This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Graph and download economic data for Unemployment Rate in Michigan (MIUR) from Jan 1976 to May 2025 about MI, unemployment, rate, and USA.

This data set contains 1-hour resolution surface meteorological data from the Michigan Automated Weather Network (MAWN) stations located across Michigan. This data set covers the period from 15 May through 15 July 2003. The data are in ASCII format.

Consumer Confidence in the United States increased to 60.70 points in June from 52.20 points in May of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Michigan Public Policy Survey (MPPS) is a program of state-wide surveys of local government leaders in Michigan. The MPPS is designed to fill an important information gap in the policymaking process. While there are ongoing surveys of the business community and of the citizens of Michigan, before the MPPS there were no ongoing surveys of local government officials that were representative of all general purpose local governments in the state. Therefore, while we knew the policy priorities and views of the state's businesses and citizens, we knew very little about the views of the local officials who are so important to the economies and community life throughout Michigan. The MPPS was launched in 2009 by the Center for Local, State, and Urban Policy (CLOSUP) at the University of Michigan and is conducted in partnership with the Michigan Association of Counties, Michigan Municipal League, and Michigan Townships Association. The associations provide CLOSUP with contact information for the survey's respondents, and consult on survey topics. CLOSUP makes all decisions on survey design, data analysis, and reporting, and receives no funding support from the associations. The surveys investigate local officials' opinions and perspectives on a variety of important public policy issues and solicit factual information about their localities relevant to policymaking. Over time, the program has covered issues such as fiscal, budgetary and operational policy, fiscal health, public sector compensation, workforce development, local-state governmental relations, intergovernmental collaboration, economic development strategies and initiatives such as placemaking and economic gardening, the role of local government in environmental sustainability, energy topics such as hydraulic fracturing ("fracking") and wind power, trust in government, views on state policymaker performance, opinions on the impacts of the Federal Stimulus Program (ARRA), and more. The program will investigate many other issues relevant to local and state policy in the future. A searchable database of every question the MPPS has asked is available on CLOSUP's website. Results of MPPS surveys are currently available as reports, and via online data tables. Out of a commitment to promoting public knowledge of Michigan local governance, the Center for Local, State, and Urban Policy is releasing public use datasets. In order to protect respondent confidentiality, CLOSUP has divided the data collected in each wave of the survey into separate datasets focused on different topics that were covered in the survey. Each dataset contains only variables relevant to that subject, and the datasets cannot be linked together. Variables have also been omitted or recoded to further protect respondent confidentiality. For researchers looking for a more extensive release of the MPPS data, restricted datasets are available through openICPSR's Virtual Data Enclave. Please note: additional waves of MPPS public use datasets are being prepared, and will be available as part of this project as soon as they are completed. For information on accessing MPPS public use and restricted datasets, please visit the MPPS data access page: http://closup.umich.edu/mpps-download-datasets

After over two years of public reporting, the State Profile Report will no longer be produced and distributed after February 2023. The final release was on February 23, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic. Data about COVID-19 will continue to be updated at CDC’s COVID Data Tracker.

The State Profile Report (SPR) is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, in collaboration with the White House. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention, the HHS Assistant Secretary for Preparedness and Response, and the Indian Health Service). The SPR provides easily interpretable information on key indicators for each state, down to the county level.

It is a weekly snapshot in time that:

• Focuses on recent outcomes in the last seven days and changes relative to the month prior
• Provides additional contextual information at the county level for each state, and includes national level information
• Supports rapid visual interpretation of results with color thresholds

This layer includes contains air quality and meteorologic measurements from air monitoring stations in Michigan that is sourced from AirNow. This dataset contains only the most recent recorded values. Note that this data is preliminary and is subject to validation and changes.

Field Name

Alias

Description

OBJECTID

N/A

N/A

StationID

Station ID

The station ID assigned by EGLE

StationName

Station Name

Station name of the air monitoring station. StationType

Station TypeThe type of air monitoring station. The value 'Permanent' indicates the station is a fixed, long-term installation.

