The Opportunity Mapping data includes census tract level information on the composite index score as well as the intermediate scores for the five key elements of neighborhood opportunity and positive life outcomes: education, economic health, housing and neighborhood quality, mobility and transportation, and health and environment. The level of opportunity score (very low, low, moderate, high, very high) is determined by sorting all census tracts into quintiles based on their index scores. Opportunity Mapping: Methodology and Technical Addendum (July 2019): https://www.psrc.org/media/3503

ObjectiveTo describe the development of an area-level measure of children's opportunity, the Ohio Children's Opportunity Index (OCOI).Data Sources/Study SettingSecondary data were collected from US census based-American Community Survey (ACS), US Environmental Protection Agency, US Housing and Urban Development, Ohio Vital Statistics, US Department of Agriculture-Economic Research Service, Ohio State University Center for Urban and Regional Analysis, Ohio Incident Based Reporting System, IPUMS National Historical Geographic Information System, and Ohio Department of Medicaid. Data were aggregated to census tracts across two time periods.Study DesignOCOI domains were selected based on existing literature, which included family stability, infant health, children's health, access, education, housing, environment, and criminal justice domains. The composite index was developed using an equal weighting approach. Validation analyses were conducted between OCOI and health and race-related outcomes, and a national index.Principal FindingsComposite OCOI scores ranged from 0–100 with an average value of 74.82 (SD, 17.00). Census tracts in the major metropolitan cities across Ohio represented 76% of the total census tracts in the least advantaged OCOI septile. OCOI served as a significant predictor of health and race-related outcomes. Specifically, the average life expectancy at birth of children born in the most advantaged septile was approximately 9 years more than those born in least advantaged septile. Increases in OCOI were associated with decreases in proportion of Black (48 points lower in the most advantaged vs. least advantaged septile), p < 0.001) and Minority populations (54 points lower in most advantaged vs. least advantaged septile, p < 0.001). We found R-squared values > 0.50 between the OCOI and the national Child Opportunity Index scores. Temporally, OCOI decreased by 1% between the two study periods, explained mainly by decreases in the children health, accessibility and environmental domains.ConclusionAs the first opportunity index developed for children in Ohio, the OCOI is a valuable resource for policy reform, especially related to health disparities and health equity. Health care providers will be able to use it to obtain holistic views on their patients and implement interventions that can tackle barriers to childhood development using a more tailored approach.

The Center for Regional Change and Rabobank have partnered to develop the Regional Opportunity Index (ROI), an index of community and regional opportunity for understanding social and economic opportunity in California’s communities. The goal of the ROI is to help target resources and policies toward people and places with the greatest need, to foster thriving communities of opportunity for all Californians. It does this by incorporating both a "people" component and a "place" component, integrating economic, infrastructure, environmental, and social indicators in to a comprehensive assessment of the factors driving opportunity.The Regional Opportunity Index was developed through a joint partnership between the UC Davis Center for Regional Change and Rabobank, N.A. Generous support also has been provided by Wells Fargo Bank for continued enhancement of the ROI.Original data sourced from: https://interact.regionalchange.ucdavis.edu/roi/data.html

This tool allows the analysis of growth that may take place in areas with moderate to high opportunity.Growth in areas of opportunity is based on an “Opportunity Index,” which combines measures of five key elements of neighborhood opportunity and positive life outcomes: education, economic health, housing and neighborhood quality, mobility and transportation, and health and environment. The level of opportunity score (very low, low, moderate, high, very high) is determined by sorting all census tracts into quintiles based on their index scores. Areas of opportunity that experience greater proportions of growth may experience an increased risk of displacement.

The Communities of Opportunity designated on the Maryland QAP Comprehensive Opportunity Maps are based on a “Composite Opportunity Index” developed by DHCD. The Composite Opportunity Index uses publicly - available data and is based on three major factors: community health, economic opportunity, and educational opportunity. To be designated a Community of Opportunity, and mapped as such to the Maryland QAP Comprehensive Opportunity Maps, the community must have a Composite Opportunity Index that it is above the statewide average. See Section 3.1 of the Program Guide for more details. https://dhcd.maryland.gov/HousingDevelopment/Documents/rhf/2020Guide.pdf

The Child Opportunity Index is calculated based on Education, Health & Built Environment and Neighborhood Social & Economic Opportunity indicators.

