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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn 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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-cfceafb8f0","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-a38342c38c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-ca70be17db","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EGeospatial Analysis in Snowflake\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/geospatial_header.png\" alt=\"assets/geospatial_header.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWelcome to the Powered by Tasty Bytes - Zero to Snowflake Quickstart focused on Geospatial Analysis!\u003C/p\u003E\n","\u003Cp\u003EWithin this Quickstart we will conduct in-depth Geospatial analysis leveraging powerful Snowflake functionality coupled with a SafeGraph POI listing from the Snowflake Marketplace.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBefore beginning, please make sure you have completed the \u003Ca href=\"/en/developers/guides/tasty-bytes-introduction/\"\u003E\u003Cstrong\u003EIntroduction to Tasty Bytes Quickstart\u003C/strong\u003E\u003C/a\u003E which provides a walkthrough on setting up a trial account and deploying the Tasty Bytes Foundation required to complete this Quickstart.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to Access the Snowflake Marketplace\u003C/li\u003E\u003Cli\u003EHow to Acquire SafeGraph POI Data from the Snowflake Marketplace\u003C/li\u003E\u003Cli\u003EHow to Create a View\u003C/li\u003E\u003Cli\u003EHow to Create a Geography Point\u003C/li\u003E\u003Cli\u003EHow to Calculate Distance between Points\u003C/li\u003E\u003Cli\u003EHow to Collect Points\u003C/li\u003E\u003Cli\u003EHow to Draw a Minimum Bounding Polygon and Calculate its Area\u003C/li\u003E\u003Cli\u003EHow to conduct Geospatial Analysis with H3 (Hexagonal Hierarchical Geospatial Indexing System)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn Analytics Ready View Complete with First and Third Party Data\u003C/li\u003E\u003Cli\u003EAn Understanding of How to Conduct Geospatial Analysis in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ECreating a Worksheet and Copying in our SQL\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWithin this Quickstart we will follow a Tasty Bytes themed story via a Snowsight SQL Worksheet with this page serving as a side by side guide complete with additional commentary, images and documentation links.\u003C/p\u003E\n","\u003Cp\u003EThis section will walk you through logging into Snowflake, Creating a New Worksheet, Renaming the Worksheet, Copying SQL from GitHub, and Pasting the SQL we will be leveraging within this Quickstart.\u003C/p\u003E\n","\u003Ch4\u003EStep 1 - Accessing Snowflake via URL\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EOpen a browser window and enter the URL of your Snowflake Account\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 2 - Logging into Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ELog into your Snowflake account.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 3 - Navigating to Worksheets\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the Projects Tab in the left-hand navigation bar and click Worksheets.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 4 - Creating a Worksheet\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWithin Worksheets, click the "+" button in the top-right corner of Snowsight.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 5 - Renaming a Worksheet\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ERename the Worksheet by clicking on the auto-generated Timestamp name and inputting "Tasty Bytes - Geospatial"\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 6 - Accessing Quickstart SQL in GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClick the button below which will direct you to our Tasty Bytes SQL file that is hosted on GitHub.\n<button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/FY25_Zero_To_Snowflake/tb_geospatial.sql\"\u003Etb_zts_geospatial.sql\u003C/a\u003E</button>\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 7 - Copying Setup SQL from GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWithin GitHub navigate to the right side and click "Copy raw contents". This will copy all of the required SQL into your clipboard.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/github_copy_raw_contents.png\" alt=\"assets/github_copy_raw_contents.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 8 - Pasting Setup SQL from GitHub into your Snowflake Worksheet\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EPath back to Snowsight and your newly created Worksheet and Paste (\u003Cem\u003ECMD + V for Mac or CTRL + V for Windows\u003C/em\u003E) what we just copied from GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 9 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EAcquiring SafeGraph POI Data from the Snowflake Marketplace\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003ETasty Bytes operates Food Trucks in numerous cities and countries across the globe with each truck having the ability to choose two different selling locations per day.\u003C/p\u003E\n","\u003Cp\u003EOne important item that our Executives are interested in is to learn more about how these locations relate to each other as well as if there are any locations we currently serve that are potentially too far away from hot selling city centers.\u003C/p\u003E\n","\u003Cp\u003EUnfortunately what we have seen so far is our first-party data does not give us the building blocks required to complete this sort of Geospatial analysis. Thankfully, the Snowflake Marketplace has great listings from SafeGraph that can assist us here.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Acquiring SafeGraph POI Data from the Snowflake Marketplace\u003C/h3\u003E\n","\u003Cp\u003ETo begin, acquire the SafeGraph listing by following the steps below within Snowsight:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn the bottom left corner, ensure you are operating as ACCOUNTADMIN\u003C/li\u003E\u003Cli\u003EIn the left pane, navigate to 'Data Products' (Cloud Icon) and select 'Marketplace'\u003C/li\u003E\u003Cli\u003EIn the search bar, enter: 'SafeGraph: frostbyte'\u003C/li\u003E\u003Cli\u003ESelect the 'SafeGraph: frostbyte' listing and click 'Get'\u003C/li\u003E\u003Cli\u003EAdjust Database name to:'TB_SAFEGRAPH'\u003C/li\u003E\u003Cli\u003EGrant access to: 'PUBLIC'\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nSafeGraph’s Places data provides detailed information about physical places. Our data is fresh, cleaned and provided in a format that empowers the user. With POI data for countries around the world, you can gain insights about any location that a person can visit aside from private residences.