There is no single “most granular” demographics dataset for every job. A high-resolution raster can estimate population across small grid cells, Census microdata can support custom analyses of sampled people and households, and Census summary files can provide detailed aggregate tables for smaller areas. The right choice depends on whether you need fine spatial coverage, respondent-level combinations, or reliable published statistics for a specific geography.
What “most granular” means in demographic data
Granularity has several meanings that should not be collapsed into one ranking:
- Spatial resolution: the size of a grid cell or smallest published area.
- Analytical unit: whether the data represent individual records, aggregate tables, or estimated values in raster cells.
- Demographic detail: which characteristics and cross-tabulations are available.
- Statistical precision and currency: how estimates are produced, how uncertain they are, and which period they describe.
A smaller cell does not automatically mean a more accurate or more current estimate. Compare the data’s geography, variables, reference period, coverage, uncertainty method, licensing, and reproducibility—not just its nominal resolution.
The 2021 Kuwala workflow: population rasters queried with H3
In an April 21, 2021 article, Matti described using Facebook Data for Good population-demographic raster files through the open-source Kuwala wrapper. The article says the source combined official census data with internal data and machine-learning image recognition to estimate building locations and types; those are the article’s descriptions, not a verified specification of a currently available dataset. Read the 2021 article.
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What the article reported
Matti reported raster cells at 1 arcsecond, approximately 30 meters. That is an approximate resolution claim from 2021, not an accuracy guarantee or a current product specification. The article named seven population groups:
- Total population
- Female
- Male
- Children under 5
- Youth aged 15–24
- People aged 60 and older
- Women of reproductive age, 15–49
It said that each country had a file for each group in GeoTIFF or CSV format; the CSV contained cell latitude, longitude, and population value. The article described preprocessing those files into Uber H3 indexes at resolution 11, using JavaScript streams and MongoDB aggregation pipelines to keep memory use low. Its wrapper offered queries by H3 cell or coordinates, point, radius, and polygon, and aggregation to areas such as ZIP-code areas.
H3 is a hierarchical hexagonal geospatial indexing system with resolutions from 0 (coarsest) through 15 (finest), according to Uber’s H3 documentation. Resolution 11 is an index level in that hierarchy, not the native resolution of the source raster. Aggregating raster cells into H3 cells changes the spatial units; do not treat the H3 resolution as equivalent to the source’s 1-arcsecond grid without examining the aggregation and its geographic effects.
The article’s examples—real-estate price prediction, vaccination planning, and rural electrification—show why gridded population estimates can be useful. However, the reviewed sources do not establish whether those files, geographic coverage, licensing, or Kuwala service remain available today. Nor do they establish an accuracy comparison against other global demographic datasets. Treat the workflow and specifications as historical, attributed descriptions.
When U.S. Census data is the better fit
For U.S.-focused work, distinguish American Community Survey Public Use Microdata Sample (ACS PUMS) records from ACS summary files. They answer different questions and are not substitutes for one another.
| Product | Analytical unit | Geography documented | Best suited to |
|---|---|---|---|
| ACS PUMS | Sampled person and household records | State and Public Use Microdata Area (PUMA) | Custom combinations of characteristics that published tables do not provide |
| ACS summary files | Aggregate cross-tabulations | Includes geographies such as block groups | Detailed published estimates for smaller areas |
The Census Bureau’s 2024 ACS API documentation describes one-year PUMS as covering approximately 1% of the U.S. population and says PUMAs contain roughly 100,000 people. Those are sampling and geography descriptions, not guarantees of adequate precision for every subgroup or locality. The same documentation describes summary-file cross-tabulations, many available down to block groups.
Rank #4
If you need individual-level records for an area smaller than a state or PUMA, PUMS does not provide respondent records at tract or block level. Check whether an appropriate aggregate summary table exists at the geography you need before deciding to use microdata.
How to make a weighted Census Microdata API query
The Microdata API can retrieve raw records and produce custom weighted tabulations. The Census Bureau’s query guide describes selecting variables, defining the universe, specifying geography, and adding an API key. The Census page dated September 17, 2026 says a key is required for data queries and lists ACS PUMS, CPS ASEC, CPS, CFS, VIUS, and SIPP among the available microdata datasets. Check the current Microdata API page and its Discovery Tool for supported variables, geographies, and examples before running a live query.
- Choose the dataset and vintage. Use the Discovery Tool to confirm the dataset, year, variables, and supported geography that match your question.
- Select variables and define the universe. Include only records and characteristics relevant to the analysis; the universe determines which records are counted.
- Specify geography. Select a supported geography, such as state or PUMA for the documented ACS PUMS data.
- Choose the tabulation and weight. Use the appropriate person or household weight for estimates representing people or households. The guide demonstrates person weight PWGTP for person estimates.
- Add an API key and submit the query. Follow the current API syntax and key instructions on the Census pages, then inspect the returned fields and geography identifiers.
Weights matter: a weighted result estimates a population quantity, while an unweighted result is a count of sample records. For multi-geography tabulations, include geography in the table layout as well as in the query universe when you want separate results by geography. Keep the universe, geography, vintage, and weight attached to any reported result so readers can interpret what it represents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose the data type that matches the question
- Need estimated population across a continuous surface? A gridded raster may be useful, provided its coverage, reference date, category definitions, and licensing are current and documented.
- Need custom combinations of individual or household characteristics? Use PUMS or another suitable microdata product where its supported geography and sample precision fit the question.
- Need detailed aggregate statistics for a block group or similar small area? Investigate ACS summary files and available tables first.
- Need statistics already published as an aggregate or time series? The Census Bureau’s Microdata API guide recommends using aggregate or time-series datasets rather than microdata when those products already provide the required statistics.
For any candidate source, verify geographic coverage and missing areas, collection or reference period, update cadence, definitions and cross-tabulations, sampling and uncertainty, privacy protections, access terms, and reproducibility. The smallest nominal unit is only one part of the decision.
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