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Snowflake geospatial functions and CARTO.","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EGeospatial query capabilities in Snowflake are built upon a combination of data types and specialized query functions that can be used to parse, construct, and run calculations over geospatial objects. This guide will introduce you to the \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E data type, help you understand geospatial formats supported by Snowflake, walk you through the use of a variety of functions on a sample geospatial data set from the Snowflake Marketplace, and show you how to analyze and visualize your Snowflake data using CARTO’s Analytics Toolbox.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_1.png\" alt=\"assets/geo_sf_carto_telco_1.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EQuick Video \u003Ca href=\"https://www.youtube.com/watch?v=fEtoYweBNQ4&ab_channel=SnowflakeInc.\"\u003EIntroduction to Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ESnowflake \u003Ca href=\"https://www.youtube.com/watch?v=us6MChC8T9Y&ab_channel=SnowflakeInc.\"\u003EData Loading Basics\u003C/a\u003E Video\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.carto.com/getting-started/carto-in-a-nutshell\"\u003ECARTO in a nutshell\u003C/a\u003Eweb guide\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.youtube.com/watch?v=9W_Attbs-fY\"\u003ECARTO Spatial Extension for Snowflake\u003C/a\u003E video\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to acquire geospatial data from the Snowflake Marketplace\u003C/li\u003E\u003Cli\u003EHow to connect Snowflake and Carto\u003C/li\u003E\u003Cli\u003EHow to load geospatial data\u003C/li\u003E\u003Cli\u003EHow to interpret the \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E data type\u003C/li\u003E\u003Cli\u003EHow to understand the different formats that \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E can be expressed in\u003C/li\u003E\u003Cli\u003EHow to do spatial analysis to understand the mobile coverage in the UK\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA supported Snowflake \u003Ca href=\"https://docs.snowflake.com/en/user-guide/setup.html\"\u003EBrowser\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ESign-up for a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake Trial\u003C/a\u003E  OR have access to an existing Snowflake account with the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role or the \u003Ccode\u003EIMPORT SHARE\u003C/code\u003Eprivilege. Select the Enterprise edition, AWS as a cloud provider and US East (Northern Virginia) or EU (Frankfurt) as a region.\u003C/li\u003E\u003Cli\u003ESign-up for a  \u003Ca href=\"http://app.carto.com/signup\"\u003ECARTO Trial\u003C/a\u003E (OR  have access to an existing CARTO account). Select the same region (continent) as for the Snowflake account.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n","\u003Cp\u003EA sample use case that involves LTE cell towers in the United Kingdom. You will answer the following questions:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWhat districts in the UK have good/poor LTE coverage?\u003C/li\u003E\u003Cli\u003EHow the strenght of the LTE signal is distributed.\u003C/li\u003E\u003Cli\u003EWhat UK motorways are poorly or not covered by LTE network.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_2.jpeg\" alt=\"assets/geo_sf_carto_telco_2.jpeg\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup your Account\u003C/h2\u003E\n","\u003Cp\u003EIf this is the first time you are logging into the Snowflake UI, you will be prompted to enter your account name or account URL that you were given when you acquired a trial. The account URL contains your \u003Ca href=\"https://docs.snowflake.com/en/user-guide/connecting.html#your-snowflake-account-name\"\u003Eaccount name\u003C/a\u003E and potentially the region. You can find your account URL in the email that was sent to you after you signed up for the trial.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ESign-in\u003C/code\u003E and you will be prompted for your user name and password.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf this is not the first time you are logging into the Snowflake UI, you should see a "Select an account to sign into" prompt and a button for your account name listed below it. Click the account you wish to access and you will be prompted for your user name and password (or another authentication mechanism).\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EIncrease Your Account Permission\u003C/h3\u003E\n","\u003Cp\u003EThe Snowflake web interface has a lot to offer, but for now, switch your current role from the default \u003Ccode\u003ESYSADMIN\u003C/code\u003E to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E. This increase in permissions will allow you to create shared databases from Snowflake Marketplace listings.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you don't have the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role, switch to a role with \u003Ccode\u003EIMPORT SHARE\u003C/code\u003E privileges instead.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_3.png\" alt=\"assets/geo_sf_carto_telco_3.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate a Virtual Warehouse\u003C/h3\u003E\n","\u003Cp\u003EYou will need to create a Virtual Warehouse to run queries.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to the \u003Ccode\u003EAdmin > Warehouses\u003C/code\u003E screen using the menu on the left side of the window\u003C/li\u003E\u003Cli\u003EClick the big blue \u003Ccode\u003E+ Warehouse\u003C/code\u003E button in the upper right of the window\u003C/li\u003E\u003Cli\u003ECreate an Large Warehouse as shown in the screen below\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_4.png\" alt=\"assets/geo_sf_carto_telco_4.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBe sure to change the \u003Ccode\u003ESuspend After (min)\u003C/code\u003E field to 5 min to avoid wasting compute credits.\u003C/p\u003E\n","\u003Ch3\u003EAcknowledge the Snowflake Third Party Terms\u003C/h3\u003E\n","\u003Cp\u003EIn order to use the packages provided by Anaconda inside Snowflake, you must acknowledge the Snowflake Third Party Terms.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect Admin » Billing & Terms.\u003C/li\u003E\u003Cli\u003EIn the Anaconda section, select Enable.\u003C/li\u003E\u003Cli\u003EIn the Anaconda Packages dialog, click the link to review the Snowflake Third Party Terms page.\u003C/li\u003E\u003Cli\u003EIf you agree to the terms, select \u003Ccode\u003EAcknowledge & Continue\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAcknowledge the Snowflake Third Party Terms\u003C/h3\u003E\n","\u003Cp\u003ENavigate to the query editor by clicking on  \u003Ccode\u003EWorksheets\u003C/code\u003E  on the top left navigation bar.\u003C/p\u003E\n","\u003Ch3\u003EOpen a New Worksheet, Choose Your Warehouse and create a Database\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClick the + Worksheet button in the upper right of your browser window. This will open a new window.\u003C/li\u003E\u003Cli\u003EIn the new Window, make sure \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E and \u003Ccode\u003EMY_WH\u003C/code\u003E (or whatever your warehouse is named) are selected in the upper right of your browser window.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_17.png\" alt=\"assets/geo_sf_carto_telco_17.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFirst, create a new database and schema where we will store datasets in the Geography data type.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace DATABASE GEOLAB;\nCREATE OR REPLACE schema GEOLAB.GEOGRAPHY;\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConnection Snowflake and Carto\u003C/h3\u003E\n","\u003Cp\u003ELet's connect your Snowflake to CARTO so you can run and visualize the queries in the following exercises of this workshop.\u003C/p\u003E\n","\u003Cp\u003EAccess the CARTO Workspace: \u003Ca href=\"http://app.carto.com/\"\u003Eapp.carto.com\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EGo to the Connections section in the Workspace, where you can find the list of all your current connections.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_5.png\" alt=\"assets/geo_sf_carto_telco_5.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo add a new connection, click on \u003Ccode\u003ENew connection\u003C/code\u003E and follow these steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect Snowflake.\u003C/li\u003E\u003Cli\u003EClick the \u003Ccode\u003ESetup connection\u003C/code\u003E button.\u003C/li\u003E\u003Cli\u003EEnter the connection parameters and credentials.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThese are the parameters you need to provide:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EName\u003C/strong\u003E for your connection: You can register different connections with the Snowflake connector. You can use the name to identify the connections.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUsername\u003C/strong\u003E: Name of the user account.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPassword\u003C/strong\u003E: Password for the user account.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAccount\u003C/strong\u003E: Hostname for your account . One way to get it is to check the Snowflake activation email which contains the account_name within the URL ( <account_name>.snowflakecomputing.com ). Just enter what's on the account_name, i.e ok36557.us-east-2.aws\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse (optional)\u003C/strong\u003E: Default warehouse that will run your queries. Use MY_WH.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EUse MY_WH or the name of the data warehouse you created in the previous step otherwise some queries will fail because CARTO won't know which warehouse to run them against.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase (optional)\u003C/strong\u003E. Default database to run your queries. Use GEOLAB.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERole (optional)\u003C/strong\u003E. Default Role to run your queries. Use ACCOUNTADMIN.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_6.png\" alt=\"assets/geo_sf_carto_telco_6.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce you have entered the parameters, you can click the Connect button. CARTO will try to connect to your Snowflake account. If everything is OK, your new connection will be registered.\u003C/p\u003E\n","\u003Ch2\u003EGet Data and Analytics Toolbox\u003C/h2\u003E\n","\u003Cp\u003EThe first step in the guide is to acquire geospatial data sets that you can freely use to explore the basics of Snowflake's geospatial functionality.  The best place to acquire this data is the Snowflake Marketplace!\u003C/p\u003E\n","\u003Cp\u003ETo try various ways to load spatial data, we will also be using a dataset that we will upload from the local desktop.