The population density of Ann Arbor, MI was 4,335 in 2018.

Population Density

Population Density is computed by dividing the total population by Land Area Per Square Mile.

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

1. ODN datasets and APIs are subject to change and may differ in format from the original source data in order to provide a user-friendly experience on this site.

2. To build your own apps using this data, see the ODN Dataset and API links.

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Geographic and Population Datasets Involving Ann Arbor, MI

  • API

    Maryland Resident Population Per Square Mile: 2010-2020

    opendata.maryland.gov | Last Updated 2024-03-11T18:51:03.000Z

    Resident population density for Maryland and Jurisdictions per square mile from 2010 to 2020. Source: U.S. Bureau of Census

  • API

    Population Density By Land Area And County In Utah 2010

    opendata.utah.gov | Last Updated 2019-02-11T21:26:09.000Z

    This data set contains population density by land area and county of residence in Utah for 2010.

  • API

    2007 Land Use Land Cover

    data.delaware.gov | Last Updated 2023-01-12T18:51:05.000Z

    <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This metadata record describes the creation of a 2007 update of the 2002 Land Use and Land Cover data set for the State of Delaware. The land use update was based on aerial imagery collected in the summer of 2007. The imagery was collected with 4 bands: B, G, R, and NIR. The update was performed using statistical differencing techniques to identify changed areas. Areas of change were photointerpreted by an analyst. The work was performed by the Sanborn Map Company, Inc. in Ann Arbor, Michigan.</SPAN></P><P><SPAN>The 2002 land use data were based on the 1997 land-use data of the State and 2002 false color infrared digital orthophotography at a scale of 1:2400.</SPAN></P></DIV></DIV></DIV>

  • API

    NOAA - Customer satisfaction with NWS services, as measured by the American Customer Satisfaction Index

    performance.commerce.gov | Last Updated 2024-03-28T20:21:54.000Z

    Weather information users are surveyed continuously by means of a web-based, pop-up survey on NWS web pages throughout the Nation. A sample size of approximately 6,000 responses is collected quarterly for a maximum of 24,000 annual responses. The Customer Satisfaction Index (CSI) score is calculated as a weighted average of three survey questions that measure different facets of satisfaction with NWS services. American Customer Satisfaction Index (ACSI) researchers use proprietary software technology to estimate the weighting. The three questions include the overall satisfaction of NWS services, expectations of service, and a comparison to an ideal organization. Indexes are reported on a 0 to 100 scale. The CSI was started in the United States in 1994 by researchers at the University of Michigan, in conjunction with the American Society for Quality in Milwaukee, Wisconsin, and CFI Group in Ann Arbor, Michigan. The Index was developed to provide information on satisfaction with the quality of products and services available to consumers. The survey data serve as inputs to an econometric model that benchmarks customer satisfaction with more than 300 companies in 43 industries and 10 economic sectors, as well as various services of federal and local government agencies.

  • API

    Demographics Stats at a Glance

    datahub.austintexas.gov | Last Updated 2023-09-19T21:56:30.000Z

    These are the statistics listed in the "Stats at a Glance" section of the City of Austin demographics website: https://demographics-austin.hub.arcgis.com/

  • API

    Internet Master Plan: Adoption and Infrastructure Data by Neighborhood

    data.cityofnewyork.us | Last Updated 2022-09-23T19:23:10.000Z

    Key indicators of broadband adoption, service and infrastructure in New York City.</p> <b>Data Limitations:</b> Data accuracy is limited as of the date of publication and by the methodology and accuracy of the original sources. The City shall not be liable for any costs related to, or in reliance of, the data contained in these datasets.