About this dataset

Context

The dataset tabulates the Chicago population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Chicago across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Chicago was 2.66 million, a 0.31% decrease year-by-year from 2022. Previously, in 2022, Chicago population was 2.67 million, a decline of 1.16% compared to a population of 2.7 million in 2021. Over the last 20 plus years, between 2000 and 2023, population of Chicago decreased by 231,271. In this period, the peak population was 2.9 million in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Chicago is shown in this column.
• Year on Year Change: This column displays the change in Chicago population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Year. You can refer the same here

Introduction This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends. Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change – birth, death and net migration – is projected separately for each five-year birth cohort and sex. The cohort – component method employs the following basic demographic balancing equation: P1 = P0 + B – D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration – Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is “survived” to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years). Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates. Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period. Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health. Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group. Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question “Where did you live five years ago?” The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods. Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.

Separate tables for the last ten years of Community Data Snapshot releases (2015-2025) are provided for three geographic levels:The seven counties in the CMAP region (with regional total)The 284 municipalities in the CMAP regionThe 77 Chicago community areas (CCAs) There is limited geographic availability (particularly at the CCA level) for some variables. Additional information on availability and data sources are found in the CDS Data Dictionary. Looking to match human-friendly labels to field names? Use the CDS Data Dictionary Crosswalk.When using multiple releases of the snapshots, please don’t compare overlapping ACS 5-Year Estimates. The Census Bureau provides specific guidance for when it is appropriate to compare ACS data across time. CMAP uses the most recently available 5-Year Estimates, which are usually available on a two year lag:CDS release yearACS 5-Year Estimates data vintageCompare to previous CDS release year20252019-20232020, 201520242018-2022201920232017-2021201820222016-2020201720212015-2019201620202014-2018201520192013-2017 20182012-2016 20172011-2015 20162010-2014 20152009-2013 NOTE: Much of the data is from five-year American Community Survey, which is a sample-based data product. This means users must exercise caution when interpreting data from low-population municipalities, as the margins of error are often large compared to the estimate. Not sure which municipality or Chicago community area you want? Explore a community's data in the interactive dashboard.Are you looking for the PDF versions? Find and download the print-friendly Community Data Snapshots from the agency website.

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 West 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 West Chicago, the median household income for the households where the householder is White increased by $35,764(40.68%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $87,918 in 2013 and $123,682 in 2023.
• Black or African American: In West Chicago, the median household income for Black or African American households was $12,014 in 2013(2023 inflation-adjusted dollars). However there is no reported data for 2023, indicating a lack of information for this specific year.
• 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 West 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 West Chicago median household income by race. You can refer the same here

Selected variables from the most recent 5 year ACS Community Survey (Released 2023) aggregated by Ward. Additional years will be added as they become available.

The underlying algorithm to create the dataset calculates the percent of a census tract that falls within the boundaries of a given ward. Given that census tracts and ward boundaries are not aligned, these figures should be considered an estimate.

Total Population in this Dataset: 2,649,803 Total Population of Chicago reported by ACS 2023: 2,664,452 % Difference: %-0.55

There are different approaches in common use for displaying Hispanic or Latino population counts. In this dataset, following the approach taken by the Census Bureau, a person who identifies as Hispanic or Latino will also be counted in the race category with which they identify. However, again following the Census Bureau data, there is also a column for White Not Hispanic or Latino.

The City of Chicago is actively soliciting community input on how best to represent race, ethnicity, and related concepts in its data and policy. Every dataset, including this one, has a "Contact dataset owner" link in the Actions menu. You can use it to offer any input you wish to share or to indicate if you would be interested in participating in live discussions the City may host.

Code can be found here: https://github.com/Chicago/5-Year-ACS-Survey-Data

Ward Shapefile:

https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Wards-2023-Map/cdf7-bgn3

Census Area Python Package Documentation:

https://census-area.readthedocs.io/en/latest/index.html

The Chicago Council Surveys are part of a long-running series of public opinion surveys conducted by The Chicago Council on Global Affairs beginning in 1974. They were conducted quadrennially from 1974 to 2002, biennially from 2002 to 2014, and are now conducted annually. The surveys are designed to investigate the opinions and attitudes of the general public on matters related to foreign policy, and to define the parameters of public opinion within which decision-makers must operate. This public opinion study of the United States focused on respondents' opinions of the United States' leadership role in the world and the challenges the country faces domestically and internationally. Data were collected on a wide range of international topics, including: United States' relations with other countries, role in foreign affairs, possible threats to vital interests in the next ten years, foreign policy goals, situations that might justify the use of United States troops in other parts of the world, international trade, United States' participation in potential treaties, U.S. policy towards Russia in Ukraine, the embargo on Cuba and the effects of renewed diplomatic relations with Havana, views of the nuclear deal with Iran and what effects that deal is likely to have, and United States' relations with allies in Asia. Respondents were also asked their opinion on domestic issues including climate change, measures to improve the United States' economic competitiveness, and their views on US immigration policy. Demographic information collected includes age, gender, race/ethnicity, marital status, left-right political self-placement, political affiliation, employment status, highest level of education, and religious preference, household income, state of residence, and living quarters ownership status.