\u003C/p\u003E\n","\u003Cp\u003EAnd we will also be accessing another asset from the Snowflake Marketplace: The CARTO Analytics Toolbox - a composed set of user-defined functions that extend the geospatial capabilities of Snowflake. The listing gives you access to Open Source modules supporting different spatial indexes and other operations: quadkeys, H3, S2, placekey, geometry constructors, accessors, transformations, etc.\u003C/p\u003E\n","\u003Ch3\u003EAcquire Data from the Snowflake Marketplace\u003C/h3\u003E\n","\u003Cp\u003ENow you can acquire sample geospatial data from the Snowflake Marketplace.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to the \u003Ccode\u003EMarketplace\u003C/code\u003E screen using the menu on the left side of the window\u003C/li\u003E\u003Cli\u003ESearch for\u003Ccode\u003EOpenCelliD\u003C/code\u003E in the search bar\u003C/li\u003E\u003Cli\u003EFind and click the\u003Ccode\u003EOpenCelliD - Open Database of Cell Towers\u003C/code\u003E tile\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_7.png\" alt=\"assets/geo_sf_carto_telco_7.png\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOnce in the listing, click the big blue \u003Ccode\u003EGet\u003C/code\u003E button\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EOn the \u003Ccode\u003EGet\u003C/code\u003E screen, you may be prompted to complete your \u003Ccode\u003Euser profile\u003C/code\u003E if you have not done so before. Click the link as shown in the screenshot below. Enter your name and email address into the profile screen and click the blue \u003Ccode\u003ESave\u003C/code\u003E button. You will be returned to the \u003Ccode\u003EGet\u003C/code\u003E screen.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_8.png\" alt=\"assets/geo_sf_carto_telco_8.png\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOn the \u003Ccode\u003EGet Data\u003C/code\u003E screen, change the name of the database from the default to \u003Ccode\u003EOPENCELLID\u003C/code\u003E, as this name is shorter, and all future instructions will assume this name for the database.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_9.png\" alt=\"assets/geo_sf_carto_telco_9.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You have just created a shared database from a listing on the Snowflake Marketplace.\u003C/p\u003E\n","\u003Cp\u003ESimilarly to the above dataset, search and get the \u003Ccode\u003EUK Open Map Data - Sonra\u003C/code\u003E dataset from the Marketplace and rename it to \u003Ccode\u003Eosm_uk\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_32.png\" alt=\"assets/geo_sf_carto_telco_32.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EInstall CARTO Analytics Toolbox from the Snowflake Marketplace\u003C/h3\u003E\n","\u003Cp\u003ENow you can acquire CARTO’s Analytics Toolbox from the Snowflake Marketplace. This will share UDFs (User defined functions) to your account that will allow you to perform even more geospatial analytics.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESimilar to how you did with the data in the previous steps, navigate to the \u003Ccode\u003EMarketplace\u003C/code\u003E screen using the menu on the left side of the window\u003C/li\u003E\u003Cli\u003ESearch for\u003Ccode\u003ECARTO\u003C/code\u003E in the search bar\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_10.png\" alt=\"assets/geo_sf_carto_telco_10.png\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFind and click the\u003Ccode\u003EAnalytics Toolbox\u003C/code\u003E  tile\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_11.png\" alt=\"assets/geo_sf_carto_telco_11.png\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on big blue\u003Ccode\u003EGet\u003C/code\u003E  button\u003C/li\u003E\u003Cli\u003EIn the options, name the database \u003Ccode\u003ECARTO\u003C/code\u003E and optionally add more roles that can access the database\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_12.png\" alt=\"assets/geo_sf_carto_telco_12.png\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on \u003Ccode\u003EGet\u003C/code\u003E and then \u003Ccode\u003EDone\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECongratulations! Now you have data and the analytics toolbox!\u003C/p\u003E\n","\u003Ch3\u003ELoad Datasets from a local file\u003C/h3\u003E\n","\u003Cp\u003ENow that you understand how to get data from Marketplace, let's try another way of getting data, namely, getting it from the external S3 storage.\u003C/p\u003E\n","\u003Cp\u003EIn this step, we're going to load data into the table from the local file. In this Quickstart, we will use a dataset with the districts' boundaries of the United Kingdom and the Netherlands, you can download the file using \u003Ca href=\"https://sfquickstarts.s3.us-west-1.amazonaws.com/vhol_spatial_analysis_geometry_geography/uk_nl_districts.csv\"\u003Ethis URL\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EIn this step, you will create an empty table where we will store boundary data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace TABLE GEOLAB.GEOGRAPHY.UK_NL_DISTRICTS (\n\tAREA VARCHAR(16777216),\n\tNAME VARCHAR(16777216),\n\tGEOMETRY GEOGRAPHY);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen click \u003Ccode\u003ERefresh\u003C/code\u003E in the sidebar, navigate to the newly created table, right-click on it, and select "Load data".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_13.png\" alt=\"assets/geo_sf_carto_telco_13.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the pop-up, choose the file you want to upload and click the Next button. When it suggests selecting the file format and some other parameters, select the values as shown below and click the Next button one more time:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_14.png\" alt=\"assets/geo_sf_carto_telco_14.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EVoila! Now you have a table with the boundaries of districts in the UK and the Netherlands.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUnderstand Geospatial Types and Formats\u003C/h2\u003E\n","\u003Cp\u003ENow we will run different queries to understand how the \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E data type works in Snowflake. First, open the worksheet you created earlier.\u003C/p\u003E\n","\u003Ch3\u003EThe GEOGRAPHY data type\u003C/h3\u003E\n","\u003Cp\u003ESnowflake's \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E data type is similar to the \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E data type in other geospatial databases in that it treats all points as longitude and latitude on a spherical earth instead of a flat plane. This is an important distinction from other geospatial types (such as \u003Ccode\u003EGEOMETRY\u003C/code\u003E). More information about Snowflake's specification can be found \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/data-types-geospatial.html\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ELook at one of the tables in the shared database which has a \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E column by running the following queries. Copy & paste the SQL below into your worksheet editor, put your cursor somewhere in the text of the query you want to run (usually the beginning or end), and either click the blue "Play" button in the upper right of your browser window, or press \u003Ccode\u003ECTRL+Enter\u003C/code\u003E or \u003Ccode\u003ECMD+Enter\u003C/code\u003E (Windows or Mac) to run the query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E// Set the working database schema\nuse schema geolab.geography;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/use-schema.html\"\u003Euse schema\u003C/a\u003E command sets the active database.schema for your future queries so you do not have to fully qualify your objects.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E// Describe the uk_nl_districts table\ndesc view uk_nl_districts;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/desc.html\"\u003Edesc or describe\u003C/a\u003E command shows you the definition of the view, including the columns, their data type, and other relevant details. Notice the \u003Ccode\u003Egeometry\u003C/code\u003E column is defined as \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E type.\u003C/p\u003E\n","\u003Ch3\u003EView GEOGRAPHY Output Formats\u003C/h3\u003E\n","\u003Cp\u003ESnowflake supports 3 primary geospatial formats and 2 additional variations on those formats. They are:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EGeoJSON\u003C/strong\u003E: a JSON-based standard for representing geospatial data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWKT & EWKT\u003C/strong\u003E: a "Well Known Text" string format for representing geospatial data and the "Extended" variation of that format\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWKB & EWKB:\u003C/strong\u003E a "Well Known Binary" format for representing geospatial data in binary and the "Extended" variation of that format\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThese formats are supported for ingestion (files containing those formats can be loaded into a \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E typed column), query result display, and data unloading to new files. You don't need to worry about how Snowflake stores the data under the covers but rather how the data is displayed to you or unloaded to files through the value of session variables called \u003Ccode\u003EGEOGRAPHY_OUTPUT_FORMAT\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ERun the queries below to make sure the current format is GeoJSON.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E// Set the output format to GeoJSON\nalter session set geography_output_format = 'GEOJSON';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/alter-session.html\"\u003Ealter session\u003C/a\u003E command lets you set a parameter for your current user session, which in this case is  \u003Ccode\u003EGEOGRAPHY_OUTPUT_FORMAT\u003C/code\u003E. The default value for those parameters is \u003Ccode\u003E'GEOJSON'\u003C/code\u003E, so normally you wouldn't have to run this command if you want that format, but this guide wants to be certain the next queries are run with the \u003Ccode\u003E'GEOJSON'\u003C/code\u003E output.\u003C/p\u003E\n","\u003Cp\u003ENow run the following query against the \u003Ccode\u003EUK_NL_DISTRICTS\u003C/code\u003E table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E// Query the uk_nl_districts table for districts areas of the UK\nselect name, geometry from geolab.geography.uk_nl_districts where area = 'UK' limit 25;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the result set, notice the \u003Ccode\u003Egeometry\u003C/code\u003E column and how it displays a JSON representation of spatial objects. It should look similar to this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E{"coordinates": [[[-0.171180, 51.92825], [-0.167960, 51.92795], .. [-0.171180, 51.92825]]],  "type": "Polygon"}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENotice that in the result set coordinates represented as pairs of longitude and latitude:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_18.png\" alt=\"assets/geo_sf_carto_telco_18.