About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in North Chicago: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 711(9.74%) households where the householder is under 25 years old, 3,244(44.44%) households with a householder aged between 25 and 44 years, 1,922(26.33%) households with a householder aged between 45 and 64 years, and 1,423(19.49%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the city of North Chicago, showcasing varying income levels among different age demographics.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 North Chicago median household income by age. You can refer the same here

Population totals for groupings commonly used in other datasets.

Not all values are available for all years.

Note that because the "Citywide" rows roll up the values from the individual ZIP Codes and the "Age 0-4," "Age 5-11," "Age 12-17," "Age 5+," "Age 18+," and "Age 65+" columns overlap other age categories, as well as each other in some cases, care should be taken in summing values to avoid accidental double-counting. The "Age 5-11" and "Age 12-17" columns only include children who live in households.

Data Sources: U.S. Census Bureau American Community Survey (ACS) 5-year estimates (ZIP Code) and 1-year estimates (Citywide). The U.S. Census Bureau did not release standard 1-year estimates from the 2020 ACS. In 2020 only, 5-year estimates were used for the Citywide estimates.

This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.

This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.

The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.

For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: All businesses identified as victims in CPD data have been removed from this dataset.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”

Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

This dataset was used by Chicago Police Department analysts to create the publicly available “Chicago Police Sentiment Dashboard” (https://home.chicagopolice.org/statistics-data/data-dashboards/sentiment-dashboard/). This online dashboard displays information related to how safe Chicago residents feel and how much trust they have in the police. The dashboard and this dataset are updated monthly and users are able to view data citywide, as well as within the five detective areas and in each of the 22 districts. Users can sort this data based on year, month and location. Information is also available based on demographics, including age, sex, race, education and income level. The dashboard is meant to improve transparency as well as work toward compliance with the consent decree. The first five columns indicate the type of organizational unit described by the records and which particular unit. Subsequent columns show either a safety or trust score for a demographic group. Scores are derived from responses to survey questions, with each response being a value that ranges from 0-10. Please note that Elucd trust and safety scores are NOT a percentage. A score of 65 means that average response to the questions is 6.5 out of 10. The final two columns show the time period in which the data were collected. The dataset was created by our partner, Elucd (https://elucd.com), through delivering short surveys to Chicago residents through digital ads. See [https://home.chicagopolice.org/wp-content/uploads/2020/12/Dashboard_FAQ_11_25_20.pdf] for more information on the project. This effort is one element of a Chicago Police Department reform process, governed by a consent decree executed between the Office of the Attorney General of the State of Illinois (OAG) and the City of Chicago. For more information on the consent decree, see https://www.chicago.gov/city/en/sites/police-reform/home/consent-decree.html.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in East Chicago: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 350(3.50%) households where the householder is under 25 years old, 3,391(33.95%) households with a householder aged between 25 and 44 years, 3,667(36.72%) households with a householder aged between 45 and 64 years, and 2,579(25.82%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of East Chicago, showcasing varying income levels among different age demographics.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 East Chicago median household income by age. You can refer the same here

Description: Pursuant to the Sex Offender and Child Murderer Community Notification Law, 730 ILCS 152/101,et seq., the Chicago Police Department maintains a list of sex offenders residing in the City of Chicago who are required to register under the Sex Offender Registration Act, 730 ILCS 150/2, et seq. To protect the privacy of the individuals, addresses are shown at the block level only and specific locations are not identified. The data are extracted from the CLEAR (Citizen Law Enforcement Analysis and Reporting) system developed by the Department. Although every effort is made to keep this list accurate and current, the city cannot guarantee the accuracy of this information. Offenders may have moved and failed to notify the Chicago Police Department as required by law. If any information presented in this web site is known to be outdated, please contact the Chicago Police Department at srwbmstr@chicagopolice.org, or mail to Sex Registration Unit, 3510 S Michigan Ave, Chicago, IL 60653. Disclaimer: This registry is based upon the legislature's decision to facilitate access to publicly available information about persons convicted of specific sexual offenses. The Chicago Police Department has not considered or assessed the specific risk of re-offense with regard to any individual prior to his or her inclusion within this registry, and has made no determination that any individual included within the registry is currently dangerous. Individuals included within this registry are included solely by virtue of their conviction record and Illinois law. The main purpose of providing this data on the internet is to make the information more available and accessible, not to warn about any specific individual.

