Canonical census data
A complete, harmonized dataset across core census domains with consistent definitions, QA, and lineage.
EtherData Canonical census data
The canonical, complete US census dataset at H3 resolution 8.
EtherData makes spatial data trustworthy and usable for every team. We keep the vision: open, rigorous, and deeply usable datasets that make decision-making fairer and faster.
3,360
H3 cells in Manhattan sample
100%
Census coverage
2
H3 resolutions: R8
Product
Built for census-native workflows.
The new release delivers canonical census data while preserving the EtherData vision: trusted, open spatial data that teams can rely on to build equitable, modern cities.
H3-native distribution
Delivered at H3 resolution 8 so you can join with spatial features instantly and keep performance predictable.
Instant warehouse access
Manhattan data is free in BigQuery. Full coverage is provisioned on request with premium support.
Data package
Everything you expect, resolved to H3.
The dataset includes population, income, education, housing, demographics, commuting, and more, mapped to H3 with standard naming and metadata.
- H3 resolution 8 tables
- Provenance and update cadence
- QA scores and missing-value flags
- BigQuery-ready schemas
Coverage
Complete US census
Sample
Manhattan free dataset
Delivery
BigQuery tables
Resolution
H3 R8
Product detail
Canonical census attributes, organized for analytics.
The etherdata.h3complete table ships a full census schema at H3 resolution 8. Use the categories below to find the attributes you need and jump straight to analysis in BigQuery.
Core identifiers and totals
geo_idtotal_poppopulation_1_year_and_overpopulation_3_years_overpop_5_years_overpop_16_overpop_25_years_overpop_25_64childrenmedian_agegroup_quartershouseholdshousing_unitsoccupied_housing_unitsvacant_housing_unitsworkers_16_and_overcommuters_16_overAge and sex distribution
female_under_5female_5_to_9female_10_to_14female_15_to_17female_18_to_19female_20female_21female_22_to_24female_25_to_29female_30_to_34female_35_to_39female_40_to_44female_45_to_49female_50_to_54female_55_to_59female_60_to_61female_62_to_64female_65_to_66female_67_to_69female_70_to_74female_75_to_79female_80_to_84female_85_and_overfemale_popmale_under_5male_5_to_9male_10_to_14male_15_to_17male_18_to_19male_20male_21male_22_to_24male_25_to_29male_30_to_34male_35_to_39male_40_to_44male_45_to_49male_45_to_64male_50_to_54male_55_to_59male_60_to_61male_62_to_64male_65_to_66male_67_to_69male_70_to_74male_75_to_79male_80_to_84male_85_and_overmale_popRace and ethnicity
white_popblack_popasian_popamerindian_popother_race_poptwo_or_more_races_pophispanic_popnot_hispanic_pophispanic_any_racewhite_including_hispanicblack_including_hispanicasian_including_hispanicamerindian_including_hispanicwhite_male_45_54white_male_55_64black_male_45_54black_male_55_64asian_male_45_54asian_male_55_64hispanic_male_45_54hispanic_male_55_64Education and schooling
less_than_high_school_graduatehigh_school_diplomahigh_school_including_gedsome_college_and_associates_degreeassociates_degreebachelors_degreebachelors_degree_2masters_degreegraduate_professional_degreeless_one_year_collegeone_year_more_collegebachelors_degree_or_higher_25_64male_45_64_associates_degreemale_45_64_bachelors_degreemale_45_64_graduate_degreemale_45_64_high_schoolmale_45_64_grade_9_12male_45_64_less_than_9_grademale_45_64_some_collegein_schoolin_grades_1_to_4in_grades_5_to_8in_grades_9_to_12in_undergrad_collegeIncome, poverty, and inequality
