Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Cuyahoga County, OH (S1701ACS039035) from 2012 to 2023 about Cuyahoga County, OH; Cleveland; OH; poverty; percent; 5-year; population; and USA.

Graph and download economic data for Estimate of People Age 0-17 in Poverty in Cuyahoga County, OH (PEU18OH39035A647NCEN) from 1989 to 2023 about Cuyahoga County, OH; Cleveland; under 18 years; OH; child; poverty; persons; and USA.

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; poverty; percent; 5-year; population; and USA.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Lorain County, OH (S1701ACS039093) from 2012 to 2023 about Lorain County, OH; Cleveland; OH; poverty; percent; 5-year; population; and USA.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Geauga County, OH (S1701ACS039055) from 2012 to 2023 about Geauga County, OH; Cleveland; OH; poverty; percent; 5-year; population; and USA.

DescriptionThis dataset includes a multimodal assessment of the Cleveland Transportation Network, conducted as part of the Cleveland Moves initiative. It evaluates need and comfort levels to improve safety and mobility on Cleveland streets.The Pedestrian Crossing Level of Stress layer was created by Toole Design and uses attributes such as number of lanes, speed limit, and presence of pedestrian islands to assess crossing stress. Data sources include Ohio and City of Cleveland street and intersection data (2024).The Bicycle Level of Traffic Stress layer, also developed by Toole Design, evaluates stress for cyclists based on lane count, speed limit, bikeway type, and other factors. This data was also generated in 2024.The ODOT Active Transportation Need layer was developed by the Ohio Department of Transportation. It incorporates factors such as vehicle access and poverty rates to determine transportation need.Update FrequencyThis dataset will be updated with additional analysis from the Cleveland Moves planning process by early 2025. After that, updates will occur annually to reflect changes aimed at improving safety and mobility.Related ApplicationsA summary of this dataset is available in the Cleveland Moves Network Assessment Dashboard.The ODOT Active Transportation Need dataset was developed by the Ohio Department of Transportation. More information is available on their website: ODOT GlossaryContactsSarah Davis, Active Transportation Senior Plannersdavis2@clevelandohio.gov

We characterize Housing Choice Voucher (HCV) use in Low-Income Housing Tax Credit (LIHTC) units with the intent to explore whether the subsidy overlap responds to needs unmet by the HCV program alone. Lacking the data to contrast HCV use in and out of LIHTC units, we turn to a comparison of HCV users in LIHTC units relative to the overall HCV population. Our analysis of 2011 tenant-level LIHTC data from Ohio and HCV data from HUD suggests place-based vouchers, which must be redeemed in an LIHTC unit, are more likely allocated to extremely low-income or special-needs households. On the other hand, HCV users who freely choose to redeem their voucher in an LIHTC unit are similar to the overall HCV population in terms of incomes and ethnicity, although they tend to be older. There is little evidence that using both programs in concert enables access to better neighborhoods for HCV users: households across both programs live in neighborhoods that tend to have poverty rates above 20 percent, with HCV-LIHTC users actually living in higher-poverty neighborhoods in most urban Ohio counties when compared to the HCV population as a whole.

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.

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; poverty; percent; 5-year; population; and USA.