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow look at the same query but in a different format. Run the following query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E// Set the output format to EWKT\nalter session set geography_output_format = 'EWKT';\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the previous queries again and when done, examine the output in the \u003Ccode\u003Egeometry\u003C/code\u003E column.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect name, geometry from geolab.geography.uk_nl_districts where area = 'UK' limit 25;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEWKT looks different than GeoJSON, and is arguably more readable. Here you can more clearly see the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/data-types-geospatial.html#geospatial-object-types\"\u003Egeospatial object types\u003C/a\u003E, which are represented below in the example output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESRID=4326;POLYGON((-0.1711802206 51.928252578,-0.1679604396 51.927957027,..., -0.1711802206 51.928252578))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEWKT also shows spatial reference identifier and since in GEOGRAPHY \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/data-types-geospatial#geography-data-type\"\u003Ewe use SRID 4326\u003C/a\u003E, you would always see this SRID.\u003C/p\u003E\n","\u003Cp\u003ELastly, look at the WKB output. Run the following query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E// Set the output format to WKB\nalter session set geography_output_format = 'WKB';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnd run the query again, and click on a cell in the \u003Ccode\u003Egeometry\u003C/code\u003E column.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect name, geometry from geolab.geography.uk_nl_districts where area = 'UK' limit 25;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENotice how WKB is incomprehensible to a human reader. However, this format is handy in data loading/unloading, as it can be more compact than WKB or GeoJSON.\u003C/p\u003E\n","\u003Ch2\u003ESpatial Analysis\u003C/h2\u003E\n","\u003Cp\u003ENow that you have a basic understanding of how the \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E data type works and what a geospatial representation of data looks like in various output formats, it's time to walkthrough a scenario that requires you to run and visualize geospatial queries to answer some questions.\u003C/p\u003E\n","\u003Ch3\u003EFind My Location\u003C/h3\u003E\n","\u003Cp\u003EPretend you are an analyst working for a telecommunication company, and you need to do an analysis around coverage of the United Kingdom by your mobile network. You will start with simple data discovery and then try to answer more complex analytical questions.\u003C/p\u003E\n","\u003Cp\u003EStart with running a query that represents your current location. This location has been preselected for the guide and represents the location of the UCL Bentham House in London. Run this query in the Snowlake editor:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect to_geography('POINT(-0.131034 51.526473)');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENotice there is no \u003Ccode\u003Efrom\u003C/code\u003E clause in this query, which allows you to construct a \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E object in a simple \u003Ccode\u003Eselect\u003C/code\u003E statement.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EPOINT(-0.131034 51.526473) is already a geography object in WKT format, so there was no real need to convert it again, but it was important to show the most basic way to use TO_GEOGRAPHY to construct a simple geography object.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENow let’s do the query in CARTO Builder to see where the point is.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a new map. Use the navigation menu on the left to get to Maps and then click on (+) New Map.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_19.png\" alt=\"assets/geo_sf_carto_telco_19.png\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the “Add Source From”\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_20.png\" alt=\"assets/geo_sf_carto_telco_20.png\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThen click on \u003Ccode\u003ECustom Query\u003C/code\u003E and make sure you have selected Snowflake Connection that you have created in previous steps.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_21.gif\" alt=\"assets/geo_sf_carto_telco_21.gif\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENow paste the query and click on the green \u003Ccode\u003ERun\u003C/code\u003E button.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect to_geography('POINT(-0.131034 51.526473)') as geom;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ECARTO requires that the column containing geospatial data be named geom so that is why we add it as geom in the query. If you don't do this it will fail.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003EUse the map zoom controls (+/- buttons) and click the zoom in (+) button until you can see the point better. You should see something like the screenshot below, though you may see more depending on your browser window size.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_22.png\" alt=\"assets/geo_sf_carto_telco_22.png\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EFeel free to use this as your SQL Editor for the next steps, you can delete and re-run the queries from the workshop here.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe green dot represents the \u003Ccode\u003EPOINT\u003C/code\u003E object location. Now you know where you are!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_23.png\" alt=\"assets/geo_sf_carto_telco_23.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EFind the Nearest Cell Towers\u003C/h3\u003E\n","\u003Cp\u003EIn the next step, you are going to use the Marketplace listing with the location of cell towers across the world and will run a query to find the ten closest LTE cell towers to your current location from above. This query will do several things:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIt will use the \u003Ccode\u003EST_DWITHIN\u003C/code\u003E function in the \u003Ccode\u003Ewhere\u003C/code\u003E clause to filter out cell towers that aren't within the stated distance. The function takes two points and a distance to determine whether those two points are less than or equal to the stated distance from each other, returning \u003Ccode\u003Etrue\u003C/code\u003E if they are and \u003Ccode\u003Efalse\u003C/code\u003E if they are not. In this function, you will use the \u003Ccode\u003Elat\u003C/code\u003E and \u003Ccode\u003Elon\u003C/code\u003E columns from OPENCELLID.PUBLIC.RAW_CELL_TOWERS table to construct GEOGRAPHY objects and compare them to your current location \u003Ccode\u003EPOINT\u003C/code\u003E, which you will construct using the previously used \u003Ccode\u003ETO_GEOGRAPHY\u003C/code\u003E. You will then use 500 meters for the distance value.\n\u003Cul\u003E\u003Cli\u003ENote that in the query below, the syntax \u003Ccode\u003EST_DWITHIN(...) = true\u003C/code\u003E is used for readability, but the \u003Ccode\u003E= true\u003C/code\u003E is not required for the filter to work. It is required if you were to need an \u003Ccode\u003E= false\u003C/code\u003E condition.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EIt will also use the \u003Ccode\u003EST_DISTANCE\u003C/code\u003E function, which actually gives you a value in meters representing the distance between the two points. When combined with \u003Ccode\u003Eorder by\u003C/code\u003E and \u003Ccode\u003Elimit\u003C/code\u003E clauses, this will help you return only a certain number of rows with the smallest distance or closest.\n\u003Cul\u003E\u003Cli\u003EAlso note in this query we use two types of constructors: \u003Ccode\u003ETO_GEOGRAPHY\u003C/code\u003E which is is a general-purpose constructor and \u003Ccode\u003EST_POINT\u003C/code\u003E constructor, which specifically makes a \u003Ccode\u003EPOINT\u003C/code\u003E object. Sometimes there is more than one valid approach to construct a geospatial object.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ERun the following query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E// calculate the distance between your location and closest cell towers \nSELECT DISTINCT ST_POINT(lon, lat) AS geom, \n// Use st_distance TO calculate the distance between your locatoin and closest cell towers \nst_distance(geom, to_geography('POINT(-0.131034 51.526473)'))::number(6, 2) AS distance_meters\nFROM OPENCELLID.PUBLIC.RAW_CELL_TOWERS \n// Filter for cell towers that have the UK country code\nWHERE mcc in ('234', '235')\n// Filter for LTE towers\nand radio = 'LTE'\n// Filter for cell towers that are within 500 meters\nand st_dwithin(geom, TO_GEOGRAPHY('POINT(-0.131034 51.526473)'), 500) = TRUE \n// ORDER the results BY the calculated distance and only return the ten lowest\nORDER BY 2 ASC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe query returns \u003Ccode\u003EPOINT\u003C/code\u003E objects, which you can visualize using CARTO.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_24.png\" alt=\"assets/geo_sf_carto_telco_24.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the previous section you've found the closest cell towers to your location. But what about answering more sophisticated questions, like what areas in the UK have very good and bad coverage by LTE network? You can use geospatial functions combined with spatial join to find out.\u003C/p\u003E\n","\u003Ch3\u003EWhat districts in the UK have good/poor LTE coverage?\u003C/h3\u003E\n","\u003Cp\u003EYou have been using one table in your queries so far: \u003Ccode\u003ERAW_CELL_TOWERS\u003C/code\u003E, which stores the locations of cell towers. To find UK districts with good and bad coverage by LTE network, we will undertake a two-step process as follows:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFor every LTE cell tower, we will calculate the coverage area.\u003C/li\u003E\u003Cli\u003EFor every UK district, calculate the area covered by LTE network.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ccode\u003EST_BUFFER\u003C/code\u003E from the Carto toolbox can be used to calculate the coverage area for each LTE cell tower. In \u003Ccode\u003ERAW_CELL_TOWERS\u003C/code\u003E table, there is a field \u003Cem\u003Ecell_range\u003C/em\u003E which can be used as a value of radius in ST_BUFFER. Since the maximum coverage area of the LTE tower is about 6km, we will cap the coverage area with this value.