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docs/guides/01-overview.md
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## Overview
For Enterprise account plans, the [Data Observatory](https://carto.com/data) provides access to a searchable catalog of advanced location data, such as census block, population segments, boundaries and so on. A set of SQL functions allow you to augment your own data and broaden your analysis by discovering boundaries and measures of data from this catalog.
This section describes the Data Observatory methods and the type of data that it returns.
### Methods Overview
There are several methods for accessing different categories of data into your visualizations. You can discover and retrieve data by requesting OBS functions from the Data Observatory. These Data Observatory functions are designed for specific, targeted methods of data analysis. The response for these functions are classified into two primary types of data results; measures and boundaries.
- Boundaries are the geospatial boundaries you need to map or aggregate your data. Examples include Country Borders, Zip Code Tabulation Areas, and Counties
- Measures are the various dimensions of information that CARTO can tell you about a place. Examples include, Population, Household Income, and Median Age
Depending on the OBS function, you will get one, or both, types of data in your result. See [Measures and Boundary Data](#measures-and-boundary-results) for details about available data.
#### Measures Methods
Use location-based measures to analyze your data by accessing population and industry measurements at point locations, or within a region or polygon. These include variables for demographic, economic, and other types of information.
- See [Measures Functions]({{ site.dataobservatory_docs }}/reference/#measures-functions) for specific OBS functions
- Returns Measures data results
#### Boundary Methods
Use global boundaries to analyze your data by accessing multi-scaled geometries for visualizations. Examples include US Block Groups and Census Tracts. These enable you to aggregate your data into geometric polygons. You can also use your own data to query specific boundaries.
- See [Boundary Functions]({{ site.dataobservatory_docs }}/reference/#boundary-functions) for specific OBS functions
- Returns Boundary data results
#### Discovery Methods
Discovery Methods provide easier ways for you to find Measures and Boundaries of interest in the Data Observatory. The Discovery functions allow you to perform targeted searches for Measures, or use your own data to discover what is available at a given location. As this is a **retrieval tool** of the Data Observatory, the query results do not change your table. The response back displays one or more identifiers as matches to your search criteria. Each unique identifier can _then_ be used as part of other OBS functions to access any of the other Data Observatory methods.
- See [Discovery Functions]({{ site.dataobservatory_docs }}/reference/#discovery-functions) for specific OBS functions
- Returns Boundary or Measures matches for your data
### Measures and Boundary Results
The response from the Data Observatory methods are classified as either Measures or Boundary. Depending on your OBS function, you will get one, or both, types of data in your result.
#### Measures Data
Measures provide details about local populations, markets, industries and other dimensions. You can search for available Measures using the Discovery methods, or by viewing the Data Catalog. Measures can be requested for Point locations, or can be summarized for Polygons (regions). In general, Point location requests will return raw aggregate values (e.g. Median Rent), or will provide amounts per square kilometer (e.g. Population). The total square kilometers of the area searched will be returned, allowing you to get raw counts, if needed. Alternatively, if you search over a polygon, raw counts will be returned.
The following table indicates where Measures data results are available. Measures can include raw measures and when indicated, can provide geometries.
Data Category | Examples | Type of Data Response | Availability
--- | ---
Housing | Vacant Housing Units, Median Rent, Units for Sale, Mortgage Count | Point measurement, Area measurement, With Geo Border | United States
Income | Median Household Income, Gini Index | Point measurement, Area measurement, With Geo Border | United States
Education | Students Enrolled in School, Population Completed H.S | Point measurement, Area measurement, With Geo Border | United States
Languages | Speaks Spanish at Home, Speaks only English at Home | Point measurement, Area measurement, With Geo Border | United States
Employment | Workers over the Age of 16 | Point measurement, Area measurement, With Geo Border | United States
Jobs and Workforce | Origin-Destination of Workforce, Job Wages by job type | Point measurement, Area measurement, With Geo Border | United States
Transportation | Commuters by Public Transportation, Work at Home | Point measurement, Area measurement, With Geo Border | United States
Race, Age and Gender | Asian Population, Median Age, Job wages by race | Point measurement, Area measurement, With Geo Border | United States, Spain
Population | Population per Square Kilometer | Point measurement, Area measurement | United States, Spain
#### Boundary Data
The following table indicates where Boundary data results are available.
