Create 06-segmentation-functions.md
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docs/guides/06-segmentation-functions.md
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docs/guides/06-segmentation-functions.md
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# Segmentation Functions
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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](https://carto.com/docs/carto-engine/dataservices-api/geocoding-functions/) documentation.
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_**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](https://github.com/CartoDB/open-segments) project repository._
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## OBS_GetSegmentSnapshot( Point Geometry )
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### Arguments
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Name | Description | Example Values
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--- | --- | ---
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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)`
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### Returns
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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.
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Name | Type | Description
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---- | ---- | -----------
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x10\_segment | text | The demographic segment location at the 10 segment level, containing populations at high-levels, broken down into 10 broad categories
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x55\_segment | text | The demographic segment location at the 55 segment level, containing more granular sub-levels to categorize the population
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An example response appears as follows:
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```json
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obs_getsegmentsnapshot: {
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"x10_segment": "Wealthy, urban without Kids",
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"x55_segment": "Wealthy city commuters",
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"us.census.acs.B01001001_quantile": "0.0180540540540541",
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"us.census.acs.B01001002_quantile": "0.0279864864864865",
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"us.census.acs.B01001026_quantile": "0.016527027027027",
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"us.census.acs.B01002001_quantile": "0.507297297297297",
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"us.census.acs.B03002003_quantile": "0.133162162162162",
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"us.census.acs.B03002004_quantile": "0.283743243243243",
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"us.census.acs.B03002006_quantile": "0.683945945945946",
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"us.census.acs.B03002012_quantile": "0.494594594594595",
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"us.census.acs.B05001006_quantile": "0.670972972972973",
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"us.census.acs.B08006001_quantile": "0.0607567567567568",
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"us.census.acs.B08006002_quantile": "0.0684324324324324",
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"us.census.acs.B08006008_quantile": "0.565135135135135",
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"us.census.acs.B08006009_quantile": "0.638081081081081",
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"us.census.acs.B08006011_quantile": "0",
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"us.census.acs.B08006015_quantile": "0.900932432432432",
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"us.census.acs.B08006017_quantile": "0.186648648648649",
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"us.census.acs.B09001001_quantile": "0.0193513513513514",
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"us.census.acs.B11001001_quantile": "0.0617972972972973",
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"us.census.acs.B14001001_quantile": "0.0179594594594595",
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"us.census.acs.B14001002_quantile": "0.0140405405405405",
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"us.census.acs.B14001005_quantile": "0",
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"us.census.acs.B14001006_quantile": "0",
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"us.census.acs.B14001007_quantile": "0",
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"us.census.acs.B14001008_quantile": "0.0609054054054054",
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"us.census.acs.B15003001_quantile": "0.0314594594594595",
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"us.census.acs.B15003017_quantile": "0.0403378378378378",
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"us.census.acs.B15003022_quantile": "0.285972972972973",
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"us.census.acs.B15003023_quantile": "0.214567567567568",
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"us.census.acs.B16001001_quantile": "0.0181621621621622",
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"us.census.acs.B16001002_quantile": "0.0463108108108108",
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"us.census.acs.B16001003_quantile": "0.540540540540541",
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"us.census.acs.B17001001_quantile": "0.0237567567567568",
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"us.census.acs.B17001002_quantile": "0.155972972972973",
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"us.census.acs.B19013001_quantile": "0.380662162162162",
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"us.census.acs.B19083001_quantile": "0.986891891891892",
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"us.census.acs.B19301001_quantile": "0.989594594594595",
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"us.census.acs.B25001001_quantile": "0.998418918918919",
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"us.census.acs.B25002003_quantile": "0.999824324324324",
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"us.census.acs.B25004002_quantile": "0.999986486486486",
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"us.census.acs.B25004004_quantile": "0.999662162162162",
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"us.census.acs.B25058001_quantile": "0.679054054054054",
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"us.census.acs.B25071001_quantile": "0.569716216216216",
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"us.census.acs.B25075001_quantile": "0.0415",
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"us.census.acs.B25075025_quantile": "0.891702702702703"
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}
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```
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The possible segments are:
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<table>
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<tr><th> X10 segment</th> <th> X55 Segment </th></tr>
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<tr><td> Hispanic and kids</td><td></td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #99C945'></div> Middle Class, Educated, Suburban, Mixed Race </td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #a3ce57'></div> Low Income on Urban Periphery</td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #add468'></div> Suburban, Young and Low-income </td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #b7d978'></div> low-income, urban, young, unmarried </td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #c1df88'></div> Low education, mainly suburban </td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #cbe598'></div> Young, working class and rural </td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #d5eba8'></div> Low-Income with gentrification </td></tr>