StationStatus

Station Status

Activity status of the station.

LastObservation

Last Observation

Date and time of the most recent recorded observation.

shape

shape

ESRI geometry field.

WD_DEGREES

Wind Direction

Wind direction for current observation expressed in degrees.

WS_MS

Wind Speed

Wind speed measured in meters per second.

TEMP_CTemperatureTemperature measure in degrees Celsius.

PM25_UGM3

PM 2.5

Concentration of particulate matter ≤ 2.5 micrometers (PM2.5) measured in micrograms per cubic meter (µg/m³).

OZONE_PPBOzone

Concentration of ozone (O3) measured in parts per billion (ppb).

NO2_PPB

NO2

Concentration of nitrogen dioxide (NO₂) measured in parts per billion (ppb).

SO2_PPB

SO2Concentration of sulfur dioxide (SO₂) measured in parts per billion (ppb).

CO_PPM

CO

Concentration of carbon monoxide (CO) measured in parts per million (ppm).

NO_PPB

NOConcentration of nitrogen monoxide (NO) measured in parts per billion (ppb).

PM10_UGM3

PM 10

Concentration of particulate matter ≤ 10 micrometers (PM10) measured in micrograms per cubic meter (µg/m³). NOX_PPB

NOxConcentration of nitrogen oxides (NOx) measured in parts per billion (ppb).RWD_DEGREESResultant Wind Direction The average wind direction expressed in degrees. NOY_PPB

NOy

Concentration of total reactive nitrogen (NOy) measured in parts per billion (ppb). RWS_KNOTS

Resultant Wind Speed

The average wind speed measured in knots.

If you have questions related to air quality, please reach out to Susan Kilmer (KilmerS@Michigan.gov or 517-242-2655). If you have map suggestions or functionality issues, please reach out to EGLE-Maps@Michigan.gov.From EPA AirNow:Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here. Visit Michigan.gov/EGLE for more information about air monitoring in Michigan.

About this dataset

Context

The dataset tabulates the Michigan City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Michigan City. The dataset can be utilized to understand the population distribution of Michigan City by age. For example, using this dataset, we can identify the largest age group in Michigan City.

Key observations

The largest age group in Michigan City, IN was for the group of age 25 to 29 years years with a population of 2,901 (9.10%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Michigan City, IN was the 80 to 84 years years with a population of 627 (1.97%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Michigan City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Michigan City total population. 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 Michigan City Population by Age. You can refer the same here

This is a subset of the SHIFDR dataset collection containing data from 14 buildings in Southeast Michigan. The full dataset collection can be found at https://deepblue.lib.umich.edu/data/collections/vh53ww273?locale=en;Organization: We include a subfolder for each building, identified by name. All buildings have been renamed after lakes to protect the identity of the building. Within each building subfolder, there is fan power (i.e. current measurements from which fan power can be computed), building automation system (BAS), whole building electrical load (WBEL), and voltage data collected over the course of our experimentation from 2017 to 2021. All experiments were conducted in the summer months and a full schedule of Demand Response (DR) events is included along with each building in the ‘Event_Schedule.csv’ file. The building information file contains general information about the buildings, pertinent to the experiments we conducted.

There is also a folder labeled ‘2021 Preprocessed data’ which contains combined BAS and fan power data from the summer of 2021. This data has been lightly processed to calculate fan power from current measurements and interpolate BAS data to 1 minute intervals. These act as an easy-to-use starting point for data analysis.

United States CCI: Michigan data was reported at 98.500 1985=100 in Apr 2025. This records an increase from the previous number of 88.600 1985=100 for Mar 2025. United States CCI: Michigan data is updated monthly, averaging 92.600 1985=100 from Feb 2007 (Median) to Apr 2025, with 219 observations. The data reached an all-time high of 151.100 1985=100 in Dec 2019 and a record low of 11.900 1985=100 in Mar 2009. United States CCI: Michigan data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H049: Consumer Confidence Index. [COVID-19-IMPACT]

This EPA SH entry contains the link to the public archive for all datasets collected by EPA and collaborators for the Lake Michigan Ozone Study conducted in 2017. The archive resides at NASA Langley Research Center. EPA-ORD data sets can be found by clicking on the individual site tabs on the main LMOS archive webpage and EPA-ORD will be listed at the PI.

