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    Segmentation Functions

    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 )


    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)


    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


    https://{username} * FROM
    OBS_GetSegmentSnapshot({{point geometry}})
    Get the Geographic Snapshot of a Segmentation

    Get the Segmentation Snapshot around the MGM Grand

    https://{username} * FROM
    OBS_GetSegmentSnapshot(CDB_LatLng(36.10222, -115.169516))

    Get the Segmentation Snapshot at CARTO’s NYC HQ

    https://{username} * FROM
    OBS_GetSegmentSnapshot(CDB_LatLng(40.704512, -73.936669))