Frequently Asked Questions
Common questions about where district statistics come from, how they're calculated, and how to interpret them.
Who created Civic Data Atlas?
Civic Data Atlas was founded by Jack Landry. Before creating Civic Data Atlas, Jack worked on economics research at the University of Chicago and then as a lead researcher at the Jain Family Institute, where his work focused on tax policy, public benefits, and microsimulation. At the Jain Family Institute, he often computed demographic statistics for city council districts on an ad hoc basis for specific projects. That experience helped inspire Civic Data Atlas, a project designed to make local district demographic statistics more widely available.
Where do the district statistics come from?
District statistics are sourced from the American Community Survey (ACS), a large, ongoing nationwide survey conducted by the U.S. Census Bureau. The Census Bureau is best known for conducting the decennial census, which aims to count every resident of the country every 10 years and serves as the underlying data source used for redistricting. However, the decennial census only collects rudimentary population data every decade. The Census Bureau conducts the American Community Survey to collect more frequent and far more detailed information about the population, covering topics as varied as disability prevalence, educational attainment, and housing costs. Nationally, the ACS surveys nearly 2 million households every year, and its data is used to allocate billions of dollars in federal funding. The district statistics shown on Civic Data Atlas cover a significant share of the topics included in the ACS but are not comprehensive.
Aggregating ACS data to city council districts is discussed in more detail on the Methodology page. In short, the Census Bureau does not reveal the precise location of ACS survey respondents, so it is not possible to simply aggregate respondents into their local districts. We take the geographic information the Census Bureau does release and overlay it with district boundaries.
Unfortunately, Census geographies do not perfectly align with district boundaries. When this occurs, we weight each geographic unit by the share of its population that falls within the district when computing district-level statistics. For example, if 40% of a tract's population falls inside a district, that tract receives 40% of its normal weight in the district calculation. For every district, we are able to test how much the mismatch between Census and district boundaries distorts statistics for a subset of measures, and we report the results under "Average Geographic Mismatch Error." Generally, these differences are less than one percentage point and often much lower.
Why aren't district statistics reported anywhere else?
The local district statistics on Civic Data Atlas generally cannot be found elsewhere because they are complicated to calculate. Similar demographic profiles can be found for non-local districts, such as congressional districts, through sources like Data USA, Census Reporter, and the Congressional District Health Dashboard, because those geographies are precalculated by the Census Bureau. Cities typically publish district population and racial composition after redistricting, based on decennial Census data, but more detailed information from the ACS is generally not available.
How do you check the district statistics for errors?
We run validation checks for every city before publishing district statistics. As one check, we reproduce district-level population and racial breakdowns from the 2020 decennial Census and compare them with figures published by the city during redistricting. This helps confirm that district boundaries are matched correctly and that Census geographies are being assigned to the right districts. We also test how much imperfect overlap between Census geographies and district boundaries affects estimates for measures where a direct comparison is possible. When large discrepancies appear, we review them before publication.
Why do the district statistics use data collected between 2020 and 2024 rather than data that is more recent?
The American Community Survey is released on a time lag—we use the most recent data available. The 2024 ACS 5-Year Estimates were released on March 5, 2026; the 2025 ACS 5-Year Estimates should be released in early 2027.
Ideally, we would use more recent data rather than aggregating five years of survey responses, some of which are up to six years old. However, 5-year estimates are necessary to have a large enough sample to compute statistics at the local district level. The four summary cards on city homepages use ACS 1-year estimates instead, since citywide sample sizes are large enough to support the more current release. Citywide comparison data on individual district pages uses the same 5-year data as the district statistics, so comparisons are made over the same time frame.
How often is the data updated?
The main demographic data on district pages is updated once a year when the Census Bureau publishes new data. The most recent ACS 5-Year Estimates were released on March 5, 2026.
We attempt to keep district representative information continually updated. For each city, we regularly check city council roster websites for updates. If you see anything out of date, please contact us at [email protected] and we will make a correction.
Why does some data differ from demographic data provided after redistricting?
As part of the redistricting process, most cities report each district's population and racial composition. See examples for Los Angeles, New York City, and Chicago. Data for these city reports come from the 2020 decennial Census, while the individual district pages use data from the 2024 ACS 5-Year Estimates. These two sources can differ for four reasons.
First, the 2024 ACS 5-Year Estimates draw from surveys conducted from 2020 through 2024, while the decennial Census was conducted in 2020 and refers to the population as it existed on April 1, 2020. The population and racial composition of the district could have shifted after 2020.
Second, the ACS is a survey with sampling error, which can make ACS-reported figures differ from the decennial Census by a few percentage points because of random variation, not because the population actually shifted. Sampling error is described in more detail on the Methodology page, and ACS margins of error are reported on all district pages.
Third, ACS estimates can include geographic aggregation error: the published ACS data cannot be restricted precisely to district boundaries, which can lead to errors. Geographic aggregation error for race variables is usually very small and directly testable. The Methodology page and each district's geographic aggregation error page reports specific error figures for all race variables.
Finally, some states adjust Census data for redistricting so that people incarcerated in prisons and jails are counted at their home addresses rather than at the correctional institution's address. In those states, the redistricting population and racial breakdown can differ slightly from the composition of people actually living in the district because the adjusted data count incarcerated people at their home addresses rather than where they are incarcerated. The ACS data has no such correction, which can create differences between ACS estimates and redistricting data.
Why do the "City at a Glance" statistics differ from those on the district pages when comparing a district to the citywide totals?
