16 KiB
16 KiB
Segmentation Functions
We will no longer support these segmentations functions from the 1st of January 2021. For a similar functionality, we recommend using Data Observatory from our Python library CARTOframes instead.
The Segmentation Snapshot functions enable you to determine the pre-calculated population segment for a location. Segmentation is a method that divides a populations into subclassifications based on common traits. For example, you can take the a store location and determine what classification of population exists around that location. If you need help creating coordinates from addresses, see the Geocoding Functions documentation.
Note: The Segmentation Snapshot functions are only available for the United States. Our first release (May 18, 2016) is derived from Census 2010 variables. Our next release will be based on Census 2014 data. For the latest information, see the Open Segments project repository.
OBS_GetSegmentSnapshot( Point Geometry )
Arguments
| Name | Description | Example Values |
|---|---|---|
| point geometry | A point geometry. You can use the helper function, CDB_LatLng to quickly generate one from latitude and longitude |
CDB_LatLng(40.760410,-73.964242) |
Returns
The segmentation function returns two segment levels for the point you requests, the x10_segment and x55_segment. These segmentation levels contain different classifications of population within with each segment. The function also returns the quantile of a number of census variables. For example, if total_poulation is at 90% quantile level then this tract has a higher total population than 90% of the other tracts.
| Name | Type | Description |
|---|---|---|
| x10_segment | text | The demographic segment location at the 10 segment level, containing populations at high-levels, broken down into 10 broad categories |
| x55_segment | text | The demographic segment location at the 55 segment level, containing more granular sub-levels to categorize the population |
An example response appears as follows:
obs_getsegmentsnapshot: {
"x10_segment": "Wealthy, urban without Kids",
"x55_segment": "Wealthy city commuters",
"us.census.acs.B01001001_quantile": "0.0180540540540541",
"us.census.acs.B01001002_quantile": "0.0279864864864865",
"us.census.acs.B01001026_quantile": "0.016527027027027",
"us.census.acs.B01002001_quantile": "0.507297297297297",
"us.census.acs.B03002003_quantile": "0.133162162162162",
"us.census.acs.B03002004_quantile": "0.283743243243243",
"us.census.acs.B03002006_quantile": "0.683945945945946",
"us.census.acs.B03002012_quantile": "0.494594594594595",
"us.census.acs.B05001006_quantile": "0.670972972972973",
"us.census.acs.B08006001_quantile": "0.0607567567567568",
"us.census.acs.B08006002_quantile": "0.0684324324324324",
"us.census.acs.B08006008_quantile": "0.565135135135135",
"us.census.acs.B08006009_quantile": "0.638081081081081",
"us.census.acs.B08006011_quantile": "0",
"us.census.acs.B08006015_quantile": "0.900932432432432",
"us.census.acs.B08006017_quantile": "0.186648648648649",
"us.census.acs.B09001001_quantile": "0.0193513513513514",
"us.census.acs.B11001001_quantile": "0.0617972972972973",
"us.census.acs.B14001001_quantile": "0.0179594594594595",
"us.census.acs.B14001002_quantile": "0.0140405405405405",
"us.census.acs.B14001005_quantile": "0",
"us.census.acs.B14001006_quantile": "0",
"us.census.acs.B14001007_quantile": "0",
"us.census.acs.B14001008_quantile": "0.0609054054054054",
"us.census.acs.B15003001_quantile": "0.0314594594594595",
"us.census.acs.B15003017_quantile": "0.0403378378378378",
"us.census.acs.B15003022_quantile": "0.285972972972973",
"us.census.acs.B15003023_quantile": "0.214567567567568",
"us.census.acs.B16001001_quantile": "0.0181621621621622",
"us.census.acs.B16001002_quantile": "0.0463108108108108",
"us.census.acs.B16001003_quantile": "0.540540540540541",
"us.census.acs.B17001001_quantile": "0.0237567567567568",
"us.census.acs.B17001002_quantile": "0.155972972972973",
"us.census.acs.B19013001_quantile": "0.380662162162162",