Context

This dataset contains information on Chicago crime reported between 2015 and 2020.

Content

This dataset is a subset of the BigQuery public database on Chicago Crime.

Acknowledgements

I appreciate the efforts of BigQuery hosting and allowing access to their public databases and Kaggle for providing a space for the widespread sharing of data and knowledge.

Inspiration

This dataset is a useful learning tool for applying descriptive statistics, analytics, and visualisations. For example, one could look at crime trends over time, identify areas with the lowest amount of crime, calculate the propability that an arrest is made based on crime type or area, and determine days of the week with the highest and lowest crime.

In 2014, Chicago Public Schools, looking to reduce the possibility of gun violence among school-aged youth, applied for a grant through the National Institute of Justice. CPS was awarded the Comprehensive School Safety Initiative grant and use said grant to establish the "Connect and Redirect to Respect" program. This program used student social media data to identify and intervene with students thought to be at higher risk for committing violence. At-risk behaviors included brandishing a weapon, instigating conflict online, signaling gang involvement, and threats towards others. Identified at-risk students would be contacted by a member of the CPS Network Safety Team or the Chicago Police Department's Gang School Safety Team, depending on the risk level of the behavior. To evaluate the efficacy of CRR, the University of Chicago Crime Lab compared outcomes for students enrolled in schools that received the program to outcomes for students enrolled in comparison schools, which did not receive the program. 32 schools were selected for the study, with a total of 44,503 students. Demographic variables included age, race, sex, and ethnicity. Misconduct and academic variables included arrest history, in-school suspensions, out-of-school suspensions, GPA, and attendance days.

In 2023, the GDP of the Chicago-Naperville-Elgin metropolitan area amounted to ****** billion chained 2017 U.S. dollars. The GDP of the United States since 1990 can be accessed here. Economic growth and unemployment in Chicago Economic growth in Chicago, measured by the growth in Gross Domestic Product (GDP), was significant in the years between 2001 and 2022. This growth occurred in a period of growth for cities nationally as seen by growth of other major American cities such as Los Angeles and San Francisco. In contrast to Chicago’s growth, San Francisco’s growth rate demonstrated the effect of a new and booming industry. The influence of technology and internet companies saw San Francisco grow nearly ** percent in comparison to the ** percent growth in GDP achieved by Chicago. As a result, Chicago-Naperville-Elgin ranked third in Gross Metropolitan Product of the United States, by metropolitan area in 2022. The drop in GDP output in 2020 can be attributed to the COVID-19 pandemic.

In 2021, the cargo tonnage registered at Chicago's O'Hare International Airport amounted to more than *** million metric tons, which represented a year-over-year increase of around ***** percent. The passenger traffic at O'Hare International reported a significant growth of ***** percent during the same year.

The annual number of passengers of the Chicago rapid transit system, also known as the "L", reported an overall decline between 2015 to 2019. In 2020, amid the COVID-19 pandemic, the ridership of the "L" plummeted **** percent, compared with 2019 levels.

The annual number of passengers of the Chicago transit bus system reported an overall decline between 2015 to 2019. In 2020, amid the COVID-19 pandemic, the ridership of the CTA bus service plummeted **** percent, compared with 2019 levels.

Most of the United States (U.S.) population live together in a few densely populated areas. While this is a well known fact, visual explanations of this characteristic can be quite striking. These four maps illustrate in different ways where we live, and how we actually inhabit so little of our country's space.Map 1 shows the coastal shoreline counties of the U.S., which are the counties that are directly adjacent to an open ocean, a major estuary, or the Great Lakes. According to 2014 Census data, 39.1 percent of the U.S. population lived in those counties, often within miles of the coast.Map 2 highlights the largest and smallest counties in the U.S. Roughly fifty percent of the U.S. population lives in the country's 144 largest counties, while the roughly other 50 percent lives in 2,998 counties.Map 3 compares America's two largest counties (Los Angeles and Downtown Chicago) with the 14 smallest states.Map 4 compares the population of these two counties with 1,437 of the country's smallest counties. Nearly five percent of America's population lives in the counties covering downtown Los Angeles and downtown Chicago, which is the same proportion as those that live in the country's 1,437 smallest counties.Source: Ana Swanson, Washington Post Wonkblog. September 3, 2015

This dataset tracks annual two or more races student percentage from 2011 to 2015 for Urban Prep Chtr Academy Englewood High School vs. Illinois and Chicago Public Schools District 299

Records from the Crimes - 2001 to Present dataset for the indicated year.

Please see the description section of the full dataset for further information about the data.

