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aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text_1":{"id":"blog-text-30d881f682","text":"\u003Cp\u003EDid you know that approximately two thirds of Snowflake customers capture the latitude and longitude of some business entity or event in their account? While latitude and longitude columns can often be used by BI tools and Python libraries to plot points on a map, or shade common administrative boundaries such as states, provinces and countries, companies can do so much more with this valuable geospatial data to perform complex analyses. \u003C/p\u003E\u003Cp\u003EWhile we covered Snowflake’s support for \u003Ca href=\"https://www.snowflake.com/blog/getting-started-geography-data/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003EGEOGRAPHY\u003C/a\u003E and \u003Ca href=\"https://www.snowflake.com/blog/blog-getting-started-geometry-data/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003EGEOMETRY\u003C/a\u003E data types in this blog series, and millions of queries run daily using these types, the reality is that there are still many who feel they need to be a geospatial expert to work with them. They need an easier access path to convert that latitude and longitude into something more powerful. \u003Ca href=\"https://youtu.be/XieZAjBTkr4?si=6XZaGFFbntYz4tsn\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003ETo paraphrase that old song from the 1960s\u003C/a\u003E, “What the world needs now” … is a hexagon.\u003C/p\u003E\u003Cp\u003EWe hear you, world. Today, we’re announcing the general availability of H3 in Snowflake. Now you can take your latitude and longitude data (and your GEOGRAPHY if you have it) and unlock the power of H3. But what is H3, and why does the world need a hexagon?\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_1":{"id":"blog-title-8bfad66fe0","propertiesId":"shall-we-play-a-game","type":"heading4","lines":["Shall we play a game?"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_2":{"id":"blog-text-5c96cca236","text":"\u003Cp\u003EI love games of all kinds, specifically strategy games. A good strategy game will both challenge you within the constructs of the game, as well as force you to weigh your navigation within those constructs against the friends and family you are playing with. \u003C/p\u003E\u003Cp\u003EStrategy games come in a variety of styles, but there is one format I want to mention specifically: the tile placement game. In a tile placement game, players frequently have to place interconnecting tiles on a board or table to generate points or currency to win the game. There are many variations to this, but one theme that tile placement games often have is that they represent two-dimensional maps of a real or imaginary world. And because these tiles often need to perfectly connect with each other to build out or populate the map, the tiles often come in one of several shapes: a square, triangle or \u003Cem\u003Ehexagon\u003C/em\u003E.\u003C/p\u003E\u003Cp\u003EThis process of placing shapes over a real or imaginary map in a game has parallels to the real world — we call it creating a \u003Cem\u003Espatial grid\u003C/em\u003E. A spatial grid attempts to organize a plane or sphere into many common shapes to be able to locate and reference each cell (shape) within that area faster. This grid, along with the metadata that represents the details of how the grid is constructed and used, is referred to as a \u003Cem\u003Espatial index\u003C/em\u003E. Snowflake partner \u003Ca href=\"https://www.carto.com\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003ECARTO\u003C/a\u003E notes that a \u003Ca href=\"https://academy.carto.com/working-with-geospatial-data/introduction-to-spatial-indexes\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Espatial index can be a powerful tool\u003C/a\u003E because “they are geolocated by a short reference string, rather than a long geometry description which is much larger to store and slower to analyze.”\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_2":{"id":"blog-title-be91e3bc77","propertiesId":"what-is-h3","type":"heading4","lines":["What is H3?"