Boundary Name | Availability
--- | ---
Countries | Global
First-level administrative subdivisions | Global
Second-level administrative subdivisions | United States
Zip Code Tabulation Areas (ZCTA) | United States
Congressional Districts | United States
Digital Marketing Areas | United States
Census Public Use Microdata Areas | United States
Census Tracts |United States
Census Block Groups | United States
US Census Blocks | United States
Disputed Areas | Global
Marine Area | Global
Oceans | Global
Continents | Global
Timezones | Global
##### Water Clipping Levels
Many geometries come with various degrees of water accuracy (how closely they follow features such as coastlines). Water clipping refers to how the level of accuracy is returned by the Data Observatory. Data results can either include no clip (no water areas are clipped in the geometry), or high clip (coastlines and inland waterways are clipped out of the final geometry). For example, US Census data might only show coastlines as a straight border line, and not as an inland water area. To find out which levels of water clipping are available for Boundary layers, refer to the [Data Catalog](https://cartodb.github.io/bigmetadata/index.html).
**Note:** While high clip water levels may be better for some kinds of maps and analysis, this type of data consumes more account storage space and may be subject to quota limitations.
For details about how to access any of this data, see [Accessing the Data Observatory]({{ site.dataobservatory_docs }}/guides/accesssing-the-data-observatory/).
docs/guides/02-accessing-the-data-observatory.md
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## Accessing the Data Observatory
The workflow for accessing the Data Observatory includes using a SQL query to apply a specific method of data enrichment or analysis to your data. You can access the Data Observatory by applying a custom query in CARTO Builder, or directly through the SQL API.
#### Prerequisites
You must have an Enterprise account and be familiar with using SQL requests.
- The Data Observatory catalog includes data that is managed by CARTO, on a SaaS cloud platform. For Enterprise users, the Data Observatory can be enabled by contacting CARTO
**Note:** The following error appears if the Data Observatory has not been enabled for your account, `You have reached the limit of your quota`. [Contact Sales](mailto:sales@carto.com) if this error message appears.
- A set of Data Observatory functions (prefaced with "OBS" for Observatory), allow you to retrieve boundaries and measures data through a SQL request. These functions should be used with UPDATE and INSERT statements, not SELECT statements, as we are currently not supporting dynamic use of the Data Observatory
**Tip:** See the recommended [Best Practices](#best-practices) for using the Data Observatory.
### Enrich from Data Observatory
As an alternative to using SQL queries, you can apply the _Enrich from Data Observatory_ ANALYSIS to a selected map layer in CARTO Builder. This enables you add a new column with contextual demographic and economic measures, without having to apply the code yourself. For details, see the [Enrich from Data Observatory Guide](https://carto.com/learn/guides/analysis/enrich-from-data-observatory) in our Learn hub.
### Apply OBS Functions to a Dataset
This procedure describes how to access the Data Observatory functions by applying SQL queries in a selected dataset.
1) Review the [prerequisites](#prerequisites) section before attempting to access any of the Data Observatory functions
2) [View the Data Observatory Catalog](https://cartodb.github.io/bigmetadata/index.html)
An overview for each of the analyzed methods of data appears, and indicates the unique function signature needed to access the catalog item. You can copy the OBS function from the Data Observatory catalog and modify the placeholder parameters shown in curly brackets (e.g. "{table_name}").
3) From _Your datasets_ dashboard in CARTO, click _NEW DATASET_ and _CREATE EMPTY DATASET_.
This creates an untitled table. You can get population measurements from the Data Observatory to build your dataset and create a map.
4) The SQL view is available when you are viewing your dataset in table view (Data View). Click the slider to switch between viewing your data by METADATA (table) to _SQL_ (opens the SQL view).
5) Apply the OBS function to modify your table.
For example, the following image displays a SQL query using the Boundary method, [`OBS_GetBoundariesByGeometry(geom geometry, geometry_id text)`](https://carto.com/docs/carto-engine/data/boundary-functions/#obsgetboundariesbygeometrygeom-geometry-geometryid-text) function. The SQL query inserts the boundary data as a single polygon geometry for each row of data.
