Dataset - opendata.hessen.de

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SPARQL

nach Themen

[{"children": [], "color": "MediumVioletRed", "highlighted": false, "icon": "fa fa-users", "id": "c51346dd-559a-413f-8f68-9aef3fbfd395", "name": "bevolkerung-und-gesellschaft", "package_count": 14, "title": "Bev\u00f6lkerung und Gesellschaft"}, {"children": [], "highlighted": false, "id": "b345842a-23c8-4855-bbf2-4be85039a3f7", "name": "bildung-kultur-und-sport", "title": "Bildung, Kultur und Sport"}, {"children": [], "highlighted": false, "id": "b5b42983-23a9-402f-b78c-dd0654538486", "name": "energie", "title": "Energie"}, {"children": [], "color": "Darkred", "highlighted": false, "icon": "fa fa-heartbeat", "id": "c51f7f61-5c5a-406e-b139-453878b112b7", "name": "gesundheit", "package_count": 4, "title": "Gesundheit"}, {"children": [], "highlighted": false, "id": "71d3dfc9-8f47-4926-9cff-3b19b11a2404", "name": "internationale-themen", "title": "Internationale Themen"}, {"children": [], "color": "DarkRed", "highlighted": false, "icon": "fa fa-balance-scale", "id": "e2d9e3b8-2e2c-4ab4-b2d4-867b4539157b", "name": "justiz-rechtssystem-und-offentliche-sicherheit", "package_count": 3, "title": "Justiz, Rechtssystem und \u00f6ffentliche Sicherheit"}, {"children": [], "highlighted": false, "id": "785c8121-9464-4bd2-a9ae-7b9a6990b201", "name": "landwirtschaft-fischerei-forstwirtschaft-und-nahrungsmittel", "title": "Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel"}, {"children": [], "color": "CadetBlue", "highlighted": false, "icon": "fa fa-bank", "id": "b739013c-1f67-4a6a-82d0-6b894f64e0d7", "name": "regierung-und-offentlicher-sektor", "package_count": 17, "title": "Regierung und \u00f6ffentlicher Sektor"}, {"children": [], "color": "DarkGoldenRod", "highlighted": false, "icon": "fa fa-city", "id": "2b667098-7c6a-4084-9497-fd0107aea558", "name": "regionen-und-stadte", "package_count": 1, "title": "Regionen und St\u00e4dte"}, {"children": [], "color": "DarkGreen", "highlighted": false, "icon": "fa fa-leaf", "id": "d06ae5e3-bfbc-4923-adcc-dc492f399458", "name": "umwelt", "package_count": 40, "title": "Umwelt"}, {"children": [], "highlighted": false, "id": "224db1bd-e82e-462c-b955-a0c79986cbef", "name": "verkehr", "title": "Verkehr"}, {"children": [], "highlighted": false, "id": "c215406d-d866-4e00-b0fb-5ebacaa2d626", "name": "vorlaufige-daten", "title": "Vorl\u00e4ufige Daten"}, {"children": [], "color": "Teal", "highlighted": false, "icon": "fa fa-line-chart", "id": "54917440-dd7c-45d6-83a7-f03099534059", "name": "wirtschaft-und-finanzen", "package_count": 4, "title": "Wirtschaft und Finanzen"}, {"children": [], "color": "DarkTurquoise", "highlighted": false, "icon": "fa fa-microscope", "id": "110f547b-8ba7-4062-bef3-f920cc7d0572", "name": "wissenschaft-und-technologie", "package_count": 5, "title": "Wissenschaft und Technologie"}]

