> ## Documentation Index > Fetch the complete documentation index at: https://private-7c7dfe99-parallel-read-in-order-multi-part.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. > Learn how to load the OpenCelliD cell tower dataset into ClickHouse Cloud, enrich it with a dictionary, query it with geo functions and visualize it with ClickHouse Agents # Geospatial analysis with the cell tower dataset export const Image = ({img, alt, size = "lg", background}) => { const normalizedSize = ["sm", "md", "lg"].includes(size) ? size : "lg"; const backgroundColor = background === "white" ? "white" : background === "black" ? "rgb(31 31 28)" : undefined; return
{alt}
; }; In this tutorial you'll explore how ClickHouse can be used for geo data using the cell tower dataset. You'll load the dataset into ClickHouse Cloud, examine its schema and run aggregation queries over tens of millions of rows. You'll then enrich the data with a dictionary, use the `pointInPolygon` function to count the towers inside Germany, and use ClickHouse Agents to visualize the result on a map.

Prerequisites

For this tutorial, you'll need: * A [ClickHouse Cloud account](https://clickhouse.cloud/signUp?loc=docs-sample-datasets-cell-towers) (\$300 in free credits when signing up) * [A ClickHouse Cloud service](/get-started/setup/cloud#1-create-a-clickhouse-service) The dataset used in this tutorial is from OpenCelliD, the world's largest Open Database of Cell Towers. As of 2021, it contains tens of millions of records about cell towers around the world with their geographical coordinates and metadata such as country code and network. 1. In ClickHouse Cloud, from the left hand menu, select your service from the dropdown 2. Select **Data sources** 3. Click the **Add sample data** card 4. Select the **Cell Towers (1.1 GB)** dataset 5. Use `default` as the destination database and click `Import dataset` You should see an entry under the **Data upload history** with a status of **success** 1. Select **SQL console** from the the left hand menu 2. Click the **+** tab next to the home icon to create a new query 3. In the SQL editor type the following query, then click **Run**: ```sql theme={null} DESCRIBE TABLE cell_towers ``` You should see the following result table: ```response theme={null} ┌─name──────────┬─type──────────────────────────────────────────────────────────────────┬ │ radio │ Enum8('' = 0, 'CDMA' = 1, 'GSM' = 2, 'LTE' = 3, 'NR' = 4, 'UMTS' = 5) │ │ mcc │ UInt16 │ │ net │ UInt16 │ │ area │ UInt16 │ │ cell │ UInt64 │ │ unit │ Int16 │ │ lon │ Float64 │ │ lat │ Float64 │ │ range │ UInt32 │ │ samples │ UInt32 │ │ changeable │ UInt8 │ │ created │ DateTime │ │ updated │ DateTime │ │ averageSignal │ UInt8 │ └───────────────┴───────────────────────────────────────────────────────────────────────┴ ``` Each entry in the table corresponds to a cell tower in an actual location on earth, given by the coordinates `lon` and `lat`. Run the following query to view the number of cell towers by type: ```sql theme={null} SELECT radio, count() AS c FROM cell_towers GROUP BY radio ORDER BY c DESC ``` ```response theme={null} ┌─radio─┬────────c─┐ │ UMTS │ 20686487 │ │ LTE │ 12101148 │ │ GSM │ 9931304 │ │ CDMA │ 556344 │ │ NR │ 867 │ └───────┴──────────┘ ``` Next, check the number of cell towers by [Mobile Country Code (MCC)](https://en.wikipedia.org/wiki/Mobile_country_code): ```sql theme={null} SELECT mcc, count() FROM cell_towers GROUP BY mcc ORDER BY count() DESC LIMIT 10 ``` ```response theme={null} ┌─mcc─┬─count()─┐ │ 310 │ 5024650 │ │ 262 │ 2622423 │ │ 250 │ 1953176 │ │ 208 │ 1891187 │ │ 724 │ 1836150 │ │ 404 │ 1729151 │ │ 234 │ 1618924 │ │ 510 │ 1353998 │ │ 440 │ 1343355 │ │ 311 │ 1332798 │ └─────┴─────────┘ ``` You can see that the countries with the most cell towers have MCCs: 310, 262 and 250. You can use a [dictionary](/concepts/features/dictionaries) to replace the numeric `mcc` column values with country names: ```sql theme={null} CREATE DICTIONARY mcc_country_dict ( MCC UInt16, Country String ) PRIMARY KEY MCC SOURCE(HTTP( url 'https://raw.githubusercontent.com/ClickHouse/examples/refs/heads/main/datasets/mcc-countries.csv' format 'CSVWithNames' )) LAYOUT(HASHED()) LIFETIME(MIN 0 MAX 0) ``` With the dictionary created, you can now use it to return the same list with the country name alongside the MCC: ```sql title="Query" theme={null} SELECT mcc, dictGet('mcc_country_dict', 'Country', mcc) AS country, count() AS cnt FROM cell_towers GROUP BY mcc ORDER BY cnt DESC LIMIT 5 ``` ```sql title="Response" theme={null} 310 United States of