides supplemental grants to state, tribal, territorial, and local governments, and certain types of private non-profits so communities can quickly respond to and recover from major disasters or emergencies. Projects can cover activities such as debris removal, emergency protective measures, and restoring public infrastructure.
IHP and PA data is published at the applicant and project level, which is aggregated by PolicyMap to produce sums and counts at the county, state, and zip (IHP only) level. PolicyMap displays this data as both individual years and multiyear ranges. Individual years may be excluded if no disasters occurred at any geographies in a given year.
The National Risk Index is a dataset and online tool to help illustrate the United States communities most at risk for 18 natural hazards. It was designed and built by FEMA in close collaboration with various stakeholders and partners in academia; local, state and federal government; and private industry.
In the National Risk Index, risk is defined as the potential for negative impacts as a result of a natural hazard. The risk equation behind the National Risk Index includes three components: a natural hazards risk component, a consequence enhancing component, and a consequence reduction component. Expected Annual Loss (EAL) is the natural hazards risk component, measuring the expected loss of building value, population, and/or agriculture value each year due to natural hazards. Social Vulnerability is the consequence enhancing component and analyzes demographic characteristics to measure the susceptibility of social groups to the adverse impacts of natural hazards. Community Resilience is the consequence reduction component and uses demographic characteristics to measure a community’s ability to prepare for, adapt to, withstand, and recover from the effects of natural hazards. The Social Vulnerability and Community Resilience components are combined into one Community Risk Factor (CRF) which is multiplied by the EAL component to calculate risk.
The 2004-2011 data is at the 2000 Census boundaries. The 2012-2019 data is at the 2010 boundaries. The 2022 data and beyond is at the 2020 boundaries. For percent changes, PolicyMap created a bridge table across 2000, 2010, and 2020 geographies in order to calculate previous years of data at proper Census boundaries. These previous years of data calculations are used for comparison to the current year of data.
In HMDA, loans guaranteed by the USDA Farm Service Agency (FSA) and those guaranteed by the USDA Rural Housing Service (RHS) are counted in the same category. FSA loans are intended for farmers who cannot qualify for conventional loans due to insufficient financial resources and farmers who have suffered financial setbacks due to natural disasters. RHS guarantees mostly apply to loans for essential community facilities in rural areas. For more on FSA-insured lending, see http://www.fsa.usda.gov/FSA/webapp?area=home&subject=fmlp&topic=landing .
The data reported on Jews and Muslims are estimates rather than counts. For more information on how these estimates were calculated, including changes in the estimation methodologies from the 2000 to 2010 surveys, see: http://www.thearda.com/mapsReports/rcms_notes.asp .
For a full report on the researchers’ findings, see http://obs.rc.fas.harvard.edu/chetty/tax_expenditure_soi_whitepaper.pdf . For more information about the Equality of Opportunity Project or the Commuting Zone geography, visit http://www.equality-of-opportunity.org .
PolicyMap downloads the geocoded Head Start locations using the Head Start locator at the website listed above. Head Start locations are classified as Early Head Start, Head Start, Migrant or Seasonal Head Start, or American Indian and Alaskan Native Head Start. Individual centers receive funding from a grantee authority and are located in defined federal regions. Head Start locations can be filtered on whether the operating status of the center is open, closed, or was unreported according to the source.
For more information on the data and methodology visit https://www.hcup-us.ahrq.gov/faststats/OpioidUseServlet .
