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This layer shows poverty status by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Poverty status is based on income in past 12 months of survey. This layer is symbolized to show the percentage of the population whose income falls below the Federal poverty line. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 7, 2023The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2022 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.
In 2023, about 13.3 percent of Ohio's population lived below the poverty line. This was no change from the previous year. The poverty rate of the United States can be accessed here.
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Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Lake County, OH (S1701ACS039085) from 2012 to 2023 about Lake County, OH; Cleveland; OH; percent; poverty; 5-year; population; and USA.
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Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Medina County, OH (S1701ACS039103) from 2012 to 2023 about Medina County, OH; Cleveland; OH; percent; poverty; 5-year; population; and USA.
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35 to 64 years Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Cleveland, Ohio by age, education, race, gender, work experience and more.
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This layer is the Neighborhood Development Index, or NDI. It is based on a study done by Community Development, and the Poverty Center at Case Western to identify parts of the city that fall in the middle of the spectrum of socioeconomic and housing conditions. Middle neighborhoods are typically areas on the edge between growth and decline. These are neither the strongest neighborhoods in a city nor are they the most distressed. In the process of those studies, the resulting NDI provided here is useful for policy work that is responsive to housing and economic conditions. The research team collected over 100 indicators which, through factor analysis, condensed to 65 variables across six distinct factors. These variables include, household income, housing value, race, education, age, poverty rate, health insurance attainment, foreign-born rates, loan rates, and more. These factors were further condensed into three categories here.1) Market-Rate - Relatively active in development, growing rents and transfer values, seeing market-driven development2) Middle Neighborhood - Stable areas both steadily improving or declining in property value and socioeconomic conditions3) Opportunity - Lower property values, income levels, and requiring substantially greater incentive for redevelopmentThese categories, are also used in conjunction with Community Development's residential tax abatement program. The neighborhood tiers are used to determine the level of tax abatement available.Data GlossaryObservations: Aggregated socieconomic indicators by Census block groupBlock Group: Geographic ID of the Census block groupClassification: One of the 3 tiers: Opportunity, Middle, MarketUpdate FrequencyThis data is set to be updated every 2 years. Last update was May 6, 2022--------------------------------UPDATE:05/05/2022The nomenclature "underinvested" has been modified to "opportunity."Many of the N/A areas have been converted to the "opportunity" designation. Current as of 5/5/2022ContactsCity of Cleveland, Department of Community Development
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This dataset includes a multimodal assessment of the Cleveland Transportation Network, conducted as part of the Cleveland Moves initiative. It assesses need and comfort levels as we work to improve safety and mobility on Cleveland streets.The Pedestrian Crossing Level of Stress layer was created by our Cleveland Moves consultant, Toole Design. It uses information about the number of lanes, the speed limit, and the presence of a pedestrian island to calculate how stressful a crossing is for someone crossing. These attributes are provided by Ohio and City of Cleveland data about streets and intersections. This data was generated in 2024. The Bicycle Level of Traffic Stress layer was created by our Cleveland Moves consultant, Toole Design. It uses information about the number of lanes, the speed limit, the type of bikeway, and more to calculate the level of stress for someone riding a bicycle on a given street. These attributes are provided by Ohio and City of Cleveland data about streets and intersections. This data was generated in 2024. The ODOT Active Transportation Need layer was created by the Ohio Department of transportation, and uses several factors to determine need including access to a vehicle, poverty rates, and more.Update FrequencyThis dataset will be updated with additional analysis from the Cleveland Moves planning process by early 2025. After that point, it will be updated annually to reflect changes to Cleveland streets geared towards improving safety and mobility. Related ApplicationsA summary of this dataset can be found in the Cleveland Moves Network Assessment Dashboard.Data GlossaryThe ODOT Active Transportation Need dataset was developed by the Ohio Department of Transportation. More information about this dataset is available on their website: https://gis.dot.state.oh.us/tims_classic/Glossary ContactSarah Davis, Active Transportation Senior Plannersdavis2@clevelandohio.gov
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Population 25 years and over Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Cleveland, Ohio by age, education, race, gender, work experience and more.
The Urban Institute undertook a comprehensive assessment of communities approaching decay to provide public officials with strategies for identifying communities in the early stages of decay and intervening effectively to prevent continued deterioration and crime. Although community decline is a dynamic spiral downward in which the physical condition of the neighborhood, adherence to laws and conventional behavioral norms, and economic resources worsen, the question of whether decay fosters or signals increasing risk of crime, or crime fosters decay (as investors and residents flee as reactions to crime), or both, is not easily answered. Using specific indicators to identify future trends, predictor models for Washington, DC, and Cleveland were prepared, based on data available for each city. The models were designed to predict whether a census tract should be identified as at risk for very high crime and were tested using logistic regression. The classification of a tract as a "very high crime" tract was based on its crime rate compared to crime rates for other tracts in the same city. To control for differences in population and to facilitate cross-tract comparisons, counts of crime incidents and other events were converted to rates per 1,000 residents. Tracts with less than 100 residents were considered nonresidential or institutional and were deleted from the analysis. Washington, DC, variables include rates for arson and drug sales or possession, percentage of lots zoned for commercial use, percentage of housing occupied by owners, scale of family poverty, presence of public housing units for 1980, 1983, and 1988, and rates for aggravated assaults, auto thefts, burglaries, homicides, rapes, and robberies for 1980, 1983, 1988, and 1990. Cleveland variables include rates for auto thefts, burglaries, homicides, rapes, robberies, drug sales or possession, and delinquency filings in juvenile court, and scale of family poverty for 1980 through 1989. Rates for aggravated assaults are provided for 1986 through 1989 and rates for arson are provided for 1983 through 1988.