median_incomeincome_per_capitagini_indexpovertypop_determined_poverty_statusincome_less_10000income_10000_14999income_15000_19999income_20000_24999income_25000_29999income_30000_34999income_35000_39999income_40000_44999income_45000_49999income_50000_59999income_60000_74999income_75000_99999income_100000_124999income_125000_149999income_150000_199999income_200000_or_morehouseholds_public_asst_or_food_stampshouseholds_retirement_incomeHousing stock, tenure, and cost
housing_unitshousing_units_renter_occupiedowner_occupied_housing_unitsrenter_occupied_housing_units_paying_cash_median_gross_rentmortgaged_housing_unitsvacant_housing_units_for_rentvacant_housing_units_for_salemobile_homesmillion_dollar_housing_unitsmedian_rentmedian_year_structure_builthousing_built_1939_or_earlierhousing_built_2000_to_2004housing_built_2005_or_laterowner_occupied_housing_units_median_valueowner_occupied_housing_units_lower_value_quartileowner_occupied_housing_units_upper_value_quartilepercent_income_spent_on_rentrent_under_10_percentrent_10_to_15_percentrent_15_to_20_percentrent_20_to_25_percentrent_25_to_30_percentrent_30_to_35_percentrent_35_to_40_percentrent_40_to_50_percentrent_over_50_percentrent_burden_not_computedHousing structure types
dwellings_1_units_detacheddwellings_1_units_attacheddwellings_2_unitsdwellings_3_to_4_unitsdwellings_5_to_9_unitsdwellings_10_to_19_unitsdwellings_20_to_49_unitsdwellings_50_or_more_unitsHousehold and family structure
family_householdsnonfamily_householdsmarried_householdsfemale_female_householdsmale_male_householdsfamilies_with_young_childrenone_parent_families_with_young_childrentwo_parent_families_with_young_childrenchildren_in_single_female_hhfather_one_parent_families_with_young_childrenfather_in_labor_force_one_parent_families_with_young_childrentwo_parents_in_labor_force_families_with_young_childrentwo_parents_father_in_labor_force_families_with_young_childrentwo_parents_mother_in_labor_force_families_with_young_childrentwo_parents_not_in_labor_force_families_with_young_childrenLabor force and employment
civilian_labor_forcepop_in_labor_forcenot_in_labor_forceemployed_popunemployed_poparmed_forcesmanagement_business_sci_arts_employedsales_office_employedemployed_agriculture_forestry_fishing_hunting_miningemployed_arts_entertainment_recreation_accommodation_foodemployed_constructionemployed_education_health_socialemployed_finance_insurance_real_estateemployed_informationemployed_manufacturingemployed_other_services_not_public_adminemployed_public_administrationemployed_retail_tradeemployed_science_management_admin_wasteemployed_transportation_warehousing_utilitiesemployed_wholesale_tradeoccupation_management_artsoccupation_sales_officeoccupation_servicesoccupation_production_transportation_materialoccupation_natural_resources_construction_maintenanceCommuting and travel behavior
aggregate_travel_time_to_workcommute_less_10_minscommute_5_9_minscommute_10_14_minscommute_15_19_minscommute_20_24_minscommute_25_29_minscommute_30_34_minscommute_35_39_minscommute_35_44_minscommute_40_44_minscommute_45_59_minscommute_60_89_minscommute_60_more_minscommute_90_more_minscommuters_by_buscommuters_by_car_truck_vancommuters_by_carpoolcommuters_by_public_transportationcommuters_by_subway_or_elevatedcommuters_drove_alonewalked_to_workworked_at_homeTransportation assets and vehicles
no_carno_carsone_cartwo_carsthree_carsfour_more_carsMobility, migration, and language
different_house_year_ago_same_citydifferent_house_year_ago_different_citynot_us_citizen_popspeak_only_english_at_homespeak_spanish_at_homespeak_spanish_at_home_low_englishExample queries
Richer use cases powered by canonical census.
Pair your operational data with H3 census attributes to generate defensible insights, equity analysis, and high-resolution planning.