For display purposes, please use the map image layer: Equity Index 2018 (Tacoma). The Equity Index is an interactive tool visually highlighting disparities in Tacoma. The 2018 iteration uses 20 data points sorted into 4 determinant categories (Accessibility, Economy, Education, and Livability) to determine where community members cannot access services or where services do not meet community needs. It is one of the primary tools that City staff, partners, and other decision-makers use to help ensure they make data-informed decisions to improve access to opportunities for all community members. Tacoma's original 2018 Equity Index was generated by The Kirwan Institute for the Study of Race & Ethnicity for Tacoma's Office of Equity and Human Rights. Please note that while the 2020 version of the Equity Index is also based on 2010 US Census block groups, the 2022 version is based on 2020 US Census block groups. To learn more about the Equity Index, visit: CityofTacoma.org/EquityIndex.For questions, please fill out the Equity Index Inquiries form.

This is a MD iMAP hosted service. Find more information at http://imap.maryland.gov. The Communities of Opportunity designated on the Maryland QAP Comprehensive Opportunity Maps are based on a 'Composite Opportunity Index' developed by DHCD. The Composite Opportunity Index uses publicly - available data and is based on three major factors: community health - economic opportunity - and educational opportunity. To be designated a Community of Opportunity - and mapped as such to the Maryland QAP Comprehensive Opportunity Maps - the community must have a Composite Opportunity Index that it is above the statewide average. See Section 3.1 of the Program Guide for more details. http://mdhousing.org/Website/Programs/rhf/documents/Guide.pdf Last Updated: 03/2016Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/BusinessEconomy/MD_HousingDesignatedAreas/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

BackgroundChildren from families with low socioeconomic status (SES), as determined by income, experience several negative outcomes, such as higher rates of newborn mortality and behavioral issues. Moreover, associations between DNA methylation and low income or poverty status are evident beginning at birth, suggesting prenatal influences on offspring development. Recent evidence suggests neighborhood opportunities may protect against some of the health consequences of living in low income households. The goal of this study was to assess whether neighborhood opportunities moderate associations between household income (HI) and neonate developmental maturity as measured with DNA methylation.MethodsUmbilical cord blood DNA methylation data was available in 198 mother-neonate pairs from the larger CANDLE cohort. Gestational age acceleration was calculated using an epigenetic clock designed for neonates. Prenatal HI and neighborhood opportunities measured with the Childhood Opportunity Index (COI) were regressed on gestational age acceleration controlling for sex, race, and cellular composition.ResultsHigher HI was associated with higher gestational age acceleration (B = .145, t = 4.969, p = 1.56x10-6, 95% CI [.087, .202]). Contrary to expectation, an interaction emerged showing higher neighborhood educational opportunity was associated with lower gestational age acceleration at birth for neonates with mothers living in moderate to high HI (B = -.048, t = -2.08, p = .03, 95% CI [-.092, -.002]). Female neonates showed higher gestational age acceleration at birth compared to males. However, within males, being born into neighborhoods with higher social and economic opportunity was associated with higher gestational age acceleration.ConclusionPrenatal HI and neighborhood qualities may affect gestational age acceleration at birth. Therefore, policy makers should consider neighborhood qualities as one opportunity to mitigate prenatal developmental effects of HI.

This 2013 dataset includes information at the block group-level for the 5-county Austin metro area. Economic, educational, housing, mobility, and environmental indicators are calculated for each block group to provide a comprehensive opportunity index score. This score reflects "opportunity" in the area, defined as a situation or condition that places individuals in a position to be more likely to succeed or excel. This data was collected and calculated by the Kirwan Institute, with collaboration from Green Doors and various community partners, and is compiled in "Geography of Opportunity in Austin" (http://www.greendoors.org/programs/docs/Geography-of-Opportunity-Austin-2013.pdf#page=43). The data can be viewed in an interactive map form here: http://www.arcgis.com/home/webmap/viewer.html?webmap=5db08646b03547abab85aec0a3592fb7. The data is also available in shapefile format for use in ESRI GIS mapping applications here: https://data.austintexas.gov/Neighborhood/Kirwin-Opportunity-Map/3ns6-m3cy.