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SummaryThe Placements dataset contains information on job placements attained through OhioMeansJobs|Cleveland-Cuyahoga County programs (July 2022 - March 2025). Includes basic job seeker information along with job placement information categorized to highlight local industry partnerships and initiatives. Data comes from ARIES, Ohio's system for workforce programs, but goes through an extensive manual cleaning and categorization. Update FrequencyQuarterlyRelated Data ItemsWorkforce Program DashboardWorkforce Program Enrollments DatasetContactsGreater Cleveland Works (formerly Cleveland-Cuyahoga County Workforce Development Board) oversees the public workforce system – helping employers find and develop the skilled workers they need and helping jobseekers find good-paying jobs. The Board currently serves over 10,000 jobseekers a year – helping the region prosper.1910 Carnegie Avenue, Cleveland, OH 44115 216-777-8200greaterclevelandworks.orgDashboard/Data-specific questions: email bryan.metlesitz@jfs.ohio.gov Data GlossaryField | Definition Customer_ID | A unique identification number for workforce data systemsCustomer_Age | The age of a customer determined by the Date of Birth entered into ARIESCustomer_Gender | The gender of a customerCustomer_Race | The race of a customerCustomer_Ethnicity | The ethnicity of a customerEmployer | The company hiring a CCWDB customerEmployer_City | The City in which the Employer is hiring a CCWDB customerEmployer_ZIP | The Zip Code in which the Employer is hiring a CCWDB customerJob_Title | The job title associated with a CCWDB customer job placement. CCWDB_Sector | A categorization of the job placement as it relates to CCWDB industry partnerships (Healthcare, Manufacturing, Information Technology)CCWDB_Job_Family | A categorization of the job placement as it related to the ONET Job Family, with minor adjustments to emphasize CCWDB industry partnerships (Built Environment, Healthcare)Program_Year | The Program Year associated with the Employment Start DateThe CCWDB Program Year runs from July-JunePY_Quarter | The Program Quarter associated with the Employment Start Date (Q1 = July - September, Q2 = October - December, Q3 = January - March, Q4 = April - June)Employment_Start_Date |Date customer begins employmentWage | The compensation associated with a new job placement. ($/hour) Enrollment_Program | Most recent workforce program a customer was enrolled before finding employmentbarriers_Low_Income | An individual or member of a family who receives now or in the last 6 months, income-based public assistance; in a family whose income is not higher than the poverty line or 705 of the lower living standard income level; is homeless; eligible for free or reduced price lunch; foster child for whom government payments are made or is an individual with a disability. barriers_Foster_Care_Status | An individual with a temporary living situation for kids whose parents cannot take care of them and whose need for care has come to the attention of child welfare agency staff. barriers_Homeless | Individual lacks a fixed, regular, and adequate nighttime residencebarriers_Veteran_Flag | Individual is a veteranbarriers_Customer_Disability_Status | An individual without the ability to work at a substantial gainful activity due to a physical or mental impairmentbarriers_Youth_Offender | A youth involved with the justice systembarriers_Adult_Offender | An Adult involved with the justice systembarriers_TANF_Recipient | An individual who receives income and/or benefits from the federal Temporary Assistance to Needy Families program barriers_SSI_Recipient | An individual who receives Supplemental Security Income from the federal Social Security Administrationbarriers_SNAP_Recipient | An individual who receives help to buy food through the Supplemental Nutrition Assistance Programbarriers_Other_Public_Assistance_Recipient | An individual who receives some form of means-tested assistanceindex | Unique identification number for the CCWDB Open Data Placement datasetCity | The City in which the customer residesPostal | The Zip Code in which the customer residesWard | The City of Cleveland Ward in which the customer resides
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Under 5 years Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in East Cleveland, Ohio by age, education, race, gender, work experience and more.
