This dataset contains a selection of 27 indicators of public health significance by Chicago community area, with the most updated information available. The indicators are rates, percents, or other measures related to natality, mortality, infectious disease, lead poisoning, and economic status. See the full description at https://data.cityofchicago.org/api/assets/BB7058D2-E8A1-4E11-86CE-6CF1738F0A02.

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: R/ECAPs must have a non-white population of 50 percent or more. Regarding the poverty threshold, Wilson (1980) defines neighborhoods of extreme poverty as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40% or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs. This translates into the following equation: Where i represents census tracts, () is the metropolitan/micropolitan (CBSA) mean tract poverty rate, is the ith tract poverty rate, () is the non-Hispanic white population in tract i, and Pop is the population in tract i.While this definition of R/ECAP works well for tracts in CBSAs, place outside of these geographies are unlikely to have racial or ethnic concentrations as high as 50 percent. In these areas, the racial/ethnic concentration threshold is set at 20 percent. Data Source: Related AFFH-T Local Government, PHA Tables/Maps: Table 4, 7; Maps 1-17.Related AFFH-T State Tables/Maps: Table 4, 7; Maps 1-15, 18.References:Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.To learn more about R/ECAPs visit:https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2017 - 2021 ACSDate Updated: 10/2023

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2007 – 2011. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table. See the full dataset description for more information at https://data.cityofchicago.org/api/assets/8D10B9D1-CCA3-4E7E-92C7-5125E9AB46E9.

An “Area of Persistent Poverty” is defined by the Bipartisan Infrastructure Law. For the purpose of a grant application, a project is located in an Area of Persistent Poverty if:the County in which the project is located consistently had greater than or equal to 20 percent of the population living in poverty in all three of the following datasets: (a) the 1990 decennial census; (b) the 2000 decennial census; and (c) the most recent (2022) Small Area Income Poverty Estimates; ORthe Census Tract in which the project is located has a poverty rate of at least 20 percent as measured by the 2014-2018 5-year data series available from the American Community Survey of the Bureau of the Census; ORthe project is located in any territory or possession of the United States.

Areas of Chicago, based on census tracts, that are the most socioeconomically disadvantaged, for the purpose of promoting equitable hiring within areas of economic need. Qualifying areas were identified using three criteria, based on data from the 2014 American Community Survey: household income, poverty rate, and unemployment rate. These area designations are used for workforce bid incentives for City contracts administered by the Department of Procurement Services. They will also be used for workforce requirements for construction at the temporary casino facility, as agreed to in the Host Community Agreement between Bally’s and the City of Chicago. The designations are made under Section 2-92-390 of the City of Chicago code. This dataset is in a forma​​t for spatial datasets that is inherently tabular but allows for a map as a derived view. Please click the indicated link below for such a map. To export the data in either tabular or geographic format, please use the Export button on this dataset.

Abstract (en): This survey was undertaken to assemble a broad range of family, household, employment, schooling, and welfare data on families living in urban poverty areas of Chicago. The researchers were seeking to test a variety of theories about urban poverty. Questions concerned respondents' current lives as well as their recall of life events from birth to age 21. Major areas of investigation included household composition, family background, education, time spent in detention or jail, childbirth, fertility, relationship history, current employment, employment history, military service, participation in informal economy, child care, child support, child-rearing, neighborhood and housing characteristics, social networks, current health, current and past public aid use, current income, and major life events. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.. Non-Hispanic whites, non-Hispanic Blacks, and persons of Mexican or Puerto Rican ethnicity, aged 18-44, residing in 1986 in Chicago census tracts with 20 percent or more persons living under the poverty line. Multistage stratified probability sample design yielding 2,490 observations (1,183 Blacks, 364 whites, 489 Mexican-origin persons, and 454 Puerto Rican-origin persons). Though Black respondents include parents (N = 1,020) and non-parents (N = 163), only parents were selected within non-Black groups. Response rates ranged from 73.8 percent for non-Hispanic whites to 82.5 percent for Black parents. 1997-11-04 The documentation and frequencies are being released as PDF files, and an SPSS export file is now available. Also, the SAS data definition statements and SPSS data definition statements have been reissued with minor changes, and SPSS value labels are being released in Part 7 due to SPSS for Windows limitations. Funding insitution(s): Carnegie Corporation. Chicago Community Trust. Ford Foundation. Institute for Research on Poverty. Joyce Foundation. Lloyd A. Fry Foundation. John D. and Catherine T. MacArthur Foundation. Rockefeller Foundation. Spencer Foundation. United States Department of Health and Human Services. William T. Grant Foundation. Woods Charitable Fund. Value labels for this study are being released in a separate file, Part 7, to assist users of SPSS Release 6.1 for Windows. The syntax window in this version of SPSS will read a maximum of 32,767 lines. If all value labels were included in the SPSS data definition file, the number of lines in the file would exceed 32,767 lines.All references to card-image data in the codebook are no longer applicable.During generation of the logical record length data file, ICPSR optimized variable widths to the width of the widest value appearing in the data collection for each variable. However, the principal investigator's user-missing data code definitions were retained even when a variable contained no missing data. As a result, when user-missing data values are defined (e.g., by uncommenting the MISSING VALUES section in the SPSS data definition statements) and exceed the optimized variable width, SPSS's display dictionary output will contain asterisks for the missing data codes.Producer: University of Chicago, Center for the Study of urban Inequality, and the National Opinion Research Center (NORC).