ObjectivesRegional sleep differences may reflect other important indicators of health and well-being. Examining sleep health at the regional level can help inform policies to improve population health. We examined the relationship between neighborhood-level adult sleep health (modeled in this study via adult sleep duration) and other health metrics and multiple indicators of child-relevant opportunity.MethodsData were obtained from the “500 Cities” data collected by the CDC, including the proportion of the adult population in each tract that report obtaining at least 7 h of sleep. The Child Opportunity Index (COI) provides indices for “education,” “health and environment,” and “social and economic” domains, as well as a global score. When data were merged, 27,130 census tracts were included. Linear regression analyses examined COI associated with the proportion of the adult population obtaining 7 h of sleep.ResultsAdult sleep duration was associated with global COI, such that for each additional percent of the population that obtains ≥ 7 h of sleep, COI increases by 3.6 points (95%CI[3.57, 3.64]). Each component of COI was separately related to adult sleep duration. All associations were attenuated but significant in adjusted analyses. In stepwise analyses, sleep health via adult sleep duration emerged as the strongest correlate of global COI, accounting for 57.2% of the variance (p 

BackgroundFragile X syndrome (FXS) is the most common inherited cause of intellectual disability and is caused by reduced or absent Fragile X messenger ribonucleoprotein (FMRP). Cognitive and adaptive skills widely vary among individuals with FXS, and these individual phenotypic differences are not fully accounted for by individual differences in FMRP expression. Social-environmental factors, including social determinants of health, may help further explain these individual differences, but these environmental factors have been under-studied in FXS.Methods175 participants with FXS (123 males; age range: 4–72 years) completed the Stanford-Binet, Fifth Edition to estimate IQ and a blood draw to quantify peripheral FMRP levels. Caregivers from a subset of participants also completed the Vineland Adaptive Behavior Scales. Neighborhood-level social-environmental information was extracted by linking participants’ home addresses to rankings of neighborhood resources (e.g., household income, pollution, healthcare access) from the Child Opportunity Index (COI). We calculated the unique variance in IQ and adaptive behaviors accounted for by these neighborhood-level social-environmental factors from the COI while covarying for FMRP expression.ResultsEven after accounting for individual differences in FMRP, numerous neighborhood factors were associated with greater IQ in males with FXS, including social resources and indicators of healthcare access. Different social-environment factors were associated with stronger adaptive skills in males with FXS, including economic and educational resources. Almost no neighborhood factors were associated with clinical outcomes in females.DiscussionOur finding of stronger links between neighborhood resources and clinical outcomes in males with FXS is consistent with previous work and may reflect increased reliance on social-environmental supports in males who typically have more significant intellectual and adaptive deficits than females. Consistent associations between greater social resources, higher IQ, and stronger adaptive skills suggest social support (e.g., social cohesion, resource and knowledge sharing) may be a particularly salient target for intervention. Associations between economic resources and adaptive communication skills also highlight the benefits of targeted economic supports for families affected by FXS. Together, our findings underscore the role of social determinants of health as key contributors to individual differences and the importance of considering these factors in clinical studies of FXS.

The Equity Index is an interactive tool visually highlighting disparities in Tacoma. The 2025 iteration uses 29 data points sorted into 5 determinant categories (Accessibility, Livability, Education, Economy, and Environmental Health) to determine where community members cannot access services or where services do not meet community needs. It is one of the primary tools that City staff, partners, and other decision-makers use to help ensure they make data-informed decisions to improve access to opportunities for all community members.Tacoma's original 2018 Equity Index was generated by The Kirwan Institute for the Study of Race & Ethnicity for Tacoma's Office of Equity and Human Rights. Please note that the 2018 & 2020 versions of the Equity Index are based on 2010 US Census block groups. The 2022, 2024, and 2025 versions are based on 2020 US Census block groups. Census block groups have been clipped to the Puget Sound shoreline. Typically, new Equity Index data has been released every 2 years. Please read the complete metadata: Tacoma Equity Index 2025 Metadata.To learn more about the Equity Index, visit: tacoma.gov/EquityIndex.For questions, please fill out the Equity Index Inquiries form. Recommended symbology:

Equity Index (Opportunity) field value

HEX Code 50% transparency

Very High

#7F0000

High

#D7301F

Moderate

#FC8D59

Low

#FDD49E

Very Low

#FAFCCF

Demographic, risk, and protective factors by neonate sex.

To download the data for analysis, please use the feature layer: Equity Index 2022 (Tacoma) - Data.Please read the complete metadata: Tacoma Equity Index 2022 Metadata.The Equity Index is an interactive tool visually highlighting disparities in Tacoma. The 2022 iteration uses 32 data points sorted into 5 determinant categories (Accessibility, Livability, Education, Economy, and Environmental Health) to determine where community members cannot access services or where services do not meet community needs. It is one of the primary tools that City staff, partners, and other decision-makers use to help ensure they make data-informed decisions to improve access to opportunities for all community members. Tacoma's original 2018 Equity Index was generated by the Kirwan Institute for the Study of Race & Ethnicity for Tacoma's Office of Equity and Human Rights. Please note that the previous two versions (2018 & 2020) of the Equity Index are based on 2010 US Census block groups. This 2022 version is based on 2020 US Census block groups. To learn more about the Equity Index, visit: CityofTacoma.org/EquityIndex.For questions, please fill out the Equity Index Inquiries form.

In 2017, the California Tax Credit Allocation Committee (CTCAC) and the Department of Housing and Community Development (HCD) created the California Fair Housing Task Force (Task Force). The Task Force was asked to assist CTCAC and HCD in creating evidence-based approaches to increasing access to opportunity for families with children living in housing subsidized by the Low-Income Housing Tax Credit (LIHTC) program.

This feature set contains Resource Opportunity Areas (ROAs) that are the results of the Task Force's analysis for the two regions used for the San Francisco Bay Region; one is for the cities and towns (urban) and the other is for the rural areas. The reason for treating urban and rural areas as separate reasons is that using absolute thresholds for place-based opportunity could introduce comparisons between very different areas of the total region that make little sense from a policy perspective — in effect, holding a farming community to the same standard as a dense, urbanized neighborhood.

ROA analysis for urban areas is based on census tract data. Since tracts in rural areas of are approximately 37 times larger in land area than tracts in non-rural areas, tract-level data in rural areas may mask over variation in opportunity and resources within these tracts. Assessing opportunity at the census block group level in rural areas reduces this difference by 90 percent (each rural tract contains three block groups), and thus allows for finer-grained analysis.

In addition, more consistent standards can be useful for identifying areas of concern from a fair housing perspective — such as high-poverty and racially segregated areas. Assessing these factors based on intraregional comparison could mischaracterize areas in more affluent areas with relatively even and equitable development opportunity patterns as high-poverty, and could generate misleading results in areas with higher shares of objectively poor neighborhoods by holding them to a lower, intraregional standard.

To avoid either outcome, the Task Force used a hybrid approach for the CTCAC/HCD ROA analysis — accounting for regional differences in assessing opportunity for most places, while applying more rigid standards for high-poverty, racially segregated areas in all regions. In particular:

Filtering for High-Poverty, Racially Segregated Areas

The CTCAC/HCD ROA filters areas that meet consistent standards for both poverty (30% of the population below the federal poverty line) and racial segregation (over-representation of people of color relative to the county) into a “High Segregation & Poverty” category. The share of each region that falls into the High Segregation & Poverty category varies from region to region.

Calculating Index Scores for Non-Filtered Areas

The CTCAC/HCD ROAs process calculates regionally derived opportunity index scores for non-filtered tracts and rural block groups using twenty-one indicators (see Data Quality section of metadata for more information). These index scores make it possible to sort each non-filtered tract or rural block group into opportunity categories according to their rank within the urban or rural areas.