The Urban Institute undertook a comprehensive assessment of communities approaching decay to provide public officials with strategies for identifying communities in the early stages of decay and intervening effectively to prevent continued deterioration and crime. Although community decline is a dynamic spiral downward in which the physical condition of the neighborhood, adherence to laws and conventional behavioral norms, and economic resources worsen, the question of whether decay fosters or signals increasing risk of crime, or crime fosters decay (as investors and residents flee as reactions to crime), or both, is not easily answered. Using specific indicators to identify future trends, predictor models for Washington, DC, and Cleveland were prepared, based on data available for each city. The models were designed to predict whether a census tract should be identified as at risk for very high crime and were tested using logistic regression. The classification of a tract as a "very high crime" tract was based on its crime rate compared to crime rates for other tracts in the same city. To control for differences in population and to facilitate cross-tract comparisons, counts of crime incidents and other events were converted to rates per 1,000 residents. Tracts with less than 100 residents were considered nonresidential or institutional and were deleted from the analysis. Washington, DC, variables include rates for arson and drug sales or possession, percentage of lots zoned for commercial use, percentage of housing occupied by owners, scale of family poverty, presence of public housing units for 1980, 1983, and 1988, and rates for aggravated assaults, auto thefts, burglaries, homicides, rapes, and robberies for 1980, 1983, 1988, and 1990. Cleveland variables include rates for auto thefts, burglaries, homicides, rapes, robberies, drug sales or possession, and delinquency filings in juvenile court, and scale of family poverty for 1980 through 1989. Rates for aggravated assaults are provided for 1986 through 1989 and rates for arson are provided for 1983 through 1988.
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SummaryThe Enrollments dataset contains basic information for local workforce program enrollments with OhioMeansJobs|Cleveland-Cuyahoga County (July 2022 - March 2025). Includes jobseeker demographic, location, employment barrier, and program enrollment information from ARIES, the state-wide case management system for workforce programs. Update FrequencyQuarterlyRelated Data ItemsWorkforce Program DashboardWorkforce Program Placements DatasetContactsGreater Cleveland Works (formerly Cleveland-Cuyahoga County Workforce Development Board) oversees the public workforce system – helping employers find and develop the skilled workers they need and helping jobseekers find good-paying jobs. Greater Cleveland Works currently serves over 10,000 jobseekers a year – helping the region prosper.1910 Carnegie Avenue, Cleveland, OH 44115 216-777-8200greaterclevelandworks.orgDashboard/Data-specific questions: email bryan.metlesitz@jfs.ohio.gov Data GlossaryField | Definition Customer_ID | A unique identification number for workforce data systemsCurrently_Enrolled | Identifies which customers are currently enrolled in a local workforce program. (snapshot in time of active enrollments) Enrollment_Program | Workforce programs available to job seekers. Enrollment_Program_Start_Date | The day a customer begins receiving services funded by a specific workforce program. Enrollment_Program_Completion_Date | The day a program enrollment is concluded. Enrollment_Completion_Reason | The outcome of a program enrollment. (the reason why a customer program enrollment is concluded) Customer_Age | The age of a customer determined by the Date of Birth entered into ARIESCustomer_Gender | The gender of a customerCustomer_Race | The race of a customerCustomer_Ethnicity | The ethnicity of a customerProgram_Year | The Program Year associated with the Employment Start DateThe CCWDB Program Year runs from July-JunePY_Quarter | The Program Quarter associated with the Employment Start Date (Q1 = July - September, Q2 = October - December, Q3 = January - March, Q4 = April - June)barriers_Low_Income | An individual or member of a family who receives now or in the last 6 months, income-based public assistance; in a family whose income is not higher than the poverty line or 705 of the lower living standard income level; is homeless; eligible for free or reduced price lunch; foster child for whom government payments are made or is an individual with a disability. barriers_Foster_Care_Status | An individual with a temporary living situation for kids whose parents cannot take care of them and whose need for care has come to the attention of child welfare agency staff. barriers_Homeless | Individual lacks a fixed, regular, and adequate nighttime residencebarriers_Veteran_Flag | Individual is a veteranbarriers_Customer_Disability_Status | An individual without the ability to work at a substantial gainful activity due to a physical or mental impairmentbarriers_Youth_Offender | A youth involved with the justice systembarriers_Adult_Offender | An Adult involved with the justice systembarriers_TANF_Recipient | An individual who receives income and/or benefits from the federal Temporary Assistance to Needy Families program barriers_SSI_Recipient | An individual who receives Supplemental Security Income from the federal Social Security Administrationbarriers_SNAP_Recipient | An individual who receives help to buy food through the Supplemental Nutrition Assistance Programbarriers_Other_Public_Assistance_Recipient | An individual who receives some form of means-tested assistanceindex | Unique identification number for the CCWDB Open Data Placement datasetCity | The City in which the customer residesPostal | The Zip Code in which the customer residesWard | The City of Cleveland Ward in which the customer resides
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Under 18 years Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Cleveland, Ohio by age, education, race, gender, work experience and more.
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Two or more races Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Cleveland, Ohio by age, education, race, gender, work experience and more.
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Asian Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Cleveland, Ohio by age, education, race, gender, work experience and more.
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Female Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Cleveland, Ohio by age, education, race, gender, work experience and more.
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Male Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Cleveland, Ohio by age, education, race, gender, work experience and more.
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White, not Hispanic Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in East Cleveland, Ohio by age, education, race, gender, work experience and more.
Male Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in East Cleveland, Ohio by age, education, race, gender, work experience and more.
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This layer shows poverty status by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Poverty status is based on income in past 12 months of survey. This layer is symbolized to show the percentage of the population whose income falls below the Federal poverty line. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 7, 2023The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2022 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.