![Query OBS Function in empty dataset](../img/obs_getboundary.jpg)
**Tip:** Want to insert population data to create a dataset? Replace `{my table name}` with your dataset name, and apply the SQL query:
```sql
INSERT INTO {my table name} (the_geom, name)
SELECT *
FROM OBS_GetBoundariesByGeometry(
st_makeenvelope(-73.97257804870605,40.671134192879286,-73.89052391052246,40.722868115036974, 4326),
'us.census.tiger.census_tract'
) As m(the_geom, geoid);
```
Another example shows how to get the local male population into your dataset. Before applying the SQL query, click _ADD COLUMN_ to create and name a column to store the [`OBS_GetMeasure`]({{ site.dataobservatory_docs}}/reference/#obsgetmeasurepolygon-geometry-measureid-text) data.
![Query local male population and apply to data](../img/local_male_pop.jpg)
**Tip:** Want to update your dataset to include the local male population from the Data Observatory? Replace `{my table name}` with your dataset name, and apply the SQL query:
```sql
UPDATE {my table name}
SET local_male_population = OBS_GetMeasure(the_geom, 'us.census.acs.B01001002')
```
6) Click _CREATE MAP_ from your dataset, to visualize the Data Observatory results. You can add custom styling, and add widgets to better visualize your data
![Visualize Data Observatory results](../img/visualize_obs_data.jpg)
### SQL API and OBS Functions
This procedure describes how to access the Data Observatory functions directly through the SQL API.
1. In order to use the SQL API, you must be [authenticated]({{ site.bdataobservatory_docs }}/guides/authentication/#authentication) using API keys
**Note:** Review the [prerequisites](#prerequisites) section before attempting to access any of the Data Observatory functions and [view the Data Observatory Catalog](https://cartodb.github.io/bigmetadata/index.html) to identify the OBS function you are looking for.
2. Query the Data Observatory directly with a specified `OBS` function to apply the results (Measures/Boundaries data) to your table, with the INSERT or UPDATE function
```sql
https://{username}.carto.com/api/v2/sql?q=UPDATE {tablename}
SET local_male_population = OBS_GetMeasure(the_geom, 'us.census.acs.B01001002')&api_key={api_key}
```
### Tips
Other useful tips about OBS functions:
- Some Data Observatory functions return geometries, enabling you to apply an UPDATE statement with an OBS function, to update `the_geom` column
- To include [water clipping levels]({{ site.dataobservatory_docs }}/guides/overview/#water-clipping-levels) as part of your results, append `_clipped` as part of the OBS function. For example:
```sql
UPDATE {tablename}
SET local_male_population = OBS_GetMeasure(the_geom, 'us.census.acs.B01001002','area','us.census.tiger.census_tract_clipped')
```
### Best Practices
The following usage notes are recommended when using the Data Observatory functions in SQL queries:
- It is discouraged to use the SELECT operation with the Data Observatory functions in your map layers. The results may be visible, but CARTO may not support dynamic rendering of the Data Observatory in the future, so your visualizations may break
The Data Observatory is **recommended** to be used with INSERT or UPDATE operations, for applying analyzed measures and boundaries data to your tables. While SELECT (retrieve) is standard for SQL API requests, be mindful of quota consumption and use INSERT (to insert a new record) or UPDATE (to update an existing record), for best practices.
**Exception:** [Discovery Methods]({{ site.dataobservatory_docs }}/guides/overview/#discovery-methods) are the exception. You can use SELECT as these functions are not actually retrieving data, they are retrieving ids that you can use for other functions.
- You can reduce storage space for unneeded geometries and optimize query optimizations by applying the PostGIS [`ST_Simplify`](http://www.postgis.org/docs/ST_Simplify.html) function. For example, you can simplify the `the_geom` for a large table of polygons and reduce the size of them for quicker rendering. For other tips, see the [most commonly used PostGIS functions]({{ site.baseurl }}/faqs/postgresql-and-postgis/#what-are-the-most-common-postgis-functions) that you can apply with CARTO
- Only point or polygon geometries are supported for OBS functions. If you attempt to apply Measures or Boundary results to line geometries, an error appears
- The Data Observatory is optimal for modifying existing tables with analytical results, not for building new tables of data
**Exception:** Exceptions apply for the following boundary functions, since they were designed to return multiple responses of geographical identifiers, as opposed to a single geometry. Create an empty dataset and build a new dataset from a SQL query, using any one of these boundary functions.