Anyone who uses information contained in the Sex Offender Database to commit a criminal act against another person is subject to criminal prosecution. Data Owner: Chicago Police Department. Frequency: Data is updated daily. Related Applications: CLEARMAP (http://j.mp/lLluSa).

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

Chicago residents who are up to date with influenza vaccines, based on the reported address, race-ethnicity, sex, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE).

“Up to date” refers to individuals aged 6 months and older who have received 1+ doses of influenza vaccine during the current season, defined as the beginning of July (MMWR week 27) through the end of the following June (MMWR week 26).

Data Notes:

Weekly cumulative counts and coverage percentages of people up to date are shown for each combination of race-ethnicity, sex, and age group. Note that race-ethnicity, age, and sex all have an option for “All” so care should be taken when summing rows. Weeks begin on a Sunday and end on a Saturday.

Coverage percentages are calculated based on the cumulative number of people in each race-ethnicity/age/sex population subgroup who are considered up to date as of the week ending date divided by the estimated number of people in that subgroup. Population counts are obtained from the 2020 U.S. Decennial Census. Actual counts may exceed population estimates and lead to coverage estimates that are greater than 100%, especially in smaller demographic groupings with smaller populations. Additionally, the medical provider may report incorrect demographic information for the person receiving the vaccination, which may lead to over- or underestimation of vaccination coverage. All coverage percentages are capped at 99%.

The Chicago Department of Public Health (CDPH) uses the most complete data available to estimate influenza vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Influenza vaccine administration is not required to be reported in Illinois, except for publicly funded vaccine (e.g., Vaccines for Children, Section 317). Individuals may receive vaccinations that are not recorded in I-CARE, such as those administered in another state, or those administered by a provider that does not submit data to I-CARE, causing underestimation of the number individuals who received an influenza vaccine for the current season.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

For all datasets related to influenza, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=flu.

Data Source: Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau 2020 Decennial Census

This collection presents survey data from 12 cities in the United States regarding criminal victimization, perceptions of community safety, and satisfaction with local police. Participating cities included Chicago, IL, Kansas City, MO, Knoxville, TN, Los Angeles, CA, Madison, WI, New York, NY, San Diego, CA, Savannah, GA, Spokane, WA, Springfield, MA, Tucson, AZ, and Washington, DC. The survey used the current National Crime Victimization Survey (NCVS) questionnaire with a series of supplemental questions measuring the attitudes in each city. Respondents were asked about incidents that occurred within the past 12 months. Information on the following crimes was collected: violent crimes of rape, robbery, aggravated assault, and simple assault, personal crimes of theft, and household crimes of burglary, larceny, and motor vehicle theft. Part 1, Household-Level Data, covers the number of household respondents, their ages, type of housing, size of residence, number of telephone lines and numbers, and language spoken in the household. Part 2, Person-Level Data, includes information on respondents' sex, relationship to householder, age, marital status, education, race, time spent in the housing unit, personal crime and victimization experiences, perceptions of neighborhood crime, job and professional demographics, and experience and satisfaction with local police. Variables in Part 3, Incident-Level Data, concern the details of crimes in which the respondents were involved, and the police response to the crimes.

Background:

Looking at Chicago's gleaming skyline today, it's surprising to remember that not so long ago many of those buildings were black with soot from coal-fired furnaces and factories all over the city. Take a look back at old photos or films, though, and that skyline isn't so pristine.

During the Industrial Age belching smokestacks were looked at as a good thing – this meant the city that works was working! Eventually, though, we learned you can have too much of a good thing. Some days, pollution turned day into night, ruining clothing, blackening buildings, sickening Chicagoans and even stopping airplanes from taking off. Today, we can see a similar situation in countries like India, Iran, Pakistan and China where coal is still widely used.

The Chicago Tribune led the crusade against Chicago’s dirty air. The newspaper began reporting on the condition of the city's air as early as the 1870s. In one report, the author Rudyard Kipling is quoted as saying simply, "the air is dirt" after a visit to Chicago.

In 1959, Chicago established the Department of Air Pollution Control to investigate and regulate emission sources. Subsequent regulations, including the federal Clean Air Act of 1970, and more recent city and state legislation have helped further mitigate city-wide emissions. Today, Chicago air pollution levels are a small fraction of their historical levels.

Standands:

The US Environmental Protection Agency (EPA) defines “moderate” air quality as air potentially unhealthy to sensitive groups including children, the elderly, and people with pre-existing cardiovascular or respiratory health conditions.