\u003C/p\u003E\n","\u003Cp\u003ERun the following query in your Snowflake's worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE TABLE geolab.geography.uk_lte_coverage AS\nSELECT radio, cell_range,\n       carto.carto.st_buffer(st_makepoint(lon, lat), least(cell_range, 6000), 5) AS coverage\nFROM OPENCELLID.PUBLIC.RAW_CELL_TOWERS \nwhere mcc in ('234', '235')\nand radio = 'LTE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow there is a table \u003Ccode\u003EUK_LTE_COVERAGE\u003C/code\u003E with areas that correspond to the coverage areas of the cell towers.\u003C/p\u003E\n","\u003Cp\u003ETo calculate the coverage of each district by LTE network, you can create a user-defined Python function that calculates an aggregated union and uses the Shapely library under the hood.\u003C/p\u003E\n","\u003Cp\u003ERun the following two queries:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE DATABASE GEOLAB;\nCREATE OR REPLACE FUNCTION GEOLAB.GEOGRAPHY.PY_UNION_AGG(g1 array)\nreturns geography\nlanguage python\nruntime_version = 3.8\npackages = ('shapely')\nhandler = 'udf'\nAS $$\nfrom shapely.ops import unary_union\nfrom shapely.geometry import shape, mapping\ndef udf(g1):\n    shape_union = unary_union([shape(i) for i in g1])\n    shape_union = shape_union.simplify(0.000001)\n    return mapping(shape_union)\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe function above gets an array of spatial objects and "dissolves" them in one large shape which is a union of all initial shapes.\u003C/p\u003E\n","\u003Cp\u003ELet's now for every district compute the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe area that is covered by the LTE network\u003C/li\u003E\u003Cli\u003EThe numerical value of coverage ratio by the LTE network\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EUse the previously created table \u003Ccode\u003EUK_LTE_COVERAGE\u003C/code\u003E, and first join it with \u003Ccode\u003EUK_NL_DISTRICTS\u003C/code\u003E using \u003Ccode\u003EST_CONTAINS\u003C/code\u003E predicate. Then use \u003Ccode\u003EST_INTERSECTION\u003C/code\u003E to find an intersection of each district with the coverage area in that district. The result will be saved in the new table. To speed up queries against that newly created table, you will enable the search optimization feature.\u003C/p\u003E\n","\u003Cp\u003ERun the following two queries:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE TABLE geolab.geography.uk_districts_coverage AS\nSELECT name,\n       to_geography(st_asgeojson(boundary)) AS county_geom,\n       st_intersection(any_value(boundary), geolab.geography.py_union_agg(ARRAY_AGG(st_asgeojson(coverage)))) AS geometry,\n       round(st_area(geometry)/st_area(any_value(boundary)), 2) AS coverage_ratio\nFROM\n  (SELECT c.coverage AS coverage,\n          b.name,\n          b.geometry AS boundary\n   FROM geolab.geography.uk_lte_coverage AS c\n   INNER JOIN geolab.geography.uk_nl_districts AS b ON st_intersects(b.geometry, c.coverage)\n   AND cell_range < 3000\n   WHERE c.radio = 'LTE'\n     AND area = 'UK' )\nGROUP BY name,\n         st_asgeojson(boundary)\nORDER BY st_geohash(geometry);\n\nALTER TABLE geolab.geography.uk_districts_coverage ADD SEARCH OPTIMIZATION ON GEO(geometry);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENice! Now you have a \u003Ccode\u003EUK_DISTRICTS_COVERAGE\u003C/code\u003E table that contains the name of the area, the boundaries of that area, and the boundaries of the LTE coverage area. Let's vizualize in Carto what UK. Paste the following query into the SQL editor and use \u003Ccode\u003ECOVERAGE_RATIO\u003C/code\u003E column to color code the coverage areas.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT geometry AS geom,\n       coverage_ratio\nFROM geolab.geography.uk_districts_coverage;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_33.gif\" alt=\"assets/geo_sf_carto_telco_33.gif\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat percent of the UK roads have LTE coverage?\u003C/h3\u003E\n","\u003Cp\u003ENow imagine you want to calculate what percentage of motorways in the UK have coverage by our network. To get the number, you can employ the \u003Ccode\u003EUK Open Map Data\u003C/code\u003E dataset that has UK roads.\u003C/p\u003E\n","\u003Cp\u003ERun the foillowing query in your Snowflake worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT round(100*sum(st_length(st_intersection(coverage.geometry, roads.geo_cordinates)))/\n               (SELECT sum(st_length(geo_cordinates))\n                FROM OSM_UK.UNITED_KINGDOM.V_ROAD  roads where class = 'motorway'), 2) AS "Coverage, %"\nFROM OSM_UK.UNITED_KINGDOM.V_ROAD roads,\n     geolab.geography.uk_districts_coverage coverage\nWHERE st_intersects(coverage.geometry, roads.geo_cordinates)\nand class = 'motorway';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIt seems our LTE network covers more than 90% of the motorways. A good number to call out in a marketing campaign.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdvanced Analysis using H3\u003C/h2\u003E\n","\u003Cp\u003EIn this section we will cover more advanced use case where we will leverage H3 functions.\u003C/p\u003E\n","\u003Ch3\u003EHow many kilometers of UK roads have poor or no LTE coverage?\u003C/h3\u003E\n","\u003Cp\u003EAs an analyst, you might want to find out how many kilometers of motorways in the UK do not have good coverage by our network. Let's use the \u003Ccode\u003EUK Open Map Data\u003C/code\u003E dataset and build a decay model of our signal using H3 functions.\u003C/p\u003E\n","\u003Cp\u003ELet's first create our signal decay model for our antennas. In the following query, we will create a table with an H3 cell id for each cell tower. To get the H3 cell id, we will use the \u003Ccode\u003EH3_LATLNG_TO_CELL\u003C/code\u003E function.\u003C/p\u003E\n","\u003Cp\u003ERun the foillowing query in your Snowflake worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE TABLE geolab.geography.uk_lte AS\nSELECT\n    row_number() over(order by null) as id\n    , cell_range\n    , H3_LATLNG_TO_CELL(lat, lon, 9) as h3\nFROM OPENCELLID.PUBLIC.RAW_CELL_TOWERS \nwhere mcc in ('234', '235')\nand radio = 'LTE'\nORDER BY h3;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow that we have our antenna geometries, we can compute the H3 cells and it's neighbors for the \u003Ccode\u003ECELL_RANGE\u003C/code\u003E accordingly.\nFirst, we will apply the \u003Ccode\u003EH3_GRID_DISK\u003C/code\u003E function to compute all neighboring H3 cells within a certain distance from a given H3 cell. The distance is calculated by dividing the \u003Ccode\u003ECELL_RANGE\u003C/code\u003E by 586 meters, which represents the spacing between H3 cells at resolution 9.  Since \u003Ccode\u003EH3_GRID_DISK\u003C/code\u003E yields an array, we must use the lateral flatten feature to cross the original rows with the array.\u003C/p\u003E\n","\u003Cp\u003EThen we will create a decay function based on the H3 distance, so we need to determine the maximum H3 distance for each antenna. We can then group the data by H3 cell and choose the highest signal strength within that cell. As we have computed H3 neighbors for each antenna, antennas in close proximity will have generated the same H3 cell multiple times; thus, we will select the one with the strongest signal.\u003C/p\u003E\n","\u003Cp\u003EThe model multiplies the "starting signal strength" of 100 by the distance between the antenna and the H3 cell, and it adds more noise as the H3 cell is further away. The signal will range from 0 (poor) to 100 (strongest).\u003C/p\u003E\n","\u003Cp\u003EOrdering by H3 will enable CARTO to execute queries faster, which is beneficial for visualization purposes.\u003C/p\u003E\n","\u003Cp\u003ERun the following query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace table geolab.geography.uk_lte_coverage_h3 as  \nwith h3_neighbors as (\n    select \n        id\n        , p.value::string as h3\n        , h3_grid_distance(h3, p.value::int) as h3_grid_distance\n    from geolab.geography.uk_lte,\n    table(flatten(input => h3_grid_disk(h3, ceil(least(cell_range, 6000) / 586)::int\n        ))) p\n), \nmax_distance_per_antena as (\n    select id, max(h3_grid_distance) as h3_max_distance\n    from h3_neighbors \n    group by id\n)\nselect \n    h3, \n    max((100 * pow(1 - h3_grid_distance / (h3_max_distance + 1), 2)) -- decay\n    ) * uniform(0.8, 1::float, random() -- noise\n    ) as signal_strength\nfrom h3_neighbors join max_distance_per_antena using(id)\ngroup by h3\norder by h3;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow that we have created our signal decay model, let’s visualize it on CARTO. For that, we can just run the following query from the query console into a new map.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect h3, signal_strength from geolab.geography.uk_lte_coverage_h3;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote that we don’t have a \u003Ccode\u003EGEOGRAPHY\u003C/code\u003E on this query. This is because CARTO has native support of H3 and can show the H3 geography representation on the browser without the need to store and move the geography from the database to the browser.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAs we create an H3 layer we will need to configure the layer type from the query console:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_26.gif\" alt=\"assets/geo_sf_carto_telco_26.gif\"\u003E\u003C/p\u003E\n","\u003Cp\u003EH3 layers allow us to show aggregated information at different resolutions for different zoom levels. Because of this, when we style the layer, we need to decide an aggregation method for the attribute to show, in this example we will use \u003Ccode\u003ESIGNAL_STRENGTH\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_27.png\" alt=\"assets/geo_sf_carto_telco_27.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ERemember to select a color palette of your liking and the color scale (the default is custom but we want to \u003Cem\u003EQuantize\u003C/em\u003E bins for this use case).\nWe can also change the relation between the zoom level and the resolution. The higher the resolution configuration, the more granularity we will see on the map but it will also take longer to load. Select resolution 5.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_28.gif\" alt=\"assets/geo_sf_carto_telco_28.gif\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet’s now use the road network from \u003Ccode\u003EUK Open Map Data\u003C/code\u003E to see which road segments have good coverage and which do not.\nTo intersect the road layer with the H3 signal strength layer, we will split the road geometries onto its minimal road segments and compute the H3 index for the centroid of each segment. We will then join on the H3 index and keep as 'No signal' all of the road segments with no coverage or coverage of under "30".\u003C/p\u003E\n","\u003Cp\u003EThen when each original road segment has an ID from 1 to n (total points in Linestring) we can create the Linestring from each point to the following point with the \u003Ccode\u003EST_COLLECT\u003C/code\u003E function.\u003C/p\u003E\n","\u003Cp\u003EFinally, we use the same \u003Ccode\u003EH3_POINT_TO_CELL\u003C/code\u003E for the selected resolution and we use the Linestring centroid for the point geography.\u003C/p\u003E\n","\u003Cp\u003ERun the following two queries.