- [`OBS_GetBoundariesByGeometry(geom geometry, geometry_id text)`]({{ site.dataobservatory_docs }}/reference/#boundary-functions#obsgetboundariesbygeometrygeom-geometry-geometryid-text)
- [`OBS_GetPointsByGeometry(polygon geometry, geometry_id text)`]({{ site.dataobservatory_docs }}/reference/#boundary-functions#obsgetpointsbygeometrypolygon-geometry-geometryid-text)
- [`OBS_GetBoundariesByPointAndRadius(point geometry, radius numeric, boundary_id text`]({{ site.dataobservatory_docs }}/reference/#boundary-functions#obsgetboundariesbypointandradiuspoint-geometry-radius-numeric-boundaryid-text)
- [`OBS_GetPointsByPointAndRadius(point geometry, radius numeric, boundary_id text`]({{ site.dataobservatory_docs }}/reference/#boundary-functions#obsgetpointsbypointandradiuspoint-geometry-radius-numeric-boundaryid-text)
- For optimal performance, each SQL request should not exceed 100 rows. As an alternative, you can use a [SQL Batch Query](/docs/carto-engine/sql-api/batch-queries) for queries with long-running CPU processing times
### Examples
View our [CARTO Blogs](https://carto.com/blog/categories/data-observatory/) for examples that highlight the benefits of using the Data Observatory.
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## Glossary
A list of boundary ids and measure_names for Data Observatory functions. For US based boundaries, the Shoreline Clipped version provides a high-quality shoreline clipping for mapping uses.
### Boundary IDs
Boundary Name | Boundary ID | Shoreline Clipped Boundary ID
--------------------- | --------------------- | ---
US States | us.census.tiger.state | us.census.tiger.state_clipped
US County | us.census.tiger.county | us.census.tiger.county_clipped
US Census Zip Code Tabulation Areas | us.census.tiger.zcta5 | us.census.tiger.zcta5_clipped
US Census Tracts | us.census.tiger.census_tract | us.census.tiger.census_tract_clipped
US Elementary School District | us.census.tiger.school_district_elementary | us.census.tiger.school_district_elementary_clipped
US Secondary School District | us.census.tiger.school_district_secondary | us.census.tiger.school_district_secondary_clipped
US Unified School District | us.census.tiger.school_district_unified | us.census.tiger.school_district_unified_clipped
US Congressional Districts | us.census.tiger.congressional_district | us.census.tiger.congressional_district_clipped
US Census Blocks | us.census.tiger.block | us.census.tiger.block_clipped
US Census Block Groups | us.census.tiger.block_group | us.census.tiger.block_group_clipped
US Census PUMAs | us.census.tiger.puma | us.census.tiger.puma_clipped
US Incorporated Places | us.census.tiger.place | us.census.tiger.place_clipped
ES Sección Censal | es.ine.geom | none
Regions (First-level Administrative) | whosonfirst.wof_region_geom | none
Continents | whosonfirst.wof_continent_geom | none
Countries | whosonfirst.wof_country_geom | none
Marine Areas | whosonfirst.wof_marinearea_geom | none
Disputed Areas | whosonfirst.wof_disputed_geom | none
### OBS_GetUSCensusMeasure Names Table
This list contains human readable names accepted in the ```OBS_GetUSCensusMeasure``` function. For the more comprehensive list of columns available to the ```OBS_GetMeasure``` function, see the [Data Observatory Catalog](https://cartodb.github.io/bigmetadata/index.html).
