All zip codes and 2020 census tract boundaries defined by the U.S. Census Bureau within the city of Detroit.
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Context
The dataset tabulates the Detroit 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 Detroit 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 Detroit was 707, a 0.57% increase year-by-year from 2022. Previously, in 2022, Detroit population was 703, a decline of 0% compared to a population of 703 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Detroit decreased by 75. In this period, the peak population was 782 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).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Detroit Population by Year. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the data for the Detroit, TX population pyramid, which represents the Detroit population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Detroit Population by Age. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Detroit 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 Detroit. The dataset can be utilized to understand the population distribution of Detroit by age. For example, using this dataset, we can identify the largest age group in Detroit.
Key observations
The largest age group in Detroit, IL was for the group of age 55 to 59 years years with a population of 10 (32.26%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Detroit, IL was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Detroit Population by Age. You can refer the same here
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.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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U.S. Census Bureau QuickFacts statistics for Detroit city, Michigan. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.
US Census Bureau ZIP Code Tabulation Areas (ZCTAs) found within or partially within the borders of the City of Detroit.
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Chart and table of population level and growth rate for the Detroit metro area from 1950 to 2025.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Note: For information on data collection, confidentiality protection, nonsampling error, subject definitions, and guidance on using the data, visit the 2020 Census Demographic and Housing Characteristics File (DHC) Technical Documentation webpage..To protect respondent confidentiality, data have undergone disclosure avoidance methods which add "statistical noise" - small, random additions or subtractions - to the data so that no one can reliably link the published data to a specific person or household. The Census Bureau encourages data users to aggregate small populations and geographies to improve accuracy and diminish implausible results..Source: U.S. Census Bureau, 2020 Census Demographic and Housing Characteristics File (DHC)
This feature service was created by calculating the percentage and acreage of impervious surface within census tract polygons. Impervious surface data used in this calculation is from 2019. Created using 2020 Detroit census tracts.
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Graph and download economic data for Income Inequality in St. Clair County, MI (2020RATIO026147) from 2010 to 2023 about St. Clair County, MI; Detroit; inequality; MI; income; and USA.
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Graph and download economic data for Income Inequality in Livingston County, MI (2020RATIO026093) from 2010 to 2023 about Livingston County, MI; Detroit; inequality; MI; income; and USA.
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Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for St. Clair County, MI (DISCONTINUED) (NETMIGNACS026147) from 2009 to 2020 about St. Clair County, MI; Detroit; migration; flow; MI; Net; 5-year; and population.
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Graph and download economic data for Civilian Labor Force in Detroit-Warren-Dearborn, MI (MSA) (LASMT261982000000006) from Jan 1990 to May 2025 about Detroit, MI, civilian, labor force, labor, household survey, and USA.
Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically
This dataset contains information on the ratio of family income to the federal poverty level at the census tract level. Each column beginning with a "T_" lists the total number of families that fall into each income category. In addition, the dataset contains information on margins of error and the reliability of each estimate, to help guide decisionmakers in more effectively using the data contained in this file. There are approximately 2,800 records in this dataset; census tract boundaries are generally drawn based on population, and are targeted to include bewteen 3,000 and 8,000 residents. Field description metadata is available for download. For more information on poverty data from the Census Bureau, please visit American Factfinder (www.factfinder2.census.gov).
B.1 Buildings Inventory
The Building Footprints data layer is an inventory of buildings in Southeast Michigan representing both the shape of the building and attributes related to the location, size, and use of the structure. The layer was first developed in 2010using heads-up digitizing to trace the outlines of buildings from 2010 one foot resolution aerial photography. This process was later repeated using six inch resolution imagery in 2015 and 2020 to add recently constructed buildings to the inventory. Due to differences in spatial accuracy between the 2010 imagery and later imagery sources, footprint polygons delineated in 2010 may appear shifted compared with imagery that is more recent.
Building Definition
For the purposes of this data layer, a building is defined as a structure containing one or more housing units AND/OR at least 250 square feet of nonresidential job space. Detached garages, pole barns, utility sheds, and most structures on agricultural or recreational land uses are therefore not considered buildings as they do not contain housing units or dedicated nonresidential job space.
