Map Your County in QGIS

Last updated on 2026-10-25 | Edit this page

Estimated time: 60 minutes

Overview

Questions

  • Can I take a Census table and boundaries all the way to a finished, shareable map on my own?
  • What makes a map “finished”?

Objectives

  • Join a Census table to tract boundaries and check the join
  • Clean Census “no estimate” codes and zero denominators before calculating rates
  • Choose a classification for a skewed rate and show missing data honestly
  • Export a print layout with a title, legend, scale bar, north arrow and source line

The capstone

This episode is a small project that uses everything in the QGIS part of Foundations: loading data from the web and making a layout (Getting Started with QGIS), and joins, the Field Calculator and selections (Tables, Joins and Selections). The instructions are shorter than before: each step says what to do and which earlier part shows how. Try first, and open the hints only when you are stuck.

The brief. A local food bank wants a one-page map of where households without a car live in Tippecanoe County, Indiana, to plan a delivery service. You will deliver it as a PNG image that a reader with no GIS experience can understand.

The data is the same as in the Python version of this capstone, so you can compare your result with a colleague who uses Python:

Dataset File
Census tracts tippecanoe_tracts.geojson
ACS 2024 table tippecanoe_acs_2024.csv

Web addresses to copy:

  • Census tracts: https://raw.githubusercontent.com/SpatialTurn/Foundations/main/data/tippecanoe_tracts.geojson
  • ACS 2024 table: https://raw.githubusercontent.com/SpatialTurn/Foundations/main/data/tippecanoe_acs_2024.csv

What a finished map needs

A map leaves your hands and is read without you there to explain it. It needs to answer four questions on its own:

Question Map element
What is shown, and in what units? A title (ideally stating the finding) and a legend with units
Where is it, and how big? Recognizable shapes, a scale bar, a north arrow
Where does the data come from, and when? A source line with the dataset and year
What is missing? A visible no data class, never a misleading zero

Steps

Callout

If QGIS asks you to choose a transformation

When layers in latitude and longitude (EPSG:4326) are drawn in a project in UTM (EPSG:26916), QGIS may open a window that begins “Multiple operations are possible for converting coordinates”. For this county any of the options is accurate to a few meters: keep the first (highlighted) one and click OK.

1. Start a project

Create a folder for the capstone, start a new QGIS project and save it there. Set the project CRS to EPSG:26916 (UTM zone 16N), so the map is not stretched and the scale bar is true.

Click the CRS button at the bottom right of the QGIS window, search for 26916 and click OK.

2. Load and join

Load both web addresses, join the table to the tracts on GEOID, and check that no tract is left without a match.

Tables, Joins and Selections, Parts 1 to 3: Properties > Joins, then select "acs_total_pop" IS NULL in the attribute table. 0 selected means every tract matched.

3. Calculate the rate

Create a virtual field pct_no_vehicle: households without a vehicle as a percentage of all households. Check what happens in Census Tract 104.

Field Calculator, virtual field (ticked automatically for this read-only layer), Decimal (double): "acs_hh_no_vehicle" / "acs_households" * 100. Tract 104 (Purdue residence halls) has 0 households, so the result is NULL there, which is correct.

4. Style the choropleth

Use Graduated symbology on pct_no_vehicle with 5 classes and a sequential color ramp. Look at the histogram first and choose a classification mode that suits a skewed distribution. Then make sure the tract with no value is shown as no data, not left out or colored like a low value.

  • In the Graduated panel, the Histogram tab (click Load Values) shows most tracts below 10% and a few far higher. Natural Breaks (Jenks) suits this.
  • QGIS leaves features with NULL values undrawn in a Graduated style. To show them, open Layer Styling or Properties > Symbology, switch to Rule-based, and add a rule with the filter "pct_no_vehicle" IS NULL, a light gray symbol and the label “No households (dorms)”. A quicker alternative: load the tracts a second time below the styled layer with a light gray hatch fill, and name it “No data”.
  • Use a pale outline (white or light gray, 0.2 mm) so small tracts in the city stay readable.

5. Compose the layout

Create a print layout with the map, a title that states the finding, a legend with units and without extra layers, a scale bar in kilometers or miles, a north arrow and a source line. Add an inset or zoom on the cities if the small urban tracts are hard to see (optional).

Getting Started with QGIS, Part 6. A good source line: “Data: U.S. Census Bureau, American Community Survey 2020-2024 5-year estimates, census tracts. Map by (your name), 2026.” For a zoom, add a second map item, set its extent to the cities, and tick Overviews on the main map to show where the zoom is.

6. Export and check

Export the layout as a PNG at 200 dpi or more. Then read it as a stranger would, using the table above.

Challenge

Challenge: A second map

Make a second finished map, of the poverty rate (below_poverty as a share of poverty_universe). Many of the highest-poverty tracts are near Purdue. Add one sentence to the map that explains why.

Virtual field poverty_rate = "acs_below_poverty" / "acs_poverty_universe" * 100 (NULL for tract 104, where the poverty universe is 0). The highest rates, around 70 to 80%, are in tracts near campus with many students, who often report low incomes while supported by family or loans. A note such as “Tracts near Purdue have many students, who often report low incomes” keeps readers from misreading the map.

Callout

Your own county

To map another county, download its state’s tract boundaries from the Census Bureau’s cartographic boundary files (a zipped shapefile can be added to QGIS without unzipping), filter it to your county with Filter… and "COUNTYFP" = '157' (your county’s code), and request the ACS table with your own Census API key as shown in the Data Collection module’s Census episode. The Python version of this capstone has a ready-made cell for the table.

Key Points
  • Join, then check for unmatched features
  • Clean special codes and zero denominators before computing rates
  • Pick classes that suit the distribution, and show missing data as its own class
  • A finished map has a title, legend with units, scale bar, north arrow and source line