Bike-share equity & mobility need (Manhattan, H3 R8)
City mobility (Etherdata demo)Data sources
- Citi Bike trips + stations (public BigQuery datasets)
- Etherdata Canonical US Census @ H3 R8 (NY free sample)
- Demographics + commuting + housing context (households, no-car rate, transit share, renter share, rent burden)
KPIs
- Mobility Need Index (census-driven need surface)
- Service Index (dock supply + utilization proxy)
- Service Gap Score = Need - Service (under/over-served signal)
- Supporting rates: trips per household, trips per worker/commuter, trips per dock, docks per 1K households
Insights
- Separate operational activity (trips) from structural mobility need (census)
- Identify under-served cells where high need is not matched by service (gap > 0)
- Distinguish tourism/CBD anomalies (high service, low resident exposure) from true residential need
- Prioritize expansion, rebalancing, and pricing programs using gap hotspots instead of trip counts alone
Health & sanitation stress signals (Manhattan, H3 R8)
Urban health & operations (Etherdata demo)Data sources
- NYC 311 service requests (public BigQuery dataset)
- Health & sanitation complaints (rodents, unsanitary conditions, water/sewer, food-related issues)
- Etherdata Canonical US Census @ H3 R8 (NY free sample)
- Population & household denominators
- Housing and vulnerability context (rent burden, income proxies)
KPIs
- Health & Sanitation Complaint Rate (requests per 1K population)
- Operational Friction (avg. hours to close health/sanitation requests)
- Health & Sanitation Risk Index
- Composite of complaint intensity + vulnerability (rent burden) + service delay
Insights
- Convert raw 311 volumes into comparable, per-capita signals using canonical census denominators
- Distinguish busy areas from true over-indexing health & sanitation stress
- Surface equity-priority cells where high complaint rates coincide with higher rent burden
- Enable transparent operational triage based on explainable drivers, not black-box scores
Member vs Casual Riders — Adoption & Usage Mix (Manhattan, H3 R8)
City mobility (Etherdata demo)Data sources
- Citi Bike trips (public BigQuery dataset)
- Trip-level events with rider type (Subscriber vs Customer), spatially indexed to H3
- Etherdata Canonical US Census @ H3 R8 (NY free sample)
KPIs
- Member Penetration Rate: subscriber trips / total trips
- Casual-to-Member Ratio: casual trips / member trips
- Trip Frequency Proxy: trips per distinct bike_id by rider type
- Socio-economic context: median income and bachelor-or-higher rate (25-64)
Insights
- Separate adoption from raw activity by tracking membership penetration vs total volume
- Identify casual-heavy corridors that need differentiated operations or pricing
- Contextualize adoption using stable income and education signals per neighborhood
- Prioritize outreach and station strategy using adoption segments instead of raw trip counts
Equitable Bike Access — Demand vs Low-Income Exposure (Manhattan, H3 R8)
City mobility (Etherdata demo)Data sources
- Citi Bike trips + stations (public BigQuery datasets)
- Trips aggregated to H3 R8 from start-station coordinates; stations aggregated to H3 R8 as dock supply proxy
- Etherdata Canonical US Census @ H3 R8 (NY free sample)
- Census context aligned to the same H3 cells (income, households, population baselines)
KPIs
- Low-Income Exposure Score (income-derived ranking across Manhattan H3 cells)
- Bike Access Score (supply proxy): docks per 1K households + station count
- Bike Demand: trips per household + trips per dock
- Equity Gap: low-income exposure minus access score (positive = lower-income areas under-served)
Insights
- Find lower-income H3 cells where dock supply lags exposure using the equity gap signal
- Balance supply and demand by comparing per-household and per-dock trip rates
- Prioritize station investment, subsidies, or pricing updates where access trails need
- Explain infrastructure decisions with transparent census-aligned demand and supply metrics
More ways to activate etherdata
Retail site selection using income, population, and car ownership.
Healthcare clinic coverage using age bands, income, and commute times.
Transit ridership planning with commute mode shares.
School capacity forecasting using children and household counts.
Senior services planning with 65+ population concentrations.
Affordable housing targeting using rent burden and median rent.
Housing redevelopment prioritization using vacancy and housing age.
Homeownership opportunity zones using owner-occupied value quartiles.
Multi-family development analysis using dwelling unit distributions.
Economic resilience scoring with poverty, gini, and unemployment.
Workforce training targeting by industry employment mix.
Remote work hotspots using worked-at-home rates.
EV charging placement using car ownership and commute distances.
Immigration services planning using language and citizenship fields.
Public safety resource allocation using population density and mobility.
Storm evacuation planning using car ownership and age distribution.
Community investment scoring using income, education, and housing cost.
Commercial leasing strategy using daytime workforce and commute modes.
Equitable green infrastructure planning with family and poverty data.
Neighborhood change monitoring using migration and housing signals.
Press
Latest announcement
Official press release covering the national H3-native census dataset launch and cloud-native availability.
Ether Data AI Launches First National H3-Native Census Intelligence Dataset for AI and Analytics
Boston, MA
A first-of-its-kind national spatial dataset that transforms U.S. Census data into a fully H3-native intelligence layer for analytics and AI workflows.
Read the press releaseAccess
Start free, scale with request-based access.
Full coverage access
Request full US coverage for production analytics, integrations, and enterprise support.
Discover with MCPX
Explore the H3-native census dataset through a multi-agent chat experience with geospatial functions and guided discovery.
Vision
Same vision. Stronger foundation.
EtherData exists to make high-quality spatial data accessible, trusted, and actionable. Canonical census data is the foundation for fairer policy, smarter infrastructure, and better business decisions.
"We are building the census-native layer that modern analytics teams can trust and ship with confidence."
— EtherData leadership