nach Schlagwörtern

[{"active": true, "count": 125, "display_name": "Hessen", "id": "ec342697-2c1f-4734-a967-9fae83190feb", "name": "Hessen", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 58, "display_name": "Bundesl\u00e4nder", "id": "c89fa8ad-3310-4cdd-8912-207a8ab7b38a", "name": "Bundesl\u00e4nder", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 33, "display_name": "Deutschland", "id": "59d12a29-faf7-4fd4-aa8a-9df52474783a", "name": "Deutschland", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 33, "display_name": "Stadtstaaten", "id": "6cba4ee8-3a9d-4256-b01d-959977986ca6", "name": "Stadtstaaten", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 33, "display_name": "Summe der L\u00e4nder", "id": "92f29afd-7d32-46ab-a7ed-6040d8ce23b1", "name": "Summe der L\u00e4nder", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 31, "display_name": "Boden", "id": "5fb13590-7897-4861-8ff5-271edcbb4441", "name": "Boden", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 30, "display_name": "Regional", "id": "e174dd36-3f03-4b1f-81c2-cb78bc1c0e9c", "name": "Regional", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 25, "display_name": "Bodenfl\u00e4chendaten", "id": "6d0a3158-edc2-46e1-9ce2-18b09ffc1b3b", "name": "Bodenfl\u00e4chendaten", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 16, "display_name": "AdVMIS", "id": "84959740-44f9-4c8d-a6ea-e3eb3dd52a53", "name": "AdVMIS", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 15, "display_name": "BFD50", "id": "eab813e1-f0cc-4627-a36a-529d37fbe236", "name": "BFD50", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 15, "display_name": "Umwelt\u00f6konomische Gesamtrechnungen der L\u00e4nder - Energierechnungen", "id": "e016e8f7-9a49-48f7-88a4-170c6cf362c3", "name": "Umwelt\u00f6konomische Gesamtrechnungen der L\u00e4nder - Energierechnungen", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 14, "display_name": "Sozialberichterstattung in der amtlichen Statistik", "id": "e7f326de-a5a1-4de8-b41f-4c23d5942e39", "name": "Sozialberichterstattung in der amtlichen Statistik", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 12, "display_name": "Armutsgef\u00e4hrdungsquote gemessen am Bundesmedian", "id": "4278eb81-2029-49c0-8484-306a8f850c56", "name": "Armutsgef\u00e4hrdungsquote gemessen am Bundesmedian", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 12, "display_name": "Armutsgef\u00e4hrdungsquote gemessen am Landesmedian", "id": "2cb0a612-c4b2-47e9-ad01-09edab561a0a", "name": "Armutsgef\u00e4hrdungsquote gemessen am Landesmedian", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 10, "display_name": "BFD5L", "id": "7a6319da-1da2-4a7a-89b8-07f7e19fc868", "name": "BFD5L", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 10, "display_name": "Umwelt\u00f6konomische Gesamtrechnungen der L\u00e4nder - Gase", "id": "d416617d-6435-4c93-bcf5-276fdfa751ed", "name": "Umwelt\u00f6konomische Gesamtrechnungen der L\u00e4nder - Gase", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 9, "display_name": "Bodenschutz", "id": "6891d940-bdda-4065-bb5b-4a223acc127c", "name": "Bodenschutz", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 9, "display_name": "Geschlecht", "id": "c81e99a9-6d05-42d2-8ff8-ee1b48a9e2ef", "name": "Geschlecht", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 9, "display_name": "m\u00e4nnlich", "id": "2b38bf58-b5b3-4e23-aef2-e8efb0969782", "name": "m\u00e4nnlich", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 9, "display_name": "Wasser", "id": "6a90026b-2557-4a49-883c-dbba1410e015", "name": "Wasser", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 9, "display_name": "weiblich", "id": "ddd3f478-ec9f-4e3e-9f96-b905097d34e4", "name": "weiblich", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 7, "display_name": "ATKIS", "id": "cad42df6-7147-4de7-b7f3-0c93c41396e1", "name": "ATKIS", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 7, "display_name": "INSPIRE-Datenmodell", "id": "de6e3bcd-c4ab-4f61-a142-b0729e401412", "name": "INSPIRE-Datenmodell", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 7, "display_name": "INSPIRE-Thema SO", "id": "2551616e-8b8d-494b-bf66-423c57354d77", "name": "INSPIRE-Thema SO", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 6, "display_name": "Gr\u00fcnlandsch\u00e4tzung", "id": "cfcf58ff-c70f-4b92-9f0b-85b57bdf6e1e", "name": "Gr\u00fcnlandsch\u00e4tzung", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 6, "display_name": "Wirtschaft A-T", "id": "9744cf17-b6af-4130-9667-c01c218bfa2e", "name": "Wirtschaft A-T", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 5, "display_name": "ATKIS_-Basis-DLM", "id": "db60880e-dc01-4ea7-9f3f-96f6154fb916", "name": "ATKIS_-Basis-DLM", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 5, "display_name": "Gebiete mit naturbedingten Risiken", "id": "79bc6453-68d7-402f-8eda-73bfc2fc013c", "name": "Gebiete mit naturbedingten Risiken", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 5, "display_name": "Hydrologie", "id": "edc5ee6b-37df-4f1e-a0a3-f70fe9b58802", "name": "Hydrologie", "vocabulary_id": null, "vocabulary_name": null}, {"active": false, "count": 5, "display_name": "Hydrology", "id": "42e34d2b-af53-4098-abd6-afd0550ebc31", "name": "Hydrology", "vocabulary_id": null, "vocabulary_name": null}]