To allow CTCAC and HCD to incentivize equitable development patterns in each region to the same degree, the CTCAC/HCD analysis 20 percent of tracts or rural block groups in each urban or rural area, respectively, with the highest relative index scores to the "Highest Resource” designation and the next 20 percent to the “High Resource” designation.

The region's urban area thus ends up with 40 percent of its total tracts with reliable data as Highest or High Resource (or 40 percent of block groups in the rural area). The remaining non-filtered tracts or rural block groups are then evenly divided into “Low Resource” and “Moderate Resource” categories.

Excluding Tracts or Block Groups

The analysis also excludes certain census areas from being categorized. To improve the accuracy of the mapping, tracts and rural bl

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

This survey monitors wellbeing among residents of the City of Guelph, located in Ontario, Canada. The survey is a joint initiative of the Canadian Index of Wellbeing in partnership with the City of Guelph. The purpose of the survey is to better understand subjective perceptions of wellbeing of residents in the survey area. The primary objectives of this survey are to (a) gather data on the wellbeing of residents which could be monitored o ver time; and, (b) to provide information on specific aspects of wellbeing that could be used to inform policy issues and community action. The survey provides information based on eight domains of wellbeing, as identified by the Canadian Index of Wellbeing: Community Vitality, Democratic Engagement, Environment, Education, Healthy Populations, Leisure and Culture, Living Standards, and Time Use. The questionnaire collected additional information about socio-economic and household characteristics, and feelings of overall satisfaction with each domain of wellbeing. A total of N=1,390 residents completed the survey.

In 2021, Suriname scored 0.7 in the gender gap index area of economic participation and opportunity. This means that women are 30 percent less likely to have equal economic participation and opportunities than men. That year, the country scored 0.56 in estimated earned income, which represents that, on average, women in Suriname only earn 56 percent of the income earned by men.

The data for the Buffering America’s Waterways Tool was created by aggregating the National Forests to Faucets 2.0 Assessment watershed importance data (IMP) with data on the percent of the riparian area in cropland (% cropland) which was calculated for this analysis.By combining importance to surface drinking water data with the percent riparian area that is cropland, the tool identifies riparian area in watersheds important to surface drinking water that have greatest opportunity for improving water quality by establishing perennial vegetation between croplands and water bodies.The resulting data, or Riparian Area Opportunity, is defined as:Riparian Area Opportunity = (IMP x %cropland)/100where,IMP = Index of Importance to Surface Drinking Water% cropland = Percent cropland in the Riparian Area (10 m, 30 m, or variable width)The result is presented in percentiles (0-100).Note: Due to the resolution of the data, the tool should only be used with other evaluation methods to prescribe practices for specific sites. Pasture land was not included in this analysis due to insufficient high resolution data. Future analysis may address pasture.Field NameDescriptionOBJECTIDOBJECTIDShapeShapeAcresAcresSTATESStatesHUC12HUC12NAMEHUC 12 NameSUM_POPSurface Drinking Water Consumers (Forests to Faucets 2.0)POP_DSDownstream Surface Drinking Water Consumers (Forests to Faucets 2.0)IMP_RForests to Faucets Important Watershed IndexREGIONFS Region NumberREGIONNAMEFS Region NameCROPAC_VarCrop Acres (variable width)PERCROP_Var% Crop (variable width)PERNAT_Var% Natural Cover (variable width)PERDEV_Var% Developed (variable width)PEROTH_Var% Other (variable width)CROPAC_30mCrop Acres (30m)PERCROP_30m% Crop (30m)PERDEV_30m% Natural Cover (30m)PERNAT_30m% Developed (30m)PEROTH_30m% Other (30m)CROPAC_10mCrop Acres (10m)PERCROP_10m% Crop (10m)PERDEV_10m% Natural Cover (10m)PERNAT_10m% Developed (10m)PEROTH_10m% Other (10m)IMPVAR_RRiparian Area Opportunity Index Percentile (variable width)IMP30m_RRiparian Area Opportunity Index Percentile (30m)IMP10m_RRiparian Area Opportunity Index Percentile (10m)