Maternity Care Health Professional Target Areas (MCTAs) are areas within an existing Primary Care Health Professional Shortage Areas (HPSA) that are experiencing a shortage of maternity health care professionals. Maternity Care Target Areas can receive a score between 0-25. What goes into the MCTA score: Population-to-Full-Time-Equivalent Maternity Care Health Professional Ratio [5 points max], Percentage of Population With Income at or Below 200 Percent of the Federal Poverty Level (FPL) [5 points max], Travel Distance/Time to Nearest Source of Accessible Care Outside of the MCTA [5 points max], Fertility Rate [2 points max], Social Vulnerability [2 points max], Maternal Health Indicators, Pre-Pregnancy Obesity [1 point max], Pre-Pregnancy Diabetes [1 point max], Pre-Pregnancy Hypertension [1 point max], Cigarette Smoking [1 point max], Prenatal Care Initiation in the 1st Trimester [1 point max], Behavioral Health Factor [1 point max]
As part of its Affirmatively Furthering Fair Housing (AFFH) initiative, HUD released a series of opportunity indices. To assist PolicyMap users engaged in AFFH efforts, PolicyMap has loaded the following opportunity indices to PolicyMap: low poverty index, labor market engagement index, school proficiency index, low transportation cost index, transit trips index, jobs proximity index and environmental health index. For more information about the component data and methodology that HUD used in creating these indices, please consult their AFFH data documentation here .
A few notes of caution with respect to percent change variables: In March 2010 the US Postal Service implemented new procedures to improve the accuracy of its vacancy indicators. This led to a large increase nationally, with much more drastic fluctuations in some local areas. Comparisons across time periods spanning the first and second quarters of 2010 may be problematic. For 2007 and 2008 the USPS geocoding methodology and some of the USPS business practices produced anomalies, which may result in spikes in the total address count in a tract that can not necessarily be attributed as growth since the previous year. Also, zip code splitting, may result in similar spikes or drops in total addresses that can not necessarily be attributed to growth or decline.
Because of pandemic-related disruptions to counts of unsheltered homeless people in January 2021, caution should be taken when using data on unsheltered homeless or comparing these values to other years. For more information see the AHAR Report here: https://www.huduser.gov/portal/sites/default/files/pdf/2021-AHAR-Part-1.pdf .
State, county, place, and tract level data from Picture of Subsidized Households are aggregated for all HUD subsidy programs, and are also available for the Housing Choice Voucher recipients and for public housing residents. Tract level data for all subsidized households is only available from 2014 on, due to missing data from the source. Data at the tract level for HCV and public housing data is available for all years. Percent calculations against the general population were made by PolicyMap by using data from the American Community Survey.
The HUD Initiative for Renewal Communities, Empowerment Zones, and Enterprise Communities (RC/EZ/EC) provided federal grants, tax incentives, and partnerships with government, for-profit, and non-profit entities to promote job creation and economic development in economically distressed communities. After the original authorization by the Omnibus Reconciliation Act of 1993, which designated 9 empowerment zones and 95 enterprise communities, there were three rounds of community applications, culminating in extension of the benefits provided through the Community Renewal Tax Relief Act of 2000. Ultimately, 19 Enterprise, 67 Renewal, and 44 Empowerment communities were designated or extended between 1994 and 2011.
HUD provided valid geocoding for 99% of the activity locations, and points with identical activity information have been removed from this dataset. To learn more about the CDBG program, please see the following HUD website: https://hub.arcgis.com/datasets/HUD::community-development-block-grant-activity/about .
PolicyMap displays the number and percentage of persons with medium income (household income below 120% AMI), low and moderate income (household income below 80% AMI), and low income (household income below 50% AMI) at the block group, census tract, place, and county geographies. This data is displayed with the most recent ACS estimates used in LMISD data, not the most recent ACS release.
Small Area FMRs (SAFMRs) are FMRs calcualted at the Zip Code level. HUD calculates SAFMR using standard quality gross rent data provided by the Census Bureau for ZIP Code Tabulation Areas (ZCTAs) when ZCTA data is statistically reliable. When ZCTA data is not reliable, HUD uses the Rent Ratio method; a calculation method where HUD divides the median gross rent across all bedrooms for the ZCTA by the similar median gross rent for the metropolitan area of the ZCTA. If the all bedroom level data is not reliable, HUD may use neighboring ZCTA data to calculate this data. For more information on this methodology, please see: https://www.huduser.gov/portal/datasets/fmr/smallarea/index.html#year2026 Prior to FY2025, HUD only published SAFMR data for metro area zip codes. Therefore, zip codes in non-metro areas prior to 2025 have been published with the “Insufficient Data” label. From 2025 on, data is available for both metro and non-metro area zip codes.
