a ; . a ; "Facilities by type"; "PREFIX rdfs: \nPREFIX owl: \n\nSELECT ?typeLabel (COUNT(?facility) AS ?count)\nWHERE {\n # relative to the endpoint this query runs on, so the app installs under any base URI:\n # the endpoint resolves to the namespace and to the dataspace's own data\n ?type a owl:Class ;\n rdfs:label ?typeLabel ;\n rdfs:isDefinedBy \n\n SERVICE \n {\n SELECT DISTINCT ?facility ?type WHERE {\n GRAPH ?graph {\n ?facility a ?type .\n }\n }\n }\n}\nGROUP BY ?type ?typeLabel\nORDER BY DESC(?count)"; . a ; "
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Check out the rest of Copenhagen's geodata in the collection below.

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"^^ . a ; ; ; ; ; ; ; ; ; "2026-09-29T09:45:10Z"^^; ; "Geospatial and other open data about cities, imported by transforming CSV files to RDF"; "2026-09-29T09:53:42.997Z"^^; "Copenhagen"; . a ; "
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What the city publishes

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Nine facility types from Copenhagen's open data, imported from CSV and given coordinates, so every\n one of them lands on a map. The chart below counts them by type; the containers hold the records.

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"^^ . a ; "
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What are the largest parking facilities in Copenhagen? Let's find out in this SPARQL-driven chart.

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"^^ . a ; ; . a ; . a ; "Facilities by type"; ; "typeLabel"; ; "count" . a ; "Copenhagen namespace"; ; . a ; "
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Copenhagen

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Geospatial and other open data about the city of Copenhagen

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Every dataset here arrived as an open CSV file from the city's data portal, lifted to RDF by LinkedDataHub's CSV imports — one mapping query per facility type.

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This dataspace was created on LinkedDataHub using the low code paradigm: no programming was required, only scripts that\n invoke LinkedDataHub's CLI commands.

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