Measure ID | Measure Name | Measure Description
--------------------- | --------------------- | ---
us.census.acs.B01002001 | Median Age | The median age of all people in a given geographic area.
us.census.acs.B15003021 | Population Completed Associate’s Degree | The number of people in a geographic area over the age of 25 who obtained a associate’s degree, and did not complete a more advanced degree.
us.census.acs.B15003022 | Population Completed Bachelor’s Degree | The number of people in a geographic area over the age of 25 who obtained a bachelor’s degree, and did not complete a more advanced degree.
us.census.acs.B15003023 | Population Completed Master’s Degree | The number of people in a geographic area over the age of 25 who obtained a master’s degree, but did not complete a more advanced degree.
us.census.acs.B14001007 | Students Enrolled in Grades 9 to 12 | The total number of people in each geography currently enrolled in grades 9 through 12 inclusive. This corresponds roughly to high school.
us.census.acs.B05001006 | Not a U.S. Citizen Population | The number of people within each geography who indicated that they are not U.S. citizens.
us.census.acs.B19001012 | Households with income of $60,000 To $74,999 | The number of households in a geographic area whose annual income was between $60,000 and $74,999.
us.census.acs.B01003001 | Total Population | The total number of all people living in a given geographic area. This is a very useful catch-all denominator when calculating rates.
us.census.acs.B01001002 | Male Population | The number of people within each geography who are male.
us.census.acs.B01001026 | Female Population | The number of people within each geography who are female.
us.census.acs.B03002003 | White Population | The number of people identifying as white, non-Hispanic in each geography.
us.census.acs.B03002004 | Black or African American Population | The number of people identifying as black or African American, non-Hispanic in each geography.
us.census.acs.B03002006 | Asian Population | The number of people identifying as Asian, non-Hispanic in each geography.
us.census.acs.B03002012 | Hispanic Population | The number of people identifying as Hispanic or Latino in each geography.
us.census.acs.B03002005 | American Indian and Alaska Native Population | The number of people identifying as American Indian or Alaska native in each geography.
us.census.acs.B03002008 | Other Race population | The number of people identifying as another race in each geography.
us.census.acs.B03002009 | Two or more races population | The number of people identifying as two or more races in each geography.
us.census.acs.B03002002 | Population not Hispanic | The number of people not identifying as Hispanic or Latino in each geography.
us.census.acs.B23025001 | Population age 16 and over | The number of people in each geography who are age 16 or over.
us.census.acs.B08006001 | Workers over the Age of 16 | The number of people in each geography who work. Workers include those employed at private for-profit companies, the self-employed, government workers and non-profit employees.
us.census.acs.B08006002 | Commuters by Car, Truck, or Van | The number of workers age 16 years and over within a geographic area who primarily traveled to work by car, truck or van. This is the principal mode of travel or type of conveyance, by distance rather than time, that the worker usually used to get from home to work.
us.census.acs.B08006003 | Commuters who drove alone | The number of workers age 16 years and over within a geographic area who primarily traveled by car driving alone. This is the principal mode of travel or type of conveyance, by distance rather than time, that the worker usually used to get from home to work.
us.census.acs.B11001001 | Households | A count of the number of households in each geography. A household consists of one or more people who live in the same dwelling and also share at meals or living accommodation, and may consist of a single family or some other grouping of people.
us.census.acs.B08006004 | Commuters by Carpool | The number of workers age 16 years and over within a geographic area who primarily traveled to work by carpool. This is the principal mode of travel or type of conveyance, by distance rather than time, that the worker usually used to get from home to work.
us.census.acs.B08301010 | Commuters by Public Transportation | The number of workers age 16 years and over within a geographic area who primarily traveled to work by public transportation. This is the principal mode of travel or type of conveyance, by distance rather than time, that the worker usually used to get from home to work.
us.census.acs.B08006009 | Commuters by Bus | The number of workers age 16 years and over within a geographic area who primarily traveled to work by bus. This is the principal mode of travel or type of conveyance, by distance rather than time, that the worker usually used to get from home to work. This is a subset of workers who commuted by public transport.
us.census.acs.B08006011 | Commuters by Subway or Elevated | The number of workers age 16 years and over within a geographic area who primarily traveled to work by subway or elevated train. This is the principal mode of travel or type of conveyance, by distance rather than time, that the worker usually used to get from home to work. This is a subset of workers who commuted by public transport.
us.census.acs.B08006015 | Walked to Work | The number of workers age 16 years and over within a geographic area who primarily walked to work. This would mean that of any way of getting to work, they travelled the most distance walking.