nach Regionen

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"wikibase": "", "wikipedia": "", "xzufi": "8999160"}, {"ags": "06436", "approval_status": "approved", "created": "2025-02-17T15:28:23.450275", "description": "", "display_name": "Main-Taunus-Kreis", "groups": [{"capacity": "public", "name": "reg-bez-darmstadt"}], "id": "09fa9689-bf54-4e16-9e4e-5afffbfb29aa", "image_display_url": "", "image_url": "", "is_organization": false, "name": "main-taunus-kreis", "num_followers": 0, "package_count": 2, "spatial": {"coordinates": [[[8.317837748110662, 49.99765967044057], [8.601755193894842, 49.99765967044057], [8.601755193894842, 50.20380596848262], [8.317837748110662, 50.20380596848262], [8.317837748110662, 49.99765967044057]]], "type": "Polygon"}, "state": "active", "title": "Main-Taunus-Kreis", "type": "region", "uri": "http://dcat-ap.de/def/politicalGeocoding/districtKey/06436", "wikibase": "Q7909", "wikipedia": "", "xzufi": "9008200"}, {"ags": "06534", "approval_status": "approved", "created": "2025-02-17T15:28:23.870921", "description": "", 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[{"approval_status": "approved", "count": 5, "created": "2025-05-14T09:25:35.978599", "description": "", "display_name": "2000", "extras": [], "groups": [], "id": "c9f4eb23-1bca-434b-9055-267f0f09a08e", "image_display_url": "", "image_url": "", "is_organization": false, "name": "2000", "num_followers": 0, "package_count": 47, "state": "active", "title": "2000", "type": "time"}, {"approval_status": "approved", "count": 5, "created": "2025-05-14T09:25:36.328648", "description": "", "display_name": "2001", "extras": [], "groups": [], "id": "4ee5a83b-f350-43eb-a419-11f943368251", "image_display_url": "", "image_url": "", "is_organization": false, "name": "2001", "num_followers": 0, "package_count": 51, "state": "active", "title": "2001", "type": "time"}, {"approval_status": "approved", "count": 5, "created": "2025-05-14T09:25:33.854993", "description": "", "display_name": "2002", "extras": [], "groups": [], "id": "121435d1-390b-43a2-be14-573a0606ae72", "image_display_url": "", "image_url": 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Räumliche Verteilung auf einen Blick

Die Karte zeigt, wie sich die aktuell gefundenen Datensätze über Hessen verteilen: je dunkler ein Gebiet, umso mehr Datensätze liegen dort vor. Dabei überlagern sich Landkreise (rot) mit Städten und Gemeinden (blau).

Datensätze, die ganze Regierungsbezirke oder ganz Hessen umfassen (auch dann, wenn sich die darin enthaltenen Daten selbst bis auf Gemeindeebene aufgliedern), werden hier weggelassen, weil ihre Anzahl keine regionalen Unterschiede ausmachen.