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecreate or replace table GEOLAB.GEOGRAPHY.OSM_UK_NOT_COVERED AS\nwith roads as (\n    select \n        row_number() over(order by null) as geoid -- creating an ID for each original road segment\n        , geo_cordinates as geom\n    from OSM_UK.UNITED_KINGDOM.V_ROAD roads\n    where class in ('primary', 'motorway')\n    and st_dimension(geo_cordinates) = 1 -- only for the linestring or multilinestring\n)\n, segment_ids as (\n    select \n        Geoid, -- ID for each minimal road segment\n        row_number() over(partition by(geoid) order by geoid) as segment_id \n        , geom\n    from roads, \n    lateral split_to_table(repeat(',', (st_npoints(geom) - 1)::int), ',')\n)\n, segments as (select \n               geoid, \n               segment_id,\n               st_makeline(\n               st_pointn(geom, segment_id), \n               st_pointn(geom, lead(segment_id) over(partition by geoid ORDER BY segment_id))) as segment\n    from segment_ids\n    order by geoid, segment_id)\nselect \n    geoid\n    , case -- No signal if no coverage, or under 30\n        when (h3 is null or signal_strength <= 30) then 'No Signal'\n        else 'OK Signal'\n    end as signal\n    , case -- This parameter we will use for visualization purposes\n        when (h3 is null or signal_strength <= 30) then 2\n        else 1\n    end as road_width\n    , st_collect(segment) as geom -- This is creating the original road segments by collecting them.\nfrom segments \nleft join GEOLAB.GEOGRAPHY.UK_LTE_COVERAGE_H3\non H3_POINT_TO_CELL(st_centroid(segment), 9) = h3\nwhere segment is not null\ngroup by 1, 2, 3\norder by st_geohash(geom);\n\nALTER TABLE GEOLAB.GEOGRAPHY.OSM_UK_NOT_COVERED ADD search optimization ON GEO(geom);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow that we have classified road segments by signal and no signal, we can run the following simple query to get the length of each geography in meters:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect signal, sum(st_length(geom)/1000)::int as total_km\nfrom GEOLAB.GEOGRAPHY.OSM_UK_NOT_COVERED\ngroup by signal;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe now know that we have 58,907 km with good coverage and 10,966 with poor/no coverage. Interestingly, that is about 15 % of the UK roads!\u003C/p\u003E\n","\u003Cp\u003ELastly, with this layer, we can add it to our CARTO map and visualize the road segment according to the signal feature we created.\u003C/p\u003E\n","\u003Cp\u003EFor this, we can add the layer from either:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAdd source from → Custom Query (SQL)\u003C/li\u003E\u003Cli\u003EAdd source from → Data Explorer\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_29.png\" alt=\"assets/geo_sf_carto_telco_29.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThen select your connection and the \u003Ccode\u003EGEOLAB.GEOGRAPHY.OSM_UK_NOT_COVERED\u003C/code\u003E table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_30.png\" alt=\"assets/geo_sf_carto_telco_30.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce we have our second layer on the map, we can click on it to style it and show the stroke color based on our “signal” column. For that create a “Custom palette” with just two colors: gray for roads with good signal and red for roads with no/poor signal.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_31.gif\" alt=\"assets/geo_sf_carto_telco_31.gif\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EYou may feel that these last several queries were a bit long and repetitive, but remember that the intention of this guide was to walk you through the progression of building these longer, more complicated queries by illustrating to you what happens at each step through the progression. By understanding how functions can be combined, it helps you to understand how you can do more advanced things with Snowflake geospatial features!\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003EIn this guide, you acquired geospatial data from the Snowflake Marketplace, explored how the GEOGRAPHY data type and its associated formats work, created data files with geospatial data in it, loaded those files into new tables with GEOGRAPHY typed columns, and queried geospatial data using parser, constructor, transformation, calculation and H3 functions on single tables and multiple tables with joins. You then saw how newly constructed geospatial objects could be visualized using CARTO.\u003C/p\u003E\n","\u003Cp\u003EYou are now ready to explore the larger world of Snowflake geospatial support and geospatial functions.\u003C/p\u003E\n","\u003Ch3\u003EWhat we've covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to acquire a shared database from the Snowflake Marketplace\u003C/li\u003E\u003Cli\u003EThe GEOGRAPHY data type, its formats GeoJSON, WKT, EWKT, WKB, and EWKB, and how to switch between them.\u003C/li\u003E\u003Cli\u003EHow to load data files with geospatial data.\u003C/li\u003E\u003Cli\u003EHow to use constructors like TO_GEOGRAPHY, ST_MAKEPOINT, ST_MAKELINE.\u003C/li\u003E\u003Cli\u003EHow to use a transformation like ST_COLLECT.\u003C/li\u003E\u003Cli\u003EHow to perform measurement calculations like ST_DISTANCE and ST_LENGTH.\u003C/li\u003E\u003Cli\u003EHow to perform relational calculations like ST_DWITHIN and ST_WITHIN.\u003C/li\u003E\u003Cli\u003EHow to use Spatial grid and H3 functions like H3_POINT_TO_CELL, H3_GRID_DISK, H3_GRID_DISTANCE\u003C/li\u003E\u003Cli\u003EHow to use Search Optimization to speed up geospatial queries.\u003C/li\u003E\u003C/ul\u003E"],"title":"Geospatial Analytics for Telecom with Snowflake and Carto","elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\n\nGeospatial query capabilities in Snowflake are built upon a combination of data types and specialized query functions that can be used to parse, construct, and run calculations over geospatial objects. This guide will introduce you to the `GEOGRAPHY` data type, help you understand geospatial formats supported by Snowflake, walk you through the use of a variety of functions on a sample geospatial data set from the Snowflake Marketplace, and show you how to analyze and visualize your Snowflake data using CARTO’s Analytics Toolbox. \n\n![assets/geo_sf_carto_telco_1.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_1.png)\n\n### Prerequisites\n* Quick Video [Introduction to Snowflake](https://www.youtube.com/watch?v=fEtoYweBNQ4&ab_channel=SnowflakeInc.)\n* Snowflake [Data Loading Basics](https://www.youtube.com/watch?v=us6MChC8T9Y&ab_channel=SnowflakeInc.) Video\n* [CARTO in a nutshell ](https://docs.carto.com/getting-started/carto-in-a-nutshell)web guide\n* [CARTO Spatial Extension for Snowflake](https://www.youtube.com/watch?v=9W_Attbs-fY) video \n\n### What You’ll Learn\n* How to acquire geospatial data from the Snowflake Marketplace\n* How to connect Snowflake and Carto \n* How to load geospatial data\n* How to interpret the `GEOGRAPHY` data type\n* How to understand the different formats that `GEOGRAPHY` can be expressed in\n* How to do spatial analysis to understand the mobile coverage in the UK\n\n### What You’ll Need\n* A supported Snowflake [Browser](https://docs.snowflake.com/en/user-guide/setup.html)\n* Sign-up for a [Snowflake Trial](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)  OR have access to an existing Snowflake account with the `ACCOUNTADMIN` role or the `IMPORT SHARE `privilege. Select the Enterprise edition, AWS as a cloud provider and US East (Northern Virginia) or EU (Frankfurt) as a region.\n* Sign-up for a  [CARTO Trial](http://app.carto.com/signup) (OR  have access to an existing CARTO account). Select the same region (continent) as for the Snowflake account.\n\n### What You’ll Build\nA sample use case that involves LTE cell towers in the United Kingdom. You will answer the following questions:\n* What districts in the UK have good/poor LTE coverage?\n* How the strenght of the LTE signal is distributed.\n* What UK motorways are poorly or not covered by LTE network.\n\n\n![assets/geo_sf_carto_telco_2.jpeg](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_2.jpeg)\n\n\u003C!-- ------------------------ --\u003E\n## Setup your Account\n\n\nIf this is the first time you are logging into the Snowflake UI, you will be prompted to enter your account name or account URL that you were given when you acquired a trial. The account URL contains your [account name](https://docs.snowflake.com/en/user-guide/connecting.html#your-snowflake-account-name) and potentially the region. You can find your account URL in the email that was sent to you after you signed up for the trial.\n\nClick `Sign-in` and you will be prompted for your user name and password.\n\n\u003E \n\u003E  If this is not the first time you are logging into the Snowflake UI, you should see a \"Select an account to sign into\" prompt and a button for your account name listed below it. Click the account you wish to access and you will be prompted for your user name and password (or another authentication mechanism).\n\n### Increase Your Account Permission\nThe Snowflake web interface has a lot to offer, but for now, switch your current role from the default `SYSADMIN` to `ACCOUNTADMIN`. This increase in permissions will allow you to create shared databases from Snowflake Marketplace listings.\n\n\u003E \n\u003E  If you don't have the `ACCOUNTADMIN` role, switch to a role with `IMPORT SHARE` privileges instead.\n\n![assets/geo_sf_carto_telco_3.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_3.png)\n\n### Create a Virtual Warehouse\n\nYou will need to create a Virtual Warehouse to run queries.\n\n- Navigate to the `Admin \u003E Warehouses` screen using the menu on the left side of the window\n- Click the big blue `+ Warehouse` button in the upper right of the window\n- Create an Large Warehouse as shown in the screen below\n\n![assets/geo_sf_carto_telco_4.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_4.png)\n\nBe sure to change the `Suspend After (min)` field to 5 min to avoid wasting compute credits.\n\n### Acknowledge the Snowflake Third Party Terms\n\nIn order to use the packages provided by Anaconda inside Snowflake, you must acknowledge the Snowflake Third Party Terms.\n\n* Select Admin » Billing & Terms.\n* In the Anaconda section, select Enable.\n* In the Anaconda Packages dialog, click the link to review the Snowflake Third Party Terms page.\n* If you agree to the terms, select `Acknowledge & Continue`.\n\n### Acknowledge the Snowflake Third Party Terms\n\nNavigate to the query editor by clicking on  `Worksheets`  on the top left navigation bar. \n\n### Open a New Worksheet, Choose Your Warehouse and create a Database\n\n* Click the + Worksheet button in the upper right of your browser window. This will open a new window.\n* In the new Window, make sure `ACCOUNTADMIN` and `MY_WH` (or whatever your warehouse is named) are selected in the upper right of your browser window.\n\n![assets/geo_sf_carto_telco_17.