us.census.acs.B08006017 | Worked at Home | The count within a geographical area of workers over the age of 16 who worked at home.
us.census.acs.B09001001 | Children under 18 Years of Age | The number of people within each geography who are under 18 years of age.
us.census.acs.B14001001 | Population 3 Years and Over | The total number of people in each geography age 3 years and over. This denominator is mostly used to calculate rates of school enrollment.
us.census.acs.B14001002 | Students Enrolled in School | The total number of people in each geography currently enrolled at any level of school, from nursery or pre-school to advanced post-graduate education. Only includes those over the age of 3.
us.census.acs.B14001005 | Students Enrolled in Grades 1 to 4 | The total number of people in each geography currently enrolled in grades 1 through 4 inclusive. This corresponds roughly to elementary school.
us.census.acs.B14001006 | Students Enrolled in Grades 5 to 8 | The total number of people in each geography currently enrolled in grades 5 through 8 inclusive. This corresponds roughly to middle school.
us.census.acs.B14001008 | Students Enrolled as Undergraduate in College | The number of people in a geographic area who are enrolled in college at the undergraduate level. Enrollment refers to being registered or listed as a student in an educational program leading to a college degree. This may be a public school or college, a private school or college.
us.census.acs.B15003001 | Population 25 Years and Over | The number of people in a geographic area who are over the age of 25. This is used mostly as a denominator of educational attainment.
us.census.acs.B15003017 | Population Completed High School | The number of people in a geographic area over the age of 25 who completed high school, and did not complete a more advanced degree.
us.census.acs.B15003019 | Population completed less than one year of college, no degree | The number of people in a geographic area over the age of 25 who attended college for less than one year and no further.
us.census.acs.B15003020 | Population completed more than one year of college, no degree | The number of people in a geographic area over the age of 25 who attended college for more than one year but did not obtain a degree.
us.census.acs.B16001001 | Population 5 Years and Over | The number of people in a geographic area who are over the age of 5. This is primarily used as a denominator of measures of language spoken at home.
us.census.acs.B16001002 | Speaks only English at Home | The number of people in a geographic area over age 5 who speak only English at home.
us.census.acs.B16001003 | Speaks Spanish at Home | The number of people in a geographic area over age 5 who speak Spanish at home, possibly in addition to other languages.
us.census.acs.B17001001 | Population for Whom Poverty Status Determined | The number of people in each geography who could be identified as either living in poverty or not. This should be used as the denominator when calculating poverty rates, as it excludes people for whom it was not possible to determine poverty.
us.census.acs.B17001002 | Income In The Past 12 Months Below Poverty Level | The number of people in a geographic area who are part of a family (which could be just them as an individual) determined to be in poverty following the Office of Management and Budget’s Directive 14. (https://www.census.gov/hhes/povmeas/methodology/ombdir14.html)
us.census.acs.B08134010 | Number of workers with a commute of over 60 minutes | The number of workers over the age of 16 who do not work from home and commute in over 60 minutes in a geographic area.
us.census.acs.B12005002 | Never Married | The number of people in a geographic area who have never been married.
us.census.acs.B12005005 | Currently married | The number of people in a geographic area who are currently married.
us.census.acs.B12005008 | Married but separated | The number of people in a geographic area who are married but separated.
us.census.acs.B12005012 | Widowed | The number of people in a geographic area who are widowed.
us.census.acs.B12005015 | Divorced | The number of people in a geographic area who are divorced.
us.census.acs.B19013001 | Median Household Income in the past 12 Months | Within a geographic area, the median income received by every household on a regular basis before payments for personal income taxes, social security, union dues, medicare deductions, etc. It includes income received from wages, salary, commissions, bonuses, and tips; self-employment income from own nonfarm or farm businesses, including proprietorships and partnerships; interest, dividends, net rental income, royalty income, or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental Security Income (SSI); any cash public assistance or welfare payments from the state or local welfare office; retirement, survivor, or disability benefits; and any other sources of income received regularly such as Veterans’ (VA) payments, unemployment and/or worker’s compensation, child support, and alimony.