Divvy Bike Trips and Stations Data

Chicago Bike-Sharing Trips and Station Details in 2015

By Philip E Cannata [source]

About this dataset

The Divvy Bike Dataset is a comprehensive compilation of data relating to the use and management of bike-sharing systems in Chicago for the year 2015. This large dataset aims to provide a detailed perspective on bike utilization across different times of the year, with individual columns detailing various aspects ranging from specific station information to user demographics.

In order to allow for easy parsing and analysis, the information is partitioned across different files according to quarters. Data for Q1 (January - March) and Q2 (April - June) are each contained within their own separate files, while data corresponding to July, August, and September are presented in dedicated monthly files.

A striking feature within this dataset lies in its granularity, as it provides a multitude of columns detailing both location-specific information regarding stations as well as time-specific details about each trip. 'Name', 'latitude', 'longitude', 'dpcapacity' ,and 'online_date' specifically describe station characteristics such as the name identification of stations along with geographical coordinates (longitude and latitude), capacity limits (defined by how many bikes each can handle), and commencement dates.

Additionally, detailed temporal data regarding individual trips have been provided which include start timings ('starttime'), end timings('stoptime'), duration('tripduration') each denoting when exactly trips took off & ended along with how long they lasted respectively. Information about from_station_name & to_station_name denote journey routes by stating where these rides originated from & where they culminated.

Moreover demographic details associated with users have been recorded too which involve user type (usertype - identifying if users were subscribers or typical customers), gender ('gender') , birthyear ('birthyear').

Overall this robust resource serves professionals such as urban planners investigating city infrastructure efficiency or businesses conducting market research in Chicago or similar urban areas on a monolithic scale by peeling back layers underpinning functions spanning across multiple months unraveling intricate details surrounding use patterns, crucial stations & common user demographics

How to use the dataset

Here's how you can put this data to good use:

• Analyze Usage Patterns: You can use parameters like tripduration, starttime, stoptime, from_station_name, and to_station_name to understand how these bicycles are being used. Identify peak usage times or most popular routes could be valuable for optimizing resource distribution or scheduling maintenance routines.

• Understand User Demographics: With access to data like gender ,birthyear,usertype, you can analyze the demographics of your users - are they predominantly male or female? What age groups are most likely to use the service? Are they regular subscribers or casual customers?

• Station Analysis: Using fields like name,latitude,longitude & dpcapacity. You could identify which stations have higher footfall (derived from from_station_name and to_station_name)and whether it relates with their capacities (dpcapacity). Finding out the busiest locations can aid promotional activities.

• Optimize Bike Stock Levels: Using origin-destination pairs (from_station_name, to_station_name) along with count of trips made(tripduration) for each pair , you could balance supply-demand gaps across different stations.

• Service Extension Strategies: Determining less popular rack locations through fewer trips originated(from_station_names) or targeting these identified areas with more advertisement/marketing efforts

6: Dates Analysis Omitted: Please note that date related analysis has been intentionally omitted here as per instruction.

This robust dataset is versatile in its utility—whether its for strategizing marketing, planning logistics or monitoring system health. Enjoy exploring it and discovering insights!

Research Ideas

• City Planning and Development: The dataset provides information about the locations of bike stations and their capacities. This data can be used by city planners to identify areas where additional stations may be required, based on usage trends and capacity. Also, traffic patterns could be studied based on start-time / stop-time of trips.
• User Behaviour Analysis: By analyzing the trip durations, user types (subscriber or customer), gender, and birth year; trends in bike usage accordi...

Graph and download economic data for Unemployment Rate in FRB-Chicago District (DISCONTINUED) (D7URN) from Jan 1990 to Nov 2015 about FRB CHI District, unemployment, rate, and USA.

Sinai Community Health Survey 2.0 was a cross-sectional health survey conducted by Sinai Urban Health Institute (SUHI), a member of Sinai Health System in Chicago. The survey's purpose was: (1) document the health status of selected Community Areas in Chicago, (2) understand the social factors associated with health-related behaviors, service utilization, and outcomes, and (3) use survey findings to develop public health interventions and policies to address health inequities. There are three datasets associated with this survey: adult, child, and a merged adult-child dataset. The final adult questionnaire includes questions about general health status, access to and utilization of health care, mental health, health behaviors, social factors, and demographic characteristics. The child dataset includes questions related to children's health, allergies, sickle cell anemia, car seat use, emotional development, and bullying. More information about the study can be found at Sinai Community Health Survey 2.0.

Ward boundaries in Chicago from May 2003 to May 2015, corresponding to the dates when a new City Council is sworn in, based on the immediately preceding elections. Neither this description nor the dataset should be relied upon in situations where legal precision is required. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

In 2021, the number of aircraft operations registered at Chicago's O'Hare International Airport amounted to ******* operations, which represented a year-over-year increase of around ***** percent as borders started to reopen throughout the COVID-19 pandemic. During the same year, the passenger traffic at O'Hare International reported a growth of ***** percent.