Data API

Jonathan Speiser edited this page May 29, 2018 · 27 revisions

Contents

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
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