America 5024650 262 Germany 2622423 250 Russia 1953176 208 France 1891187 724 Brazil 1836150 ``` You might also be interested in knowing how many cell towers are within a specific geographical area. ClickHouse has many useful geo functions for this use case, such as the [`pointInPolygon`](/reference/functions/regular-functions/geo/coordinates#pointinpolygon) function. Let's imagine you're interested in seeing how many cell towers are located within Germany. Create a table called `germany` which contains a single column of type `polygon`: ```sql theme={null} CREATE TABLE germany (polygon Array(Tuple(Float64, Float64))) ORDER BY polygon; ``` Now insert the co-ordinates for the outline of (mainland) Germany into the table: ```sql expandable theme={null} INSERT INTO germany VALUES ([(10.454459535579133, 47.55573834324997), (10.890209019630618, 47.537202200721026), (10.970328786144592, 47.40002058348318), (11.272891854275713, 47.39785257163891), (11.637139099589831, 47.59425903862376), (12.20388194699433, 47.60680973608049), (12.162598450658038, 47.70112977791695), (12.256972280946059, 47.7430071703447), (12.255189497297522, 47.679294992270115), (12.440126465064338, 47.69517254727526), (12.499216429994306, 47.625017004631104), (12.781115045223203, 47.674141358936595), (12.80384823830559, 47.54988690165925), (13.04750486076074, 47.49208302661043), (13.080793954277624, 47.68702414364935), (12.905046866219948, 47.72364341860896), (13.003294582878766, 47.85031169133157), (12.758258591061178, 48.126325188057024), (13.329748843534333, 48.32352787700995), (13.508992162958009, 48.59060155142049), (13.727359451977179, 48.51300820806239), (13.839506769122124, 48.771604902544254), (13.628668330878838, 48.949206192112115), (13.402895552887514, 48.98734273487935), (13.029109282267598, 49.304348045426025), (12.655893345501568, 49.43454683436897), (12.521638213932476, 49.68676182809594), (12.40071737081007, 49.75384886863043), (12.547730388346793, 49.92034323954891), (12.201422342567128, 50.10850072273024), (12.10090036995615, 50.31802824350871), (12.184453107724778, 50.32232471771158), (12.289803626736045, 50.176930077860845), (12.334553826997592, 50.17174892257475), (12.512047935363967, 50.39725816952898), (12.819380927587815, 50.45979531680047), (12.912391784952945, 50.42372909162435), (12.937308320622549, 50.406328326990774), (12.947972824086605, 50.40427966057791), (13.370982951801807, 50.65054589707552), (13.464767584956121, 50.6018925959666), (13.526820353776372, 50.70501677580364), (13.855192645324394, 50.727109704649195), (14.387635007249855, 50.899087975929376), (14.258647344299561, 50.98753751504432), (14.301858340490071, 51.05504617034455), (14.508211954118451, 51.043154596807256), (14.59917754360879, 50.98711100167145), (14.564810750215997, 50.91835296052079), (14.650215415109358, 50.93153456746637), (14.618868455215079, 50.85771029809183), (14.793911832764934, 50.82014753807846), (15.041789401659628, 51.274035224546026), (14.949088456067443, 51.47126488866371), (14.729118627322862, 51.53143962304023), (14.7575478735356, 51.66150403168575), (14.590144372564794, 51.82100998951944), (14.759042582749032, 52.06476202734575), (14.681595447571908, 52.11665177535917), (14.715746889307525, 52.23588355048554), (14.576039609740235, 52.288366710755156), (14.534357221808364, 52.39500777668758), (14.63911057749118, 52.57299886300717), (14.12292694822014, 52.83765710679012), (14.143657774686574, 52.96136543636584), (14.348570677994417, 53.05471830950245), (14.450570288887775, 53.26224993248843), (14.30255731786599, 53.55340466159589), (14.283595832881417, 53.772302946323975), (13.822463699939874, 53.847704554621544), (13.914106243308538, 53.921857458754744), (13.743604331474785, 54.02871742866648), (13.808750796911795, 54.10045250547881), (13.7062583845169, 54.17118193160093), (13.489404453952432, 54.083845783755976), (13.142765860556096, 54.2536547023027), (13.178427336852167, 54.26927398011014), (13.132873048252634, 54.27941493335254), (13.138935061603831, 54.319290755963664), (13.393587180304166, 54.220971308479136), (13.350821423170203, 54.269835443950626), (13.57531796425269, 54.352998349893994), (13.723904396071077, 54.273407259737326), (13.766909407942876, 54.34183448887063), (13.57052592673017, 54.45765298249921), (13.661158812029612, 54.57578947060762), (13.425207789507908, 54.57776585760831), (13.377190829792994, 54.63485405846808), (13.429096601926688, 54.68488300844416), (13.250612882278745, 54.660459153766965), (13.160936899918056, 54.559083360308875), (13.28297271262062, 54.64558892578833), (13.245451284549176, 