us.census.acs.B25001001 | Housing Units | A count of housing units in each geography. A housing unit is a house, an apartment, a mobile home or trailer, a group of rooms, or a single room occupied as separate living quarters, or if vacant, intended for occupancy as separate living quarters.
us.census.acs.B25002003 | Vacant Housing Units | The count of vacant housing units in a geographic area. A housing unit is vacant if no one is living in it at the time of enumeration, unless its occupants are only temporarily absent. Units temporarily occupied at the time of enumeration entirely by people who have a usual residence elsewhere are also classified as vacant.
us.census.acs.B25004002 | Vacant Housing Units for Rent | The count of vacant housing units in a geographic area that are for rent. A housing unit is vacant if no one is living in it at the time of enumeration, unless its occupants are only temporarily absent. Units temporarily occupied at the time of enumeration entirely by people who have a usual residence elsewhere are also classified as vacant.
us.census.acs.B19001013 | Households with income of $75,000 To $99,999 | The number of households in a geographic area whose annual income was between $75,000 and $99,999.
us.census.acs.B19001014 | Households with income of $100,000 To $124,999 | The number of households in a geographic area whose annual income was between $100,000 and $124,999.
us.census.acs.B25004004 | Vacant Housing Units for Sale | The count of vacant housing units in a geographic area that are for sale. A housing unit is vacant if no one is living in it at the time of enumeration, unless its occupants are only temporarily absent. Units temporarily occupied at the time of enumeration entirely by people who have a usual residence elsewhere are also classified as vacant.
us.census.acs.B25058001 | Median Rent | The median contract rent within a geographic area. The contract rent is the monthly rent agreed to or contracted for, regardless of any furnishings, utilities, fees, meals, or services that may be included. For vacant units, it is the monthly rent asked for the rental unit at the time of interview.
us.census.acs.B25071001 | Percent of Household Income Spent on Rent | Within a geographic area, the median percentage of household income which was spent on gross rent. Gross rent is the amount of the contract rent plus the estimated average monthly cost of utilities (electricity, gas, water, sewer etc.) and fuels (oil, coal, wood, etc.) if these are paid by the renter. Household income is the sum of the income of all people 15 years and older living in the household.
us.census.acs.B25075025 | Owner-occupied Housing Units valued at $1,000,000 or more. | The count of owner occupied housing units in a geographic area that are valued at $1,000,000 or more. Value is the respondent’s estimate of how much the property (house and lot, mobile home and lot, or condominium unit) would sell for if it were for sale.
us.census.acs.B25081002 | Owner-occupied Housing Units with a Mortgage | The count of housing units within a geographic area that are mortagaged. Mortgage refers to all forms of debt where the property is pledged as security for repayment of the debt, including deeds of trust, trust deed, contracts to purchase, land contracts, junior mortgages, and home equity loans.
us.census.acs.B23025002 | Population in Labor Force | The number of people in each geography who are either in the civilian labor force or are members of the U.S. Armed Forces (people on active duty with the United States Army, Air Force, Navy, Marine Corps, or Coast Guard).
us.census.acs.B23025003 | Population in Civilian Labor Force | The number of civilians 16 years and over in each geography who can be classified as either employed or unemployed below.
us.census.acs.B08135001 | Aggregate travel time to work | The total number of minutes every worker over the age of 16 who did not work from home spent spent commuting to work in one day in a geographic area.
us.census.acs.B19001002 | Households with income less than $10,000 | The number of households in a geographic area whose annual income was less than $10,000.
us.census.acs.B19001003 | Households with income of $10,000 to $14,999 | The number of households in a geographic area whose annual income was between $10,000 and $14,999.
us.census.acs.B19001004 | Households with income of $15,000 to $19,999 | The number of households in a geographic area whose annual income was between $15,000 and $19,999.
us.census.acs.B23025004 | Employed Population | The number of civilians 16 years old and over in each geography who either (1) were at work, that is, those who did any work at all during the reference week as paid employees, worked in their own business or profession, worked on their own farm, or worked 15 hours or more as unpaid workers on a family farm or in a family business; or (2) were with a job but not at work, that is, those who did not work during the reference week but had jobs or businesses from which they were temporarily absent due to illness, bad weather, industrial dispute, vacation, or other personal reasons. Excluded from the employed are people whose only activity consisted of work around the house or unpaid volunteer work for religious, charitable, and similar organizations; also excluded are all institutionalized people and people on active duty in the United States Armed Forces.
us.census.acs.B23025005 | Unemployed Population | The number of civilians in each geography who are 16 years old and over and are classified as unemployed.
us.census.acs.B23025006 | Population in Armed Forces | The number of people in each geography who are members of the U.S. Armed Forces (people on active duty with the United States Army, Air Force, Navy, Marine Corps, or Coast Guard).
us.census.acs.B23025007 | Population Not in Labor Force | The number of people in each geography who are 16 years old and over who are not classified as members of the labor force. This category consists mainly of students, homemakers, retired workers, seasonal workers interviewed in an off season who were not looking for work, institutionalized people, and people doing only incidental unpaid family work.
us.census.acs.B12005001 | Population 15 Years and Over | The number of people in a geographic area who are over the age of 15. This is used mostly as a denominator of marital status.
us.census.acs.B08134001 | Workers age 16 and over who do not work from home | The number of workers over the age of 16 who do not work from home in a geographic area.
us.census.acs.B08134002 | Number of workers with less than 10 minute commute | The number of workers over the age of 16 who do not work from home and commute in less than 10 minutes in a geographic area.
us.census.acs.B08303004 | Number of workers with a commute between 10 and 14 minutes | The number of workers over the age of 16 who do not work from home and commute in between 10 and 14 minutes in a geographic area.
us.census.acs.B08303005 | Number of workers with a commute between 15 and 19 minutes | The number of workers over the age of 16 who do not work from home and commute in between 15 and 19 minutes in a geographic area.
us.census.acs.B08303006 | Number of workers with a commute between 20 and 24 minutes | The number of workers over the age of 16 who do not work from home and commute in between 20 and 24 minutes in a geographic area.
us.census.acs.B08303007 | Number of workers with a commute between 25 and 29 minutes | The number of workers over the age of 16 who do not work from home and commute in between 25 and 29 minutes in a geographic area.
us.census.acs.B08303008 | Number of workers with a commute between 30 and 34 minutes | The number of workers over the age of 16 who do not work from home and commute in between 30 and 34 minutes in a geographic area.
us.census.acs.B08134008 | Number of workers with a commute between 35 and 44 minutes | The number of workers over the age of 16 who do not work from home and commute in between 35 and 44 minutes in a geographic area.
us.census.acs.B08303011 | Number of workers with a commute between 45 and 59 minutes | The number of workers over the age of 16 who do not work from home and commute in between 45 and 59 minutes in a geographic area.
us.census.acs.B19001005 | Households with income of $20,000 To $24,999 | The number of households in a geographic area whose annual income was between $20,000 and $24,999.
us.census.acs.B19001006 | Households with income of $25,000 To $29,999 | The number of households in a geographic area whose annual income was between $20,000 and $24,999.
us.census.acs.B19001007 | Households with income of $30,000 To $34,999 | The number of households in a geographic area whose annual income was between $30,000 and $34,999.
us.census.acs.B19001008 | Households with income of $35,000 To $39,999 | The number of households in a geographic area whose annual income was between $35,000 and $39,999.
us.census.acs.B19001009 | Households with income of $40,000 To $44,999 | The number of households in a geographic area whose annual income was between $40,000 and $44,999.
us.census.acs.B19001010 | Households with income of $45,000 To $49,999 | The number of households in a geographic area whose annual income was between $45,000 and $49,999.
us.census.acs.B19001011 | Households with income of $50,000 To $59,999 | The number of households in a geographic area whose annual income was between $50,000 and $59,999.
us.census.acs.B19001015 | Households with income of $125,000 To $149,999 | The number of households in a geographic area whose annual income was between $125,000 and $149,999.
us.census.acs.B19001016 | Households with income of $150,000 To $199,999 | The number of households in a geographic area whose annual income was between $150,000 and $1999,999.
us.census.acs.B19001017 | Households with income of $200,000 Or More | The number of households in a geographic area whose annual income was more than $200,000.