--- slug: /en/getting-started/example-datasets/cell-towers sidebar_label: Cell Towers sidebar_position: 3 title: "Cell Towers" --- import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import CodeBlock from '@theme/CodeBlock'; import ActionsMenu from '@site/docs/en/_snippets/_service_actions_menu.md'; import SQLConsoleDetail from '@site/docs/en/_snippets/_launch_sql_console.md'; import SupersetDocker from '@site/docs/en/_snippets/_add_superset_detail.md'; ## Goal In this guide you will learn how to: - load the OpenCellID data in Clickhouse - connect Apache Superset to ClickHouse - build a dashboard based on data available in the dataset - how to choose data types for your table fields - how to choose a primary key Here is a preview of the dashboard created in this guide: ![Dashboard of cell towers by radio type in mcc 204](@site/docs/en/getting-started/example-datasets/images/superset-cell-tower-dashboard.png) ## Get the Dataset {#get-the-dataset} This dataset is from [OpenCellid](https://www.opencellid.org/) - The world's largest Open Database of Cell Towers. As of 2021, it contains more than 40 million records about cell towers (GSM, LTE, UMTS, etc.) around the world with their geographical coordinates and metadata (country code, network, etc). OpenCelliD Project is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, and we redistribute a snapshot of this dataset under the terms of the same license. The up-to-date version of the dataset is available to download after sign in. ClickHouse Cloud provides an easy-button for uploading this dataset from S3. Log in to your ClickHouse Cloud organization, or create a free trial at [ClickHouse.cloud](https://clickhouse.cloud). Choose the **Cell Towers** dataset from the **Sample data** tab, and **Load data**: ![Load cell towers dataset](@site/docs/en/_snippets/images/cloud-load-data-sample.png) Examine the schema of the cell_towers table: ```sql DESCRIBE TABLE cell_towers ``` 1. Download the snapshot of the dataset from February 2021: [cell_towers.csv.xz](https://datasets.clickhouse.com/cell_towers.csv.xz) (729 MB). 2. Validate the integrity (optional step): ```bash md5sum cell_towers.csv.xz ``` ```response 8cf986f4a0d9f12c6f384a0e9192c908 cell_towers.csv.xz ``` 3. Decompress it with the following command: ```bash xz -d cell_towers.csv.xz ``` 4. Create a table: ```sql CREATE TABLE cell_towers ( 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 ) ENGINE = MergeTree ORDER BY (radio, mcc, net, created); ``` 5. Insert the dataset: ```bash clickhouse-client --query "INSERT INTO cell_towers FORMAT CSVWithNames" < cell_towers.csv ``` ## Example queries {#examples} 1. A number of cell towers by type: ```sql SELECT radio, count() AS c FROM cell_towers GROUP BY radio ORDER BY c DESC ``` ```response ┌─radio─┬────────c─┐ │ UMTS │ 20686487 │ │ LTE │ 12101148 │ │ GSM │ 9931312 │ │ CDMA │ 556344 │ │ NR │ 867 │ └───────┴──────────┘ 5 rows in set. Elapsed: 0.011 sec. Processed 43.28 million rows, 43.28 MB (3.83 billion rows/s., 3.83 GB/s.) ``` 2. Cell towers by [mobile country code (MCC)](https://en.wikipedia.org/wiki/Mobile_country_code): ```sql SELECT mcc, count() FROM cell_towers GROUP BY mcc ORDER BY count() DESC LIMIT 10 ``` ```response ┌─mcc─┬─count()─┐ │ 310 │ 5024650 │ │ 262 │ 2622423 │ │ 250 │ 1953176 │ │ 208 │ 1891187 │ │ 724 │ 1836150 │ │ 404 │ 1729151 │ │ 234 │ 1618924 │ │ 510 │ 1353998 │ │ 440 │ 1343355 │ │ 311 │ 1332798 │ └─────┴─────────┘ 10 rows in set. Elapsed: 0.019 sec. Processed 43.28 million rows, 86.55 MB (2.33 billion rows/s., 4.65 GB/s.) ``` Based on the above query and the [MCC list](https://en.wikipedia.org/wiki/Mobile_country_code), the countries with the most cell towers are: the USA, Germany, and Russia. You may want to create an [External Dictionary](../../sql-reference/dictionaries/external-dictionaries/external-dicts.md) in ClickHouse to decode these values. ## Use case: Incorporate geo data {#use-case} Using the [`pointInPolygon`](/docs/en/sql-reference/functions/geo/coordinates.md/#pointinpolygon) function. 1. Create a table where we will store polygons: ```sql CREATE TABLE moscow (polygon Array(Tuple(Float64, Float64))) ORDER BY polygon; ``` ```sql CREATE TEMPORARY TABLE moscow (polygon Array(Tuple(Float64, Float64))); ``` 2. This is a rough shape of Moscow (without "new Moscow"): ```sql INSERT INTO moscow VALUES ([(37.84172564285271, 55.78000432402266), (37.8381207618713, 55.775874525970494), (37.83979446823122, 55.775626746008065), (37.84243326983639, 55.77446586811748), (37.84262672750849, 55.771974101091104), (37.84153238623039, 55.77114545193181), (37.841124690460184, 55.76722010265554), (37.84239076983644, 55.76654891107098), (37.842283558197025, 55.76258709833121), (37.8421759312134, 55.758073999993734), (37.84198330422974, 55.75381499999371), (37.8416827275085, 55.749277102484484), (37.84157576190186, 55.74794544108413), (37.83897929098507, 