These datasets are mapped according to CPD’s custom boundaries for Formula Allocations. Data are mapped to the most recent boundaries available. See: https://hudgis-hud.opendata.arcgis.com/
The Housing Choice Voucher Marketing Opportunity Index is an index for every Census tract and block group to identify the area’s potential opportunity for Housing Choice Voucher holders seeking housing. It is a measure of neighborhoods’ high quality housing and neighborhood conditions. A higher number indicates a higher potential opportunity for HCV holders seeking housing. It can be used by Public Housing Authorities to help voucher holders find neighborhoods that have low poverty rates, available rental units at or below Fair Market Rent limits, a high level of employment and educational opportunities, and a low density of households who receive housing assistance. The calculation through which the index is calculated is available in a PDF document at the link provided. The calculation through which the index is calculated is available in a PDF document downloadable from the page at the link provided above.
Many non-federal and non-housing programs also use HUD’s income guidelines, often specifying a percentage of the median income that a household’s income must fall below in order to qualify. PolicyMap includes AMI at a variety of percentages for a variety of household sizes. The 30%, 50% (Very Low Income), and 80% (Low Income) of median income by family size as well as the overall area median income are provided by HUD. PolicyMap calculated 60% of Area Median Income by multiplying the 50% threshold by 1.2 and calculated 120% of AMI by multiplying the 50% threshold by 2.4, per instructions in the LIHTC legislation, on HUD’s website, and in communications between PolicyMap and the HUD User electronic help desk resource. The income thresholds as they are calculated in PolicyMap may not be appropriate for your needs if your programs or requirements specify a different method for determining income thresholds. In particular, the Housing and Economic Recovery Act of 2008 (HERA) specifies different Income Limits for qualification levels and rental rates under section 42 of the Internal Revenue Code and projects financed with tax-exempt housing bonds under section 142 of the Code. Projects in service in 2007 or 2008 should rely on the Multifamily Tax Subsidy Income Limits (MTSP). See: http://www.huduser.org/portal/datasets/mtsp.html .
A very thorough explanation of the calculations made by HUD are available here: https://www.hudexchange.info/programs/location-affordability-index/ .
PolicyMap downloaded the properties listed in HUD’s LIHTC Database in June 2025. HUD provided valid geocoding for 96% of projects. The LIHTC program was created by the Tax Reform Act of 1986, and gives state and local LIHTC allocating agencies authority to issue tax credits for acquisition, rehabilitation or new construction of low income rental housing.
This data is displayed with HUD’s A Picture of Subsidized Households , which shows data about the subsidized households at these properties. Picture data is provided by HUD at the contract level, not property level. For PolicyMap, contracts are aggregated together to create single values for each property. All contracts available in the Picture data are included, regardless of the contracts listed in the Multifamily Assistance database. Only properties listed in the HUD Multifamily database are included.
In areas designated as Promise Zones, the federal government will partner to help local agencies access resources. Under the Promise Zones program, federal partners will collaborate with local leaders on economic development, education, and crime-related projects. Federal incentives available to areas designated as Promise Zones are listed at https://www.hudexchange.info/promise-zones/federal-partner-funding-and-technical-assistance-opportunities/ . The twelve federal partners in the Promise Zones program include the U.S. Department of Agriculture, U.S. Department of Commerce, Corporation for National and Community Service, U.S. Department of Education, U.S. Department of Health and Human Services, U.S. Department of Housing and Urban Development, U.S. Department of Justice, U.S. Department of Labor, National Endowment for the Arts, Small Business Administration, U.S. Department of Transportation, and U.S. Department of the Treasury. In total, 20 urban, rural, and tribal communities will be designated as Promise Zones by the end of 2016.