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_17.png)\n\n\nFirst, create a new database and schema where we will store datasets in the Geography data type.\n\n\n```\ncreate or replace DATABASE GEOLAB;\nCREATE OR REPLACE schema GEOLAB.GEOGRAPHY;\n\n```\n\n### Connection Snowflake and Carto\n\nLet's connect your Snowflake to CARTO so you can run and visualize the queries in the following exercises of this workshop.\n\nAccess the CARTO Workspace: [app.carto.com](http://app.carto.com/)\n\nGo to the Connections section in the Workspace, where you can find the list of all your current connections.\n\n![assets/geo_sf_carto_telco_5.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_5.png)\n\nTo add a new connection, click on `New connection` and follow these steps:\n\n1. Select Snowflake.\n2. Click the `Setup connection` button.\n3. Enter the connection parameters and credentials.\n\nThese are the parameters you need to provide:\n\n- **Name** for your connection: You can register different connections with the Snowflake connector. You can use the name to identify the connections.\n- **Username**: Name of the user account.\n- **Password**: Password for the user account.\n- **Account**: Hostname for your account . One way to get it is to check the Snowflake activation email which contains the account_name within the URL ( \u003Caccount_name\u003E.snowflakecomputing.com ). Just enter what's on the account_name, i.e ok36557.us-east-2.aws\n- **Warehouse (optional)**: Default warehouse that will run your queries. Use MY_WH.\n\n\u003E \n\u003E  Use MY_WH or the name of the data warehouse you created in the previous step otherwise some queries will fail because CARTO won't know which warehouse to run them against.\n\n- **Database (optional)**. Default database to run your queries. Use GEOLAB.\n- **Role (optional)**. Default Role to run your queries. Use ACCOUNTADMIN.\n\n![assets/geo_sf_carto_telco_6.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_6.png)\n\nOnce you have entered the parameters, you can click the Connect button. CARTO will try to connect to your Snowflake account. If everything is OK, your new connection will be registered.\n\n## Get Data and Analytics Toolbox\n\n\nThe first step in the guide is to acquire geospatial data sets that you can freely use to explore the basics of Snowflake's geospatial functionality.  The best place to acquire this data is the Snowflake Marketplace!  \n\nTo try various ways to load spatial data, we will also be using a dataset that we will upload from the local desktop.\n\nAnd we will also be accessing another asset from the Snowflake Marketplace: The CARTO Analytics Toolbox - a composed set of user-defined functions that extend the geospatial capabilities of Snowflake. The listing gives you access to Open Source modules supporting different spatial indexes and other operations: quadkeys, H3, S2, placekey, geometry constructors, accessors, transformations, etc.\n\n### Acquire Data from the Snowflake Marketplace\nNow you can acquire sample geospatial data from the Snowflake Marketplace.\n* Navigate to the `Marketplace` screen using the menu on the left side of the window\n* Search for` OpenCelliD` in the search bar\n* Find and click the` OpenCelliD - Open Database of Cell Towers` tile\n\n![assets/geo_sf_carto_telco_7.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_7.png)\n\n* Once in the listing, click the big blue `Get` button\n\n\u003E \n\u003E  On the `Get` screen, you may be prompted to complete your `user profile` if you have not done so before. Click the link as shown in the screenshot below. Enter your name and email address into the profile screen and click the blue `Save` button. You will be returned to the `Get` screen.\n\n![assets/geo_sf_carto_telco_8.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_8.png)\n\n* On the `Get Data` screen, change the name of the database from the default to `OPENCELLID`, as this name is shorter, and all future instructions will assume this name for the database.\n\n![assets/geo_sf_carto_telco_9.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_9.png)\n\nCongratulations! You have just created a shared database from a listing on the Snowflake Marketplace. \n\nSimilarly to the above dataset, search and get the `UK Open Map Data - Sonra` dataset from the Marketplace and rename it to `osm_uk`.\n\n![assets/geo_sf_carto_telco_32.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_32.png)\n\n### Install CARTO Analytics Toolbox from the Snowflake Marketplace\n\nNow you can acquire CARTO’s Analytics Toolbox from the Snowflake Marketplace. This will share UDFs (User defined functions) to your account that will allow you to perform even more geospatial analytics. \n\n* Similar to how you did with the data in the previous steps, navigate to the `Marketplace` screen using the menu on the left side of the window\n* Search for` CARTO` in the search bar\n\n![assets/geo_sf_carto_telco_10.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_10.png)\n\n* Find and click the` Analytics Toolbox`  tile \n\n![assets/geo_sf_carto_telco_11.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_11.png)\n\n* Click on big blue` Get`  button \n* In the options, name the database `CARTO` and optionally add more roles that can access the database \n\n![assets/geo_sf_carto_telco_12.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_12.png)\n\n* Click on `Get` and then `Done`. \n\nCongratulations! Now you have data and the analytics toolbox! \n\n\n### Load Datasets from a local file\n\nNow that you understand how to get data from Marketplace, let's try another way of getting data, namely, getting it from the external S3 storage. \n\nIn this step, we're going to load data into the table from the local file. In this Quickstart, we will use a dataset with the districts' boundaries of the United Kingdom and the Netherlands, you can download the file using [this URL](https://sfquickstarts.s3.us-west-1.amazonaws.com/vhol_spatial_analysis_geometry_geography/uk_nl_districts.csv).\n\nIn this step, you will create an empty table where we will store boundary data.\n\n```\ncreate or replace TABLE GEOLAB.GEOGRAPHY.UK_NL_DISTRICTS (\n\tAREA VARCHAR(16777216),\n\tNAME VARCHAR(16777216),\n\tGEOMETRY GEOGRAPHY);\n```\n\nThen click `Refresh` in the sidebar, navigate to the newly created table, right-click on it, and select \"Load data\".\n\n![assets/geo_sf_carto_telco_13.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_13.png)\n\n\nIn the pop-up, choose the file you want to upload and click the Next button. When it suggests selecting the file format and some other parameters, select the values as shown below and click the Next button one more time:\n\n![assets/geo_sf_carto_telco_14.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_14.png)\n\nVoila! Now you have a table with the boundaries of districts in the UK and the Netherlands.\n\n\u003C!-- ------------------------ --\u003E\n## Understand Geospatial Types and Formats\n\n\nNow we will run different queries to understand how the `GEOGRAPHY` data type works in Snowflake. First, open the worksheet you created earlier.\n\n### The GEOGRAPHY data type\n\nSnowflake's `GEOGRAPHY` data type is similar to the `GEOGRAPHY` data type in other geospatial databases in that it treats all points as longitude and latitude on a spherical earth instead of a flat plane. This is an important distinction from other geospatial types (such as `GEOMETRY`). More information about Snowflake's specification can be found [here](https://docs.snowflake.com/en/sql-reference/data-types-geospatial.html).\n\nLook at one of the tables in the shared database which has a `GEOGRAPHY` column by running the following queries. Copy & paste the SQL below into your worksheet editor, put your cursor somewhere in the text of the query you want to run (usually the beginning or end), and either click the blue \"Play\" button in the upper right of your browser window, or press `CTRL+Enter` or `CMD+Enter` (Windows or Mac) to run the query.\n\n\n```\n// Set the working database schema\nuse schema geolab.geography;\n```\n\nThe [use schema](https://docs.snowflake.com/en/sql-reference/sql/use-schema.html) command sets the active database.schema for your future queries so you do not have to fully qualify your objects.\n\n\n```\n// Describe the uk_nl_districts table\ndesc view uk_nl_districts;\n```\n\nThe [desc or describe](https://docs.snowflake.com/en/sql-reference/sql/desc.html) command shows you the definition of the view, including the columns, their data type, and other relevant details. Notice the `geometry` column is defined as `GEOGRAPHY` type. \n\n\n### View GEOGRAPHY Output Formats\n\nSnowflake supports 3 primary geospatial formats and 2 additional variations on those formats. They are:\n\n* **GeoJSON**: a JSON-based standard for representing geospatial data\n* **WKT & EWKT**: a \"Well Known Text\" string format for representing geospatial data and the \"Extended\" variation of that format\n* **WKB & EWKB:** a \"Well Known Binary\" format for representing geospatial data in binary and the \"Extended\" variation of that format\n\nThese formats are supported for ingestion (files containing those formats can be loaded into a `GEOGRAPHY` typed column), query result display, and data unloading to new files. You don't need to worry about how Snowflake stores the data under the covers but rather how the data is displayed to you or unloaded to files through the value of session variables called `GEOGRAPHY_OUTPUT_FORMAT`.\n\nRun the queries below to make sure the current format is GeoJSON.\n\n\n```\n// Set the output format to GeoJSON\nalter session set geography_output_format = 'GEOJSON';\n```\n\nThe [alter session](https://docs.snowflake.com/en/sql-reference/sql/alter-session.html) command lets you set a parameter for your current user session, which in this case is  `GEOGRAPHY_OUTPUT_FORMAT`. The default value for those parameters is `'GEOJSON'`, so normally you wouldn't have to run this command if you want that format, but this guide wants to be certain the next queries are run with the `'GEOJSON'` output.\n\nNow run the following query against the `UK_NL_DISTRICTS` table.\n\n\n```\n// Query the uk_nl_districts table for districts areas of the UK\nselect name, geometry from geolab.geography.uk_nl_districts where area = 'UK' limit 25;\n```\n\nIn the result set, notice the `geometry` column and how it displays a JSON representation of spatial objects. It should look similar to this:\n\n```\n{\"coordinates\": [[[-0.171180, 51.92825], [-0.167960, 51.92795], .. [-0.171180, 51.92825]]],  \"type\": \"Polygon\"}\n```\n\nNotice that in the result set coordinates represented as pairs of longitude and latitude:\n\n![assets/geo_sf_carto_telco_18.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_18.png)\n\nNow look at the same query but in a different format. Run the following query:\n\n\n```\n// Set the output format to EWKT\nalter session set geography_output_format = 'EWKT';\n\n```\n\nRun the previous queries again and when done, examine the output in the `geometry` column.