Mit einem Klick auf eine Region fügen sie diese dem Datensatz-Filter hinzu. Zugleich wird damit die darunterliegende Ebene aufgedeckt, d.h. ein Klick auf einen Landkreis zeigt seine Gemeinden.

Tipps und Tricks

Kreisfreie Städte werden wie Städte und Gemeinden behandelt. Sie erscheinen in blau, auf der Ebene der Landkreise (rot) sind ihre Flächen leer.
Mehrere Filter sind UND-verknüpft. Wenn Sie etwa Darmstadt und Kassel als Filter hinzufügen, dann suchen Sie nach Datensätzen, die Daten sowohl über Darmstadt als auch über Kassel enthalten (also nicht nach solchen, die entweder nur Darmstadt oder nur Kassel enthalten).
Die Filter von Landkreisen und Gemeinden wirken getrennt. Wenn Sie also nach einem Landkreis filtern, sind nicht automatisch die Datensätze aller kreisangehörigen Gemeinden gleich mit enthalten.
Wenn sie nur nach einer Gemeinde filtern wollen, dann klicken sie zunächst auf ihren Landkreis - und haben ihn dadurch sogleich als Filter hinzugefügt. Klicken Sie dann erst auf die Gemeinde und aktivieren Sie sie somit ebenfalls als Filter; danach entfernen Sie wieder den Landkreis in der Reihe der aktiven Filter durch Klick auf das x.
Diese Karte dient nur der schnellen Orientierung. Wenn Sie Datensätze gezielt über Regionen und Verwaltungsebenen suchen, dann wechseln Sie zum Dashboard "Regionen". Dort können Sie auch alle Ebenen einzeln ein- und ausblenden und sehen eine Legende über die zahlenmäßige Verteilung.
Region ist nicht identisch mit Herausgeber. Daten über eine Gemeinde können, müssen aber nicht von dieser Gebietskörperschaft stammen, sondern ebenso gut von einer ganz anderen Behörde.
Die Anzahl der Datensätze sagt noch nichts aus über die Menge an Daten, die darin enthalten ist. So kann z.B. ein WMS-Dienst aus lediglich einem Layer bestehen oder aber zahlreiche Layer enthalten zu ganz unterschiedlichen Aspekten.

Dataset · Hessisches Landesamt für Bodenmanagement und Geoinformation

Präsentationsgraphik PG 50

Die Präsentationsgraphik im Maßstab 1:50.000 wird automatisiert aus dem modellgeneralisierten DLM50 abgeleitet und ist damit ein Folgeprodukt des tagesaktuellen ATKIS®-Basis-...
- GML
- RDF
- HTML
- WMS
Dataset · Hessisches Landesamt für Bodenmanagement und Geoinformation

Präsentationsgraphik PG 25

Die Präsentationsgraphik im Maßstab 1:25.000 wird automatisiert aus dem tagesaktuellen ATKIS®-Basis-DLM abgeleitet. Das in der Graphik dargestellte Schriftgut wird aus den...
- GML
- RDF
- HTML
- WMS
Dataset · Hessisches Landesamt für Bodenmanagement und Geoinformation

Präsentationsgraphik PG 100

Die Präsentationsgraphik im Maßstab 1:100.000 wird automatisiert aus dem modellgeneralisierten DLM50 abgeleitet und ist damit ein Folgeprodukt des tagesaktuellen ATKIS®-Basis-...
- GML
- RDF
- HTML
- WMS
Dataset · Hessisches Landesamt für Bodenmanagement und Geoinformation

Präsentationsgraphik PG 10

Die Präsentationsgraphik im Maßstab 1:10.000 wird automatisiert aus dem aktuellen ATKIS®-Basis-DLM abgeleitet. Die in der Graphik dargestellten Straßennamen stammen aus dem...
- GML
- RDF
- HTML
- WMS
Dataset · Hessisches Landesamt für Bodenmanagement und Geoinformation