How Current is the Buildings Footprints Layer
The building footprints data layer is current as of April, 2020. This date was chose to align with the timing of the 2020 Decennial Census, so that accurate comparisons of housing unit change can be made to evaluate the quality of Census data.
Temporal Aspects
The building footprints data layer is designed to be temporal in nature, so that an accurate inventory of buildings at any point in time since the origination of the layer in April 2010 can be visualized. To facilitate this, when existing buildings are demolished the demolition date is recorded but they are not removed from the inventory. To view only current buildings, you must filter the data layer using the expression, WHERE DEMOLISHED IS NULL.
B.2 Building Footprints Attributes
Table B-1 list the current attributes of the building footprints data layer. Additional information about certain fields follows the attribute list.
Table B-1 Building Footprints Attributes
FIELD | TYPE | DESCRIPTION |
BUILDING_ID | Long Integer | Unique identification number assigned to each building. |
PARCEL_ID | Long Integer | Identification number of the parcel on which the building is located. |
APN | Varchar(24) | Tax assessing parcel number of the parcel on which the building is located. |
CITY_ID | Integer | SEMCOG identification number of the municipality, or for Detroit, master plan neighborhood, in which the building is located. |
BUILD_TYPE | Integer | Building type. Please see section B.3 for a detailed description of the types. |
RES_SQFT | Long Integer | Square footage devoted to residential use. |
NONRES_SQFT | Long Integer | Square footage devoted to nonresidential activity. |
YEAR_BUILT | Integer | Year structure was built. A value of 0 indicates the year built is unknown. |
DEMOLISHED | Date | Date structure was demolished. |
STORIES | Float(5.2) | Number of stories. For single-family residential this number is expressed in quarter fractions from 1 to 3 stories: 1.00, 1.25, 1.50, etc. |
MEDIAN_HGT | Integer | Median height of the building from LiDAR surveys, NULL if unknown. |
HOUSING_UNITS | Integer | Number of residential housing units in the building. |
GQCAP | Integer | Maximum number of group quarters residents, if any. |
SOURCE | Varchar(10) | Source of footprint polygon: NEARMAP, OAKLAND, SANBORN, SEMCOG or AUTOMATIC. |
ADDRESS | Varchar(100) | Street address of the building. |
ZIPCODE | Varchar(5) | USPS postal code for the building address. |
REF_NAME | Varchar(40) | Owner or business name of the building, if known. |
CITY_ID
Please refer to the SEMCOG CITY_ID Code List for a list identifying the code for each municipality AND City of Detroit master plan neighborhood.
RES_SQFT and NONRES_SQFT
Square footage evenly divisible by 100 is an estimate, based on size and/or type of building, where the true value is unknown.
SOURCE
Footprints from OAKLAND County are derived from 2016 EagleView imagery. Footprints from SEMCOG are edits of shapes from another source. AUTOMATIC footprints are those created by algorithm to represent mobile homes in manufactured housing parks.
ADDRESS
Buildings with addresses on multiple streets will have each street address separated by the “ | “ symbol within the field.
B.3 Building Types
Each building footprint is assigned one of 26 building types to represent how the structure is currently being used. The overwhelming majority of buildings
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Detroit 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 Detroit. The dataset can be utilized to understand the population distribution of Detroit by age. For example, using this dataset, we can identify the largest age group in Detroit.
Key observations
The largest age group in Detroit, OR was for the group of age 65 to 69 years years with a population of 21 (19.44%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Detroit, OR was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Detroit Population by Age. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The UHI index in this analysis is based on a study, Sangiorgio et al. (2020), that quantifies the factors that cause developed areas to heat up. Climate Central adapted the modeling approach in Sangiorgio et al. (2020) using data on the distribution of land cover types in each city (from green space to paved areas) as well as building height and population density, to estimate how urban heat island intensity varies within each of the 44 cities included in this analysis.
This is a cut of the data for census tracts coded for Detroit
All zip codes and 2020 census tract boundaries defined by the U.S. Census Bureau within the city of Detroit.