AQI ratings are calculated by weighting 6 key criteria pollutants for their risk to health. The pollutant with the highest individual AQI becomes the ‘main pollutant’ and dictates the overall air quality index. Fine particulate matter (PM2.5) and ozone represent two of the most common ‘main pollutants’ responsible for a city’s AQI due to the weight the formula ascribes to them for their potential harm and prevalence at high levels.

PM2.5 pollution is fine particle pollution with a range of chemical compositions that measures 2.5 microns in diameter or less. The US EPA recommends that annual PM2.5 exposure not exceed 12 μg/m3. The World Health Organization (WHO), meanwhile, employs a more stringent standard, recommending that exposure remain below 10 μg/m3 annually.

learn more: https://www.iqair.com/usa/illinois/chicago

In this dataset we explore the pollution levels and learn EDA techniques in the process.

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. 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/views/fwb8-6aw5/files/A5KBlegGR2nWI1jgP6pjJl32CTPwPbkl9KU3FxlZk-A?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\ECONOMIC_INDICATORS\Dataset_Description_socioeconomic_indicators_2012_FOR_PORTAL_ONLY.pdf

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.

This dataset contains our most up to date list of Unclaimed Persons found in Cook County.

If you feel you are the next of kin to any of the individuals listed, Please contact Rebeca Perrone, Indigent Coordinator, at 312-997-4480. During regular business hours: Monday through Friday, 7 a.m. to 3 p.m.

Statement of Public Notice: Unclaimed indigents will be cremated or buried at County expense within 60 days of admission to our facility as dictated by the Cook County Medical Examiner Ordinance

Each record in this dataset shows information about an arrest executed by the Chicago Police Department (CPD). Source data comes from the CPD Automated Arrest application. This electronic application is part of the CPD CLEAR (Citizen Law Enforcement Analysis and Reporting) system, and is used to process arrests Department-wide.

A more-detailed version of this dataset is available to media by request. To make a request, please email dataportal@cityofchicago.org with the subject line: Arrests Access Request. Access will require an account on this site, which you may create at https://data.cityofchicago.org/signup. New data fields may be added to this public dataset in the future. Requests for individual arrest reports or any other related data other than access to the more-detailed dataset should be directed to CPD, through contact information on that site or a Freedom of Information Act (FOIA) request.

The data is limited to adult arrests, defined as any arrest where the arrestee was 18 years of age or older on the date of arrest. The data excludes arrest records expunged by CPD pursuant to the Illinois Criminal Identification Act (20 ILCS 2630/5.2).

Department members use charges that appear in Illinois Compiled Statutes or Municipal Code of Chicago. Arrestees may be charged with multiple offenses from these sources. Each record in the dataset includes up to four charges, ordered by severity and with CHARGE1 as the most severe charge. Severity is defined based on charge class and charge type, criteria that are routinely used by Illinois court systems to determine penalties for conviction. In case of a tie, charges are presented in the order that the arresting officer listed the charges on the arrest report. By policy, Department members are provided general instructions to emphasize seriousness of the offense when ordering charges on an arrest report.

Each record has an additional set of columns where a charge characteristic (statute, description, type, or class) for all four charges, or fewer if there were not four charges, is concatenated with the | character. These columns can be used with the Filter function's "Contains" operator to find all records where a value appears, without having to search four separate columns.

Users interested in learning more about CPD arrest processes can review current directives, using the CPD Automated Directives system (http://directives.chicagopolice.org/directives/). Relevant directives include:

• Special Order S06-01-11 – CLEAR Automated Arrest System: describes the application used by Department members to enter arrest data. • Special Order S06-01-04 – Arrestee Identification Process: describes processes related to obtaining and using CB numbers. • Special Order S09-03-04 – Assignment and Processing of Records Division Numbers: describes processes related to obtaining and using RD numbers. • Special Order 06-01 – Processing Persons Under Department Control: describes required tasks associated with arrestee processing, include the requirement that Department members order charges based on severity.