54.5578326488577), (13.368071411966127, 54.61469746081923), (13.446130971966852, 54.55160125899096), (13.511133452346996, 54.566114026241394), (13.557162122533953, 54.438539184735646), (13.345708024605699, 54.52048608046829), (13.368973991356654, 54.57891966081377), (13.304904763544073, 54.51411689467898), (13.144180806199302, 54.546965665732955), (13.270112698742082, 54.47988865533722), (13.149423426549959, 54.42813433982798), (13.260337438621946, 54.38045384347015), (13.129103578567367, 54.32114076573379), (13.015778021838287, 54.43939322591672), (12.809663608988387, 54.34360109191692), (12.668525237349854, 54.408133446301576), (12.42937122989838, 54.29356120888275), (12.436138349505256, 54.37908880570387), (12.693160014523073, 54.432254058577996), (12.926951675779264, 54.42796397405823), (12.514359690347476, 54.484348643375256), (12.100993421009605, 54.16928820758477), (11.682936883230639, 54.1532711821194), (11.552519675043015, 54.09268650258008), (11.626244382272489, 54.09015710806415), (11.452965579797706, 53.894822219270054), (11.179984660863454, 54.01516631277519), (10.898693904600066, 53.95665538983445), (10.760186530184285, 54.02460718607284), (11.09341311572274, 54.19866713068359), (11.07191809484425, 54.34224630552262), (11.125609497880077, 54.373613504659545), (11.118558230278154, 54.39442093987452), (10.706974464899702, 54.30503645926524), (10.321948337845583, 54.43574765223241), (10.174162085012256, 54.34574538594086), (10.199382745317507, 54.45556154343052), (10.134027446509492, 54.48433790277204), (9.85537006887364, 54.456283168433515), (10.027329336579669, 54.55311722278168), (10.036023455118425, 54.68841701763131), (9.909510390991784, 54.79991988959131), (9.845578708190885, 54.75603640555573), (9.591910925348998, 54.886970216702196), (9.343610061754987, 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53.88211822337735), (8.483191313228588, 53.694068058094444), (8.556142860704483, 53.526062883695204), (8.28300703264847, 53.61001675866652), (8.226915998274762, 53.52008371229357), (8.315820116805014, 53.461673297584696), (8.249573034967625, 53.398362336752484), (8.071310815641652, 53.46743230775485), (8.170974825128098, 53.54047017575806), (8.00872870406647, 53.71213492358788), (7.296326719002934, 53.680098653130415), (7.090335704828988, 53.579652246054366), (7.129998987962949, 53.532359981627906), (7.034145450951826, 53.545670230296935), (7.012918474807805, 53.34466909190513), (7.265760915633791, 53.32719714862958), (7.191115728008469, 53.31760156675898), (7.217281326671923, 53.00698383493608), (7.087323855511556, 52.84992858723456), (7.055577137075261, 52.64336989040817), (6.75264448085818, 52.64813255966993), (6.766621548046032, 52.561512582439775), (6.680868309524897, 52.553336398755846), (6.753103465152833, 52.46379697395122), (6.991802701633446, 52.467452567881764), (7.065868436279345, 52.24125898091728), (6.694652945177324, 52.06980283263465), (6.830419114950246, 51.98620651731699), (6.732607205258375, 51.898707142520664), (6.401752481791675, 51.82729228551062), (6.11804667081725, 51.901669637044165), (6.166407642798504, 51.840834718447525), (5.945029570158567, 51.82365634356546), (6.212084663676308, 51.513378407087885), (6.226129918361096, 51.36051764687198), (6.072656924877208, 51.24258730406086), (6.175415266801167, 51.15847977362273), (5.867096631104687, 51.04667907857669), (6.093924751825909, 50.921325464498636), (5.974861423316099, 50.79797295258976), (6.266895404299532, 50.64152787241346), (6.196995433692337, 50.531031950664044), (6.351259021186934, 50.48826779162533), (6.407870066950068, 50.33513396126335), (6.175687402551659, 50.235415978964284), (6.112562492565019, 50.05922877348917), (6.322636569122437, 49.838032009569645), (6.529314320899232, 49.8093868550194), (6.357852033844324, 49.573820737836854), (6.367083526083889, 49.46951204834244), (6.550815842976817, 49.425379524945185), (6.738600535586272, 49.16367513140227), (6.938108243600595, 49.222433998330416), (7.052445915346766, 49.11275784459889), (7.293176904690426, 49.11490732950972), (7.445646679250899, 49.18415102974626), (7.631201346365117, 49.05488570651022), (8.232632820348442, 48.9665714470147), (7.839740454658283, 48.64127586299071), (7.744638258948555, 48.32781093512659), (7.577585310114046, 48.12065583849619), (7.621952269681572, 47.97280642807425), (7.511871830220855, 47.696037521514484), (7.604484107155884, 47.57779110477571), (7.693596586013655, 47.60053085751309), (7.667618493298903, 47.536221133898266), (8.582542232737921, 47.59610411291453), (8.60662329822668, 47.67212011105431), (8.405559327800802, 