Data API - DataUSA/datausa-api GitHub Wiki
Contents
Legacy PUMS API:
-
Variables by Dataset
Introduction
Let's breakdown an example data call:
http://api.datausa.io/api/?show=cip&sumlevel=all
In its most basic form, all API calls required two parameters: a show variable and a sumlevel variable. In this example we are showing "cip" (aka CIP, Classification of Instructional Programs) across all sumlevels. CIP codes are available at four basic sumlevels: 2-digit (high level), 4 digit, 6-digit (most detailed) and all (2, 4 and 6).
If we only want the high level CIP codes, we could ask for:
http://api.datausa.io/api/?show=cip&sumlevel=2
If we look at the response, we are receiveing many columns. If we are only interested an a particular column we can ask for just that:
http://api.datausa.io/api/?show=cip&sumlevel=2&required=grads_total
If we want multiple columns, we can provide a comma-separated list to required:
http://api.datausa.io/api/?show=cip&sumlevel=2&required=grads_men,grads_women
Query Parameters
| parameter | accepted values | description |
|---|---|---|
| force | schema_name.table_name (Example) | Forces the use of a particular data table. |
| limit | integer | Limits the number of rows returned by the query. |
| order | any available column name | Column name to use for ordering the resulting data array. |
| show (required) | any available attribute | A comma-separated list of attributes to show in the query. |
| sort | desc or asc |
Changes the sort order of the returned data array. |
| sumlevel (required) | any available sumlevel for the given attribute | This restricts the data fetched to only display the specified sumlevel(s). If more than one "show" attribute is specified, sumlevel must be a comma-separated list with a value for each attribute. |
| required | any available column name | A comma-separated list of column names to be returned in the query. |
| where | see documentation | Advanced filtering of columns, similar to the WHERE clause on SQL. |
| year | latest, oldest, all, 4-digit year |
Filters the returned data to the given year. |
Basic Filtering
There are two main ways to filter rows in the DataUSA API. One way is through strict equality. For example, if you were looking at all top level NAICS:
http://api.datausa.io/api?show=naics&sumlevel=0
You could filter the results to only show data from NAICS code 23, by adding &naics=23 to the URL:
http://api.datausa.io/api?show=naics&sumlevel=0&naics=23
This is true for any column: you may restrict API results to that column through strict equality by using column_name=value.
Advanced Filtering
Sometimes, however, it's necessary to filter not merely by equality but by other mechanisms. Using the where query parameter provides access to more complicated expressions. The basic syntax of using where is: &where**=column_name:condition
Let's say we wanted to filter the results of an API call to only show top-level industries containing more than an estimated 10M people in the workforce. Here is how we would write that:
http://api.datausa.io/api/?show=naics&sumlevel=0&where=num_ppl:>10000000
Here is a list of the available expressions:
| expression | operator | example syntax |
|---|---|---|
| greater than | > | &where=num_ppl:>10000000 |
| less than | < | &where=num_ppl:<10000000 |
| string starts with | ^ | &where=naics:^23 |
| string ends with | $ (placed after text) | &where=naics:3$ |
| not equal (integer) | ! | &where=avg_wage_rank:!1 |
| not equal (string) | str! | &where=geo:str!04000US25 |
Substitutions
Sometimes, when requesting data for a specific attribute, the underlying dataset may only contain data for a higher level. For example, the County Health Records (CHR) dataset only contains geographies down to the county, so when requesting health data for a deeper geography the API will automatically find the closest parent. Take this example:
http://api.datausa.io/api/?show=geo&required=diabetes&sumlevel=all&geo=31000US14460
Here we are requesting the diabetes rate for the Boston-Cambridge-Newtown, MA-NH Metro Area, but because that depth is not available for this dataset, the API returns data for the state of Massachusetts. When looking at the JSON data that is returned, this substitution is passed as the "subs" key:
{
"data": ...,
"headers": ...,
"source": ...,
"subs": {
"geo": "04000US25"
},
"logic": ...
}
Limitations
The API mirrors the underlying aggregated table structure. Consequently, currently not all possible variables are available for simultaneous retrieval from the API. Check each dataset's table lists to understand supported variable combinations. To address this issue we have created the Join API.
Download as CSV
To download the data as a CSV, simply add csv into the URL as follows:
http://api.datausa.io/api/csv/?show=cip&sumlevel=2&required=grads_men,grads_women
IPEDS
Tuition
| column name | description |
|---|---|
| year | 4-digit year value |
| cip | course ID |
| university | university ID |
| sector | sector ID |
| oos_tuition | out of state tuition |
| oos_fee | out of state fee |
| state_tuition | in-state tuition |
| state_fee | in-state fee |
| district_tuition | in-district tuition |
| district_fee | in-district fee |
| state_tuition_grads | state tuition (graduate students) |
| district_tuition_grads | district tuition (graduate students) |
| oss_tuition_grads | out of state tuition (graduate students) |
| state_fee_grads | state fee (graduate students) |
| district_fee_grads | district fee (graduate students) |
| oss_fee_grads | out of state fee (graduate students) |
Enrollment
(5/29/18) Updated documentation on expanded enrollment data coming soon
Graduates
| column name | description |
|---|---|
| year | 4-digit year value |
| university | university ID |
| cip | course ID |
| degree | degree ID |
| geo_id | location ID |
| grads_total | total graduates |
| grads_men | men graduates |
| grads_women | women graduates |
| grads_black_men | black men graduates |
| grads_multi | multiracial graduates |
| grads_white_women | white women graduates |
| grads_hispanic_men | hispanic men graduates |
| grads_nonresident | non-resident graduates |
| grads_native | native American graduates |
| grads_hispanic | hispanic graduates |
| grads_hawaiian | hawaiian and Pacific Islander graduates |
| grads_multi_men | multiracial men graduates |
| grads_black | black graduates |
| grads_native_women | native women graduates |
| grads_multi_women | multiracial women graduates |
| grads_asian_women | asian women graduates |
| grads_unknown | unknown race graduates |
| grads_hawaiian_men | hawaiian men graduates |
| grads_hawaiian_women | hawaiian women graduates |
| grads_asian | asian graduates |
| grads_nonresident_women | non-resident women graduates |
| grads_unknown_men | unknown race men graduates |
| grads_native_men | native American men graduates |
| grads_asian_men | asian men graduates |
| grads_unknown_women | unknown race women graduates |
| grads_black_women | black women graduates |
| grads_white_men | white men graduates |
| grads_white | white graduates |
| grads_nonresident_men | non-resident men graduates |
| grads_hispanic_women | hispanic women graduates |
University Financials
| column name | description |
|---|---|
| year | 4-digit year value |
| university | university ID |
| total_expenses | Total expenses as represented by F1D02, F2B02 and F3B02 from IPEDS |
| total_salaries | Total salaries as represented by F1C192, F2E132, and F3E072 from IPEDS |
| endowment_value_fiscal_year_end | Total endowment at fiscal year year as represented by F1H02 and F1H02 |
| investment_income | Income on investments represented by F1B17, F2D10, F3D05 |
| research_total | Total spending on research represented by F1C021, F2E021, F3E02A1 |
| federal_grants_and_contracts | Total value of federal grants and contracts based on F1B02, F2D05, F3D02B |
| state_grants_and_contracts | State grants and contracts based on: F1B03, F2D06, F3D03B |
| local_grants_and_contracts | Local grants and contracts based on: F1B04A, F2D07, F3D03D |
| private_grants | |
| pell_grants | |
| other_federal_grants | |
| state_grants | |
| local_grants | |
| tuition_and_fees |
ACS PUMS
| column name | description |
|---|---|
| num_records | number of PUMS records collapsed to generate the estimate |
| num_records_ft | number of full-time records collapsed to generate the estimate |
| num_records_pt | number of part-time records collapsed to generate the estimate |
| avg_wage | average wage estimate |
| avg_wage_ft | average wage full-time employees estimate |
| avg_wage_pt | average wage part-time employees estimate |
| avg_wage_moe | average wage margin of error |
| avg_wage_ft_moe | average wage full-time margin of error |
| avg_wage_pt_moe | average wage part-time margin of error |
| num_ppl | estimated number of people in the workforce |
| num_ppl_ft | estimated number of full-time employed people |
| num_ppl_pt | estimated number of part-time employed people |
| num_ppl_moe | estimated number of people margin of error |
| num_ppl_ft_moe | estimated number of people full-time margin of error |
| num_ppl_pt_moe | estimated number of people part-time margin of error |
| avg_age | average age estimate |
| avg_age_ft | average age full-time employees estimate |
| avg_age_pt | average age part-time employees estimate |
| avg_age_moe | average age margin of error |
| avg_age_ft_moe | average age margin of error full-time employees |
| avg_age_pt_moe | average age margin of error part-time employees |
Note: num_ppl represents people over the age of 16 in the workforce. In the ygb table, num_ppl is replaced by num_over5 to represent all people over the age of 5 years old.
Aggregation Methodology
Data USA incorporates 4 universes from the ACS PUMS dataset (the default usually being "Workforce"). Here are is the aggregation criteria for these workforces:
Workforce Universe (e.g. num_ppl)
- WAGP > 0
- AGEP >= 16
- ESR is one of: 1, 2, 4, 5
Full-time Universe (e.g. num_ppl_ft)
- WAGP > 0
- AGEP >= 16
- WKHP >= 35
- ESR is one of: 1, 2, 4, 5
Part-time Universe (e.g. num_ppl_pt)
- WAGP > 0
- AGEP >= 16
- WKHP < 35
- ESR is one of: 1, 2, 4, 5
5+ Universe (only for num_over5)
- AGEP >= 5
All wage estimate values are adjusted using the ADJINC variable.
5-year Estimates
Geographies, industries and occupation classifications can change in the middle of a single 5-year PUMS estimate. Since data are recorded in the classification in which they are collected, these changes add additional uncertainty to the PUMS 5-year estimate. In order to compensate in many cases for the lack of 1:1 mappings between old and new classifications, we will, apply a randomized redistribution from the old classification system to the new one. For occupations and industries the probability is based upon the Census Bureau's Industry and Occupation Conversion Rates tables. For geography (the issue only arises with PUMA-level data) it is based upon IPUMS' crosswalk methodology.
Column Mapping
| Data USA | Base PUMS |
|---|---|
| birthplace | POBP |
| cip | FOD1P |
| degree | SCHL |
| naics | NAICSP |
| soc | SOCP |
| race | RAC1P |
| age | AGEP |
| sex | SEX |
American Community Survey (ACS)
Data USA provides a view onto several American Community survey data tables. These tables include information on: population, income, veteran status, nativity, poverty, race, property, transportation, language and more. Below are a list of the support data tables and variables for ACS:
yg
Sources: B01002, B01003, B19013, B08136, B08006, B05001, B25003, B25077, B16001
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| age | median Age |
| pop | population |
| non_us_citizens | percentage of population that are Non-US Citizens |
| mean_commute_minutes | mean Commute Time in Minutes |
| income | median Household Income |
| owner_occupied_housing_units | percentage of housing units that are Owner occupied |
| median_property_value | median property value |
| median_property_value_moe | median property value, margin of error |
| pop_rank | rank of population (for its sumlevel) |
| income_rank | rank of income (for its sumlevel) |
| us_citizens | percentage of population that are US citizen |
| non_eng_speakers_pct | percentage of population that speak a language other than English |
yg_conflict
Source: B21002 Universe: Civilian Veterans 18 Years and Over
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| conflict_total | total number of Veterans |
| conflict_wwii | total number of World War II Veterans |
| conflict_korea | total number of Korean War Veterans |
| conflict_vietnam | total number of Vietnam Veterans |
| conflict_gulf90s | total number of Gulf War (90s) Veterans |
| conflict_gulf01 | total number of Gulf War (01-present) Veterans |
yg_income
Source: B19013 Universe: Households
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| income_black | median income for Black people |
| income_native | median income for Native American people |
| income_asian | median income for Asian people |
| income_hawaiian | median income for Hawaiian people |
| income_white | median income for White people |
| income_hispanic | median income for Hispanic people |
| income_2ormore | median income for people of two or more |
yg_income_distribution
Source: B19001 Universe: Households
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| income_under10 | number of households whose income is $10K or less |
| income_10to15 | number of households whose income is between $10-15K |
| income_15to20 | number of households whose income is between $15-20K |
| income_20to25 | number of households whose income is between $20-25K |
| income_25to30 | number of households whose income is between $25-30K |
| income_30to35 | number of households whose income is between $30-35K |
| income_35to40 | number of households whose income is between $35-40K |
| income_40to45 | number of households whose income is between $40-45K |
| income_45to50 | number of households whose income is between $45-50K |
| income_50to60 | number of households whose income is between $50-60K |
| income_50to60 | number of households whose income is between $50-60K |
| income_60to75 | number of households whose income is between $60-75K |
| income_75to100 | number of households whose income is between $75-100K |
| income_100to125 | number of households whose income is between $100-125K |
| income_125to150 | number of households whose income is between $125-150K |
| income_150to200 | number of households whose income is between $150-200K |
| income_200over | number of households whose income is $200K+ |
| totalhouseholds | total number of households |
yg_nativity_age
Source: B06001 Universe: Total Population in the United States
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| nativity_foreign | number of foreign born (non-Native citizens) |
| nativity_foreign_under5 | population of Foreign born people under the age of 5 |
| nativity_foreign_5to17 | foreign born people 5-17 years old |
| nativity_foreign_18to24 | foreign born people 18-24 years old |
| nativity_foreign_25to34 | foreign born people 25-34 years old |
| nativity_foreign_35to44 | foreign born people 35-44 years old |
| nativity_foreign_45to54 | foreign born people 45-54 years old |
| nativity_foreign_55to59 | foreign born people 54-59 years old |
| nativity_foreign_60to61 | foreign born people 60-61 years old |
| nativity_foreign_62to64 | foreign born people 62-64 years old |
| nativity_foreign_65to74 | foreign born people 65-74 years old |
| nativity_foreign_75over | foreign born people 75+ years old |
| nativity_us | number of US natives (includes those born in the US as well as those born as natives outside the US) |
| nativity_us_under5 | number of US natives under 5 years old |
| nativity_us_5to17 | US natives 5-17 years old |
| nativity_us_18to24 | US natives 18-24 years old |
| nativity_us_25to34 | US natives 25-34 years old |
| nativity_us_35to44 | US natives 35-44 years old |
| nativity_us_45to54 | US natives 45-54 years old |
| nativity_us_55to59 | US natives 54-59 years old |
| nativity_us_60to61 | US natives 60-61 years old |
| nativity_us_62to64 | US natives 62-64 years old |
| nativity_us_65to74 | US natives 65-74 years old |
| nativity_us_75over | US natives 75+ years old |
yg_poverty
Source: B17001 Universe: Population for Whom Poverty Status Is Determined
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| income_below_poverty | population living below the poverty line, for whom poverty status is determined |
| pop_poverty_status | population for whom poverty status is determined |
| poverty_female | females with income below poverty level |
| poverty_male | males with income below poverty level |
| poverty_female_5 | females under 5 years old with income below poverty level |
| poverty_female_6to11 | females 6-11 years old with income below poverty level |
| poverty_female_12to14 | females 12-14 years old with income below poverty level |
| poverty_female_15 | females 15 years old with income below poverty level |
| poverty_female_16to17 | females 16-17 years old with income below poverty level |
| poverty_female_18to24 | females 18-24 years old with income below poverty level |
| poverty_female_25to34 | females 25-34 years old with income below poverty level |
| poverty_female_35to44 | females 35-44 years old with income below poverty level |
| poverty_female_45to54 | females 45-54 years old with income below poverty level |
| poverty_female_55to64 | females 55-64 years old with income below poverty level |
| poverty_female_65to74 | females 65-74 years old with income below poverty level |
| poverty_female_75over | females 75+ years old with income below poverty level |
| poverty_male_5 | males under 5 years old with income below poverty level |
| poverty_male_6to11 | males 6-11 years old with income below poverty level |
| poverty_male_12to14 | males 12-14 years old with income below poverty level |
| poverty_male_15 | males 15 years old with income below poverty level |
| poverty_male_16to17 | males 16-17 years old with income below poverty level |
| poverty_male_18to24 | males 18-24 years old with income below poverty level |
| poverty_male_25to34 | males 25-34 years old with income below poverty level |
| poverty_male_35to44 | males 35-44 years old with income below poverty level |
| poverty_male_45to54 | males 45-54 years old with income below poverty level |
| poverty_male_55to64 | males 55-64 years old with income below poverty level |
| poverty_male_65to74 | males 65-74 years old with income below poverty level |
| poverty_male_75over | males 75+ years old with income below poverty level |
yg_poverty_race
Source: B17001A, B17001B, B17001C, B17001D, B17001E, B17001F Universe: Population for Whom Poverty Status Is Determined
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| poverty_asian | asian population below poverty level |
| poverty_white | white population below poverty level |
| poverty_black | black population below poverty level |
| poverty_native | american Indian and Alaska Native Alone population below poverty level |
| poverty_other | other Race population below poverty level |
| poverty_hawaiian | native Hawaiian and Other Pacific Islander population below poverty level |
| poverty_2ormore | 2 or more races population below poverty level |
| poverty_white_non_hispanic | white non-Hispanic population below poverty level |
| poverty_hispanic | hispanic population below poverty level |
yg_property_tax
Source: B25102 Universe: Total population Owner-occupied Housing Units (Only looking at units with a mortgage)
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| propertytax_less800 | owner-occupied housing units with a mortgage and property taxes less than $800 |
| propertytax_800to1500 | owner-occupied housing units with a mortgage and property taxes $800-$1,499 |
| propertytax_1500to2000 | owner-occupied housing units with a mortgage and property taxes $1,500-$1,999 |
| propertytax_2000to3000 | owner-occupied housing units with a mortgage and property taxes $2,000-$2,999 |
| propertytax_3000over | owner-occupied housing units with a mortgage and property taxes $3000+ |
| propertytax_none | owner-occupied housing units with a mortgage and no property taxes |
yg_property_value
Source: B25075 Universe: Total population Owner-occupied Housing Units
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| propertyval_less10k | number of units with property value less than $10,000 |
| propertyval_10kto15k | $10,000 to $14,999 |
| propertyval_15kto20k | $15,000 to $19,999 |
| propertyval_20kto25k | $20,000 to $24,999 |
| propertyval_25kto30k | $25,000 to $29,999 |
| propertyval_30kto35k | $30,000 to $34,999 |
| propertyval_35kto40k | $35,000 to $39,999 |
| propertyval_40kto50k | $40,000 to $49,999 |
| propertyval_50kto60k | $50,000 to $59,999 |
| propertyval_60kto70k | $60,000 to $69,999 |
| propertyval_70kto80k | $70,000 to $79,999 |
| propertyval_80kto90k | $80,000 to $89,999 |
| propertyval_90kto100k | $90,000 to $99,999 |
| propertyval_100kto125k | $100,000 to $124,999 |
| propertyval_125kto150k | $125,000 to $149,999 |
| propertyval_150kto175k | $150,000 to $174,999 |
| propertyval_175kto200k | $175,000 to $199,999 |
| propertyval_200kto250k | $200,000 to $249,999 |
| propertyval_250kto300k | $250,000 to $299,999 |
| propertyval_300kto400k | $300,000 to $399,999 |
| propertyval_400kto500k | $400,000 to $499,999 |
| propertyval_500kto750k | $500,000 to $749,999 |
| propertyval_750kto1M | $750,000 to $999,999 |
| propertyval_1Mormore | $1,000,000 or more |
yg_race
Source: B03002 Universe: Total population
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| pop_native | population that is American Indian and Alaska Native |
| pop_black | population that is Black or African American |
| pop_white | population that is White |
| pop_asian | population that is Asian |
| pop_hawaiian | population that is Native Hawaiian and Other Pacific Islander |
| pop_other | population that is some other race |
| pop_2ormore | population that is 2 or more races |
| pop_latino | population that is Hispanic or Latino |
yg_tenure
Source: B25003 Universe: Occupied Housing Units
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| households_renter_occupied | number of renter-occupied households |
| households_owner_occupied | number of owner-occupied households |
| households | number of households |
yg_transport
Source: B08301 Universe: Workers 16 Years and Over
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| transport_bicycle | number of workers who commute by bicycle |
| transport_carpooled | number of workers who commute by carpool |
| transport_drove | number of workers who commute by driving alone? |
| transport_motorcycle | number of workers who commute by carpool |
| transport_other | number of workers who commute by other methods |
| transport_publictrans | number of workers who commute by public transit (excluding taxi) |
| transport_taxi | number of workers who commute by taxi |
| transport_walked | number of workers who commute by walking |
| transport_home | number of workers who work at home |
| workers | total number of workers |
yg_travel_time
Source: B08303 Universe: Workers 16 Years and Over Who Did Not Work at Home
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| travel_less5 | number of households whose commute time is less than 5 minutes |
| travel_5to9 | number of households whose commute time is 5-9 minutes |
| travel_10to14 | number of households whose commute time is 10-14 minutes |
| travel_15to19 | number of households whose commute time is 15-19 minutes |
| travel_20to24 | number of households whose commute time is 20-24 minutes |
| travel_25to29 | number of households whose commute time is 25-29 minutes |
| travel_30to34 | number of households whose commute time is 30-34 minutes |
| travel_35to39 | number of households whose commute time is 35-39 minutes |
| travel_40to44 | number of households whose commute time is 40-44 minutes |
| travel_45to59 | number of households whose commute time is 45-59 minutes |
| travel_60to89 | number of households whose commute time is 60-89 minutes |
| travel_90over | number of households whose commute time is 90+ minutes |
yg_vehicles
Source: B08014 Universe: Workers 16 Years and Over in Households
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| vehicles_none | households with no vehicles |
| vehicles_1 | households with 1 vehicle |
| vehicles_2 | households with 2 vehicles |
| vehicles_3 | households with 3 vehicles |
| vehicles_4 | households with 4 vehicles |
| vehicles_5over | households with 5+ vehicles |
ygi_num_emp
Source: C24030 Universe: Civilian Employed Population 16 Years and Over
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| num_emp | number of employees |
| num_emp_rca | RCA metric based on the number of employees in a given year and geography for a particular industry |
ygl_speakers
Source: B16001 Universe: Population 5 Years and Over
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| language | language ID |
| num_speakers | number of speakers of the language |
ygo_med_earnings
Source: B24012 Universe: Civilian Employed Population 16 Years and Over With Earnings
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| acs_occ | occupation ID |
| med_earnings | median earnings |
ygo_num_emp
Source: C24010 Universe: Civilian Employed Population 16 Years and Over
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID |
| acs_occ | occupation ID |
| num_emp | number of employees |
| num_emp_male | number of male employees |
| num_emp_female | number of female employees |
| num_emp_rca | number of employees RCA |
| num_emp_rca | RCA metric based on the number of employees in a given year and geography for a particular industry |
O*NET
Data USA uses the Skills.txt file from revision 19.0 of the O*NET Database.
Skills by Course
We crosswalk the O*NET codes to CIP codes using data from the National Crosswalk Service Center and compute mean level values for each CIP at 2, 4 and 6-digit levels.
| column name | description |
|---|---|
| cip | course ID |
| skill | O*NET skill identifier |
| value | value describing the importance of the skill to the course |
| value_rca | RCA calculation for the skill value |
Skills by Occupation
We use the level values (SCALEID=LV) for each skill. In addition the values provided directly by ONET we also compute additional values based on parent grouping. This allows to provide reference data for an occupation's parent grouping if it is not available in the ONET skills database.
| column name | description |
|---|---|
| soc | occupation ID |
| skill | O*NET Skill identifier |
| value | value describing the importance of the skill to the course |
| value_rca | RCA calculation for the skill value |
Bureau of Labor and Statistics (BLS)
Data USA contains growth projections for industries and occupations, as well as CES data on employment from the Bureau of Labor and Statistics.
Industry Growth
| column name | description |
|---|---|
| naics | industry ID |
| emp_2002_thousands | number of employees in 2002 (in thousands) |
| emp_2012_thousands | number of employees in 2012 (in thousands) |
| emp_2022_thousands | number of employees in 2022 (in thousands) |
Occupation Growth
| column name | description |
|---|---|
| soc | occupation ID |
| emp_2012_thousands | number of employees in 2012 (in thousands) |
| emp_2022_thousands | number of employees in 2022 (in thousands) |
CES Data
| column name | description |
|---|---|
| year | 4-digit year value |
| naics | industry ID |
| employees_thousands | employees (in thousands) |
| avg_hrly_earnings | average hourly earnings |
| avg_wkly_hrs | average weekly hours |
County Health Rankings (CHR)
Data USA contains data from the University of Wisconsin's County Health Rankings. Visit the CHR website for more detailed documentation.
| column name | description |
|---|---|
| year | 4-digit year value |
| geo | location ID (State or County) |
| primary_care_physicians | ratio of the population to total primary care physicians |
| dentists | ratio of the county population to total dentists in the county |
| mental_health_providers | ratio of the county population to the number of mental health providers including psychiatrists, psychologists, licensed clinical social workers, counselors, marriage and family therapists and advanced practice nurses specializing in mental health care |
| other_primary_care_providers | number of other primary care provders per the population of a county, which include nurse practitioners, physician assistants, and clinical nurse specialists |
| adult_smoking | percentage of adults that reported currently smoking |
| adult_obesity | percentage of adults that report BMI >= 30 |
| excessive_drinking | percentage of adults that report excessive drinking |
| motor_vehicle_crash_deaths | motor vehicle crash deaths per 100,000 population |
| homicide_rate | number or deaths due to homicide per 100,000 population |
| sexually_transmitted_infections | number of newly diagnosed chlamydia cases per 100,000 population |
| health_care_costs | amount of price-adjusted Medicare reimbursements per enrollee |
| diabetes | percentage of adults aged 20 and above with diagnosed diabetes |
| hiv_prevalence_rate | number of persons living with a diagnosis of human immunodeficiency virus (HIV) infection per 100,000 population |
| violent_crime | number of reported violent crime offenses per 100,000 population |
| alcoholimpaired_driving_deaths | percentage of driving deaths with alcohol involvement |
| premature_death | years of potential life lost before age 75 per 100,000 population |
| poor_or_fair_health | percentage of adults reporting fair or poor health (age-adjusted) |
| poor_physical_health_days | average number of physically unhealthy days reported in past 30 days (age-adjusted) |
| poor_mental_health_days | average number of mentally unhealthy days reported in past 30 days (age-adjusted) |
| low_birthweight | percentage of live births with low birthweight (< 2500 grams) |
| food_environment_index | takes both proximity to healthy foods and income into account |
| physical_inactivity | percentage of adults aged 20 and over reporting no leisure-time physical activity |
| access_to_exercise_opportunities | percentage of individuals who live reasonably close to a location for physical activity |
| teen_births | number of births per 1,000 female population, ages 15-19 |
| uninsured | percentage of the population under age 65 that has no health insurance coverage |
| preventable_hospital_stays | the hospital discharge rate for ambulatory care-sensitive conditions per 1,000 fee-for-service Medicare enrollees |
| diabetic_screening | percentage of diabetic fee-for-service Medicare patients ages 65-75 whose blood sugar control was monitored in the past year using a test of their glycated hemoglobin (HbA1c) levels |
| mammography_screening | percentage of female fee-for-service Medicare enrollees age 67-69 that had at least one mammogram over a two-year period |
| high_school_graduation | percentage of the ninth-grade cohort in public schools that graduates from high school in four years |
| some_college | percentage of the population ages 25-44 with some post-secondary education, such as enrollment in vocational/technical schools, junior colleges, or four-year colleges, including individuals who pursued education following high school but did not receive a degree |
| children_in_poverty | percentage of children under age 18 living in poverty |
| children_in_singleparent_households | percentage of children in family households that live in a household headed by a single parent (male or female head of household with no spouse present) |
| social_associations | number of social associations per 10,000 population, including membership organizations such as civic organizations, bowling centers, golf clubs, fitness centers, sports organizations, religious organizations, political organizations, labor organizations, business organizations, and professional organizations |
| injury_deaths | number of deaths due to injury per 100,000 population |
| polution_ppm | average daily density of fine particulate matter in micrograms per cubic meter (PM2.5) |
| drinking_water_violations | percentage of population being served by community water systems with health-based drinking water violations |
| severe_housing_problems | percentage of households with at least 1 or more of the following housing problems: housing unit lacks complete kitchen facilities, housing unit lacks complete plumbing facilities, household is severely overcrowded, and/or household is severely cost burdened |
| population_living_in_a_rural_area | percentage of population living in a rural area |
| premature_ageadjusted_mortality | years of potential life lost before age 75 per 100,000 population (age-adjusted to the 2000 US population) |
| infant_mortality | number of deaths among children less than one year of age per 1,000 live births |
| child_mortality | number of deaths among children under age 18 per 100,000 population |
| food_insecurity | percentage of the population who did not have access to a reliable source of food during the past year |
| limited_access_to_healthy_foods | percentage of the population who are low income and do not live close to a grocery store |
| drug_poisoning_deaths | number of deaths due to drug poisoning per 100,000 population |
| uninsured_adults | percentage of the population ages 18 to 65 that has no health insurance coverage |
| uninsured_children | percentage of children under age 19 without health insurance |
| could_not_see_doctor_due_to_cost | percentage of adults who could not see a doctor in the past 12 months because of cost |
| children_eligible_for_free_lunch | percentage of children enrolled in public schools eligible for free lunch |
| unemployment | percentage of the civilian labor force, age 16 and older, that is unemployed but seeking work |
| income_inequality | ratio of household income at the 80th percentile to that at the 20th percentile |
| driving_along_to_work | percentage of the workforce that usually drives alone to work |
| long_commute__driving_alone | percentage of commuters, among those who commute to work by car, truck, or van alone, who drive longer than 30 minutes to work each day |
| population_that_is_not_proficient_in_english | percentage of population that is not proficient in English |
| population_estimate | resident population. |
| median_household_income | income at which half the households earn more and half the households earn less |
Bureau of Economic Analysis (BEA)
Data USA contains data from the Input/Output tables published by the Bureau of Economic Analysis County Health Rankings.
| column name | description |
|---|---|
| year | 4-digit year value |
| industry_iocode | BEA industry ID |
| commodity_iocode | BEA commodity ID |
| value_millions | production value in millions USD |
| industry_level | industry depth level |
Dartmouth Atlas of Health
ygc_post_discharge
| column name | description |
|---|---|
| year | year |
| geo | location ID (Nation, State, County) |
| cohort | Cohort (AMI, pneumonia, CHF, surgical, medical) |
| patients_readmitted_within_30_days_of_discharge | Patients of patients who return back to hospital within 30 days after discharge |
| patients_seeing_a_primary_care_physician_within_14_days | Patients of patients who see a primary care physician 2 weeks after discharge |
| patients_having_an_ambulatory_visit_within_14_days | Patients of patients who require medical procedures within 2 weeks that can be performed on an outpatient basis |
| patients_having_an_emergency_room_visit_within_30_days | Patients of patients who require an emergency room visit within 30 days of discharge |
yg_reimbursements
| column name | description |
|---|---|
| year | year |
| geo | location ID (Nation, State, County) |
| total_medicare_enrollees | Total number of Medicare Enrollees |
| total_reimbursements_a | Total reimbursements per enrollee (age, sex & race-adjusted) |
| total_reimbursements_b | Total reimbursements per enrollee (price, age, sex & race-adjusted) |
| hospital_reimbursements_a | Hospital & skilled nursing facility reimbursements per enrollee (age, sex & race-adjusted) |
| hospital_reimbursements_b | Total number of Medicare Part A hospital reimbursements (price, age, sex & race-adjusted) |
| physician_reimbursements_a | Physician reimbursements per enrollee (age, sex & race-adjusted) |
| physician_reimbursements_b | Physician reimbursements per enrollee (price, age, sex & race-adjusted) |
| outpatient_reimbursements_a | Outpatient facility reimbursements per enrollee (age, sex & race-adjusted) |
| outpatient_reimbursements_b | Outpatient facility reimbursements per enrollee (price, age, sex & race-adjusted) |
| home_health_reimbursements_a | Home health agency reimbursements per enrollee (age, sex & race-adjusted) |
| home_health_reimbursements_b | Home health agency reimbursements per enrollee (price, age, sex & race-adjusted) |
| hospice_reimbursements_a | Hospice reimbursements per enrollee (age, sex & race-adjusted) |
| hospice_reimbursements_b | Hospice reimbursements per enrollee (price, age, sex & race-adjusted) |
| medical_equip_reimbursements_a | Durable medical equipment reimbursements per enrollee (age, sex & race-adjusted) |
| medical_equip_reimbursements_b | Durable medical equipment reimbursements per enrollee (price, age, sex & race-adjusted) |
yg_prim_care_access
| column name | description |
|---|---|
| year | year |
| geo | location ID (Nation, State, County) |
| medicare_beneficiaries_total | Total number of Medicare enrollees |
| medicare_beneficiaries_black | Total number of Black Medicare enrollees |
| medicare_beneficiaries_white | Total number of Non-Black Medicare enrollees |
| patients_with_one_ambulatory_visit_to_pc_total | Medicare enrollees having at least one ambulatory visit to a primary care clinician |
| patients_with_one_ambulatory_visit_to_pc_black | Black Medicare enrollees having at least one ambulatory visit to a primary care clinician |
| patients_with_one_ambulatory_visit_to_pc_white | White Medicare enrollees having at least one ambulatory visit to a primary care clinician |
| diabetic_medicare_enrollees_65_75_total | Total number of diabetic Medicare enrollees between age 65 and 75 having eye exam |
| diabetic_medicare_enrollees_65_75_black | Total number of diabetic Black Medicare enrollees between age 65 and 75 having eye exam |
| diabetic_medicare_enrollees_65_75_white | Total number of diabetic Non-Black Medicare enrollees between age 65 and 75 having eye exam |
| patients_diabetic_medicare_enrollees_65_75_hemoglobin_total | Total number of Medicare enrollees having hemoglobin test |
| patients_diabetic_medicare_enrollees_65_75_hemoglobin_black | Total number of Black Medicare enrollees having hemoglobin test |
| patients_diabetic_medicare_enrollees_65_75_hemoglobin_white | Total number of Non-Black Medicare enrollees having hemoglobin test |
| patients_diabetic_medicare_enrollees_65_75_eye_exam_total | Total number of diabetic Medicare enrollees between age 65 and 75 having eye exam |
| patients_diabetic_medicare_enrollees_65_75_eye_exam_black | Total number of black diabetic Medicare enrollees between age 65 and 75 having eye exam |
| patients_diabetic_medicare_enrollees_65_75_eye_exam_white | Total number of non-black diabetic Medicare enrollees between age 65 and 75 having eye exam |
| patients_diabetic_medicare_enrollees_65_75_lipid_test_total | Total number of diabetic Medicare enrollees between age 65 and 75 having lipids test |
| patients_diabetic_medicare_enrollees_65_75_lipid_test_black | Total number of black diabetic Medicare enrollees between age 65 and 75 having lipids test |
| patients_diabetic_medicare_enrollees_65_75_lipid_test_white | Total number of non-black diabetic Medicare enrollees between age 65 and 75 having lipids test |
| number_of_females_enrolled_67_69_total | Number of female Medicare enrollees age 67-69 |
| number_of_females_enrolled_67_69_black | Number of black female Medicare enrollees age 67-69 |
| number_of_females_enrolled_67_69_white | Number of non-black female Medicare enrollees age 67-69 |
| patients_females_67_69_having_mammogram_total | Female Medicare enrollees age 67-69 having at least one mammogram over a two-year period |
| patients_females_67_69_having_mammogram_black | Black female Medicare enrollees age 67-69 having at least one mammogram over a two-year period |
| patients_females_67_69_having_mammogram_white | Non-black female Medicare enrollees age 67-69 having at least one mammogram over a two-year period |
| beneficiaries_part_a_eligible_total | Number of Medicare beneficiaries (Part A eligible) |
| beneficiaries_part_a_eligible_black | Number of black Medicare beneficiaries (Part A eligible) |
| beneficiaries_part_a_eligible_white | Number of non-black Medicare beneficiaries (Part A eligible) |
| leg_amputations_per_1000_enrollees_total | Leg amputations per 1,000 Medicare enrollees |
| leg_amputations_per_1000_enrollees_black | Leg amputations per 1,000 black Medicare enrollees |
| leg_amputations_per_1000_enrollees_white | Leg amputations per 1,000 non-black Medicare enrollees |
| discharges_for_ambulatory_conditions_per_1000_total | Number of hospital discharges for ambulatory sensitive conditions per 1000 medicare enrollees, total |
| discharges_for_ambulatory_conditions_per_1000_black | Number of hospital discharges for ambulatory sensitive conditions per 1000 medicare enrollees, black |
| discharges_for_ambulatory_conditions_per_1000_white | Number of hospital discharges for ambulatory sensitive conditions per 1000 medicare enrollees, non-black |
| beneficiaries_part_a_eligible_total | Number of Medicare beneficiaries (Part A eligible) |
| medicare_beneficiaries_total | Number of Medicare beneficiaries |
County Business Patterns (CBP)
| column name | description |
|---|---|
| year | year |
| geo | location ID (Nation, State, County) |
| naics | industry ID |
| est | number of establishments |
| ap | annual payroll |
| ap_nf | annual payroll noise flag (see CBP methodology) |
| emp | number of employees |
| emp_nf | number of employees noise flag (see CBP methodology) |
Opiods
| column name | description |
|---|---|
| year | year |
| geo | location ID (Nation, State, County) |
| opioid_overdose_deathrate_ageadjusted | Opioid overdose death rate per 100,000 population (age-adjusted) |
| column name | description |
|---|---|
| year | year |
| geo | location ID (Nation, State, County) |
| drug_overdose_ageadjusted | All drugs overdose death rate per 100,000 population (age-adjusted) |
| column name | description |
|---|---|
| year | year |
| geo | location ID (Nation, State, County) |
| non_medical_use_of_pain_relievers | Nonmedical use of prescription pain relievers in the past year among people aged 12 or older, by region and state: percentages, annual averages based on combined 2012 to 2014 NSDUHs |
| non_medical_use_of_pain_relievers_lci | Lower Confidence Interval |
| non_medical_use_of_pain_relievers_uci | Upper Confidence Interval |
All variables by table
For a complete listing of all tables and their available variables, visit https://api.datausa.io/api/variables/
Data Coming Soon
Undergraduate Admissions by Institution
| column name | description |
|---|---|
| year | 4-digit year value |
| university | University ID |
| applicants_total | Coming soon... |
| applicants_men | Coming soon... |
| applicants_women | Coming soon... |
| admissions_total | Coming soon... |
| admissions_men | Coming soon... |
| admissions_women | Coming soon... |
| enrolled_total | Coming soon... |
| enrolled_men | Coming soon... |
| enrolled_women | Coming soon... |
| enrolled_ft_total | Coming soon... |
| enrolled_ft_men | Coming soon... |
| enrolled_ft_women | Coming soon... |
| enrolled_pt_total | Coming soon... |
| enrolled_pt_men | Coming soon... |
| enrolled_pt_women | Coming soon... |
| sub_sat_scores_num | Coming soon... |
| sub_act_scores_num | Coming soon... |
| sub_sat_scores_pct | Coming soon... |
| sub_act_scores_pct | Coming soon... |
| sat_cr_25 | Coming soon... |
| sat_cr_75 | Coming soon... |
| sat_math_25 | Coming soon... |
| sat_math_75 | Coming soon... |
| sat_writing_25 | Coming soon... |
| sat_writing_75 | Coming soon... |
| act_composite_25 | Coming soon... |
| act_composite_75 | Coming soon... |
| act_english_25 | Coming soon... |
| act_english_75 | Coming soon... |
| act_math_25 | Coming soon... |
| act_math_75 | Coming soon... |
| act_writing_25 | Coming soon... |
| act_writing_75 | Coming soon... |
Graduation Rate Data
| column name | description |
|---|---|
| year | 4-digit year value |
| university | University ID |
| sex | Sex |
| ipeds_race | Coming soon... |
| cohort_size | Enrollment in year t-4 or t-2 for 4/2 year institutions respectively |
| num_finishers | Number of student who completed degree within 150% of completion time |
| grad_rate | Percentage of cohort completing degree within 150% of completion time |
Retention Rate Data
Descriptions for columns in this table come directly from IPEDS.
| column name | description |
|---|---|
| year | 4-digit year value |
| university | University ID |
| retention_rate_pt | The part-time retention rate is the percent of the (fall part-time cohort from the prior year minus exclusions from the fall part-time cohort) that re-enrolled at the institution as either full- or part-time in the current year |
| retention_rate_ft | The full-time retention rate is the percent of the (fall full-time cohort from the prior year minus exclusions from the fall full-time cohort) that re-enrolled at the institution as either full- or part-time in the current year |
| student_faculty_ratio | Student-to-faculty ratio - Total FTE students not in graduate or professional programs divided by total FTE instructional staff not teaching in graduate or professional programs. |
Freight Analysis Framework (FAF) data
Descriptions for columns in this table come directly from IPEDS.
| column name | description |
|---|---|
| year | 4-digit year value |
| origin_geo | Origin geography |
| destination_geo | Destination geography |
| transportation_mode | Mode of Transportation |
| sctg | SCTG product code |
| tons | Tonnage of goods |
| millions_of_2012_dollars | Value of goods in millions of 2012 dollars |
New PUMS MOE Methodology
Moving forward, the PUMS margins of error will be calculated using a modified version of ACS' generalized approximation formula instead of the previous replicate weight method. The trade-off is that this less computational intense methodology will allow us to provide deeper cuts to the PUMS data while still retaining an element of the margin of error.