About this dataset

Context

The dataset presents the median household income across different racial categories in Chicago Heights. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Chicago Heights population by race & ethnicity, the population is predominantly Black or African American. This particular racial category constitutes the majority, accounting for 40.34% of the total residents in Chicago Heights. Notably, the median household income for Black or African American households is $51,010. Interestingly, despite the Black or African American population being the most populous, it is worth noting that White households actually reports the highest median household income, with a median income of $78,294. This reveals that, while Black or African Americans may be the most numerous in Chicago Heights, White households experience greater economic prosperity in terms of median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Chicago Heights.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Chicago Heights median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household income across different racial categories in West Chicago. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of West Chicago population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 44.63% of the total residents in West Chicago. Notably, the median household income for White households is $123,682. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $123,682.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in West Chicago.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for West Chicago median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Chicago. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Chicago, the median household income for the households where the householder is White increased by $22,056(27.31%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $80,765 in 2013 and $102,821 in 2023.
• Black or African American: In Chicago, the median household income for the households where the householder is Black or African American increased by $3,868(9.70%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $39,876 in 2013 and $43,744 in 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Chicago.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Chicago median household income by race. You can refer the same here

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

Background

In the wake of a nationwide controversy over policing, we've decided to study one of the largest police departments in the United States: the Chicago Police Department (CPD). Thanks to the Invisible Institute, a non-profit journalism organization, we acquired and analyzed comprehensive data on police brutality in Chicago. However, there is still much to consider:

What effects do education, income, and marital status have on crime rates and policing patterns? Is the CPD allocating its resources in the most effective manner? Who are the people being policed?

With detailed demographic data, we can more confidently explore these difficult questions.

Content (to be expanded)

A beat is a subdivision of a police district. See more here and here

General Data

beatpop.txt: population and square mileage

beathh.txt: number of households

Demographic Data

beatage.txt: populations of age groups

beatrace.txt: populations of ethnic groups

Socioeconomic Data

beathi.txt: average median household income

beatfs.txt: number on food stamps

Educational Data

beatea.txt: number with bachelor's, HS diploma, and none

beatse.txt: number enrolled in some school by age

Even Lines > 1: beat name Odd Lines > 1: White, Hispanic, Black, Asian, Mixed, Other populations

Median Household Income

beathi.txt

Lines > 1: beat name, average median household income

Population Living on Food Stamps

beatfs.txt

Lines > 1: beat name, number living on food stamps

Methods

We acquired 2 GeoJSON files describing block group and beat boundaries. Using each geographical division has its distinct benefit: block groups have corresponding census data; beats are used in police records.

We then created two 10,000x10,000 arrays of strings, one for each division, where each position (or pixel) represents a 13.8x13.8 ft region of Chicago, and each string assigns that pixel to its block group / beat. The scalings for the two arrays are the same, meaning that pixel (x, y) in the block group array is geographically identical to (x, y) in the beat array.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1718624%2F30d6995d49d129bf86f0a7a20541721e%2Fbgs.png?generation=1595103318202402&alt=media%20=500x500" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1718624%2Fedadcf95d70d3ace473dab02fc440255%2Fbeats.png?generation=1595103442331258&alt=media%20=500x500" alt=""> Each 10,000x10,000 array converted to an image, with each division receiving a unique color. Block groups (L), beats (R)

We scraped each block group's demographic data from [3]. Under the simplifying assumption that any two pixels within the same block group have the same data, we "distributed" each block group's data among its constituent pixels. Lastly, we calculated the data for each beat by "summing up" the data of its constituent pixels.

Caveats

1) The above procedure of "distributing" and "summing up" data, which enables the conversion from block groups to beats, is an approximation. However, since beats are much larger than block groups (as can be seen in the above maps), we have sufficient reason to trust the accuracy of this approximation method. See the Accuracy section for more details.

2) The CPD has made slight changes to its beat boundaries over the years. The beats described in this dataset are up-to-date. This data still can be used with older police records, but with minor hiccups for a few beats.

Accuracy

The main cause of errors in our data is the imperfect overlapping between block group and beat boundaries. Such overlapping is broken into 2 groups: 1) those along Chicago's boundaries; and 2) those within Chicago's boundaries.

We checked for the impact of 1) by "adding up" the estimated data for each beat and cross-referencing them to Chicago's totals (e.g. adding every beat's population, and comparing the sum to Chicago's population). Since many block groups straddle Chicago's borders, how well our approximation algorithm handles their data determines the accuracy of our totals. We calculated percentage errors between our totals and those from [3], finding that none exceeded 2% (see ERRORLOG.txt for more details). Thus, our data is, for the large part, clear of errors caused by 1).

Since inaccuracies committed by our algorithm within Chicago's boundaries have no impact on our totals, the above method can't find any inaccuracies caused by 2). Indeed, since there exists no official demographic data for beats, it's probably impossible to precisely check for this. The best we could do was to create map...

Prior research has established the greater exposure of African Americans from all income groups to disadvantaged environments compared to whites, but the traditional focus in studies of neighborhood stratification obscures heterogeneity within racial/ethnic groups in residential attainment over time. Also obscured are the moderating influences of broader social changes on the life-course and the experiences of Latinos, a large and growing presence in American cities. We address these issues by examining group-based trajectory models of residential neighborhood disadvantage among white, Black, and Latino individuals in a multi-cohort longitudinal research design of over 1,000 children from Chicago as they transitioned to adulthood over the last quarter century. We find considerable temporal consistency among white individuals compared to dynamic heterogeneity among nonwhite individuals in exposure to residential disadvantage, especially Black individuals and those born in the 1980s compared to the 1990s. Racial and cohort differences are not accounted for by early-life characteristics that predict long-term attainment. Inequalities by race in trajectories of neighborhood disadvantage are thus at once more stable and more dynamic than previous research suggests, and they are modified by broader social changes. These findings offer insights on the changing pathways by which neighborhood racial inequality is produced.

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

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

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

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

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

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

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