In 2021, Philadelphia, Pennsylvania was the city with the highest poverty rate of the United States' most populated cities. In this statistic, the cities are sorted by poverty rate, not population. The most populated city in 2021 according to the source was New York city - which had a poverty rate of 18 percent.

This paper uses an overlapping-generations dynamic general equilibrium model of residential sorting and intergenerational human capital accumulation to investigate the effects of neighborhood externalities. In the model, households choose where to live and how much to invest toward the production of their child’s human capital. The return on parents’ investment is determined in part by the child’s ability and in part by an externality from the average human capital in their neighborhood. We use the model to test a prominent hypothesis about the concentration of poverty within racially-segregated neighborhoods (Wilson 1987). We first impose segregation on a model with two neighborhoods and match the model steady state to income and housing data from Chicago in 1960. Next, we lift the restriction on moving and compute the new steady state and corresponding transition path. The transition implied by the model qualitatively supports Wilson’s hypothesis: high-income residents of the low average human capital neighborhood move out, reducing the returns to investment in their old neighborhood. Sorting decreases citywide human capital and produces congestion in the high-income neighborhood, increasing the average cost of housing. On net, average welfare decreases by 3.0 percent of presorting steady state consumption, and 0.01 percent of households starting in the low-income neighborhood receive positive welfare.

This study sought to answer the question: If a woman is experiencing intimate partner violence, does the collective efficacy and community capacity of her neighborhood facilitate or erect barriers to her ability to escape violence, other things being equal? To address this question, longitudinal data on a sample of 210 abused women from the CHICAGO WOMEN'S HEALTH RISK STUDY, 1995-1998 (ICPSR 3002) were combined with community context data for each woman's residential neighborhood taken from the Chicago Alternative Policing Strategy (CAPS) evaluation, LONGITUDINAL EVALUATION OF CHICAGO'S COMMUNITY POLICING PROGRAM, 1993-2000 (ICPSR 3335). The unit of analysis for the study is the individual abused woman (not the neighborhood). The study takes the point of view of a woman standing at a street address and looking around her. The characteristics of the small geographical area immediately surrounding her residential address form the community context for that woman. Researchers chose the police beat as the best definition of a woman's neighborhood, because it is the smallest Chicago area for which reliable and complete data are available. The characteristics of the woman's police beat then became the community context for each woman. The beat, district, and community area of the woman's address are present. Neighborhood-level variables include voter turnout percentage, organizational involvement, percentage of households on public aid, percentage of housing that was vacant, percentage of housing units owned, percentage of feminine poverty households, assault rate, and drug crime rate. Individual-level demographic variables include the race, ethnicity, age, marital status, income, and level of education of the woman and the abuser. Other individual-level variables include the Social Support Network (SSN) scale, language the interview was conducted in, Harass score, Power and Control score, Post-Traumatic Stress Disorder (PTSD) diagnosis, other data pertaining to the respondent's emotional and physical health, and changes over the past year. Also included are details about the woman's household, such as whether she was homeless, the number of people living in the household and details about each person, the number of her children or other children in the household, details of any of her children not living in her household, and any changes in the household structure over the past year. Help-seeking in the past year includes whether the woman had sought medical care, had contacted the police, or had sought help from an agency or counselor, and whether she had an order of protection. Several variables reflect whether the woman left or tried to leave the relationship in the past year. Finally, the dataset includes summary variables about violent incidents in the past year (severity, recency, and frequency), and in the follow-up period.

Note that S is the average of the columns in Table 7

Number of Tracts Meeting Uncertainty Threshold (CV = 0.12), Poverty Scenario for Chicago MSA.

Regionalization Results Summary, Variation in Maximum CV Value, Poverty Scenario for Chicago MSA.

Comprehensive demographic dataset for Austin, Chicago, IL, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.