Description: Pursuant to the Sex Offender and Child Murderer Community Notification Law, 730 ILCS 152/101,et seq., the Chicago Police Department maintains a list of sex offenders residing in the City of Chicago who are required to register under the Sex Offender Registration Act, 730 ILCS 150/2, et seq. To protect the privacy of the individuals, addresses are shown at the block level only and specific locations are not identified. The data are extracted from the CLEAR (Citizen Law Enforcement Analysis and Reporting) system developed by the Department. Although every effort is made to keep this list accurate and current, the city cannot guarantee the accuracy of this information. Offenders may have moved and failed to notify the Chicago Police Department as required by law. If any information presented in this web site is known to be outdated, please contact the Chicago Police Department at srwbmstr@chicagopolice.org, or mail to Sex Registration Unit, 3510 S Michigan Ave, Chicago, IL 60653. Disclaimer: This registry is based upon the legislature's decision to facilitate access to publicly available information about persons convicted of specific sexual offenses. The Chicago Police Department has not considered or assessed the specific risk of re-offense with regard to any individual prior to his or her inclusion within this registry, and has made no determination that any individual included within the registry is currently dangerous. Individuals included within this registry are included solely by virtue of their conviction record and Illinois law. The main purpose of providing this data on the internet is to make the information more available and accessible, not to warn about any specific individual.

Anyone who uses information contained in the Sex Offender Database to commit a criminal act against another person is subject to criminal prosecution. Data Owner: Chicago Police Department. Frequency: Data is updated daily. Related Applications: CLEARMAP (http://j.mp/lLluSa).

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. 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/views/fwb8-6aw5/files/A5KBlegGR2nWI1jgP6pjJl32CTPwPbkl9KU3FxlZk-A?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\ECONOMIC_INDICATORS\Dataset_Description_socioeconomic_indicators_2012_FOR_PORTAL_ONLY.pdf

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)

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.

This dataset contains individual-level homicide and non-fatal shooting victimizations, including homicide data from 1991 to the present, and non-fatal shooting data from 2010 to the present (2010 is the earliest available year for shooting data). This dataset includes a "GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized-access dataset, but with "UNKNOWN" in the shooting column.

Each row represents a single victimization, i.e., a unique event when an individual became the victim of a homicide or non-fatal shooting. Each row does not represent a unique victim—if someone is victimized multiple times there will be multiple rows for each of those distinct events.

The dataset is refreshed daily, but excludes the most recent complete day to allow the Chicago Police Department (CPD) time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

A version of this dataset with additional crime types is available by request. To make a request, please email dataportal@cityofchicago.org with the subject line: Violence Reduction Victims Access Request. Access will require an account on this site, which you may create at https://data.cityofchicago.org/signup.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset. For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.” Officer-involved shootings are not included.

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

Note: In some instances, CPD's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most reliable crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: Homicide victims names are delayed by two weeks to allow time for the victim’s family to be notified of their passing.

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

Note: This dataset includes variables referencing administrative or political boundaries that are subject to change. These include Street Outreach Organization boundary, Ward, Chicago Police Department District, Chicago Police Department Area, Chicago Police Department Beat, Illinois State Senate District, and Illinois State House of Representatives District. These variables reflect current geographic boundaries as of November 1st, 2021. In some instances, current boundaries may conflict with those that were in place at the time that a given incident occurred in prior years. For example, the Chicago Police Department districts 021 and 013 no longer exist. Any historical violent crime victimization that occurred in those districts when they were in existence are marked in this dataset as having occurred in the current districts that expanded to replace 013 and 021."

This dataset includes aggregated weekly data on the percent of emergency department visits and the percent of hospital inpatient admissions due to influenza-like illness (ILI), COVID-19, influenza, RSV, and acute respiratory illness. The Illinois Department of Public Health (IDPH) collects data for Emergency Department visits to all 185 acute care hospitals in Illinois. The data are submitted from IDPH to the CDC’s BioSense Platform for access and analysis by health departments via the ESSENCE system.

The CDC National Syndromic Surveillance Program (NSSP) utilizes diagnostic codes and clinical terms to create definitions for diagnosed COVID-19, influenza, RSV, and acute respiratory illness. For more information on diagnostic codes and clinical terms used, visit: https://www.cdc.gov/nssp/php/onboarding-resources/companion-guide-ed-data-respiratory-illness.html

The data is characterized by selected demographic groups including age group and race/ethnicity.

The dataset also includes percent of weekly outpatient visits due to ILI as reported by several outpatient clinics throughout Chicago that participate in CDC’s Influenza-like Illness Surveillance Network (ILINet).

For more information on ESSENCE, see https://www.dph.illinois.gov/data-statistics/syndromic-surveillance

For more information on ILINet, see https://www.cdc.gov/fluview/overview/index.html#cdc_generic_section_3-outpatient-illness-surveillance

All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources.