
Community Outcomes Data
Sources & Methodology
Overview
This dashboard was created through an 18-month collaborative effort involving direct service providers, local program evaluators, community members, and data professionals across Montgomery County. Together, these partners selected meaningful indicators to better understand the conditions, strengths, and opportunities within our schools and neighborhoods.
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The goal of this tool is to support community members, families, service providers, elected officials, youth, and local leaders in using reliable, place-based data to guide decisions, shape programming, and inform policy.
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This report prioritizes place as the central lens through which data is organized and interpreted. Data is structured around school buildings and neighborhoods (represented by census tracts) to help users understand what is happening at the most local levels. By focusing on community conditions, the dashboard is designed to support place-based strategies that recognize the unique assets and challenges in each part of Montgomery County.​
Data Sources
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American Community Survey (ACS) – Neighborhood-Level Data
Neighborhood-level data are drawn from the U.S. Census Bureau’s American Community Survey (ACS) 5-Year Estimates, which provide detailed information on population, housing, education, and economic well-being at the census tract level. These data serve as proxies for neighborhood conditions.
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B15003 Educational attainment of adults age 25+ from no formal schooling through doctorate degrees
B17003 & B17018
Poverty status of individuals and families by sex, family type, and educational attainment
B19001
Household income by detailed income brackets
B20004
Median earnings by sex and educational attainment
B21003
Veteran status by educational attainment
B25002, B25070, B25091, B25106
Housing characteristics: occupancy, rent and mortgage burden, and housing cost as a % of income
B27019
Health insurance status by age and educational attainment
DP03
Selected economic characteristics, including employment status, income, and poverty indicators
S1101, DP04
Household structure and selected housing unit values
C15002A–I
Educational attainment by race/ethnicity and sex for adults age 25+
B23025
Employment status for the population 16 and older
B01001
Age and sex breakdown of the population
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CDC PLACES – Neighborhood-Level Health & Well-being
Health-related and social determinant data are sourced from the CDC’s PLACES Project, which provides modeled estimates at the census tract level using data from the Behavioral Risk Factor Surveillance System (BRFSS). These indicators estimate adult health outcomes and barriers to well-being.
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Lack of health insurance (ages 18–64)
Percentage of adults without insurance
Depression
Adults ever diagnosed with a depressive disorder
Feeling socially isolated
Adults reporting social isolation
Food insecurity (past 12 months)
Adults reporting difficulty affording food
Frequent mental or physical distress
Adults reporting ≥14 days of poor mental or physical health in the past month
Housing insecurity (past 12 months)
Adults worried about losing housing
Lack of reliable transportation
Adults unable to access consistent transportation
Lack of social/emotional support
Adults reporting inadequate support
No leisure-time physical activity
Adults reporting no exercise outside of work
Short sleep duration
Adults sleeping less than 7 hours per night
Received SNAP (past 12 months)
Adults receiving food stamps
Utility shut-off threat (past 12 months)
Adults threatened with utility disconnection
Visited dentist / doctor in past year
Access to routine health and dental care
All data are modeled at the census tract level to reflect local neighborhood conditions.
Ohio Department of Education and Workforce (ODEW) – School Building-Level Data
Academic performance and educational readiness indicators are sourced from the Ohio Department of Education and Workforce (ODEW) and reflect publicly reported data at the individual school building level. These indicators highlight progress at key points along the student journey from kindergarten through high school.
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Kindergarten Readiness (KRA-R)
Students classified as Emerging, Approaching, or Demonstrating Readiness in early skills
Third Grade Reading Proficiency
Students proficient or higher on Ohio’s Grade 3 English Language Arts assessment
Eighth Grade Math Proficiency
Students proficient or higher on Ohio’s Grade 8 Mathematics assessment
Four-Year High School Graduation Rate
Percentage of students graduating within four years of entering Grade 9
Geographic Level: School building
Timeframe: School years 2018–2019 through 2023–2024
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Geographic Crosswalks and Mapping Resources
To enable place-based analysis of educational, economic, and social data, geographic crosswalks and mapping resources are employed to align disparate data sources at consistent levels of geography.
NCES School District–to–Census Tract Crosswalk
The National Center for Education Statistics (NCES) school district–to–census tract crosswalk is used to match educational datasets reported at the school district level to U.S. Census Bureau demographic and economic data available at the census tract level. This crosswalk accounts for instances where census tracts overlap multiple school districts and applies proportional allocation based on residential population.
Missouri Census Data Center’s GeoCorr 2022 Application
For geographic entities not directly linked in existing crosswalk datasets—such as cities, townships, or custom regions—the Missouri Census Data Center’s Geographic Correspondence Engine (GeoCorr 2022) is used to generate allocation tables. This tool enables mapping between different geographic units (e.g., townships to census tracts or cities to school districts) using census block-level population weights. The application is available at: https://mcdc.missouri.edu/applications/geocorr2022.html.
TIGER/Line Shapefiles – Montgomery County, Ohio
U.S. Census Bureau Topologically Integrated Geographic Encoding and Referencing (TIGER/Line) shapefiles are used to produce geographic boundary maps for Montgomery County, Ohio, including census tracts, municipal boundaries, and other relevant geographic layers. These shapefiles provide the spatial framework for integrating and visualizing datasets across multiple geographic levels.
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Data Limitations
Small Demographic Group Data Masking: In accordance with FERPA and HIPAA, any group with fewer than 10 individuals within a school building or census tract geography is masked (displayed as “<10”) to protect privacy. When viewing multiple masked values across buildings or census tracts that roll up into a larger area, cumulative data loss may occur. This limitation may be amplified in dashboards that allow users to compare or aggregate multiple school buildings within the same report. Users seeking comprehensive statistics at broader geographic levels (e.g., city, county, state) are encouraged to consult additional sources such as the Ohio Department of Education, CDC, National Student Clearinghouse, American Community Survey, Bureau of Labor Statistics, or the U.S. Census Bureau.
Special Thanks
This work reflects the knowledge and lived experience of many contributors. Special thanks to the Montgomery County 2 Generation Outcomes Committee and community partners who guided the development of this dashboard.
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Contributors:
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Jen Brauer – Preschool Promise
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Hope Cypryla – Miami Valley Child Development Centers
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Erica Fields – Learn to Earn Dayton
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Philip Ferrari – Learn to Earn Dayton
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Jessica Salem – Dayton Children's Hospital
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Lisa Henderson – Greater Dayton Area Hospital Association
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Jan Lepore-Jentleson – East End Community Services
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Lizz Mahar – Montgomery County Human Services Planning and Development
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Mary Reid – Catholic Social Services
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Emma Smales – Learn to Earn Dayton
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Nichole Smith – East End Community Services
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Bob Stoughton – Montgomery County Human Services and Planning Department (ret.)
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Scott Siegfried – Miami Valley Child Development Centers
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Richard Stock – University of Dayton
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Berta Velilla – Miami Valley Child Development Centers
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Rachel Ward – Omega Community Development Corporation
We are deeply grateful for their guidance and shared commitment to using data to drive meaningful, place-based change.
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Questions or Suggestions?
For questions, suggestions, or revisions to the dashboard, please contact Philip Ferrari at
Philip.Ferrari@learntoearndayton.org.