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_3":{"id":"blog-text-ce19f68151","text":"\u003Cp\u003EUber, a popular rideshare company, had a \u003Ca href=\"https://www.uber.com/blog/h3/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Eneed to optimize rideshare pricing and dispatch\u003C/a\u003E. They could have used standard government-defined polygons based on any number of existing levels of grouping within a municipality, but those are subject to change and are highly irregular. Consider the following image from \u003Ca href=\"https://www.snowflake.com/blog/blog-getting-started-geometry-data/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Epart 2\u003C/a\u003E of this blog series:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1":{"id":"image-7fc34b4a0e","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--e14e12b6-472a-41c3-a03b-59a7cfb3a6f3/irregular-boundaries.png?quality=85&preferwebp=true","alt":"","lazyEnabled":true,"height":"537","width":"994",":type":"snowflake-site/components/image"},"blog_text_4":{"id":"blog-text-7a2a8a51f6","text":"\u003Cp\u003EThe polygons for the United States, Great Britain, Germany, the Netherlands and Belgium are highly irregular shapes. Their \u003Ca href=\"https://academy.carto.com/working-with-geospatial-data/introduction-to-spatial-indexes\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Eoutlines require an incredibly dense series of points\u003C/a\u003E to render, and mathematical calculations against these polygons can be slow for some use cases. Uber needed the ability to represent a geographical area using a more uniform grid system that would allow fast calculations between predictable cells in the grid, so the company invented and eventually open sourced the spatial index \u003Ca href=\"https://h3geo.org/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EH3\u003C/a\u003E.\u003C/p\u003E\u003Cp\u003EThe first challenge was to determine what shape to use for their grid system. As I explained earlier, triangles, squares and hexagons are commonly used in a grid system because they interlock, or space-fill, very well. But hexagons have one important advantage over triangles and squares: they have \u003Ca href=\"https://h3geo.org/docs/highlights/aggregation\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Eequidistant neighbors\u003C/a\u003E:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_2":{"id":"image-e07db69aea","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--39e1f645-e8cb-4226-9478-ea3410ee88af/screen-shot-2024-01-18-at-14.11.46.png?quality=85&preferwebp=true","alt":"","lazyEnabled":true,"height":"716","width":"1868",":type":"snowflake-site/components/image"},"blog_text_5":{"id":"blog-text-2c4c38a3ba","text":"\u003Cp\u003EA hexagon within a grid has six neighboring hexagons whose centroids are a uniform distance apart from each other. This is not true for triangles or squares, making hexagons perfect for Uber’s geospatial use case because they could have a fast, singular distance category. This also allows for a simple \u003Ca href=\"https://h3geo.org/docs/core-library/coordsystems/#:~:text=IJK%20Coordinates%E2%80%8B,using%20the%20structure%20type%20CoordIJK%20.\"\u003Ecoordinate system\u003C/a\u003E by creating a ring around a given hexagon:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_3":{"id":"image-934910b579","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--cbbd0c83-f240-4663-ba4d-eba80112d11c/screenshot-2024-02-29-at-7.30.03%25E2%2580%25AFam.png?quality=85&preferwebp=true","alt":"","lazyEnabled":true,"height":"440","width":"1090",":type":"snowflake-site/components/image"},"blog_text_6":{"id":"blog-text-e4d13957d3","text":"\u003Cp\u003EThe last element of H3 worth covering is the concept of resolution. Like video or TV resolution, which involves how many pixels are packed into a display or video file size, the grid system is designed with the concept of resolution to allow for different size hexagons to be used depending on the size of the area to be analyzed, or the granularity of the analysis required. This resolution is created as a hierarchy by layering one grid on top of another, with each layer having a slightly different orientation as shown below. When this is done correctly, seven hexagons centroids fit within a larger hexagon using a method called \u003Ca href=\"https://h3geo.org/docs/highlights/indexing\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Eaperture 7\u003C/a\u003E, and there is a parent-child relationship created between the hexagons at every layer. Check different resolutions and play with hierarchy levels using the widget below.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"iframe_1":{"id":"iframe-5d1ab49365","iFrameContent":"\u003Ciframe loading=\"lazy\" src=\"https://sfblogresolutions.streamlit.app/?embed=true\" height=\"550px\" width=\"100%\" style=\"border: none\"\u003E\u003C/iframe\u003E",":type":"snowflake-site/components/iframe"},"blog_text_7":{"id":"blog-text-0be473b220","text":"\u003Cp class=\"has-text-align-center\"\u003E\u003Cem\u003ECaption: A demo Streamlit app visualizes H3 hexagons at different resolutions for the same data.\u003C/em\u003E\u003C/p\u003E\u003Cp\u003EThere are 16 layers, or levels, of resolution in H3. In the demo\u003Ca href=\"https://h3-snow.streamlit.app/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003E Streamlit app\u003C/a\u003E below, you can see how cell tower concentration in the United States can be shown at different levels of resolution, which results in very different size hexagons:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"iframe_2":{"id":"iframe-7143dd3bcc","iFrameContent":"\u003Ciframe loading=\"lazy\" src=\"https://sfblogcelltowers.streamlit.app/?embed=true\" height=\"550px\" width=\"100%\" style=\"border: none\"\u003E\u003C/iframe\u003E",":type":"snowflake-site/components/iframe"},"markup_editor_copy":{"id":"markup-editor-df4f52732a","title":"iframe","cssContent":".iframe-demo{display:block;width:100%;max-width:1200px;min-height:900px;aspect-ratio:1200 / 900;border:none;margin:0 auto}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"blog_text_8":{"id":"blog-text-1c5587b5c1","text":"\u003Cp class=\"has-text-align-center\"\u003E\u003Cem\u003ECaption: A demo Streamlit app visualizes H3 hexagons at different resolutions for the same data.\u003C/em\u003E\u003C/p\u003E\u003Cp\u003EBut H3 isn’t just valuable for rideshare companies, it also works well for a number of use cases across a variety of industries:\u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003E\u003Cstrong\u003ETelecoms:\u003C/strong\u003E Network monitoring and planning. H3 enables the processing of massive network performance data volumes and the generation of unique customer usage insights.\u003C/li\u003E\n\n\n\n\u003Cli\u003E\u003Cstrong\u003EInsurance:\u003C/strong\u003E Defining pricing strategies, and analyzing risk patterns against assets and infrastructure. \u003C/li\u003E\n\n\n\n\u003Cli\u003E\u003Cstrong\u003ESustainability and climate resilience analytics: \u003C/strong\u003EHelping analysts to overcome the processing limitations and costs associated with handling and visualizing large raster data sets.\u003C/li\u003E\n\n\n\n\u003Cli\u003E\u003Cstrong\u003ELogistics and fleet management:\u003C/strong\u003E Optimizing routings and delivery times, and better management of fleets at scale.\u003C/li\u003E\n\n\n\n\u003Cli\u003E\u003Cstrong\u003EDelivery and quick commerce:\u003C/strong\u003E Evaluating business-critical performance indicators such as delivery delays and errors.\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"iframe_3":{"id":"iframe-b02d064017","iFrameContent":"\u003Ciframe loading=\"lazy\" src=\"https://clausa.app.carto.com/map/f0219681-c8b3-48a7-ab2c-d410372e86c0\" height=\"550px\" width=\"100%\" style=\"border: none\"\u003E\u003C/iframe\u003E",":type":"snowflake-site/components/iframe"},"blog_text_9":{"id":"blog-text-d364393465","text":"\u003Cp class=\"has-text-align-center\"\u003E\u003Cem\u003ECaption: This map leverages H3 for the creation of a Wildfire Risk Index for California. Land cover type, temperature and wind speeds are all incorporated into one single score to help insurers understand the risk of wildfire. Source: \u003Ca href=\"https://clausa.app.carto.com/map/f0219681-c8b3-48a7-ab2c-d410372e86c0\"\u003ECARTO\u003C/a\u003E\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_3":{"id":"blog-title-ae932519eb","propertiesId":"h3-in-snowflake","type":"heading4","lines":["H3 in Snowflake"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_10":{"id":"blog-text-4159d20341","text":"\u003Cp\u003EIt’s easy to get started with H3 in Snowflake. Unlike GEOGRAPHY and GEOMETRY, H3 isn’t a data type. Instead, you use a collection of H3 \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/data-types-geospatial#label-data-types-geospatial-h3\"\u003Efunctions\u003C/a\u003E with GEOGRAPHY data that is either stored in a GEOGRAPHY data type, or stored as discrete latitude and longitude columns. By using Streamlit in Snowflake and the PyDeck package, you can build dashboards that visualize this data.\u003C/p\u003E\u003Cp\u003EThe easiest set of functions to get started with is H3_[object]_TO_CELL, where [object] can either be a GEOGRAPHY POINT or a combination of latitude and longitude columns. These functions return the H3 cell id for a given object. There are two variations that work with either type of object, and there are two variations that return the H3 cell id in integer or hexadecimal format:\u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003EH3_POINT_TO_CELL\u003C/li\u003E\n\n\n\n\u003Cli\u003EH3_POINT_TO_CELL_STRING\u003C/li\u003E\n\n\n\n\u003Cli\u003EH3_LATLNG_TO_CELL\u003C/li\u003E\n\n\n\n\u003Cli\u003EH3_LATLNG_TO_CELL_STRING\u003C/li\u003E\n\u003C/ul\u003E\u003Cp\u003EWhether you choose integer or hexadecimal depends on your use case. Hexadecimal is more broadly supported across visualization tools and libraries that support H3, but integers will be more efficient for storage, as well as join or aggregation performance. So choose the variation that best suits your use case.\u003C/p\u003E\u003Cp\u003EYou can see below that I have a table called ALL_NODES, which has a column S_GEOGRAPHY stored in the GEOGRAPHY data type. I also have a column TAGS, which I will use to isolate the location of the Eiffel Tower in Paris.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_4":{"id":"image-bacfc53e5c","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--00014203-580d-4a67-8254-7a13c000ad62/screen-shot-2024-01-18-at-16.08.03.png?quality=85&preferwebp=true","alt":"","lazyEnabled":true,"height":"530","width":"2338",":type":"snowflake-site/components/image"},"blog_text_11":{"id":"blog-text-2ca7f166f0","text":"\u003Cp\u003EThere are two things happening in the above query:\u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003EFirst, we’re using both H3_POINT_TO_CELL (lines 2 & 3) and H3_POINT_TO_CELL_STRING (lines 4 & 5) to return the H3 cell id for the Eiffel Tower in both integer and hexadecimal representation, respectively.\u003C/li\u003E\n\n\n\n\u003Cli\u003ESecond, both functions require a specific resolution. Remember that H3 actually has 16 layers of hexagonal grids, so to get the corresponding H3 cell id for a point, we need to know which resolution we want it at. In this case, we used resolutions 8 and 9.\u003C/li\u003E\n\u003C/ul\u003E\u003Cp\u003EBut again, we don’t \u003Cem\u003Eneed\u003C/em\u003E data already stored in a GEOGRAPHY data type — we just need latitude and longitude columns. So let’s create a view of ALL_NODES called ALL_NODES_V, which extracts the latitude and longitude from the S_GEOGRAPHY column and stores them as separate columns. We can then replicate the above query below, substituting the appropriate columns from the new view and different functions:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_5":{"id":"image-534bdd9013","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--eb9519e2-00fd-4339-8f84-94075be410eb/screen-shot-2024-01-18-at-16.09.38.png?quality=85&preferwebp=true","alt":"","lazyEnabled":true,"height":"524","width":"2338",":type":"snowflake-site/components/image"},"blog_text_12":{"id":"blog-text-c44c9a46a7","text":"\u003Cp\u003EThere are two things to note in the above two queries:\u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003EFirst, we’re switching to H3_LATLNG_TO_CELL (lines 14 & 15) and H3_LATLNG_TO_CELL_STRING (lines 16 & 17) functions, which use discrete latitude and longitude columns as arguments. Remember: two thirds of you already have this data in your Snowflake account!\u003C/li\u003E\n\n\n\n\u003Cli\u003ESecond, notice that we get the exact same H3 cell ids in query 1 as we do in query 2.\u003C/li\u003E\n\u003C/ul\u003E\u003Cp\u003EThese same transformation capabilities that exist for a POINT or a latitude/longitude combination also exist for a POLYGON. These functions allow you to overlay the H3 grid over your polygons, an action also known as \u003Cstrong\u003Epolyfill.\u003C/strong\u003E These functions behave a little differently, though: \u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003EH3_POLYGON_TO_CELLS is planar and does a centroid-based polyfill. This method will produce better results in more localized areas.\u003C/li\u003E\n\n\n\n\u003Cli\u003EH3_COVERAGE uses spherical geometry calculations and returns cells that fully cover a shape. It works well for data on a much larger scale.\u003C/li\u003E\n\u003C/ul\u003E\u003Cp\u003EThe difference between these two polyfill approaches can be seen in the\u003Ca href=\"https://h3-snow.streamlit.app/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003E Streamlit app\u003C/a\u003E mentioned earlier:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"iframe_4":{"id":"iframe-9271cf9ef3","iFrameContent":"\u003Ciframe loading=\"lazy\" src=\"https://sfblogtessellation.streamlit.app/?embed=true\" height=\"550px\" width=\"100%\" style=\"border: none\"\u003E\u003C/iframe\u003E",":type":"snowflake-site/components/iframe"},"blog_text_13":{"id":"blog-text-ca8db37acb","text":"\u003Cp class=\"has-text-align-center\"\u003E\u003Cem\u003ECaption: A Streamlit app showing the difference between two different polyfill functions at different H3 resolutions.\u003C/em\u003E\u003C/p\u003E\u003Cp\u003ENotice how H3_POLYGON_TO_CELLS doesn’t align well at the more global scale on the left, but it polyfills a little cleaner than H3_COVERAGE for the smaller area on the right.\u003C/p\u003E\u003Cp\u003EThere are another set of functions that let you navigate up and down the resolution hierarchy within H3. As noted before, the 16 layers all behave in a parent/child relationship. First, we find the parent of any given H3 cell id at a particular resolution using H3_CELL_TO_PARENT:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_6":{"id":"image-f95d0fb730","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--00d75c8b-4711-4ef3-9414-969019350429/screen-shot-2024-02-01-at-10.15.55.png?quality=85&preferwebp=true","alt":"","lazyEnabled":true,"height":"524","width":"2334",":type":"snowflake-site/components/image"},"blog_text_14":{"id":"blog-text-f1c088039d","text":"\u003Cp\u003EOn lines 23 and 25 in the query above, we’re wrapping the H3_LATLNG_TO_CELL and H3_LATLNG_TO_CELL_STRING functions at resolution 9 with H3_CELL_TO_PARENT to give us the parent cell id at resolution 8. Leaving in the original resolution 8 columns on lines 22 and 24, we can see in the results how the values match. We could have also skipped resolution 8 and jumped to resolution 7 or lower, all the way down to resolution 0. A parent resolution number is always a lower number.\u003C/p\u003E\u003Cp\u003EWe can use the same type of function in reverse to find all of the child cells for a given parent cell. This function is named H3_CELL_TO_CHILDREN, and it goes in the opposite resolution direction: lower to higher. Let’s simplify the query a bit by removing a few columns because the results will look different from the previous queries:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_7":{"id":"image-16f38f94a1","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--911ca385-a7cf-4e45-a340-6cf64446e210/screen-shot-2024-02-01-at-10.22.12.png?quality=85&preferwebp=true","alt":"","lazyEnabled":true,"height":"748","width":"2334",":type":"snowflake-site/components/image"},"blog_text_15":{"id":"blog-text-f84b66ddbf","text":"\u003Cp\u003ELine 31 is asking for the children of a particular cell at resolution 9, and line 30 represents the Eiffel Tower cell at resolution 9. You can see how the children are returned in an array and there are seven of them. If we isolate that array in the sidebar, we can see that indeed the Eiffel Tower cell is one of the cells listed in the array (red highlight box).\u003C/p\u003E\u003Cp\u003EThis is all well and good, but we haven’t really done anything interesting yet. These functions are useful for transforming your geospatial data into H3, but once you’re there, what can you do with it? Let’s consult some outside help to go deeper.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_4":{"id":"blog-title-837364e2ce","propertiesId":"h3-in-snowflake-with-carto","type":"heading4","lines":["H3 in Snowflake with CARTO"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_16":{"id":"blog-text-1fc524250e","text":"\u003Cp\u003ESnowflake partner \u003Ca href=\"https://carto.com/?utm_source=snowflakeH3&utm_medium=blog&utm_campaign=snowflakeH3\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003ECARTO\u003C/a\u003E provides a wide range of features for deriving insights from spatial data.\u003C/p\u003E\u003Cp\u003EOne key feature is CARTO’s \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZT0Z4CM1E9FM/carto-carto-analytics-toolbox-core?utm_source=snowflakeH3&utm_medium=blog&utm_campaign=snowflakeH3\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EAnalytics Toolbox\u003C/a\u003E, available as a Snowflake Native App from Snowflake Marketplace. This allows you to leverage advanced analytical procedures — from running statistical analyses such as Geographically Weighted Regression to enriching your H3 index with data from another enrichment query that contains geographies (such as administrative regions). These advanced stored procedures are accessible in Snowflake through the CALL command, and each procedure takes a set of arguments to make them easier to use than writing a series of large SQL statements by hand.\u003C/p\u003E\u003Cp\u003ELet’s look at an example using the Analytics Toolbox procedure called \u003Cstrong\u003EENRICH_GRID\u003C/strong\u003E. This procedure aggregates columns from a \u003Cstrong\u003Esource\u003C/strong\u003E table based on the area which overlaps the \u003Cstrong\u003Etarget\u003C/strong\u003E H3 table: \u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_8":{"id":"image-c693d0fbc0","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d0042141-3e7a-4da0-b326-0a03f7966329/snowflake-carto-h3-blog-enrichment-code.png?quality=85&preferwebp=true","alt":"","lazyEnabled":true,"height":"754","width":"1600",":type":"snowflake-site/components/image"},"blog_text_17":{"id":"blog-text-5107b41da5","text":"\u003Cp\u003EIn the example above, Los Angeles property data is aggregated to a H3 grid in the following steps:\u003C/p\u003E\u003Col class=\"wp-block-list\"\u003E\n\u003Cli\u003ELines 2-4: The user specifies the target grid type (‘h3’), the target table (‘LOS_ANGELES_H3R9_POP’) and the target H3 column (‘h3’).\u003C/li\u003E\n\n\n\n\u003Cli\u003ELine 5: the source table and geography column are specified (‘LA_SALES_PARCELS’ and ‘GEOM’ respectively).\u003C/li\u003E\n\n\n\n\u003Cli\u003ELines 6-9: an array is constructed specifying the columns to be aggregated and an aggregation method. In this example, both the year built (‘EFFECTIVE_YEARBUILT’) and property price (‘SALEPRICE’) are averaged.\u003C/li\u003E\n\n\n\n\u003Cli\u003ELine 10: the output table is specified.\u003C/li\u003E\n\u003C/ol\u003E\u003Cp\u003EMore information on this process can be found in the \u003Ca href=\"https://docs.carto.com/data-and-analysis/analytics-toolbox-for-snowflake/sql-reference/data?utm_source=snowflakeH3&utm_medium=blog&utm_campaign=snowflakeH3#enrich_grid\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EAnalytics Toolbox documentation\u003C/a\u003E. \u003C/p\u003E\u003Cp\u003ELet’s look at the difference between visualizing the source table and the target table:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"iframe_5":{"id":"iframe-a33828a20d","iFrameContent":"\u003Ciframe loading=\"lazy\" src=\"https://clausa.app.carto.com/map/a43e9ab6-2514-42f2-ba97-0f527c506f10\" height=\"550px\" width=\"100%\" style=\"border: none\"\u003E\u003C/iframe\u003E",":type":"snowflake-site/components/iframe"},"blog_text_18":{"id":"blog-text-1f2d717a63","text":"\u003Cp class=\"has-text-align-center\"\u003E\u003Cem\u003ECaption: Maps in CARTO showing the difference between visualizing data via points vs H3. Source: carto.com.\u003C/em\u003E\u003C/p\u003E\u003Cp\u003EWe can see the results of this analysis on the maps above. The property parcels (source table) is on the left-hand map, with the resulting H3 aggregated grid (target table) on the right — both are showing the average sale price data. Note how much easier it is to interpret spatial patterns through the H3 grid, not to mention the data loads more quickly as you explore the map due to the speed of H3 calculations. CARTO progressively generates tiles on demand based on highly optimized SQL queries that are pushed down to your Snowflake database with a mechanism named \u003Ca href=\"https://carto.com/blog/dynamic-tiling-for-highly-performant-cloud-native-maps\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EDynamic Tiling\u003C/a\u003E, and it’s able to directly translate the H3 indexes present in your data into the hexagonal cells that are rendered in the map.\u003C/p\u003E\u003Cp\u003EEven if you aren’t a geospatial expert, you can expand the use of your latitude and longitude data by embracing the H3 spatial index. Using a combination of Snowflake and CARTO, you can power more performant and sophisticated analyses with more impactful visualizations to gain better insights into your business. If you want to learn more about H3, check out our geospatial \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/data-types-geospatial#label-data-types-geospatial-h3\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Edocumentation\u003C/a\u003E and our new \u003Ca href=\"https://quickstarts.snowflake.com/guide/geo_performance/index.html?index=../..index#0\"\u003EQuickstart\u003C/a\u003E for performance optimization approaches with geospatial data. Join the \u003Ca href=\"https://go.carto.com/webinars/boosting-geospatial-analysis-h3-in-carto-snowflake\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Elive webinar \u003C/a\u003Ehosted by CARTO on March 13 at 12 pm ET //5 pm CET, where we will share even more techniques and use 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