55.74525257875241), (37.83739676451868, 55.74404373042019), (37.838732481460525, 55.74298009816793), (37.841183997352545, 55.743060321833575), (37.84097476190185, 55.73938799999373), (37.84048155819702, 55.73570799999372), (37.840095812164286, 55.73228210777237), (37.83983814285274, 55.73080491981639), (37.83846476321406, 55.729799917464675), (37.83835745269769, 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(37.392680272491454, 55.84502158126453), (37.39241188227847, 55.84659117913199), (37.392529730163616, 55.84816071336481), (37.39486835714723, 55.85288092980303), (37.39873052645878, 55.859893456073635), (37.40272161111449, 55.86441833633205), (37.40697072750854, 55.867579567544375), (37.410007082016016, 55.868369880337), (37.4120992989502, 55.86920843741314), (37.412668021163924, 55.87055369615854), (37.41482461111453, 55.87170587948249), (37.41862266137694, 55.873183961039565), (37.42413732540892, 55.874879126654704), (37.4312182698669, 55.875614937236705), (37.43111093783558, 55.8762723478417), (37.43332105622856, 55.87706546369396), (37.43385747619623, 55.87790681284802), (37.441303050262405, 55.88027084462084), (37.44747234260555, 55.87942070143253), (37.44716141796871, 55.88072960917233), (37.44769797085568, 55.88121221323979), (37.45204320500181, 55.882080694420715), (37.45673176190186, 55.882346110794586), (37.463383999999984, 55.88252729504517), (37.46682797486874, 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55.88037213862385), (37.73394330422971, 55.877941504088696), (37.745339592590376, 55.87208120378722), (37.75525267724611, 55.86703807949492), (37.76919976190188, 55.859821640197474), (37.827835219574, 55.82962968399116), (37.83341438888553, 55.82575289922351), (37.83652584655761, 55.82188784027888), (37.83809213491821, 55.81612575504693), (37.83605359521481, 55.81460347077685), (37.83632178569025, 55.81276696067908), (37.838623105812026, 55.811486181656385), (37.83912198147584, 55.807329380532785), (37.839079078033414, 55.80510270463816), (37.83965844708251, 55.79940712529036), (37.840581150787344, 55.79131399999368), (37.84172564285271, 55.78000432402266)]); ``` 3. Check how many cell towers are in Moscow: ```sql SELECT count() FROM cell_towers WHERE pointInPolygon((lon, lat), (SELECT * FROM moscow)) ``` ```response ┌─count()─┐ │ 310463 │ └─────────┘ 1 rows in set. Elapsed: 0.067 sec. Processed 43.28 million rows, 692.42 MB (645.83 million rows/s., 10.33 GB/s.) ``` ## Review of the schema This dataset primarily provides the location (Longitude and Latitude) and radio types at mobile cellular towers worldwide. The column descriptions can be found in the [community forum](https://community.opencellid.org/t/documenting-the-columns-in-the-downloadable-cells-database-csv/186). The columns used in the visualizations that will be built are described below Here is a description of the columns taken from the OpenCellID forum: | Column | Description | |--------------|--------------------------------------------------------| | radio | Technology generation: CDMA, GSM, UMTS, 5G NR | | mcc | Mobile Country Code: `204` is The Netherlands | | lon | Longitude: With Latitude, approximate tower location | | lat | Latitude: With Longitude, approximate tower location | :::tip mcc To find your MCC check [Mobile network codes](https://en.wikipedia.org/wiki/Mobile_country_code), and use the first three digits in the `MCC / MNC` column. ::: ## Build visualizations with Apache Superset Superset is easy to run from Docker. If you already have Superset running, all you need to do is add ClickHouse Connect with `pip install clickhouse-connect`. If you need to install Superset open the **Launch Apache Superset in Docker** directly below. ## Build a dashboard 1. foo ![Dashboard of cell towers by radio type in mcc 204](@site/docs/en/getting-started/example-datasets/images/superset-cell-tower-dashboard.png) 1. foo ![Add ClickHouse as a Superset datasource](@site/docs/en/getting-started/example-datasets/images/superset-connect-a-database.png) 1. foo ![Create a map in Superset](@site/docs/en/getting-started/example-datasets/images/superset-create-map.png) 1. foo ![Specify longitude and latitude fields](@site/docs/en/getting-started/example-datasets/images/superset-lon-lat.png) 1. foo ![Filter on MCC 204](@site/docs/en/getting-started/example-datasets/images/superset-mcc-204.png) 1. foo ![Filter on radio = UMTS](@site/docs/en/getting-started/example-datasets/images/superset-radio-umts.png) 1. foo ![Chart for UMTS radios in MCC 204](@site/docs/en/getting-started/example-datasets/images/superset-umts-netherlands.png) The data is also available for interactive queries in the [Playground](https://play.clickhouse.com/play?user=play), [example](https://play.clickhouse.com/play?user=play#U0VMRUNUIG1jYywgY291bnQoKSBGUk9NIGNlbGxfdG93ZXJzIEdST1VQIEJZIG1jYyBPUkRFUiBCWSBjb3VudCgpIERFU0M=). Although you cannot create temporary tables there.