"us.census.acs.B19083001_quantile": "0.986891891891892",
"us.census.acs.B19301001_quantile": "0.989594594594595",
"us.census.acs.B25001001_quantile": "0.998418918918919",
"us.census.acs.B25002003_quantile": "0.999824324324324",
"us.census.acs.B25004002_quantile": "0.999986486486486",
"us.census.acs.B25004004_quantile": "0.999662162162162",
"us.census.acs.B25058001_quantile": "0.679054054054054",
"us.census.acs.B25071001_quantile": "0.569716216216216",
"us.census.acs.B25075001_quantile": "0.0415",
"us.census.acs.B25075025_quantile": "0.891702702702703"
}
The possible segments are:
| X10 segment | X55 Segment |
|---|---|
| Hispanic and kids | |
| Middle Class, Educated, Suburban, Mixed Race | |
| Low Income on Urban Periphery | |
| Suburban, Young and Low-income | |
| low-income, urban, young, unmarried | |
| Low education, mainly suburban | |
| Young, working class and rural | |
| Low-Income with gentrification | |
| Low Income and Diverse | |
| High school education Long Commuters, Black, White Hispanic mix | |
| Rural, Bachelors or college degree, Rent owned mix | |
| Rural,High School Education, Owns property | |
| Young, City based renters in Sparse neighborhoods, Low poverty | |
| Low income, minority mix | |
| Predominantly black, high high school attainment, home owners | |
| White and minority mix multilingual, mixed income / education. Married | |
| Hispanic Black mix multilingual, high poverty, renters, uses public transport | |
| Predominantly black renters, rent own mix | |
| Middle income, single family homes | |
| Lower Middle Income with higher rent burden | |
| Black and mixed community with rent burden | |
| Lower Middle Income with affordable housing | |
| Relatively affordable, satisfied lower middle class | |
| Satisfied Lower Middle Income Higher Rent Costs | |
| Suburban/Rural Satisfied, decently educated lower middle class | |
| Struggling lower middle class with rent burden | |
| Older white home owners, less comfortable financially | |
| Older home owners, more financially comfortable, some diversity | |
| Native American | |
| Younger, poorer,single parent family Native Americans | |
| Older, middle income Native Americans once married and Educated | |
| Old Wealthy, White | |
| Older, mixed race professionals | |
| Works from home, Highly Educated, Super Wealthy | |
| Retired Grandparents | |
| Wealthy and Rural Living | |
| Wealthy, Retired Mountains/Coasts | |
| Wealthy Diverse Suburbanites On the Coasts | |
| Retirement Communitties | |
| Low Income African American | |
| Urban - Inner city | |
| Rural families | |
| Residential institutions, young people | |
| College towns | |
| College town with poverty | |
| University campus wider area | |
| City Outskirt University Campuses | |
| City Center University Campuses | |
| Wealthy Nuclear Families | |
| Lower educational attainment, Homeowner, Low rent | |
| Younger, Long Commuter in dense neighborhood | |
| Long commuters White black mix | |
| Low rent in built up neighborhoods | |
| Renters within cities, mixed income areas, White/Hispanic mix, Unmarried | |
| Older Home owners with high income | |
| Older home owners and very high income | |
| White Asian Mix Big City Burbs Dwellers | |
| Bachelors degree Mid income With Mortgages | |
| Asian Hispanic Mix, Mid income | |
| Bachelors degree Higher income Home Owners | |
| Wealthy, urban, and kid-free | |
| Wealthy city commuters | |
| New Developments | |
| Very wealthy, multiple million dollar homes | |
| High rise, dense urbanites |
Examples
https://{username}.carto.com/api/v2/sql?q=SELECT * FROM
OBS_GetSegmentSnapshot({{point geometry}})
Get the Geographic Snapshot of a Segmentation
Get the Segmentation Snapshot around the MGM Grand
https://{username}.carto.com/api/v2/sql?q=SELECT * FROM
OBS_GetSegmentSnapshot(CDB_LatLng(36.10222, -115.169516))
Get the Segmentation Snapshot at CARTO's NYC HQ
https://{username}.carto.com/api/v2/sql?q=SELECT * FROM
OBS_GetSegmentSnapshot(CDB_LatLng(40.704512, -73.936669))