\n\n```\nselect name, geometry from geolab.geography.uk_nl_districts where area = 'UK' limit 25;\n```\n\n\nEWKT looks different than GeoJSON, and is arguably more readable. Here you can more clearly see the [geospatial object types](https://docs.snowflake.com/en/sql-reference/data-types-geospatial.html#geospatial-object-types), which are represented below in the example output:\n\n\n```\nSRID=4326;POLYGON((-0.1711802206 51.928252578,-0.1679604396 51.927957027,..., -0.1711802206 51.928252578))\n```\n\n\nEWKT also shows spatial reference identifier and since in GEOGRAPHY [we use SRID 4326](https://docs.snowflake.com/en/sql-reference/data-types-geospatial#geography-data-type), you would always see this SRID.\n\nLastly, look at the WKB output. Run the following query:\n\n\n```\n// Set the output format to WKB\nalter session set geography_output_format = 'WKB';\n```\n\nAnd run the query again, and click on a cell in the `geometry` column.\n\n```\nselect name, geometry from geolab.geography.uk_nl_districts where area = 'UK' limit 25;\n```\n\nNotice how WKB is incomprehensible to a human reader. However, this format is handy in data loading/unloading, as it can be more compact than WKB or GeoJSON.\n\n## Spatial Analysis\n\n\nNow that you have a basic understanding of how the `GEOGRAPHY` data type works and what a geospatial representation of data looks like in various output formats, it's time to walkthrough a scenario that requires you to run and visualize geospatial queries to answer some questions.\n\n\n### Find My Location\n\nPretend you are an analyst working for a telecommunication company, and you need to do an analysis around coverage of the United Kingdom by your mobile network. You will start with simple data discovery and then try to answer more complex analytical questions.\n\nStart with running a query that represents your current location. This location has been preselected for the guide and represents the location of the UCL Bentham House in London. Run this query in the Snowlake editor:\n\n\n```\nselect to_geography('POINT(-0.131034 51.526473)');\n```\n\n\nNotice there is no `from` clause in this query, which allows you to construct a `GEOGRAPHY` object in a simple `select` statement.\n\n\n\u003E \n\u003E  POINT(-0.131034 51.526473) is already a geography object in WKT format, so there was no real need to convert it again, but it was important to show the most basic way to use TO_GEOGRAPHY to construct a simple geography object.\n\nNow let’s do the query in CARTO Builder to see where the point is.\n\n\n* Create a new map. Use the navigation menu on the left to get to Maps and then click on (+) New Map.\n\n![assets/geo_sf_carto_telco_19.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_19.png)\n\n* Click on the “Add Source From”\n\n![assets/geo_sf_carto_telco_20.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_20.png)\n\n* Then click on `Custom Query` and make sure you have selected Snowflake Connection that you have created in previous steps. \n\n![assets/geo_sf_carto_telco_21.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_21.gif)\n\n\n* Now paste the query and click on the green `Run` button. \n\n```\nselect to_geography('POINT(-0.131034 51.526473)') as geom;\n```\n\n\u003E \n\u003E  CARTO requires that the column containing geospatial data be named geom so that is why we add it as geom in the query. If you don't do this it will fail. \n\n* Use the map zoom controls (+/- buttons) and click the zoom in (+) button until you can see the point better. You should see something like the screenshot below, though you may see more depending on your browser window size. \n\n![assets/geo_sf_carto_telco_22.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_22.png)\n\n\n\u003E \n\u003E  Feel free to use this as your SQL Editor for the next steps, you can delete and re-run the queries from the workshop here. \n\nThe green dot represents the `POINT` object location. Now you know where you are!\n\n![assets/geo_sf_carto_telco_23.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_23.png)\n\n\n### Find the Nearest Cell Towers\n\nIn the next step, you are going to use the Marketplace listing with the location of cell towers across the world and will run a query to find the ten closest LTE cell towers to your current location from above. This query will do several things:\n\n* It will use the `ST_DWITHIN` function in the `where` clause to filter out cell towers that aren't within the stated distance. The function takes two points and a distance to determine whether those two points are less than or equal to the stated distance from each other, returning `true` if they are and `false` if they are not. In this function, you will use the `lat` and `lon` columns from OPENCELLID.PUBLIC.RAW_CELL_TOWERS table to construct GEOGRAPHY objects and compare them to your current location `POINT`, which you will construct using the previously used `TO_GEOGRAPHY`. You will then use 500 meters for the distance value.\n    * Note that in the query below, the syntax `ST_DWITHIN(...) = true` is used for readability, but the `= true` is not required for the filter to work. It is required if you were to need an `= false` condition.\n* It will also use the `ST_DISTANCE` function, which actually gives you a value in meters representing the distance between the two points. When combined with `order by` and `limit` clauses, this will help you return only a certain number of rows with the smallest distance or closest.\n    * Also note in this query we use two types of constructors: `TO_GEOGRAPHY` which is is a general-purpose constructor and `ST_POINT` constructor, which specifically makes a `POINT` object. Sometimes there is more than one valid approach to construct a geospatial object.\n\nRun the following query: \n\n\n```\n// calculate the distance between your location and closest cell towers \nSELECT DISTINCT ST_POINT(lon, lat) AS geom, \n// Use st_distance TO calculate the distance between your locatoin and closest cell towers \nst_distance(geom, to_geography('POINT(-0.131034 51.526473)'))::number(6, 2) AS distance_meters\nFROM OPENCELLID.PUBLIC.RAW_CELL_TOWERS \n// Filter for cell towers that have the UK country code\nWHERE mcc in ('234', '235')\n// Filter for LTE towers\nand radio = 'LTE'\n// Filter for cell towers that are within 500 meters\nand st_dwithin(geom, TO_GEOGRAPHY('POINT(-0.131034 51.526473)'), 500) = TRUE \n// ORDER the results BY the calculated distance and only return the ten lowest\nORDER BY 2 ASC\nLIMIT 10;\n```\n\nThe query returns `POINT` objects, which you can visualize using CARTO.\n\n![assets/geo_sf_carto_telco_24.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_24.png)\n\nIn the previous section you've found the closest cell towers to your location. But what about answering more sophisticated questions, like what areas in the UK have very good and bad coverage by LTE network? You can use geospatial functions combined with spatial join to find out.\n\n### What districts in the UK have good/poor LTE coverage?\n\nYou have been using one table in your queries so far: `RAW_CELL_TOWERS`, which stores the locations of cell towers. To find UK districts with good and bad coverage by LTE network, we will undertake a two-step process as follows:\n\n* For every LTE cell tower, we will calculate the coverage area.\n* For every UK district, calculate the area covered by LTE network.\n\n`ST_BUFFER` from the Carto toolbox can be used to calculate the coverage area for each LTE cell tower. In `RAW_CELL_TOWERS` table, there is a field _cell_range_ which can be used as a value of radius in ST_BUFFER. Since the maximum coverage area of the LTE tower is about 6km, we will cap the coverage area with this value.\n\nRun the following query in your Snowflake's worksheet: \n\n```\nCREATE OR REPLACE TABLE geolab.geography.uk_lte_coverage AS\nSELECT radio, cell_range,\n       carto.carto.st_buffer(st_makepoint(lon, lat), least(cell_range, 6000), 5) AS coverage\nFROM OPENCELLID.PUBLIC.RAW_CELL_TOWERS \nwhere mcc in ('234', '235')\nand radio = 'LTE';\n```\n\n\nNow there is a table `UK_LTE_COVERAGE` with areas that correspond to the coverage areas of the cell towers. \n\nTo calculate the coverage of each district by LTE network, you can create a user-defined Python function that calculates an aggregated union and uses the Shapely library under the hood. \n\nRun the following two queries:\n\n```\nUSE DATABASE GEOLAB;\nCREATE OR REPLACE FUNCTION GEOLAB.GEOGRAPHY.PY_UNION_AGG(g1 array)\nreturns geography\nlanguage python\nruntime_version = 3.8\npackages = ('shapely')\nhandler = 'udf'\nAS $$\nfrom shapely.ops import unary_union\nfrom shapely.geometry import shape, mapping\ndef udf(g1):\n    shape_union = unary_union([shape(i) for i in g1])\n    shape_union = shape_union.simplify(0.000001)\n    return mapping(shape_union)\n$$;\n```\n\nThe function above gets an array of spatial objects and \"dissolves\" them in one large shape which is a union of all initial shapes.\n\nLet's now for every district compute the following:\n\n* The area that is covered by the LTE network\n* The numerical value of coverage ratio by the LTE network\n\nUse the previously created table `UK_LTE_COVERAGE`, and first join it with `UK_NL_DISTRICTS` using `ST_CONTAINS` predicate. Then use `ST_INTERSECTION` to find an intersection of each district with the coverage area in that district. The result will be saved in the new table. To speed up queries against that newly created table, you will enable the search optimization feature.\n\nRun the following two queries:\n\n```\nCREATE OR REPLACE TABLE geolab.geography.uk_districts_coverage AS\nSELECT name,\n       to_geography(st_asgeojson(boundary)) AS county_geom,\n       st_intersection(any_value(boundary), geolab.geography.py_union_agg(ARRAY_AGG(st_asgeojson(coverage)))) AS geometry,\n       round(st_area(geometry)/st_area(any_value(boundary)), 2) AS coverage_ratio\nFROM\n  (SELECT c.coverage AS coverage,\n          b.name,\n          b.geometry AS boundary\n   FROM geolab.geography.uk_lte_coverage AS c\n   INNER JOIN geolab.geography.uk_nl_districts AS b ON st_intersects(b.geometry, c.coverage)\n   AND cell_range \u003C 3000\n   WHERE c.radio = 'LTE'\n     AND area = 'UK' )\nGROUP BY name,\n         st_asgeojson(boundary)\nORDER BY st_geohash(geometry);\n\nALTER TABLE geolab.geography.uk_districts_coverage ADD SEARCH OPTIMIZATION ON GEO(geometry);\n```\n\nNice! Now you have a `UK_DISTRICTS_COVERAGE` table that contains the name of the area, the boundaries of that area, and the boundaries of the LTE coverage area. Let's vizualize in Carto what UK. Paste the following query into the SQL editor and use `COVERAGE_RATIO` column to color code the coverage areas.\n\n```\nSELECT geometry AS geom,\n       coverage_ratio\nFROM geolab.geography.uk_districts_coverage;\n```\n\n![assets/geo_sf_carto_telco_33.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_33.gif)\n\n### What percent of the UK roads have LTE coverage?\n\nNow imagine you want to calculate what percentage of motorways in the UK have coverage by our network. To get the number, you can employ the `UK Open Map Data` dataset that has UK roads.