The "City at a Glance" statistics use the latest ACS 1-year data, while citywide statistics used in district comparisons use ACS 5-year data. District-level data can only be produced using 5-year ACS data, while citywide data is available in both 1-year and 5-year versions. To compare districts to the entire city on an apples-to-apples basis, we use the citywide 5-year data. But to show the most current citywide statistics, we use 1-year data, which better reflect present conditions, though in practice the differences are typically very small.
What does "Universe" mean in the data tables?
In the data tables, "universe" refers to the population the statistic applies to. For instance, when the universe is the total population, the statistic counts everyone living in the district. If the universe is "Population 25 years and over," (the universe for the education table), the statistic counts everyone age 25 or older; younger residents are not included.
Sometimes universes refer to "households" rather than individuals. For instance, the Household Size table's universe is "Occupied housing units." This means that the statistic counts all occupied housing units: each household is counted once, regardless of how many people live in the household. This is an important distinction. For instance, if a district had 50% one-person households and 50% two-person households, most residents would live in a two-person household, but households would be evenly split between one-person and two-person households.
Sometimes a universe is at the household level but refers to a specific individual: the Primary Resident in Household. These statistics refer to one person per household, even though other people in the household may have different answers. For instance, the Years in Current Residence table's universe is Primary Resident in Household—some household members could have a different number of years living there. The definition of primary resident is the person who owns or leases the residence. If co-owned or multiple names are on the lease, either person could be the primary resident. If no one formally owns or rents the property, any adult member of the household could be the primary resident.
Finally, some universes refer to the "civilian noninstitutionalized population." This generally means the entire population of the district except people living in correctional facilities, nursing homes, and certain other facilities that the ACS considers "institutions."
What does "Geography" mean in the data tables?
All district statistics are produced by aggregating smaller Census-defined geographies to district boundaries. Those smaller Census-defined geographies are either census block groups or census tracts. Census block groups are smaller than census tracts and are generally preferred because smaller geographies can better match district boundaries. However, not all statistics of interest are released by the Census Bureau at the block-group level; some are only released at the tract level. For these statistics, we are forced to use tract-level data. Each table shows what geographic level the statistic is aggregated from: block-group level or tract level.
What happens when a Census tract or block group crosses a district boundary?
When a census tract or block-group crosses over a district boundary, we incorporate data from the entire tract or block group, as it is not possible to restrict the published Census data to the exact district boundaries. For each tract or block group that extends past the district boundary, we account for its contribution to the overall district average by weighting it according to the fraction of its population that falls within the district boundary (using 2020 decennial census block-level data, which is disaggregated at high levels of geographic detail that perfectly align with district boundaries).
What does the "±" (Margin of Error) mean on the data tables?
The American Community Survey is run by the U.S. Census Bureau, but it is not the same as the decennial Census. The ACS collects responses from a representative sample of households rather than attempting to count everyone. Because the ACS is based on a sample, it comes with sampling error: random variation that occurs because a random sample will not select people with the exact same characteristics every time. The tables provide more information about this random variation by allowing users to toggle margins of error on or off. A margin of error is a way of quantifying that random variation. The margin of error means that if the ACS were conducted repeatedly with new samples, the result would fall within the margin of error about 90% of the time. For instance, if a district page reports that 50% of households are renters, with a ±2 percentage point margin of error, the true renter share would be expected to fall between 48% and 52% about 90% of the time across repeated samples.
How do you handle prison or jail populations?
If a district includes a prison or jail, the people incarcerated there will be reflected in some of the district's demographic statistics. Any table where the universe is labeled as total population or population of a certain age range will include the incarcerated population. People who are incarcerated will be excluded from many tables where their inclusion would not make sense. For instance, a district's median rent comes from surveys of housing units and excludes "group quarters," a category that includes prison and jail populations. More broadly, any table whose universe is "households," "housing units," or "noninstitutionalized" will not include people incarcerated in prisons or jails.
When people incarcerated in prisons or jails are included in district-level estimates, they are counted where the jail or prison is located, not at the home address where they would live if they were not incarcerated. This differs from how some states treat incarcerated people for redistricting: they count incarcerated people at their home addresses rather than where the prison or jail is located.
How are other special populations counted, including students in college dorms, juvenile detention residents, nursing-home residents, military barracks residents, and people in shelters or group homes?
These special populations, along with people incarcerated in prisons and jails, are considered to be living in "group quarters." This means they are counted in tables whose universe is "total population," but they are excluded from tables whose universe is "households" or "housing units." Tables where the universe is labeled "civilian noninstitutionalized population" exclude people incarcerated in prisons and jails, people in juvenile detention, nursing home residents, people in mental hospitals and similar institutions, and active-duty military personnel living in barracks or military quarters. College dorm residents are included, as are some group-home and shelter residents who are not categorized as living in institutions.
Can I bulk-download the data for my own use?
We are happy to provide all district-level data for non-commercial uses—please contact us at [email protected].
What are your plans for the future?
We have many ideas for expanding the data and adding features that make local district statistics easier to understand and compare to serve a wide variety of use cases. High-level ideas include adding more features to district pages, such as comparing districts to one another rather than just to the city as a whole. Historical data could be added to show how districts have changed over time. Data could be sourced from places beyond the Census Bureau's American Community Survey, and far more ACS statistics could be published than are currently displayed. And of course, more cities could be added and as well as types of local governments beyond city councils. We welcome your ideas and input—please contact us at [email protected].
Are you affiliated with the U.S. Census Bureau or any local government?
No. Civic Data Atlas is an independent project. While we use data from the U.S. Census Bureau and city data portals, we are not officially affiliated with or endorsed by them.