This dataset, provided by the US Department of Housing and Urban Development (HUD), identifies census tracts that meet or exceed HUD’s established thresholds for racially and ethnically-concentrated areas of poverty (R/ECAPs). These tracts have a non-white population that is greater than or equal to 50% and meet either of the following poverty criteria: the poverty rate of a tract is 1) higher than 40% or 2) more than three times the average poverty rate of tracts in the metropolitan area. The racial/ethnic threshold is lowered to 20% for tracts that are located outside of metropolitan/micropolitan areas. HUD used component data from the decennial census and the American Community Survey to determine which geographies met these criteria in 2010 and 2020, and component data from Brown Longitudinal Tract Database , based on decennial census data from 2000 and 1990, to determine R/ECAP geographies for 1990 and 2000. Data from 1990-2010 are at the 2010 census tract boundaries. Data from 2020 is at the 2020 census tract boundaries.
PolicyMap downloads geocoded data on HUD’s multifamily and public housing sites from three different resources at HUD and wherever possible PolicyMap linked the data using the property ID. The three HUD housing datasets included are the Multifamily Assistance and Section 8 Contracts, A Picture of Subsidized Households and the REAC assessment scores report. All points are geocoded by HUD; where coordinates were not available, the point was not included on the map. In 2025, there was no update for the public housing inspection scores. The latest data available from HUD (2023) was used.
Incentive zones that were authorized before 2000 were specified in terms of 1990 Census Tracts. In PolicyMap it is only possible to display shading for 2000 Census Tracts. If 75% or more of the area of a 2000 Census Tract was deemed an Empowerment Zone, Renewal Community, or Enterprise Community in 1990 (according to the overlap of the 1990 boundary file), then that Census Tract is designated to be of that Zone or Community in PolicyMap.
The Museum Universe Data file contains the locations and basic information of known museums and related organizations. Museums include aquariums, arboretums, botanical gardens, art museums, children’s museums, general museums, historic houses and sites, history museums, nature centers, natural history and anthropology museums, planetariums, science and technology centers, specialized museums, and zoological parks. IMLS compiled this information from federal tax records, private foundations, and third-party commercial vendors. Points were geocoded by IMLS.
Library outlet locations include central libraries, branches, bookmobiles, and books-by-mail locations. Points were geocoded by IMLS based on addresses provided by the survey respondent (library administrator), and in some cases were matched to the center point of the postal zip code or zip code division. IMLS was able to locate 99.9% of library outlets on a map. The reporting period varies among localities for states; however, each public library provided data for a 12-month period. When calculating weekly hours, PolicyMap applied IMLS’ methodology–suppressing values where average hours per week is less than 11 or greater than 130. Refer to the Data File Documentation and User’s Guide for more details.
The International Business Innovation Association is a trade group serving over 2,100 business incubators and related organizations worldwide. Business incubators are programs that provide support services and resources for entrepreneurial companies during their “start-up” phase. InBIA provided PolicyMap with a list of business incubators in the United States.
Numbers of returns and aggregate amounts are made available by the IRS. Percents and averages are calculated by PolicyMap. Percents use total returns as a denominator, and averages use returns of that type as a denominator (for example, average charitable contributions is total charitable contributions divided by the number of returns claiming any charitable contributions). Generally, returns represent households, not individuals.
Data from the IRS is given for the year of the tax filing; people generally file their returns the year following the tax year (tax year 2013 returns are filed in 2014). PolicyMap displays the tax year, which is the year the migration occurred, not the filing year.
The researchers also published a calculator that assesses individual risk of death from COVID-19 here: http://covid19risktools.com/ .
Intensive care units (ICU) are stocked with the specialized equipment needed to save the lives of people hospitalized with the most severe COVID-19 symptoms. Kaiser Health News estimated the number of available ICU beds in each county using data from the Healthcare Provider Cost Reporting Information System database, published by Centers for Medicare and Medicaid Services (CMS). For this analysis, Kaiser Health News included intensive care unit beds, surgical intensive care unit beds, coronary care unit beds, and burn intensive care unit beds reported by CMS. These calculations do not include intensive care unit beds in Veterans Affairs hospitals.