Präsentationsgraphik PG 4

Die Präsentationsgraphik im Maßstab 1:4.000 wird automatisiert aus dem aktuellen ATKIS®-Basis-DLM abgeleitet. Die in der Graphik vollständig dargestellten Straßennamen stammen...
- GML
- RDF
- HTML
- WMS
Dataset · Hessisches Landesamt für Bodenmanagement und Geoinformation

ATKIS - DLM50

Das digitale Landschaftsmodell beschreibt die topographischen Objekte der Landschaft im Vektorformat. Die Objekte werden einer bestimmten Objektart zugeordnet und mit ihrer...
- GML
- RDF
- HTML
- WMS
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Haushaltstyp (8) - Jahr -regionale Tiefe:...

Armutsgefährdungsquote nach Haushaltstyp (8) - Jahr -regionale Tiefe: Bundesländer (2005-2019)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Personentyp (2) undBildungsstand (3) - Jahr -...

Armutsgefährdungsquote nach Personentyp (2) undBildungsstand (3) - Jahr - regionale Tiefe: Bundesländer(2005-2019)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Erwerbsstatus - Jahr - regionaleTiefe:...

Armutsgefährdungsquote nach Erwerbsstatus - Jahr - regionaleTiefe: Bundesländer (2005-2019)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Staatsangehörigkeit undMigrationshintergrund -...

Armutsgefährdungsquote nach Staatsangehörigkeit undMigrationshintergrund - Jahr - regionale Tiefe:Bundesländer (2005-2019)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Geschlecht und Altersgruppen (5)(Landesmedian) -...

Armutsgefährdungsquote nach Geschlecht und Altersgruppen (5)(Landesmedian) - Jahr - regionale Tiefe: Bundesländer(2005-2019)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Geschlecht und Altersgruppen (5)(Bundesmedian) -...

Armutsgefährdungsquote nach Geschlecht und Altersgruppen (5)(Bundesmedian) - Jahr - regionale Tiefe: Bundesländer(2005-2019)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Geschlecht in Deutschland - Jahr- regionale...

Armutsgefährdungsquote nach Geschlecht in Deutschland - Jahr- regionale Tiefe: Bundesländer (2005-2019)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Haushaltstyp (8) - Jahr -regionale Tiefe:...

Armutsgefährdungsquote nach Haushaltstyp (8) - Jahr -regionale Tiefe: Bundesländer (ab 2020)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Personentyp (2) undBildungsstand (3) - Jahr -...

Armutsgefährdungsquote nach Personentyp (2) undBildungsstand (3) - Jahr - regionale Tiefe: Bundesländer (ab2020)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Erwerbsstatus (5) - Jahr -regionaleTiefe:...

Armutsgefährdungsquote nach Erwerbsstatus (5) - Jahr -regionaleTiefe: Bundesländer (ab 2020)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Staatsangehörigkeit undMigrationshintergrund -...

Armutsgefährdungsquote nach Staatsangehörigkeit undMigrationshintergrund - Jahr - regionale Tiefe:Bundesländer (ab 2020)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Geschlecht und Altersgruppen (5)(Landesmedian) -...

Armutsgefährdungsquote nach Geschlecht und Altersgruppen (5)(Landesmedian) - Jahr - regionale Tiefe: Bundesländer (ab2020)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Geschlecht und Altersgruppen (5)(Bundesmedian) -...

Armutsgefährdungsquote nach Geschlecht und Altersgruppen (5)(Bundesmedian) - Jahr - regionale Tiefe: Bundesländer (ab2020)
- TXT
- CSV
- JSON
- REST
Dataset · Hessisches Statistisches Landesamt

Armutsgefährdungsquote nach Geschlecht in Deutschland - Jahr- regionale...

Armutsgefährdungsquote nach Geschlecht in Deutschland - Jahr- regionale Tiefe: Bundesländer (ab 2020)
- TXT
- CSV
- JSON
- REST

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125 datasets found

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