Business Applications for Michigan was 10.09000 % Chg. from Yr. Ago in May of 2025, according to the United States Federal Reserve. Historically, Business Applications for Michigan reached a record high of 700.00000 in January of 2013 and a record low of -88.85000 in January of 2025. Trading Economics provides the current actual value, an historical data chart and related indicators for Business Applications for Michigan - last updated from the United States Federal Reserve on July of 2025.

The Southeast Michigan Operational Data Environment (SEMI-ODE) is a real-time data acquisition and distribution software system that processes vehicle and infrastructure data collected from sources such as the Southeast Michigan testbed Situational Data Clearinghouse (SDC) and the Situational Data Warehouse (SDW), along with other non-connected vehicle sources of data. The ODE offers four core functions to supply tailored and custom-requested data from the SEMI Testbed to subscribing client software applications. The core functions are: 1) Valuation (V), 2) Integration (I), 3) Sanitization (S) (also called de-identification), and 4) Aggregation (A). These four VISA functions are critical to the field test as they enable the subscribing emulated applications to receive data tailored to support their operation. These functions also serve to increase the general usability of the data being generated in the SEMI Test Bed. This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

These data describe the catch and biological data from 363 bottom-set gill-net lifts distributed throughout Lake Michigan (including main basin and Green Bay) between April and November in 1930–1932. Data collected from the R/V Fulmar were recorded in notebooks and are now archived at the U.S. Geological Survey’s Great Lakes Science Center. Each lift included 1–7 gangs of linen gill nets. Each gang comprised 3–5 panels each having a length of 155 m, a height of 1.5 m, and a (stretch-)mesh size of either 60, 64, 67, 70, or 76 mm. The digitization of the Fulmar data notebooks was started in the late 1990s and finished in this study.

Graph and download economic data for Real Median Household Income in Michigan (MEHOINUSMIA672N) from 1984 to 2023 about MI, households, median, income, real, and USA.

These data were automated to provide an accurate high-resolution historical shoreline of Lake Michigan suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution...

In 1954 researchers at the USGS Great Lakes Science Center conducted 11 research cruises on Lake Michigan during which 779 bathythermographs were cast to collect temperature profile data (temperature at depth). Bathythermographs of that era recorded water pressure and temperature data by mechanically etching them as a curve on a glass slide. Data was collected from the glass slide by projecting the image of the curve, superimposing a grid, and taking a photo of it, thereby creating a bathythermogram. Data collection personnel could then read the data from the curve. This USGS data release is a digitized set of those original bathythermogram print photos and the temperature and depth data the project team collected from them using the open-source software, Web Plot Digitizer, as well as metadata describing each. In addition, because of their historical value as well as potential future use, this data release includes the cruise logs, which include nautical and research notes beyond the logical scope of this data release.

This data collection contains county-level information on churches and church membership by denomination in Michigan for 1950 and 1960. Information is given on the names of the county, presbytery, and church. Other variables provide information on the number of churches and church members for each denomination. Additional variables give the number and percentage of the state population who were 14 years and older in each county in 1950 and in 1960, the percentage of this age group who attended churches in 1950 and in 1960, and the percentage of the change in membership in each denomination between 1950 and 1960.

The Annual Average Daily Traffic (AADT) is the estimated mean daily traffic volume and the Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles. For continuous sites, estimates are calculated by summing the Annual Average Days of the Week and dividing by seven. For short-count sites, estimates are made by factoring a short count using seasonal and day-of-week adjustment factors.Data Coverage: The dataset covers the entire Federal Aid System in the State of Michigan.Update Cycle: AADT & CAADT volumes are created and released every year.Transportation Data Management System (TDMS) AADT Calculation HelpTraffic Monitoring Program

These data were automated to provide an accurate high-resolution historical shoreline of Saginaw River, Michigan suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808