Mapbox Satellite hosts satellite imagery for use in web-based mapping applications. Mapbox’s satellite image base maps also contain overlying road, place name, and other geographic data sourced from OpenStreetMap. Mapbox Satellite images are sourced from several different satellite programs or commercial aggregators of satellite data, including MODIS, Landsat 5, Landsat 7, and Maxar Vivid. Mapbox continuously updates their satellite data, and these updates are incorporated into PolicyMap as they are released by Mapbox.
MapTiler styles and hosts base maps for web-based mapping applications. Their base maps provide complete coverage of the United States at all available zoom levels. MapTiler sources their map data from OpenStreetMap. They pull in data updates from OpenStreetMap regularly, roughly on a weekly basis. These updates are incorporated into PolicyMap as they are released by MapTiler.
The Housing Gap Estimates use American Communities Survey data to estimate the level of units of housing, both owner and renter, a given census tract is short. Moody’s Analytics and Reinvestment Fund use a methodology to create estimates of the number of housing units a given tract is short by comparing a tract’s vacancy rate to the historical “equilibrium” vacancy rate of the City in which the tract sits. The equilibrium rate is derived by taking the average vacancy rate during a stable housing market. Data is available for the most recent 5 year ACS period (2019-2023), and for census tracts within 350 of the largest cities in the United States. Each tract additionally has a “Severity” estimate that contextualizes the percentage of vacant units as either close to balanced, or substantially or modestly oversupplied or undersupplied.
The full methodology can be found in the published paper here https://www.policymap.com/data/moodys-housing-shortfall .
Urban Institute nonprofits data is downloadable only for individual Census Tracts and Block Groups.
The Common Core of Data (CCD) is a program of the U.S. Department of Education’s National Center for Education Statistics that collects selected data about all public schools, public school districts and state education agencies in the United States every year. Data are supplied by state education agency officials through the Common Core of Data (CCD), Local Education Agency (School District) Universe Survey, accessed from https://nces.ed.gov/ccd/files.asp . For schools that do not report free and reduced-price lunch program (FRPL) eligibility data data, direct certification counts are used exclusively. Direct certification applies to children from households participating in the Supplemental Nutrition Assistance Program (SNAP), Temporary Assistance for Needy Families (TANF), the Food Distribution Program on Indian Reservations (FDPIR), or (in some states) Medicaid, as well as children who are migrant, experiencing homelessness, in foster care, or enrolled in Head Start. These students are categorically eligible to receive free meals at school. Note that NCES CCD data may indicate participation rates above 100% in some schools due to reporting variations or administrative discrepancies. Eligibility data is suppressed when fewer than 6 students are present to protect confidentiality.
Data Note: Illinois and Virginia opted not to collect this data under the pandemic meal provisions that gave free meals universally, thus leading to gaps in the usual reporting for these states in the 2020-21 period. Additionally, in the August 2025 data update, PolicyMap recalculated and reloaded historic percent data for the 2019-2020 school year.
Schools without coordinates are excluded from the data.
The point-level community college locations data on PolicyMap consist of all the institutions in the IPEDS’ universe classified as 2-but-less-than-4-year or less than 2-year. The 4-year colleges and universities data consist of all institutions in the IPEDS’ universe classified as four or more years. Most of the data in these datasets is for the 2016-2017 academic year (with enrollment data applying to Fall of 2016). Graduation rates, however, apply to August of 2016. Data regarding institutions’ financial performance reflect Government Accounting Standards Board (GASB) reporting. PolicyMap downloaded these data from the IPEDS data center in August 2018. The latitude and longitude of the points were provided by the source.
The Private School Universe Survey (PSS) is a program of the U.S. Department of Education’s National Center for Education Statistics that collects selected data about all private schools every two years. Data are supplied through a survey that is compiled by administrative personnel in private schools. The NCES definition of a private school is “A private school is not supported primarily by public funds, provides classroom instruction for one or more of grades K-12 or comparable ungraded levels, and has one or more teachers. Organizations or institutions that provide support for home schooling without offering classroom instruction for students are not included.”
