Frequently Asked Questions
Common questions about where district and neighborhood statistics come from, how they're calculated, and how to interpret them.
Who created Civic Data Atlas?
Jack Landry is the founder and research lead of Civic Data Atlas. 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.
What are the data sources behind district and neighborhood statistics?
Most district and neighborhood 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 administers 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 and neighborhood statistics shown on Civic Data Atlas cover a significant share of the topics included in the ACS but are not comprehensive.
Health statistics come from the CDC PLACES data, and estimates of air conditioning access and heat-related social vulnerability come from the U.S. Census Bureau's Local Air Conditioning Estimates and Community Resilience Estimates for Heat. Job and commuting statistics also come from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES). Tree canopy and land cover data comes from the National Baseline Assessment of Urban and Community Forests and housing assistance statistics come from the U.S. Department of Housing and Urban Development's Picture of Subsidized Households. Statistics on the adult outcomes of children who grew up in a given district or neighborhood come from The Opportunity Atlas, which sources its data from U.S. Internal Revenue Service tax records. Additional information on these sources is detailed on the Methodology page.
How are the district and neighborhood statistics computed?
Our methodology is described in detail on the Methodology page. In short, the Census Bureau does not release the precise location of survey respondents, so we combine data from the smaller geographic areas it does publish that fall within or overlap a given neighborhood or district. Some of these areas do not perfectly align with district boundaries: part of the area may be inside the district, while another part is outside of it. When this occurs, we weight the data by the fraction of the area's population that falls within the district boundaries. While this geographic mismatch is unavoidable, it has a very small impact on the accuracy of the resulting statistics. For every district or neighborhood, 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 and neighborhood 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. Without pre-calculation, gathering Census data is difficult and requires a multi-step process described in detail on the Methodology page.
Cities typically publish local council district population and racial composition figures after redistricting based on decennial Census data. Ensuring districts are racially representative and have similar population sizes is important to the redistricting process. However, only knowing the racial breakdown and size of a district gives an extremely limited view of the composition of a district.
The same is broadly true of neighborhoods. Some cities publish basic neighborhood profiles from other sources—but not with the range of demographic, housing, and economic detail along with the comparison tools and visualizations published here. Like city council districts, city neighborhood data is not pre-packaged by the Census Bureau, which makes it more difficult to calculate.
How do you check the district and neighborhood statistics for errors?
We run validation checks for every city before publishing district or neighborhood 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 (community areas, Neighborhood Tabulation Areas, and CDND neighborhoods aren't subject to redistricting, so for those we instead confirm boundaries are matched correctly against the source boundary files). This helps confirm that Census geographies are being assigned to the right district or neighborhood. We also test how much imperfect overlap between Census geographies and district or neighborhood boundaries affects estimates for measures where a direct comparison is possible. When large discrepancies appear, we review them before publication.
Why do most 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. We use 5-year estimates because they are the only release with a large enough sample to compute statistics at the local district or neighborhood level. 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 and neighborhood pages uses the same 5-year data as the district and neighborhood statistics, so comparisons are made over the same time frame.
How often is the data updated?
We attempt to update statistics as soon as new data is released. Information on each dataset's historical update timeline is available on the Methodology page, along with a link to the latest data. For information about each district's elected representative, we regularly check city council roster websites for updates. If you see anything out of date, please contact us at contact@civicdataatlas.org and we will make a correction.
Why does some district 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 comes 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, while the decennial Census attempts to count everyone. ACS-reported figures differ from the decennial Census by a few percentage points because of random sampling variation that's inherent to all surveys, not because the population actually shifted between years. 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. The ACS makes no such correction—people incarcerated in prisons and jails are counted as living in the location of the prison or jail. For the states that make this adjustment, 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—creating a difference between the ACS estimates and redistricting data.
Why do the "City at a Glance" statistics differ from those on the district and neighborhood pages when comparing a district or neighborhood to the citywide totals?
The "City at a Glance" statistics use the latest ACS 1-year data, while citywide statistics used in district and neighborhood comparisons use ACS 5-year data. District- and neighborhood-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 and neighborhoods 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 reflects 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 or neighborhood. 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 or neighborhood 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 or neighborhood 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?
Most district and neighborhood statistics are produced by aggregating smaller Census-defined geographies to district or neighborhood boundaries. Those smaller Census-defined geographies are typically 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 or neighborhood 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 the geographic level from which the statistic is aggregated: block groups or tracts.
A subset of statistics is produced by aggregating data at the census-block level. Census blocks are the smallest Census-defined geography and generally align more closely with district and neighborhood boundaries, so these statistics do not require the tract- or block-group-level aggregation used for most other tables. The geography for these statistics is labeled "Census Blocks."
What happens when a census tract or block group crosses a district or neighborhood boundary?
When a census tract or block group crosses over a district or neighborhood 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 align closely with district and neighborhood boundaries). This process is described in greater detail on the Methodology page.
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 or neighborhood 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 or neighborhood includes a prison or jail, the people incarcerated there will be reflected in some of its 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- or neighborhood-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?
Individual tables can be downloaded directly from each district or neighborhood page using the download button on the table. Data for all districts or all neighborhoods for a specific metric can be downloaded via the "compare all districts" pages. For bulk access to all district-level and neighborhood-level data at once, for non-commercial uses, please contact us at contact@civicdataatlas.org.
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.