Minneapolis poverty dashboard

You can explore data about poverty in Minneapolis.

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Minneapolis poverty dashboard

How to use the dashboard

View the data

For best results:

  • View the dashboard in full screen.
  • Use Chrome or Firefox as your browser.

Read the data

Most pages contain map(s) of the Minneapolis neighborhoods or census tracts, along with charts.  Use the controls to look at the demographics of people living above and below poverty.

Click the button to select your desired dashboard:

  • Overview
  • Poverty Ratio
  • Race/Ethnicity
  • Citizenship
  • Language
  • Education
  • Age
  • Disability
  • Household Type
  • Families/Children

Hover over the area in which you want to get more details.

Search the data

Controls are located in the upper middle of most pages. Controls are located at the bottom of the Overview page.

Map controls

These controls let you choose the demographic to display on the maps.

Chart control

The Poverty Ratio and Household Type pages lets you pick a Minneapolis neighborhood to change the neighborhood charts. All pages let you choose whether to display chart labels.

Charts with controls

  • The chart at the bottom of the Overview page has its own filters. The filters allow you to choose which neighborhoods to display. You can also select the neighborhoods that will display based on the percentage of people in poverty.

Download the data

At the top right of each page is a button to download data. When clicked, the Neighborhood Data Download dashboard appears.

  • Read the directions to understand what information is available and when.
  • On the Neighborhood Data Download you may:
    • Download neighborhood data
    • Download tract data
    • View neighborhood boundaries
    • Get information on the U.S. Census Bureau site

 

Technical notes

Source of data

The data in this dashboard come from the U.S. Census Bureau. For additional information:

Margin of error

The data are part of the American Community Survey (ACS), conducted by the U.S. Census Bureau. ACS data represent statistical estimates based on population samples, rather than population counts. Therefore, the data do not represent true population values and have error associated with them. This error is called the “Margin of Error” (MOE).

For example, a population estimate may be written as “52 +/- 18”, where “18” is the MOE. The MOE is subtracted from and added to the estimate to create a range. In this case the range is 34 to 70. This range indicates there is a 90% chance the true population count falls between 34 and 70. There is a 10% chance the true value falls outside of the range.

Note that if the bottom of the range is a negative number, it is assumed to be zero if a negative number doesn’t make sense. For example, if a count of people under age ten is 124 +/- 200, it doesn’t make sense to say there are -76 children. The range is instead interpreted as 0 to 324.

Understanding the estimate reliability

A statistical value called the Coefficient of Variation (CV) measures how big an MOE is compared to an estimate. An estimate is assigned a reliability level based on the value of its CV.

Smaller coefficients of variation

Indicate that the MOE is smaller compared to the estimate. These estimates are more likely to be close to the true population count.

Larger coefficients of variation

Indicate that the MOE is larger compared to the estimate. These estimates are less likely to be close to the true population count.

Estimate reliability level

An estimate reliability level is assigned based on the CV of an estimate as follows:

High: CV < 12%

Medium: CV between 12% and 40%

Low: CV > 40%

Interpreting the reliability level

How you interpret the reliability of an estimate depends on how you are using the estimate. There are no specific rules for interpretation. Here is an example of how you might use the reliability level:

Suppose there are 51,293 +/- 1,681 children under age ten in Minneapolis. Of these, 349 +/- 399 children under ten live in the Kenwood neighborhood. The estimate reliability is low.

  • You are organizing a children’s festival in Minneapolis, and you want to locate it near a lot of children under ten. You can be confident that Kenwood is not a good neighborhood to pick.
  • You are starting a daycare/afterschool care to serve fifteen children under ten. You prefer that children live in the neighborhood where the facility is located. If you find the perfect building in the Kenwood neighborhood, do not rely on this estimate to decide whether to rent the building. Instead, seek more reliable information about the presence of children under ten in the neighborhood.