47.67451554525627), (8.56785207691189, 47.80848124891054), (8.893659067611907, 47.6521787220704), (8.989864116335923, 47.743465936104144), (9.21772790891589, 47.66612201881617), (9.03953653098614, 47.81748197511314), (9.680775632889208, 47.542264915293686), (9.80050512701007, 47.59598448329734), (9.97089855812203, 47.54568043790192), (10.091470374358323, 47.45907615923744), (10.09978048058474, 47.35473600336394), (10.23619637995904, 47.381860769459024), (10.172036399760088, 47.27904122237459), (10.232299537493702, 47.27047759546474), (10.436472139577631, 47.38064780435275), (10.454459535579133, 47.55573834324997)]); ``` You can now check how many cell towers are located within mainland Germany using the `pointInPolygon` function: ```sql title="Query" theme={null} SELECT formatReadableQuantity(count()) FROM cell_towers WHERE pointInPolygon((lon, lat), (SELECT * FROM germany)) ``` ```sql title="Response" theme={null} 2.62 million ``` Next we'd like to visualize this data. This is an optional example run: ClickHouse Agents is model-driven beta functionality, so its plan, tool trace and output can differ between runs. [ClickHouse Agents](/products/cloud/features/ai-ml/agents) lets you easily query and explore your ClickHouse data through conversation, without writing SQL or orchestration logic yourself. The agent interprets your intent, plans steps, calls the tools you’ve configured, and returns the results to you. Click **ClickHouse agents** underneath your organization name in the bottom of the left hand menu to open ClickHouse Agents. You can message the ClickHouse agent through the [chat](/products/cloud/features/ai-ml/agents/chat). Type "Visualize cell towers within Germany on a map using the `default.cell_towers` and `default.germany` tables" and hit send. On one run, the agent carried out the following steps: ClickHouse Agent chat showing the steps taken to build a cell tower density grid for Germany * It looked up Germany's Mobile Country Code in `mcc_country_dict` and found MCC `262`. * It counted the towers with `mcc = 262` that fall inside the polygon in `germany` using `pointInPolygon`, which gave 2,588,635 towers. This is slightly fewer than the count above because the extra `mcc` filter excludes towers inside the polygon that are registered to another country. * Rather than plotting 2.6 million individual points, it grouped the towers into a 0.1° by 0.1° grid by rounding `lon` and `lat`, which produced 4,821 cells. It checked that the cell counts sum back to exactly 2,588,635, so nothing was lost or double counted in the binning. * It retrieved the polygon from `germany` and drew it as an outline over the grid, using a logarithmic color scale so that dense urban cells and sparse rural cells are both visible. Open the generated file to see the result. The bright clusters are the major metropolitan areas: Berlin, Hamburg, Munich and Frankfurt. Heatmap of cell tower density in Germany on a 0.1 degree grid with a logarithmic color scale

Next steps

In this tutorial you loaded the OpenCelliD cell tower dataset into ClickHouse Cloud, examined its schema, and ran aggregation queries over tens of millions of rows. You created a dictionary to enrich the numeric `mcc` column with country names, stored the outline of Germany as a polygon, and used `pointInPolygon` to count the towers inside it. Finally, you used ClickHouse Agents to turn that question into a density map without writing the aggregation or plotting code yourself. From here you could: * **Ask the agent follow-up questions.** Try asking how density differs between radio types such as `LTE` and `GSM`, or which cities have the most `NR` towers. Save prompts you reuse in the [prompt library](/products/cloud/features/ai-ml/agents/prompts), or build a specialised agent in the [Agent Builder](/products/cloud/features/ai-ml/agents/builder). * **Compare countries.** Insert polygons for other countries and run the same `pointInPolygon` query against each one. The dictionary you created lets you label the results with country names. * **Bucket towers with H3 or geohash.** The agent used a simple rounding grid. ClickHouse also provides [H3](/reference/functions/regular-functions/geo/h3) and [geohash](/reference/functions/regular-functions/geo/geohash) functions, which produce hierarchical cells that suit zoomable maps. * **Explore other datasets.** The [NYC taxi](/get-started/sample-datasets/nyc-taxi) dataset also contains pickup and dropoff coordinates, and the [sample datasets](/get-started/sample-datasets) index lists many more.