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 - Evaluating SafeGraph POI Data\u003C/h3\u003E\n","\u003Cp\u003ENow that we have the \u003Ccode\u003Etb_safegraph\u003C/code\u003E database live in our account, let's run the next three queries to set our Worksheet Role, Warehouse and Database context and see what data is available for Paris.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE sysadmin;\nUSE WAREHOUSE tb_de_wh;\nUSE DATABASE tb_101;\n\nSELECT \n    cpg.placekey,\n    cpg.location_name,\n    cpg.longitude,\n    cpg.latitude,\n    cpg.street_address,\n    cpg.city,\n    cpg.country,\n    cpg.polygon_wkt\nFROM tb_safegraph.public.frostbyte_tb_safegraph_s cpg\nWHERE 1=1\n    AND cpg.top_category = 'Museums, Historical Sites, and Similar Institutions'\n    AND cpg.sub_category = 'Museums'\n    AND cpg.city = 'Paris'\n    AND cpg.country = 'France';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/safegraph_data.png\" alt=\"assets/safegraph_data.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAmazing!\u003C/strong\u003E These sort of POI metrics will be immensely valuable in conducting Geospatial analysis to assist our Executives in making data driven location decisions.\u003C/p\u003E\n","\u003Cp\u003ELet's get this harmonized with our Sales data in the next section.\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EHarmonizing and Promoting First and Third Party Data\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003ETo make our Geospatial analysis seamless, let's make sure to get SafeGraph POI data included in the \u003Ccode\u003Eanalytics.orders_v\u003C/code\u003E so all of our downstream users can also access it.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Enriching our Analytics View\u003C/h3\u003E\n","\u003Cp\u003EPlease execute this steps query which will enrich our \u003Ccode\u003Eanalytics.orders_v\u003C/code\u003E by adding all available, new SafeGraph POI metrics.\u003C/p\u003E\n","\u003Cp\u003EWithin this \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-view\"\u003ECREATE VIEW\u003C/a\u003E statement, you will see \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/to_date\"\u003EDATE\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/select#parameters\"\u003ESELECT * EXCLUDE ..\u003C/a\u003E functionality leveraged.\u003C/p\u003E\n","\u003Cp\u003ETo highlight what our EXCLUDE is doing we are using it to simplify the SQL required to SELECT all of the available SafeGraph columns but not the ones we already have available in the \u003Ccode\u003Eharmonized.orders_v\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EThis query will provide a \u003Ccode\u003EView ORDERS_V successfully created.\u003C/code\u003E result.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW analytics.orders_v\nCOMMENT = 'Tasty Bytes Order Detail View'\n    AS\nSELECT \n    DATE(o.order_ts) AS date,\n    o.* ,\n    cpg.* EXCLUDE (location_id, region, phone_number, country)\nFROM tb_101.harmonized.orders_v o\nJOIN tb_safegraph.public.frostbyte_tb_safegraph_s cpg\n    ON o.location_id = cpg.location_id;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\n\u003Cstrong\u003ENote:\u003C/strong\u003E For demonstration purposes the SafeGraph listing already has the Tasty Bytes \u003Cem\u003Elocation_id's\u003C/em\u003E included within. In a real-world scenario the mapping would leverage public \u003Ca href=\"https://www.placekey.io/\"\u003EPlacekeys\u003C/a\u003E which \u003Ca href=\"https://www.safegraph.com/blogs/safegraph-joins-placekey-initiative-as-a-founding-partner\"\u003ESafeGraph is a founding partner of\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003ECreating Geograpy Points from Latitude and Longitude\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWith Point of Interest metrics now readily available from the Snowflake Marketplace without any ETL required, let's start on our Geospatial analysis journey.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Creating a Geography Point\u003C/h3\u003E\n","\u003Cp\u003ELatitude and Longitude are two building block POI metrics we now have access to thanks to SafeGraph.\u003C/p\u003E\n","\u003Cp\u003EPlease execute the next two queries to re-assume to \u003Ccode\u003Etb_data_engineer\u003C/code\u003E role and create our Geography Point leveraging the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/st_makepoint\"\u003EST_MAKEPOINT/ST_POINT\u003C/a\u003E function.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EST_MAKEPOINT/ST_POINT:\u003C/strong\u003E Constructs a GEOGRAPHY object that represents a point with the specified longitude and latitude.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tb_data_engineer;\n\nSELECT TOP 10\n    location_id,\n    ST_MAKEPOINT(longitude, latitude) AS geo_point,\n    SUM(price) AS total_sales_usd\nFROM analytics.orders_v\nWHERE primary_city = 'Paris'\nGROUP BY location_id, latitude, longitude\nORDER BY total_sales_usd DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/st_makepoint.png\" alt=\"assets/st_makepoint.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe results we see in the geo_point column will be essential for everything else we do moving forward.\u003C/p\u003E\n","\u003Cp\u003EOne thing to note here is despite typically hearing Latitude then Longitude this function requires these values to be input in the Longitude, Latitude order.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003ECalculating Straight Line Distance between Points\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EStarting with our Geographic Point, we can now begin to dive into some of the powerful Geospatial functions Snowflake offers natively.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - ST_DISTANCE\u003C/h3\u003E\n","\u003Cp\u003ELet's first start with calculating the distances in Miles and Kilometers between those top selling locations we generated previously by executing our next query which leverages the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/st_distance\"\u003EST_DISTANCE\u003C/a\u003E function.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EST_DISTANCE:\u003C/strong\u003E Returns the minimum geodesic distance between two GEOGRAPHY or the minimum Euclidean distance between two GEOMETRY objects.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWithin this query we also leverage a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/queries-cte\"\u003ECommon Table Expression or CTE\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/constructs/qualify\"\u003EQUALIFY\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/lag\"\u003ELAG\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWITH _top_10_locations AS\n(\n    SELECT TOP 10\n        location_id,\n        ST_MAKEPOINT(longitude, latitude) AS geo_point,\n        SUM(price) AS total_sales_usd\n    FROM analytics.orders_v\n    WHERE primary_city = 'Paris'\n    GROUP BY location_id, latitude, longitude\n    ORDER BY total_sales_usd DESC\n)\nSELECT\n    a.location_id,\n    b.location_id,\n    ROUND(ST_DISTANCE(a.geo_point, b.geo_point)/1609,2) AS geography_distance_miles,\n    ROUND(ST_DISTANCE(a.geo_point, b.geo_point)/1000,2) AS geography_distance_kilometers\nFROM _top_10_locations a\nJOIN _top_10_locations b\n    ON a.location_id <> b.location_id -- avoid calculating the distance between the point itself\nQUALIFY a.location_id <> LAG(b.location_id) OVER (ORDER BY geography_distance_miles) -- avoid duplicate: a to b, b to a distances\nORDER BY geography_distance_miles;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/st_distance.png\" alt=\"assets/st_distance.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWow!\u003C/strong\u003E Look at how far we have already came in so little time. Let's keep diving deeper into what Geospatial insights we can derive in our next section.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003ECollecting Coordinates, Creating a Bounding Polygon & Finding its Center Point\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003ENow that we understand how to create points, and calculate distance, we will now pile on a large set additional Snowflake Geospatial functionality to further our analysis.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Create a Geographic Collection of Points and build our Minimum Bounding Polygon\u003C/h3\u003E\n","\u003Cp\u003EPlease execute the next query which will take the building blocks from the previous section and now collect all of our Geography Points and build a Minimum Bounding Polygon from them.\u003C/p\u003E\n","\u003Cp\u003EWithin this query we will see the usage of \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/st_npoints\"\u003EST_NPOINTS\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/st_collect\"\u003EST_COLLECT\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/st_envelope\"\u003EST_ENVELOPE\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/st_area\"\u003EST_AREA\u003C/a\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EST_NPOINTS/ST_NUMPOINTS:\u003C/strong\u003E Returns the number of points in a GEOGRAPHY or GEOGRAPHY object.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EST_COLLECT:\u003C/strong\u003E This function combines all the GEOGRAPHY objects in a column into one GEOGRAPHY object.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EST_ENVELOPE:\u003C/strong\u003E Returns the minimum bounding box (a rectangular “envelope”) that encloses a specified GEOMETRY object.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EST_AREA:\u003C/strong\u003E Returns the area of the Polygon(s) in a GEOGRAPHY or GEOMETRY object.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003EWITH _top_10_locations AS\n(\n    SELECT TOP 10\n        location_id,\n        ST_MAKEPOINT(longitude, latitude) AS geo_point,\n        SUM(price) AS total_sales_usd\n    FROM analytics.orders_v\n    WHERE primary_city = 'Paris'\n    GROUP BY location_id, latitude, longitude\n    ORDER BY total_sales_usd DESC\n)\nSELECT\n    ST_NPOINTS(ST_COLLECT( geo_point)) AS count_points_in_collection,\n    ST_COLLECT(geo_point) AS collection_of_points,\n    ST_ENVELOPE(collection_of_points) AS minimum_bounding_polygon,\n    ROUND(ST_AREA(minimum_bounding_polygon)/1000000,2) AS area_in_sq_kilometers\nFROM _top_10_locations;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/collect_draw_area.png\" alt=\"assets/collect_draw_area.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELook at how much we accomplished within a single SQL query, but we can't stop yet, let's keep going to see what else we can accomplish.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Finding our Top Selling Locations Center Point\u003C/h3\u003E\n","\u003Cp\u003ENow that we have collected geographic information about where our Trucks sell the most in Paris.  Please execute the next query which will utilize \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/st_centroid\"\u003EST_CENTROID\u003C/a\u003E to find the top selling center point.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EST_CENTROID:\u003C/strong\u003E Returns the Point representing the geometric center of a GEOGRAPHY or GEOMETRY object.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003EWITH _top_10_locations AS\n(\n    SELECT TOP 10\n        o.location_id,\n        ST_MAKEPOINT(o.longitude, o.latitude) AS geo_point,\n        SUM(o.price) AS total_sales_usd\n    FROM analytics.orders_v o\n    WHERE primary_city = 'Paris'\n    GROUP BY o.location_id, o.latitude, o.longitude\n    ORDER BY total_sales_usd DESC\n)\nSELECT\n    ST_COLLECT(tl.geo_point) AS collect_points,\n    ST_CENTROID(collect_points) AS geometric_center_point\nFROM _top_10_locations tl;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/st_centroid.png\" alt=\"assets/st_centroid.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Storing our Center Point in a Variable\u003C/h3\u003E\n","\u003Cp\u003ESo we can use this Center Point of our Top Selling locations in a future query, please execute the query below to SET our center_point variable equal to our \u003Ccode\u003Egeometric_center_point\u003C/code\u003E result from above.\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003ESET center_point = '{ "coordinates": [ 2.323399542234561e+00, 4.885933767767676e+01 ], "type": "Point" }';\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce pasted in, please execute the query which will yield a \u003Ccode\u003EStatement executed succesfully.\u003C/code\u003E message. Now we can use this \u003Ccode\u003Ecenter_point\u003C/code\u003E Variable to help conclude our Geospatial analysis.\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EFinding Locations Furthest Away from our Top Selling Center Point\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EAs mentioned earlier, our Tasty Bytes Executives are interested in seeing what locations we may want to stop having our Food Trucks visit in their weekly schedules. Thankfully, every step we have taken so far has now enabled us to deliver on this exact request.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Top 50 Locations furthest away from our Top Selling Center Point in Paris\u003C/h3\u003E\n","\u003Cp\u003EExecuting the next query we can identify which Paris locations we may want to take off our schedules. Within the query we will see a new function we haven't used yet, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/to_geography\"\u003ETO_GEOGRAPHY\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWITH _paris_locations AS\n(\n    SELECT DISTINCT\n        location_id,\n        location_name,\n        ST_MAKEPOINT(longitude, latitude) AS geo_point\n    FROM analytics.orders_v\n    WHERE primary_city = 'Paris'\n)\nSELECT TOP 50\n    location_id,\n    location_name,\n    ROUND(ST_DISTANCE(geo_point, TO_GEOGRAPHY($center_point))/1000,2) AS kilometer_from_top_selling_center\nFROM _paris_locations\nORDER BY kilometer_from_top_selling_center DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/far_out.png\" alt=\"assets/far_out.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFantastic work! We have now delivered on the exact as from our Executive Team through a simple process by leveraging SafeGraph from the Snowflake Marketplace.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EGeospatial Analysis with H3 (Hexagonal Hierarchical Geospatial Indexing System)\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EH3 is a way of dividing the Earth's surface into hexagonal shapes, organizing them into levels of resolution, and assigning unique codes to each hexagon for easy reference.\u003C/p\u003E\n","\u003Cp\u003EThis system was created by Uber for tasks like mapping, location-based services, and market\nanalysis. A lower resolution corresponds to larger hexagons covering broader areas, while a higher resolution means smaller hexagons representing more specific locations.\u003C/p\u003E\n","\u003Cp\u003EAs Snowflake offers H3 functionality, the Tasty Bytes Leadership Team has tasked our Data Engineer with generating H3 Codes and finding the Top Selling Hexagons.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Locate H3 Codes in Integer and String form\u003C/h3\u003E\n","\u003Cp\u003EThe next query will locate H3 Codes for the \u003Ca href=\"https://en.wikipedia.org/wiki/Louvre\"\u003ELouvre\u003C/a\u003E in both Integer and String form at varying resolutions usings the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/h3_latlng_to_cell\"\u003EH3_LATLNG_TO_CELL\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/h3_latlng_to_cell_string\"\u003EH3_LATLNG_TO_CELL_STRING\u003C/a\u003E functions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT DISTINCT\n    location_id,\n    location_name,\n    H3_LATLNG_TO_CELL(latitude, longitude, 4) AS h3_integer_resolution_4, \n    H3_LATLNG_TO_CELL_STRING(latitude, longitude, 4) AS h3_hex_resolution_4,\n    H3_LATLNG_TO_CELL(latitude, longitude, 8) AS h3_integer_resolution_8, \n    H3_LATLNG_TO_CELL_STRING(latitude, longitude, 8) AS h3_hex_resolution_8,\n    H3_LATLNG_TO_CELL(latitude, longitude, 12) AS h3_integer_resolution_12,\n    H3_LATLNG_TO_CELL_STRING(latitude, longitude, 12) AS h3_hex_resolution_12\nFROM analytics.orders_v\nWHERE location_name = 'Musee du Louvre';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/h3_louvre.png\" alt=\"assets/h3_louvre.png\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EResolution 4\u003C/strong\u003E = 288 thousand hexagons covering the globe\n\u003Cstrong\u003EResolution 8\u003C/strong\u003E = 92 billion hexagons covering the globe\n\u003Cstrong\u003EResolution 12\u003C/strong\u003E = 1.6 billion hexagons covering the globe\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFor more detail on Resolutions please visit: \u003Ca href=\"https://h3geo.org/docs/core-library/restable/\"\u003EH3 Geo: Tables of Cell Statistics Across Resolutions\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 - Parent and Children Cell ID's\u003C/h3\u003E\n","\u003Cp\u003EExecute the next query to locate the Parent Cell Id and an array containing all of the associated Children Id's for the Louvre by leveraging \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/h3_cell_to_parent\"\u003EH3_CELL_TO_PARENT\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/h3_int_to_string\"\u003EH3_INT_TO_STRING\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/h3_cell_to_children\"\u003EH3_CELL_TO_CHILDREN\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT DISTINCT\n    location_id,\n    location_name,\n    H3_LATLNG_TO_CELL(latitude, longitude, 8) AS h3_int_resolution_8,\n    H3_INT_TO_STRING(h3_int_resolution_8) AS h3_hex_resolution_8, -- convert above to hexadecimal format\n    H3_CELL_TO_PARENT(h3_int_resolution_8, 5) AS h3_int_parent_resolution_5, -- locate resolution 5 parent of our H3 cell\n    H3_CELL_TO_CHILDREN(h3_int_resolution_8, 10) AS h3_int_children_resolution_10 -- locate all children at resolution 10\nFROM analytics.orders_v\nWHERE location_name = 'Musee du Louvre';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/h3louvre_parent_child.png\" alt=\"assets/h3louvre_parent_child.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Locations within Top Selling Hexagons\u003C/h3\u003E\n","\u003Cp\u003EUsing the H3 Cell IDs from the previous step, let's now produce a list of all locations within these Top Hexagons and the Hexagons Total Sales.\u003C/p\u003E\n","\u003Cp\u003EThese results will help the business locate areas and locations that we should ensure are always occupied by our trucks.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWITH _top_50_locations AS\n(\n    SELECT TOP 50\n        location_id,\n        ARRAY_SIZE(ARRAY_UNIQUE_AGG(customer_id)) AS customer_loyalty_visitor_count,\n        H3_LATLNG_TO_CELL(latitude, longitude, 7) AS h3_integer_resolution_6,\n        H3_LATLNG_TO_CELL_STRING(latitude, longitude, 7) AS h3_hex_resolution_6,\n        SUM(price) AS total_sales_usd\n    FROM analytics.orders_v\n    WHERE primary_city = 'Paris'\n    GROUP BY ALL\n    ORDER BY total_sales_usd DESC\n)\nSELECT\n    h3_hex_resolution_6,\n    COUNT(DISTINCT location_id) AS number_of_top_50_locations,\n    SUM(customer_loyalty_visitor_count) AS customer_loyalty_visitor_count,\n    SUM(total_sales_usd) AS total_sales_usd\nFROM _top_50_locations\nGROUP BY ALL\nORDER BY total_sales_usd DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4 - H3_GRID_DISTANCE\u003C/h3\u003E\n","\u003Cp\u003ETo conclude, in the next query we will see if our two Top Selling Resolution 6 Hexagons border each other using H3_GRID_DISTANCE(https://docs.snowflake.com/en/sql-reference/functions/h3_grid_distance).\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nA neighbor cell is one step away and two cells with one hexagon between them are two steps apart.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT H3_GRID_DISTANCE('871fb4671ffffff', '871fb4670ffffff') AS cell_distance;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/h3_grid_distance.png\" alt=\"assets/h3_grid_distance.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 5 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EConclusion and Next Steps\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFantastic work!\u003C/strong\u003E You have successfully completed the Tasty Bytes - Zero to Snowflake - Geospatial Quickstart.\u003C/p\u003E\n","\u003Cp\u003EBy doing so you have now:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAccessed the Snowflake Marketplace\u003C/li\u003E\u003Cli\u003EAcquired SafeGraph POI Data from the Snowflake Marketplace\u003C/li\u003E\u003Cli\u003ECreated a View\u003C/li\u003E\u003Cli\u003ECreated a Geography Point\u003C/li\u003E\u003Cli\u003ECalculated Distance between Points\u003C/li\u003E\u003Cli\u003ECollected Points\u003C/li\u003E\u003Cli\u003EDrew a Minimum Bounding Polygon and Calculated its Area\u003C/li\u003E\u003Cli\u003EConducted Geospatial Analysis with H3 (Hexagonal Hierarchical Geospatial Indexing System)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you would like to re-run this Quickstart please leverage the Reset scripts in the bottom of your associated Worksheet.\u003C/p\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n","\u003Cp\u003ETo continue your journey in the Snowflake AI Data Cloud, please now visit the link below to see all other Powered by Tasty Bytes - Quickstarts available to you.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Ch3\u003E\u003Ca href=\"/en/developers/guides/tasty-bytes-introduction/\"\u003EPowered by Tasty Bytes - Quickstarts Table of Contents\u003C/a\u003E\u003C/h3\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Tasty Bytes - Zero to Snowflake - Geospatial",":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n\r\n## Geospatial Analysis in Snowflake\r\n![assets/geospatial_header.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/geospatial_header.png)\r\n\r\n###  Overview \r\nWelcome to the Powered by Tasty Bytes - Zero to Snowflake Quickstart focused on Geospatial Analysis!\r\n\r\nWithin this Quickstart we will conduct in-depth Geospatial analysis leveraging powerful Snowflake functionality coupled with a SafeGraph POI listing from the Snowflake Marketplace.\r\n\r\n### Prerequisites\r\n- Before beginning, please make sure you have completed the [**Introduction to Tasty Bytes Quickstart**](/en/developers/guides/tasty-bytes-introduction/) which provides a walkthrough on setting up a trial account and deploying the Tasty Bytes Foundation required to complete this Quickstart.\r\n\r\n### What You Will Learn\r\n- How to Access the Snowflake Marketplace\r\n- How to Acquire SafeGraph POI Data from the Snowflake Marketplace\r\n- How to Create a View\r\n- How to Create a Geography Point\r\n- How to Calculate Distance between Points\r\n- How to Collect Points\r\n- How to Draw a Minimum Bounding Polygon and Calculate its Area\r\n- How to conduct Geospatial Analysis with H3 (Hexagonal Hierarchical Geospatial Indexing System)\r\n\r\n### What You Will Build\r\n- An Analytics Ready View Complete with First and Third Party Data\r\n- An Understanding of How to Conduct Geospatial Analysis in Snowflake\r\n\r\n## Creating a Worksheet and Copying in our SQL\r\n\r\n### Overview\r\nWithin this Quickstart we will follow a Tasty Bytes themed story via a Snowsight SQL Worksheet with this page serving as a side by side guide complete with additional commentary, images and documentation links.\r\n\r\nThis section will walk you through logging into Snowflake, Creating a New Worksheet, Renaming the Worksheet, Copying SQL from GitHub, and Pasting the SQL we will be leveraging within this Quickstart.\r\n\r\n#### Step 1 - Accessing Snowflake via URL\r\n- Open a browser window and enter the URL of your Snowflake Account \r\n\r\n### Step 2 - Logging into Snowflake\r\n- Log into your Snowflake account.\r\n\r\n### Step 3 - Navigating to Worksheets\r\n- Click on the Projects Tab in the left-hand navigation bar and click Worksheets.\r\n\r\n### Step 4 - Creating a Worksheet\r\n- Within Worksheets, click the \"+\" button in the top-right corner of Snowsight.\r\n\r\n### Step 5 - Renaming a Worksheet\r\n- Rename the Worksheet by clicking on the auto-generated Timestamp name and inputting \"Tasty Bytes - Geospatial\"\r\n\r\n### Step 6 - Accessing Quickstart SQL in GitHub\r\n- Click the button below which will direct you to our Tasty Bytes SQL file that is hosted on GitHub.\r\n\u003Cbutton\u003E[tb_zts_geospatial.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/FY25_Zero_To_Snowflake/tb_geospatial.sql)\u003C/button\u003E\r\n\r\n### Step 7 - Copying Setup SQL from GitHub\r\n- Within GitHub navigate to the right side and click \"Copy raw contents\". This will copy all of the required SQL into your clipboard.\r\n    - ![assets/github_copy_raw_contents.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/github_copy_raw_contents.png)\r\n\r\n### Step 8 - Pasting Setup SQL from GitHub into your Snowflake Worksheet\r\n- Path back to Snowsight and your newly created Worksheet and Paste (*CMD + V for Mac or CTRL + V for Windows*) what we just copied from GitHub.\r\n\r\n### Step 9 - Click Next --\u003E\r\n\r\n\r\n## Acquiring SafeGraph POI Data from the Snowflake Marketplace\r\n\r\n### Overview\r\nTasty Bytes operates Food Trucks in numerous cities and countries across the globe with each truck having the ability to choose two different selling locations per day.\r\n\r\nOne important item that our Executives are interested in is to learn more about how these locations relate to each other as well as if there are any locations we currently serve that are potentially too far away from hot selling city centers.\r\n\r\nUnfortunately what we have seen so far is our first-party data does not give us the building blocks required to complete this sort of Geospatial analysis. Thankfully, the Snowflake Marketplace has great listings from SafeGraph that can assist us here.\r\n\r\n\r\n### Step 1 - Acquiring SafeGraph POI Data from the Snowflake Marketplace \r\nTo begin, acquire the SafeGraph listing by following the steps below within Snowsight:\r\n\r\n- In the bottom left corner, ensure you are operating as ACCOUNTADMIN\r\n- In the left pane, navigate to 'Data Products' (Cloud Icon) and select 'Marketplace'\r\n- In the search bar, enter: 'SafeGraph: frostbyte'\r\n- Select the 'SafeGraph: frostbyte' listing and click 'Get'\r\n- Adjust Database name to:'TB_SAFEGRAPH'\r\n- Grant access to: 'PUBLIC'\r\n\r\n\r\n\u003Easide positive\r\n\u003ESafeGraph’s Places data provides detailed information about physical places. Our data is fresh, cleaned and provided in a format that empowers the user. With POI data for countries around the world, you can gain insights about any location that a person can visit aside from private residences. \r\n\u003E\r\n\r\n### Step 2 - Evaluating SafeGraph POI Data\r\nNow that we have the `tb_safegraph` database live in our account, let's run the next three queries to set our Worksheet Role, Warehouse and Database context and see what data is available for Paris.\r\n\r\n```\r\nUSE ROLE sysadmin;\r\nUSE WAREHOUSE tb_de_wh;\r\nUSE DATABASE tb_101;\r\n\r\nSELECT \r\n    cpg.placekey,\r\n    cpg.location_name,\r\n    cpg.longitude,\r\n    cpg.latitude,\r\n    cpg.street_address,\r\n    cpg.city,\r\n    cpg.country,\r\n    cpg.polygon_wkt\r\nFROM tb_safegraph.public.frostbyte_tb_safegraph_s cpg\r\nWHERE 1=1\r\n    AND cpg.top_category = 'Museums, Historical Sites, and Similar Institutions'\r\n    AND cpg.sub_category = 'Museums'\r\n    AND cpg.city = 'Paris'\r\n    AND cpg.country = 'France';\r\n```\r\n\r\n![assets/safegraph_data.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/safegraph_data.png)\r\n\r\n**Amazing!** These sort of POI metrics will be immensely valuable in conducting Geospatial analysis to assist our Executives in making data driven location decisions. \r\n\r\nLet's get this harmonized with our Sales data in the next section.\r\n\r\n### Step 3 - Click Next --\u003E\r\n\r\n## Harmonizing and Promoting First and Third Party Data\r\n\r\n### Overview\r\nTo make our Geospatial analysis seamless, let's make sure to get SafeGraph POI data included in the `analytics.orders_v` so all of our downstream users can also access it.\r\n\r\n### Step 1 - Enriching our Analytics View\r\nPlease execute this steps query which will enrich our `analytics.orders_v` by adding all available, new SafeGraph POI metrics.\r\n\r\nWithin this [CREATE VIEW](https://docs.snowflake.com/en/sql-reference/sql/create-view) statement, you will see [COMMENT](https://docs.snowflake.com/en/sql-reference/sql/comment), [DATE](https://docs.snowflake.com/en/sql-reference/functions/to_date) and [SELECT * EXCLUDE ..](https://docs.snowflake.com/en/sql-reference/sql/select#parameters) functionality leveraged. \r\n\r\nTo highlight what our EXCLUDE is doing we are using it to simplify the SQL required to SELECT all of the available SafeGraph columns but not the ones we already have available in the `harmonized.orders_v`.\r\n\r\nThis query will provide a `View ORDERS_V successfully created.` result.\r\n\r\n```\r\nCREATE OR REPLACE VIEW analytics.orders_v\r\nCOMMENT = 'Tasty Bytes Order Detail View'\r\n    AS\r\nSELECT \r\n    DATE(o.order_ts) AS date,\r\n    o.* ,\r\n    cpg.* EXCLUDE (location_id, region, phone_number, country)\r\nFROM tb_101.harmonized.orders_v o\r\nJOIN tb_safegraph.public.frostbyte_tb_safegraph_s cpg\r\n    ON o.location_id = cpg.location_id;\r\n```\r\n\r\n\r\n\u003Easide negative\r\n\u003E **Note:** For demonstration purposes the SafeGraph listing already has the Tasty Bytes *location_id's* included within. In a real-world scenario the mapping would leverage public [Placekeys](https://www.placekey.io/) which [SafeGraph is a founding partner of](https://www.safegraph.com/blogs/safegraph-joins-placekey-initiative-as-a-founding-partner).\r\n\u003E\r\n\r\n### Step 2 - Click Next --\u003E\r\n\r\n## Creating Geograpy Points from Latitude and Longitude\r\n\r\n### Overview\r\nWith Point of Interest metrics now readily available from the Snowflake Marketplace without any ETL required, let's start on our Geospatial analysis journey.\r\n\r\n### Step 1 - Creating a Geography Point\r\nLatitude and Longitude are two building block POI metrics we now have access to thanks to SafeGraph. \r\n\r\nPlease execute the next two queries to re-assume to `tb_data_engineer` role and create our Geography Point leveraging the [ST_MAKEPOINT/ST_POINT](https://docs.snowflake.com/en/sql-reference/functions/st_makepoint) function.\r\n\r\n\u003Easide positive\r\n\u003E **ST_MAKEPOINT/ST_POINT:** Constructs a GEOGRAPHY object that represents a point with the specified longitude and latitude.\r\n\u003E\r\n\r\n```\r\nUSE ROLE tb_data_engineer;\r\n\r\nSELECT TOP 10\r\n    location_id,\r\n    ST_MAKEPOINT(longitude, latitude) AS geo_point,\r\n    SUM(price) AS total_sales_usd\r\nFROM analytics.orders_v\r\nWHERE primary_city = 'Paris'\r\nGROUP BY location_id, latitude, longitude\r\nORDER BY total_sales_usd DESC;\r\n```\r\n\r\n![assets/st_makepoint.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/st_makepoint.png)\r\n\r\nThe results we see in the geo_point column will be essential for everything else we do moving forward.\r\n\r\nOne thing to note here is despite typically hearing Latitude then Longitude this function requires these values to be input in the Longitude, Latitude order.\r\n\r\n### Step 2 - Click Next --\u003E\r\n\r\n## Calculating Straight Line Distance between Points\r\n\r\n### Overview\r\nStarting with our Geographic Point, we can now begin to dive into some of the powerful Geospatial functions Snowflake offers natively. \r\n\r\n### Step 1 - ST_DISTANCE\r\n\r\nLet's first start with calculating the distances in Miles and Kilometers between those top selling locations we generated previously by executing our next query which leverages the [ST_DISTANCE](https://docs.snowflake.com/en/sql-reference/functions/st_distance) function.\r\n\r\n\u003Easide positive\r\n\u003E**ST_DISTANCE:** Returns the minimum geodesic distance between two GEOGRAPHY or the minimum Euclidean distance between two GEOMETRY objects.\r\n\u003E\r\n\r\nWithin this query we also leverage a [Common Table Expression or CTE](https://docs.snowflake.com/en/user-guide/queries-cte), [QUALIFY](https://docs.snowflake.com/en/sql-reference/constructs/qualify) and [LAG](https://docs.snowflake.com/en/sql-reference/functions/lag).\r\n\r\n```\r\nWITH _top_10_locations AS\r\n(\r\n    SELECT TOP 10\r\n        location_id,\r\n        ST_MAKEPOINT(longitude, latitude) AS geo_point,\r\n        SUM(price) AS total_sales_usd\r\n    FROM analytics.orders_v\r\n    WHERE primary_city = 'Paris'\r\n    GROUP BY location_id, latitude, longitude\r\n    ORDER BY total_sales_usd DESC\r\n)\r\nSELECT\r\n    a.location_id,\r\n    b.location_id,\r\n    ROUND(ST_DISTANCE(a.geo_point, b.geo_point)/1609,2) AS geography_distance_miles,\r\n    ROUND(ST_DISTANCE(a.geo_point, b.geo_point)/1000,2) AS geography_distance_kilometers\r\nFROM _top_10_locations a\r\nJOIN _top_10_locations b\r\n    ON a.location_id \u003C\u003E b.location_id -- avoid calculating the distance between the point itself\r\nQUALIFY a.location_id \u003C\u003E LAG(b.location_id) OVER (ORDER BY geography_distance_miles) -- avoid duplicate: a to b, b to a distances\r\nORDER BY geography_distance_miles;\r\n```\r\n\r\n![assets/st_distance.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/st_distance.png)\r\n\r\n**Wow!** Look at how far we have already came in so little time. Let's keep diving deeper into what Geospatial insights we can derive in our next section.\r\n\r\n### Step 2 - Click Next --\u003E\r\n\r\n## Collecting Coordinates, Creating a Bounding Polygon & Finding its Center Point\r\n\r\n### Overview\r\nNow that we understand how to create points, and calculate distance, we will now pile on a large set additional Snowflake Geospatial functionality to further our analysis.\r\n\r\n### Step 1 - Create a Geographic Collection of Points and build our Minimum Bounding Polygon\r\nPlease execute the next query which will take the building blocks from the previous section and now collect all of our Geography Points and build a Minimum Bounding Polygon from them.\r\n\r\nWithin this query we will see the usage of [ST_NPOINTS](https://docs.snowflake.com/en/sql-reference/functions/st_npoints), [ST_COLLECT](https://docs.snowflake.com/en/sql-reference/functions/st_collect), [ST_ENVELOPE](https://docs.snowflake.com/en/sql-reference/functions/st_envelope) and [ST_AREA](https://docs.snowflake.com/en/sql-reference/functions/st_area).\r\n\r\n\u003Easide positive\r\n\u003E**ST_NPOINTS/ST_NUMPOINTS:** Returns the number of points in a GEOGRAPHY or GEOGRAPHY object.\r\n\u003E\r\n\u003E**ST_COLLECT:** This function combines all the GEOGRAPHY objects in a column into one GEOGRAPHY object.\r\n\u003E\r\n\u003E**ST_ENVELOPE:** Returns the minimum bounding box (a rectangular “envelope”) that encloses a specified GEOMETRY object.\r\n\u003E\r\n\u003E**ST_AREA:** Returns the area of the Polygon(s) in a GEOGRAPHY or GEOMETRY object.\r\n\u003E\r\n\r\n```\r\nWITH _top_10_locations AS\r\n(\r\n    SELECT TOP 10\r\n        location_id,\r\n        ST_MAKEPOINT(longitude, latitude) AS geo_point,\r\n        SUM(price) AS total_sales_usd\r\n    FROM analytics.orders_v\r\n    WHERE primary_city = 'Paris'\r\n    GROUP BY location_id, latitude, longitude\r\n    ORDER BY total_sales_usd DESC\r\n)\r\nSELECT\r\n    ST_NPOINTS(ST_COLLECT( geo_point)) AS count_points_in_collection,\r\n    ST_COLLECT(geo_point) AS collection_of_points,\r\n    ST_ENVELOPE(collection_of_points) AS minimum_bounding_polygon,\r\n    ROUND(ST_AREA(minimum_bounding_polygon)/1000000,2) AS area_in_sq_kilometers\r\nFROM _top_10_locations;\r\n```\r\n\r\n![assets/collect_draw_area.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/collect_draw_area.png)\r\n\r\nLook at how much we accomplished within a single SQL query, but we can't stop yet, let's keep going to see what else we can accomplish.\r\n\r\n### Step 2 - Finding our Top Selling Locations Center Point\r\nNow that we have collected geographic information about where our Trucks sell the most in Paris.  Please execute the next query which will utilize [ST_CENTROID](https://docs.snowflake.com/en/sql-reference/functions/st_centroid) to find the top selling center point.\r\n\r\n\u003Easide positive\r\n\u003E**ST_CENTROID:** Returns the Point representing the geometric center of a GEOGRAPHY or GEOMETRY object.\r\n\u003E\r\n\r\n```\r\nWITH _top_10_locations AS\r\n(\r\n    SELECT TOP 10\r\n        o.location_id,\r\n        ST_MAKEPOINT(o.longitude, o.latitude) AS geo_point,\r\n        SUM(o.price) AS total_sales_usd\r\n    FROM analytics.orders_v o\r\n    WHERE primary_city = 'Paris'\r\n    GROUP BY o.location_id, o.latitude, o.longitude\r\n    ORDER BY total_sales_usd DESC\r\n)\r\nSELECT\r\n    ST_COLLECT(tl.geo_point) AS collect_points,\r\n    ST_CENTROID(collect_points) AS geometric_center_point\r\nFROM _top_10_locations tl;\r\n```\r\n\r\n![assets/st_centroid.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/st_centroid.png)\r\n\r\n\r\n### Step 3 - Storing our Center Point in a Variable\r\nSo we can use this Center Point of our Top Selling locations in a future query, please execute the query below to SET our center_point variable equal to our `geometric_center_point` result from above.\r\n\r\n``\r\nSET center_point = '{   \"coordinates\": [     2.323399542234561e+00,     4.885933767767676e+01   ],   \"type\": \"Point\" }';\r\n``\r\n\r\nOnce pasted in, please execute the query which will yield a `Statement executed succesfully.` message. Now we can use this `center_point` Variable to help conclude our Geospatial analysis.\r\n\r\n\r\n### Step 4 - Click Next --\u003E\r\n\r\n## Finding Locations Furthest Away from our Top Selling Center Point\r\n\r\n### Overview\r\nAs mentioned earlier, our Tasty Bytes Executives are interested in seeing what locations we may want to stop having our Food Trucks visit in their weekly schedules. Thankfully, every step we have taken so far has now enabled us to deliver on this exact request.\r\n\r\n### Step 1 - Top 50 Locations furthest away from our Top Selling Center Point in Paris\r\nExecuting the next query we can identify which Paris locations we may want to take off our schedules. Within the query we will see a new function we haven't used yet, [TO_GEOGRAPHY](https://docs.snowflake.com/en/sql-reference/functions/to_geography).\r\n\r\n```\r\nWITH _paris_locations AS\r\n(\r\n    SELECT DISTINCT\r\n        location_id,\r\n        location_name,\r\n        ST_MAKEPOINT(longitude, latitude) AS geo_point\r\n    FROM analytics.orders_v\r\n    WHERE primary_city = 'Paris'\r\n)\r\nSELECT TOP 50\r\n    location_id,\r\n    location_name,\r\n    ROUND(ST_DISTANCE(geo_point, TO_GEOGRAPHY($center_point))/1000,2) AS kilometer_from_top_selling_center\r\nFROM _paris_locations\r\nORDER BY kilometer_from_top_selling_center DESC;\r\n```\r\n\r\n![assets/far_out.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/far_out.png)\r\n\r\nFantastic work! We have now delivered on the exact as from our Executive Team through a simple process by leveraging SafeGraph from the Snowflake Marketplace.\r\n\r\n### Step 2 - Click Next --\u003E\r\n\r\n## Geospatial Analysis with H3 (Hexagonal Hierarchical Geospatial Indexing System)\r\n\r\n### Overview\r\nH3 is a way of dividing the Earth's surface into hexagonal shapes, organizing them into levels of resolution, and assigning unique codes to each hexagon for easy reference. \r\n \r\nThis system was created by Uber for tasks like mapping, location-based services, and market\r\nanalysis. A lower resolution corresponds to larger hexagons covering broader areas, while a higher resolution means smaller hexagons representing more specific locations.\r\n\r\nAs Snowflake offers H3 functionality, the Tasty Bytes Leadership Team has tasked our Data Engineer with generating H3 Codes and finding the Top Selling Hexagons.\r\n\r\n\r\n### Step 1 - Locate H3 Codes in Integer and String form\r\nThe next query will locate H3 Codes for the [Louvre](https://en.wikipedia.org/wiki/Louvre) in both Integer and String form at varying resolutions usings the [H3_LATLNG_TO_CELL](https://docs.snowflake.com/en/sql-reference/functions/h3_latlng_to_cell) and [H3_LATLNG_TO_CELL_STRING](https://docs.snowflake.com/en/sql-reference/functions/h3_latlng_to_cell_string) functions.\r\n\r\n```\r\nSELECT DISTINCT\r\n    location_id,\r\n    location_name,\r\n    H3_LATLNG_TO_CELL(latitude, longitude, 4) AS h3_integer_resolution_4, \r\n    H3_LATLNG_TO_CELL_STRING(latitude, longitude, 4) AS h3_hex_resolution_4,\r\n    H3_LATLNG_TO_CELL(latitude, longitude, 8) AS h3_integer_resolution_8, \r\n    H3_LATLNG_TO_CELL_STRING(latitude, longitude, 8) AS h3_hex_resolution_8,\r\n    H3_LATLNG_TO_CELL(latitude, longitude, 12) AS h3_integer_resolution_12,\r\n    H3_LATLNG_TO_CELL_STRING(latitude, longitude, 12) AS h3_hex_resolution_12\r\nFROM analytics.orders_v\r\nWHERE location_name = 'Musee du Louvre';\r\n```\r\n\r\n![assets/h3_louvre.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/h3_louvre.png)\r\n\r\n\u003Easide positive\r\n\u003E **Resolution 4** = 288 thousand hexagons covering the globe\r\n\u003E **Resolution 8** = 92 billion hexagons covering the globe\r\n\u003E **Resolution 12** = 1.6 billion hexagons covering the globe\r\n\u003E  - For more detail on Resolutions please visit: [H3 Geo: Tables of Cell Statistics Across Resolutions](https://h3geo.org/docs/core-library/restable/)\r\n\r\n### Step 2 - Parent and Children Cell ID's\r\nExecute the next query to locate the Parent Cell Id and an array containing all of the associated Children Id's for the Louvre by leveraging [H3_CELL_TO_PARENT](https://docs.snowflake.com/en/sql-reference/functions/h3_cell_to_parent), [H3_INT_TO_STRING](https://docs.snowflake.com/en/sql-reference/functions/h3_int_to_string) and [H3_CELL_TO_CHILDREN](https://docs.snowflake.com/en/sql-reference/functions/h3_cell_to_children).\r\n\r\n```\r\nSELECT DISTINCT\r\n    location_id,\r\n    location_name,\r\n    H3_LATLNG_TO_CELL(latitude, longitude, 8) AS h3_int_resolution_8,\r\n    H3_INT_TO_STRING(h3_int_resolution_8) AS h3_hex_resolution_8, -- convert above to hexadecimal format\r\n    H3_CELL_TO_PARENT(h3_int_resolution_8, 5) AS h3_int_parent_resolution_5, -- locate resolution 5 parent of our H3 cell\r\n    H3_CELL_TO_CHILDREN(h3_int_resolution_8, 10) AS h3_int_children_resolution_10 -- locate all children at resolution 10\r\nFROM analytics.orders_v\r\nWHERE location_name = 'Musee du Louvre';\r\n```\r\n\r\n![assets/h3louvre_parent_child.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/h3louvre_parent_child.png)\r\n\r\n\r\n### Step 3 - Locations within Top Selling Hexagons\r\nUsing the H3 Cell IDs from the previous step, let's now produce a list of all locations within these Top Hexagons and the Hexagons Total Sales.\r\n\r\nThese results will help the business locate areas and locations that we should ensure are always occupied by our trucks.\r\n\r\n```\r\nWITH _top_50_locations AS\r\n(\r\n    SELECT TOP 50\r\n        location_id,\r\n        ARRAY_SIZE(ARRAY_UNIQUE_AGG(customer_id)) AS customer_loyalty_visitor_count,\r\n        H3_LATLNG_TO_CELL(latitude, longitude, 7) AS h3_integer_resolution_6,\r\n        H3_LATLNG_TO_CELL_STRING(latitude, longitude, 7) AS h3_hex_resolution_6,\r\n        SUM(price) AS total_sales_usd\r\n    FROM analytics.orders_v\r\n    WHERE primary_city = 'Paris'\r\n    GROUP BY ALL\r\n    ORDER BY total_sales_usd DESC\r\n)\r\nSELECT\r\n    h3_hex_resolution_6,\r\n    COUNT(DISTINCT location_id) AS number_of_top_50_locations,\r\n    SUM(customer_loyalty_visitor_count) AS customer_loyalty_visitor_count,\r\n    SUM(total_sales_usd) AS total_sales_usd\r\nFROM _top_50_locations\r\nGROUP BY ALL\r\nORDER BY total_sales_usd DESC;\r\n```\r\n\r\n### Step 4 - H3_GRID_DISTANCE\r\nTo conclude, in the next query we will see if our two Top Selling Resolution 6 Hexagons border each other using H3_GRID_DISTANCE(https://docs.snowflake.com/en/sql-reference/functions/h3_grid_distance).\r\n\r\n\u003Easide positive\r\n\u003E A neighbor cell is one step away and two cells with one hexagon between them are two steps apart.\r\n\u003E\r\n\r\n\r\n```\r\nSELECT H3_GRID_DISTANCE('871fb4671ffffff', '871fb4670ffffff') AS cell_distance;\r\n```\r\n\r\n![assets/h3_grid_distance.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-geospatial/h3_grid_distance.png)\r\n\r\n### Step 5 - Click Next --\u003E\r\n\r\n## Conclusion and Next Steps\r\n\r\n### Conclusion\r\n**Fantastic work!** You have successfully completed the Tasty Bytes - Zero to Snowflake - Geospatial Quickstart. \r\n\r\nBy doing so you have now:\r\n- Accessed the Snowflake Marketplace\r\n- Acquired SafeGraph POI Data from the Snowflake Marketplace\r\n- Created a View\r\n- Created a Geography Point\r\n- Calculated Distance between Points\r\n- Collected Points\r\n- Drew a Minimum Bounding Polygon and Calculated its Area\r\n- Conducted Geospatial Analysis with H3 (Hexagonal Hierarchical Geospatial Indexing System)\r\n\r\nIf you would like to re-run this Quickstart please leverage the Reset scripts in the bottom of your associated Worksheet.\r\n\r\n### Next Steps\r\nTo continue your journey in the Snowflake AI Data Cloud, please now visit the link below to see all other Powered by Tasty Bytes - 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Analysis in Snowflake\u003C/h2\u003E","\u003Ch2\u003ECreating a Worksheet and Copying in our SQL\u003C/h2\u003E","\u003Ch2\u003EAcquiring SafeGraph POI Data from the Snowflake Marketplace\u003C/h2\u003E","\u003Ch2\u003EHarmonizing and Promoting First and Third Party Data\u003C/h2\u003E","\u003Ch2\u003ECreating Geograpy Points from Latitude and Longitude\u003C/h2\u003E","\u003Ch2\u003ECalculating Straight Line Distance between Points\u003C/h2\u003E","\u003Ch2\u003ECollecting Coordinates, Creating a Bounding Polygon & Finding its Center Point\u003C/h2\u003E","\u003Ch2\u003EFinding Locations Furthest Away from our Top Selling Center Point\u003C/h2\u003E","\u003Ch2\u003EGeospatial Analysis with H3 (Hexagonal Hierarchical Geospatial Indexing System)\u003C/h2\u003E","\u003Ch2\u003EConclusion and Next 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