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<tr><td>Low Income and Diverse</td><td></td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #52BCA3'></div> High school education Long Commuters, Black, White Hispanic mix</td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #66c5ae'></div> Rural, Bachelors or college degree, Rent owned mix</td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #79cdb7'></div> Rural,High School Education, Owns property</td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #8bd5c1'></div> Young, City based renters in Sparse neighborhoods, Low poverty </td></tr>
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<tr><td>Low income, minority mix</td><td></td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #5D69B1'></div> Predominantly black, high high school attainment, home owners </td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #7b83c6'></div> White and minority mix multilingual, mixed income / education. Married </td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #9095d2'></div> Hispanic Black mix multilingual, high poverty, renters, uses public transport</td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #a3a7df'></div> Predominantly black renters, rent own mix </td></tr>
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<tr><td>Middle income, single family homes</td><td></td></tr>
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<tr><td></td> <td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #E58606'></div> Lower Middle Income with higher rent burden </td></tr>
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<tr><td></td> <td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #f0983b'></div> Black and mixed community with rent burden</td></tr>
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<tr><td></td> <td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #f4a24e'></div> Lower Middle Income with affordable housing</td></tr>
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<tr><td></td> <td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #f8ab5f'></div> Relatively affordable, satisfied lower middle class</td></tr>
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<tr><td></td> <td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #fcb470'></div> Satisfied Lower Middle Income Higher Rent Costs</td></tr>
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<tr><td></td> <td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #ffbe81'></div> Suburban/Rural Satisfied, decently educated lower middle class</td></tr>
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<tr><td></td> <td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #ffc792'></div> Struggling lower middle class with rent burden</td></tr>
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<tr><td></td> <td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #ffd0a3'></div> Older white home owners, less comfortable financially </td></tr>
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<tr><td></td> <td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #ffdab4'></div> Older home owners, more financially comfortable, some diversity</td></tr>
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<tr><td>Native American</td><td></td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #2F8AC4'></div>Younger, poorer,single parent family Native Americans</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background: #77b8ee'></div>Older, middle income Native Americans once married and Educated </td></tr>
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<tr><td>Old Wealthy, White</td><td></td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#24796C'></div> Older, mixed race professionals</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#388d7e'></div> Works from home, Highly Educated, Super Wealthy </td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#4ca191'></div> Retired Grandparents</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#60b5a5'></div> Wealthy and Rural Living</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#73c9b8'></div> Wealthy, Retired Mountains/Coasts</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#87decc'></div> Wealthy Diverse Suburbanites On the Coasts</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#9bf3e1'></div> Retirement Communitties</td></tr>
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<tr><td>Low Income African American</td><td></td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#c23a7e'></div>Urban - Inner city</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#d86298'></div>Rural families</td></tr>
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<tr><td>Residential institutions, young people</td><td></td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#764e9f'></div>College towns</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#8a64b1'></div>College town with poverty</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#9e7ac3'></div>University campus wider area</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#b491d5'></div>City Outskirt University Campuses</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#c9a8e8'></div>City Center University Campuses</td></tr>
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<tr><td>Wealthy Nuclear Families</td><td></td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#ed645a'></div>Lower educational attainment, Homeowner, Low rent</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#ee7655'></div>Younger, Long Commuter in dense neighborhood</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#f38060'></div>Long commuters White black mix</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#f98a6b'></div>Low rent in built up neighborhoods</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#fe9576'></div>Renters within cities, mixed income areas, White/Hispanic mix, Unmarried</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#ff9f82'></div>Older Home owners with high income</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#ffa98d'></div>Older home owners and very high income</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#ffb399'></div>White Asian Mix Big City Burbs Dwellers</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#ffbda5'></div>Bachelors degree Mid income With Mortgages</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#ffc8b1'></div>Asian Hispanic Mix, Mid income</td></tr>
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<tr><td></td><td><div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#ffd2bd'></div>Bachelors degree Higher income Home Owners</td></tr>
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<tr><td>Wealthy, urban, and kid-free</td><td></td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#CC61B0'></div>Wealthy city commuters </td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#d975bd'></div>New Developments</td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#e488c9'></div>Very wealthy, multiple million dollar homes</td></tr>
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<tr><td></td> <td> <div style='float:left;margin-right:10px;width:20px; height:20px; border-radius:20px;background:#ee9ad4'></div>High rise, dense urbanites</td></tr>
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</table>
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### Examples
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```bash
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https://{username}.carto.com/api/v2/sql?q=SELECT * FROM
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OBS_GetSegmentSnapshot({{point geometry}})
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```
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##### Get the Geographic Snapshot of a Segmentation
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__Get the Segmentation Snapshot around the MGM Grand__
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```bash
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https://{username}.carto.com/api/v2/sql?q=SELECT * FROM
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OBS_GetSegmentSnapshot(CDB_LatLng(36.10222, -115.169516))
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```
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__Get the Segmentation Snapshot at CARTO's NYC HQ__
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```bash
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https://{username}.carto.com/api/v2/sql?q=SELECT * FROM
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OBS_GetSegmentSnapshot(CDB_LatLng(40.704512, -73.936669))
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```
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