According to the NCED, the data is not complete. The NCED provides estimates of completeness by state, which can be accessed here: https://www.conservationeasement.us/completeness/ .
The National Credit Union Administration (NCUA) provides data for all U.S. credit unions as well as the addresses of credit union branches. These data are from the 5300 Call Report, submitted quarterly by credit unions to the NCUA, and downloaded in April 2025 from https://www.ncua.gov/analysis/Pages/call-report-data/quarterly-data.aspx . All information in this data, aside from office type and contact information, applies to the credit union and not the individual branch. Financial counseling/education, online banking, and percent loans delinquent were calculated by PolicyMap. PolicyMap geocoded all branch location points, and was able to locate 95% of the given addresses on a map.
The National Oceanic and Atmospheric Association (NOAA) and the National Weather Service provide Annual Time Series UV Index data through their Climate Prediction Center. The average UV Index is an average of every UV Index issued within the year by the NOAA/National Weather Service for selected cities. When issuing the UV Index, the NOAA uses the World Health Organization’s Exposure Categories of 0-2 as being low, 3-5 as moderate, 6-7 as high, 8-10 as very high, and 11 or more as extreme.
Each of these indicators is available for the continental United States and for selected parts of Alaska and Hawaii. They are not available for Puerto Rico.
The Federal Historic Preservation Tax Incentives Program is administered by the National Park Service, State Historic Preservation Offices, and the Internal Revenue Service. For more information on Federal Historic Tax Credits visit the National Park Service: http://www.nps.gov/tps/index.htm . For additional information visit the National Trust Community Investment Corporation: https://ntcic.com/invest/htc/
This index was developed by New Localism Advisors to determine priority investment locations in Qualified Opportunity Zones. To learn more please visit https://www.thenewlocalism.com/wp-content/uploads/2018/03/Guiding-Principles-for-Opportunity-Zones_TheNewLocalism_March92018.pdf.
PolicyMap calculated the percent change in cases and deaths and rates per 100,000 people. Rates were calculated using the 2014-2018 population estimates published by the U.S. Census Bureau. The populations of Platte, Clay, Jackson, and Cass counties in Missouri excluding the portion of the population that lives within Kansas City were estimated using block group data. This dataset is updated daily by the New York Times, and downloaded and published frequently by PolicyMap.
OpenFlights.org collects information on airport locations and airline routes. PolicyMap downloaded this information, and used spatial analysis to simulate flights in order to estimate which neighborhoods and zip codes likely fall into the path of airplanes taking off and landing at nearby airports. This data is used to indicate the possible presence of noise pollution from airplanes flying over neighborhoods.
ZIP Code boundary files on PolicyMap are licensed from Precisely (formerly Pitney Bowes). Precisely builds its ZIP codes boundary files from individual addresses to align boundaries with streets; it is the source recommended for business by the US Postal Service.
Normalized scores were then converted to percentiles and z scores for easier interpretation. Percentiles rank counties from the lowest score to the highest on a scale of 0 to 100, where a score of 50 represents the median value. A county’s z score shows how many standard deviations above or below the average a county’s risk level falls. A score of 0.6, for example, would mean that the county has a higher risk than average, but is still within one standard deviation of the average and is therefore not unusually high. Risk categories from very low to very high are assigned based on z scores.
Please be aware that the thresholds and data sources used in this calculation can vary, and federal agencies may require specific calculations for some program applications. The Community Development Block Grant (CDBG) program defines low and moderate income tracts based on what percent of the population is low or moderate income, rather than by comparing median local values to the surrounding metro area (See HUD Community Development Block Grant Eligibility Criteria above). The Community Reinvestment Act (CRA) specifies what years of income data to include in the calculation – 2000 data for local median income and 2004 data for area median income. (See Community Reinvestment Act Eligibility Criteria above). Both CDBG and CRA low and moderate income calculations can be found on PolicyMap under the Federal Guidelines tab.
Geographies with percent calculations are suppressed in cases where the denominator of the calculation was less than 10 households.