\n\nRun the foillowing query in your Snowflake worksheet:\n\n```\nSELECT round(100*sum(st_length(st_intersection(coverage.geometry, roads.geo_cordinates)))/\n               (SELECT sum(st_length(geo_cordinates))\n                FROM OSM_UK.UNITED_KINGDOM.V_ROAD  roads where class = 'motorway'), 2) AS \"Coverage, %\"\nFROM OSM_UK.UNITED_KINGDOM.V_ROAD roads,\n     geolab.geography.uk_districts_coverage coverage\nWHERE st_intersects(coverage.geometry, roads.geo_cordinates)\nand class = 'motorway';\n```\n\nIt seems our LTE network covers more than 90% of the motorways. A good number to call out in a marketing campaign.\n\n\u003C!-- ------------------------ --\u003E\n## Advanced Analysis using H3\n\n\nIn this section we will cover more advanced use case where we will leverage H3 functions.\n\n### How many kilometers of UK roads have poor or no LTE coverage?\n\nAs an analyst, you might want to find out how many kilometers of motorways in the UK do not have good coverage by our network. Let's use the `UK Open Map Data` dataset and build a decay model of our signal using H3 functions.\n\nLet's first create our signal decay model for our antennas. In the following query, we will create a table with an H3 cell id for each cell tower. To get the H3 cell id, we will use the `H3_LATLNG_TO_CELL` function.\n\nRun the foillowing query in your Snowflake worksheet:\n\n```\nCREATE OR REPLACE TABLE geolab.geography.uk_lte AS\nSELECT\n    row_number() over(order by null) as id\n    , cell_range\n    , H3_LATLNG_TO_CELL(lat, lon, 9) as h3\nFROM OPENCELLID.PUBLIC.RAW_CELL_TOWERS \nwhere mcc in ('234', '235')\nand radio = 'LTE'\nORDER BY h3;\n```\n\nNow that we have our antenna geometries, we can compute the H3 cells and it's neighbors for the `CELL_RANGE` accordingly.\nFirst, we will apply the `H3_GRID_DISK` function to compute all neighboring H3 cells within a certain distance from a given H3 cell. The distance is calculated by dividing the `CELL_RANGE` by 586 meters, which represents the spacing between H3 cells at resolution 9.  Since `H3_GRID_DISK` yields an array, we must use the lateral flatten feature to cross the original rows with the array.\n\nThen we will create a decay function based on the H3 distance, so we need to determine the maximum H3 distance for each antenna. We can then group the data by H3 cell and choose the highest signal strength within that cell. As we have computed H3 neighbors for each antenna, antennas in close proximity will have generated the same H3 cell multiple times; thus, we will select the one with the strongest signal.\n\nThe model multiplies the \"starting signal strength\" of 100 by the distance between the antenna and the H3 cell, and it adds more noise as the H3 cell is further away. The signal will range from 0 (poor) to 100 (strongest).\n\nOrdering by H3 will enable CARTO to execute queries faster, which is beneficial for visualization purposes.\n\nRun the following query.\n\n```\ncreate or replace table geolab.geography.uk_lte_coverage_h3 as  \nwith h3_neighbors as (\n    select \n        id\n        , p.value::string as h3\n        , h3_grid_distance(h3, p.value::int) as h3_grid_distance\n    from geolab.geography.uk_lte,\n    table(flatten(input =\u003E h3_grid_disk(h3, ceil(least(cell_range, 6000) / 586)::int\n        ))) p\n), \nmax_distance_per_antena as (\n    select id, max(h3_grid_distance) as h3_max_distance\n    from h3_neighbors \n    group by id\n)\nselect \n    h3, \n    max((100 * pow(1 - h3_grid_distance / (h3_max_distance + 1), 2)) -- decay\n    ) * uniform(0.8, 1::float, random() -- noise\n    ) as signal_strength\nfrom h3_neighbors join max_distance_per_antena using(id)\ngroup by h3\norder by h3;\n```\n\nNow that we have created our signal decay model, let’s visualize it on CARTO. For that, we can just run the following query from the query console into a new map.\n\n```\nselect h3, signal_strength from geolab.geography.uk_lte_coverage_h3;\n```\n\n\u003E \n\u003E  Note that we don’t have a `GEOGRAPHY` on this query. This is because CARTO has native support of H3 and can show the H3 geography representation on the browser without the need to store and move the geography from the database to the browser. \n\nAs we create an H3 layer we will need to configure the layer type from the query console:\n\n![assets/geo_sf_carto_telco_26.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_26.gif)\n\nH3 layers allow us to show aggregated information at different resolutions for different zoom levels. Because of this, when we style the layer, we need to decide an aggregation method for the attribute to show, in this example we will use `SIGNAL_STRENGTH`.\n\n![assets/geo_sf_carto_telco_27.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_27.png)\n\nRemember to select a color palette of your liking and the color scale (the default is custom but we want to *Quantize* bins for this use case).\nWe can also change the relation between the zoom level and the resolution. The higher the resolution configuration, the more granularity we will see on the map but it will also take longer to load. Select resolution 5.\n\n![assets/geo_sf_carto_telco_28.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_28.gif)\n\nLet’s now use the road network from `UK Open Map Data` to see which road segments have good coverage and which do not.\nTo intersect the road layer with the H3 signal strength layer, we will split the road geometries onto its minimal road segments and compute the H3 index for the centroid of each segment. We will then join on the H3 index and keep as 'No signal' all of the road segments with no coverage or coverage of under \"30\". \n\nThen when each original road segment has an ID from 1 to n (total points in Linestring) we can create the Linestring from each point to the following point with the `ST_COLLECT` function.\n\nFinally, we use the same `H3_POINT_TO_CELL` for the selected resolution and we use the Linestring centroid for the point geography.\n\nRun the following two queries.\n\n```\ncreate or replace table GEOLAB.GEOGRAPHY.OSM_UK_NOT_COVERED AS\nwith roads as (\n    select \n        row_number() over(order by null) as geoid -- creating an ID for each original road segment\n        , geo_cordinates as geom\n    from OSM_UK.UNITED_KINGDOM.V_ROAD roads\n    where class in ('primary', 'motorway')\n    and st_dimension(geo_cordinates) = 1 -- only for the linestring or multilinestring\n)\n, segment_ids as (\n    select \n        Geoid, -- ID for each minimal road segment\n        row_number() over(partition by(geoid) order by geoid) as segment_id \n        , geom\n    from roads, \n    lateral split_to_table(repeat(',', (st_npoints(geom) - 1)::int), ',')\n)\n, segments as (select \n               geoid, \n               segment_id,\n               st_makeline(\n               st_pointn(geom, segment_id), \n               st_pointn(geom, lead(segment_id) over(partition by geoid ORDER BY segment_id))) as segment\n    from segment_ids\n    order by geoid, segment_id)\nselect \n    geoid\n    , case -- No signal if no coverage, or under 30\n        when (h3 is null or signal_strength \u003C= 30) then 'No Signal'\n        else 'OK Signal'\n    end as signal\n    , case -- This parameter we will use for visualization purposes\n        when (h3 is null or signal_strength \u003C= 30) then 2\n        else 1\n    end as road_width\n    , st_collect(segment) as geom -- This is creating the original road segments by collecting them.\nfrom segments \nleft join GEOLAB.GEOGRAPHY.UK_LTE_COVERAGE_H3\non H3_POINT_TO_CELL(st_centroid(segment), 9) = h3\nwhere segment is not null\ngroup by 1, 2, 3\norder by st_geohash(geom);\n\nALTER TABLE GEOLAB.GEOGRAPHY.OSM_UK_NOT_COVERED ADD search optimization ON GEO(geom);\n```\n\nNow that we have classified road segments by signal and no signal, we can run the following simple query to get the length of each geography in meters:\n\n```\nselect signal, sum(st_length(geom)/1000)::int as total_km\nfrom GEOLAB.GEOGRAPHY.OSM_UK_NOT_COVERED\ngroup by signal;\n```\n\nWe now know that we have 58,907 km with good coverage and 10,966 with poor/no coverage. Interestingly, that is about 15 % of the UK roads!\n\nLastly, with this layer, we can add it to our CARTO map and visualize the road segment according to the signal feature we created.\n\nFor this, we can add the layer from either:\n* Add source from → Custom Query (SQL) \n* Add source from → Data Explorer\n\n![assets/geo_sf_carto_telco_29.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_29.png)\n\nThen select your connection and the `GEOLAB.GEOGRAPHY.OSM_UK_NOT_COVERED` table.\n\n![assets/geo_sf_carto_telco_30.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_30.png)\n\nOnce we have our second layer on the map, we can click on it to style it and show the stroke color based on our “signal” column. For that create a “Custom palette” with just two colors: gray for roads with good signal and red for roads with no/poor signal.\n\n![assets/geo_sf_carto_telco_31.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/geo-analysis-telecom/geo_sf_carto_telco_31.gif)\n\n\u003E \n\u003E  You may feel that these last several queries were a bit long and repetitive, but remember that the intention of this guide was to walk you through the progression of building these longer, more complicated queries by illustrating to you what happens at each step through the progression. By understanding how functions can be combined, it helps you to understand how you can do more advanced things with Snowflake geospatial features!\n\n## Conclusion\n\nIn this guide, you acquired geospatial data from the Snowflake Marketplace, explored how the GEOGRAPHY data type and its associated formats work, created data files with geospatial data in it, loaded those files into new tables with GEOGRAPHY typed columns, and queried geospatial data using parser, constructor, transformation, calculation and H3 functions on single tables and multiple tables with joins. You then saw how newly constructed geospatial objects could be visualized using CARTO.\n\nYou are now ready to explore the larger world of Snowflake geospatial support and geospatial functions.\n\n### What we've covered\n* How to acquire a shared database from the Snowflake Marketplace\n* The GEOGRAPHY data type, its formats GeoJSON, WKT, EWKT, WKB, and EWKB, and how to switch between them.\n* How to load data files with geospatial data.\n* How to use constructors like TO_GEOGRAPHY, ST_MAKEPOINT, ST_MAKELINE.\n* How to use a transformation like ST_COLLECT.\n* How to perform measurement calculations like ST_DISTANCE and ST_LENGTH.\n* How to perform relational calculations like ST_DWITHIN and ST_WITHIN.\n* How to use Spatial grid and H3 functions like H3_POINT_TO_CELL, H3_GRID_DISK, H3_GRID_DISTANCE\n* How to use Search Optimization to speed up geospatial queries.\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c95d6c2cb1","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-32684f1bf8","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-8318a87c6c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-69edb2cc7b","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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