PolicyMap and the Federal Reserve Bank of Philadelphia developed estimates of housing repair costs for each occupied housing unit surveyed in the 2017 American Housing Survey, then aggregated them to the MSA level. The researchers developed a set of repair scenarios based on responses to questions about housing problems and structural characteristics and worked with construction experts at Gordian to arrive at estimated repair costs using their RSMeans database. This dataset was used to inform a national housing quality analysis. Estimated repair needs for the largest 15 Metropolitan Statistical Areas were calculated using this dataset and scaled based on local differences in construction costs. Cost estimates are provided in 2018 dollars. See policymap.com/issues/housing-quality for more information on this dataset.
PolicyMap calculated the shortest distance (within 50 miles) from each block group to the nearest public school with a Niche Overall School Rating of A+ or A, within the same state. For block groups with more than 50% water area, block-level center points were used instead. The Distance to Nearest High-Performing Public School for each Census Tract is the average of these minimum distances for the majority-land block groups within the tract. This representation of access to high performing public schools is limited by the fact that Niche does not assign a rating to every public school in the nation. This analysis does not take into account political boundaries or catchment areas within states that may make a public school inaccessible. Areas where all eligible block groups are located more than 50 miles from any A+ or A school are labeled “Insufficient Data.” A tract may show an average distance even if no high-performing schools are within its boundaries, as the metric reflects proximity to nearby schools rather than the presence within the tract.
Niche is a platform that provides rankings for schools based on public data and user reviews. PolicyMap licenses Niche ratings for Pre-K through 12th grade schools. Niche-generated fields reflect the data available at the time of licensing, while the other fields are sourced from recent public-use datasets, such as those from the U.S. Department of Education. The Overall Rating and Academic Rating are based on Niche’s proprietary algorithms and follow a grading scale from A+ to D-. If a school’s coordinates fall outside its listed county or state, PolicyMap will re-geocode the address. Schools are excluded from the map if their address is missing or cannot be geocoded.Niche’s methodology includes a Bayesian adjustment to account for confidence in user-submitted data, standardized z-scores for comparison, and factor weighting based on both statistical analysis and user priorities. Schools missing over 50% of the weighted factors are excluded from rankings or only receive a grade to ensure reliability.
The 2019-2023 calculations rely on HUD’s FY2023 Area Median Income (AMI) data. The 30%, 50% (Very Low Income), and 80% (Low Income) of median income by family size as well as the overall area median income are provided by HUD. PolicyMap calculated 60% of Area Median Income by multiplying the 50% threshold by 1.2 and calculated 120% of AMI by multiplying the 50% threshold by 2.4, per instructions in the Low Income Housing Tax Credit (LIHTC) legislation, on HUD’s website, and in communications between PolicyMap and the HUD User electronic help desk resource. Counts of owner- and renter-occupied housing units by value or rental price were obtained from the Census Bureau’s 2019-2023 American Community Survey. Census tracts and block groups show reduced data availability compared to the previous 2017-2021 period.
Locations in one dataset are matched with those in the other if they share the same physical address and/or project name, and if the projects are within three years of one another. Before matching, the physical addresses are standardized using a proprietary geocoder licensed by PolicyMap. The project names are matched using a “fuzzy” matching method that determines how similar two text values are to one another. Project names that were highly similar were considered to match. Because of the methods used to match projects between the two datasets, there may be some “false positives” and “false negatives” in the dataset.
PolicyMap created estimates of the average cost of public college in the United States using NCES’s Integrated Postesecondary Education Data System (IPEDS) data on 2-year and 4-year public institutions. For each institution category, the sum of the cost for in-state tuition, in-state fees, books and supplies, on-campus room and board, and other on-campus expenses was multiplied by the full-time undergraduate enrollment, using the total full-time enrollment within the state to calculate the state average. Institutions with either no full-time undergraduate enrollment, or no information for both in-state tuition and in-state fees were excluded from the calculations. Cost of public college data is based on a custom download of preliminary data from http://nces.ed.gov/ipe