{"product_id":"denmark-e-on-ev","title":"E.ON EV Charging Station Locations Dataset – Denmark","description":"\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\/\",\n  \"@type\": \"Dataset\",\n  \"name\": \"E.ON EV Charging Station Locations Dataset – Denmark\",\n  \"description\": \"Download a geocoded dataset containing 978 E.ON ev charging station locations across Denmark. Includes addresses, administrative areas, and WGS84 latitude\/longitude coordinates in CSV format for GIS, market research, logistics, site selection, and location intelligence applications. Updated: 15 July 2026.\",\n  \"url\": \"https:\/\/geolocet.com\/products\/denmark-e-on-ev\",\n  \"isAccessibleForFree\": false,\n  \"creator\": {\n    \"@type\": \"Organization\",\n    \"sameAs\": \"https:\/\/geolocet.com\",\n    \"name\": \"Geolocet\"\n  },\n  \"publisher\": {\n    \"@type\": \"Organization\",\n    \"name\": \"Geolocet\",\n    \"sameAs\": \"https:\/\/geolocet.com\",\n    \"url\": \"https:\/\/geolocet.com\"\n  },\n  \"dateModified\": \"2026-07-15\",\n  \"lastVerified\": \"2026-07-15\",\n  \"spatialCoverage\": {\n    \"@type\": \"Place\",\n    \"name\": \"Denmark\"\n  },\n  \"size\": \"978 locations\",\n  \"measurementTechnique\": \"Web scraping, geocoding, validation, and standardization\",\n  \"license\": \"https:\/\/geolocet.com\/pages\/terms-of-use\",\n  \"distribution\": {\n    \"@type\": \"DataDownload\",\n    \"encodingFormat\": \"text\/csv\"\n  },\n  \"hasPart\": {\n    \"@type\": \"Dataset\",\n    \"name\": \"Free Sample: E.ON EV Charging Stations Locations Dataset – Denmark\",\n    \"description\": \"A subset of the full dataset demonstrating data structure, geospatial precision, and column headers.\",\n    \"isAccessibleForFree\": true,\n    \"distribution\": {\n      \"@type\": \"DataDownload\",\n      \"encodingFormat\": \"text\/csv\",\n      \"contentUrl\": \"https:\/\/geolocet.com\/products\/denmark-e-on-ev\"\n    }\n  },\n  \"variableMeasured\": [\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"GUID\",\n      \"description\": \"Unique global identifier for the location record.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Title\",\n      \"description\": \"The official name, brand, or title of the store\/location.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Latitude\",\n      \"description\": \"Precise WGS84 latitude coordinate.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Longitude\",\n      \"description\": \"Precise WGS84 longitude coordinate.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Street No\",\n      \"description\": \"The specific building or street number.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Street\",\n      \"description\": \"The street name where the location is situated, excluding the building number.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"City\",\n      \"description\": \"City, municipality, or primary settlement area.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Kommune\",\n      \"description\": \"Specific data attribute detailing the kommune of the location.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Region\",\n      \"description\": \"Macro-administrative boundary, such as a large geographic region, state, or province.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Population\",\n      \"description\": \"Specific data attribute detailing the population of the location.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Postal Code\",\n      \"description\": \"Zip code or postal routing code for the region.\"\n    },\n    {\n      \"@type\": \"PropertyValue\",\n      \"name\": \"Address\",\n      \"description\": \"Full, standardized street address of the location.\"\n    }\n  ],\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"price\": \"72.00\",\n    \"priceCurrency\": \"EUR\",\n    \"seller\": {\n      \"@type\": \"Organization\",\n      \"sameAs\": \"https:\/\/geolocet.com\",\n      \"name\": \"Geolocet\"\n    },\n    \"description\": \"Full dataset. Digital product for instant download. No shipping required.\"\n  },\n  \"keywords\": [\n    \"E.ON\",\n    \"Denmark\",\n    \"EV Charging Stations\",\n    \"geospatial data\",\n    \"location data\",\n    \"spatial retail analytics\",\n    \"retail site selection\",\n    \"forecourt geospatial dataset\",\n    \"GIS-ready retail location data\",\n    \"geocoded retail locations\"\n  ],\n  \"alternateName\": [\n    \"E.ON EV Charging Station Denmark address list\",\n    \"Geocoded E.ON locations - Denmark CSV\",\n    \"E.ON Denmark geospatial dataset\",\n    \"E.ON EV Charging Station locations Denmark\"\n  ],\n  \"temporalCoverage\": \"2026-07-15\",\n  \"mentions\": {\n    \"@type\": \"Organization\",\n    \"name\": \"E.ON\",\n    \"url\": \"https:\/\/www.eon.dk\",\n    \"alternateName\": \"E.ON Drive \/ E.ON Drive Infrastructure (EDRI)\",\n    \"sameAs\": \"https:\/\/en.wikipedia.org\/wiki\/E.ON\"\n  }\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"BreadcrumbList\",\n  \"itemListElement\": [\n    {\n      \"@type\": \"ListItem\",\n      \"position\": 1,\n      \"name\": \"Home\",\n      \"item\": \"https:\/\/geolocet.com\"\n    },\n    {\n      \"@type\": \"ListItem\",\n      \"position\": 2,\n      \"name\": \"Brands Locations\",\n      \"item\": \"https:\/\/geolocet.com\/collections\/brands-locations\"\n    },\n    {\n      \"@type\": \"ListItem\",\n      \"position\": 3,\n      \"name\": \"Denmark\",\n      \"item\": \"https:\/\/geolocet.com\/pages\/denmark\"\n    },\n    {\n      \"@type\": \"ListItem\",\n      \"position\": 4,\n      \"name\": \"E.ON EV Charging Stations Dataset\",\n      \"item\": \"https:\/\/geolocet.com\/products\/denmark-e-on-ev\"\n    }\n  ]\n}\n\u003c\/script\u003e\n\u003cnav aria-label=\"Page sections\" style=\"background:#f8f9fa; border:1px solid #ddd; border-radius:4px; padding:10px 14px; margin-bottom:18px;\"\u003e\u003cstrong style=\"font-size:0.85em; margin-right:10px;\"\u003eQuick links:\u003c\/strong\u003e\u003cul style=\"display:inline; list-style:none; margin:0; padding:0;\"\u003e\n\u003cli style=\"display:inline; margin-right:12px;\"\u003e\u003ca href=\"#dataset-summary\" style=\"font-size:0.85em; color:#2c6fad; text-decoration:none;\"\u003eDataset Summary\u003c\/a\u003e\u003c\/li\u003e\n\u003cli style=\"display:inline; margin-right:12px;\"\u003e\u003ca href=\"#methodology\" style=\"font-size:0.85em; color:#2c6fad; text-decoration:none;\"\u003eMethodology\u003c\/a\u003e\u003c\/li\u003e\n\u003cli style=\"display:inline; margin-right:12px;\"\u003e\u003ca href=\"#download\" style=\"font-size:0.85em; color:#2c6fad; text-decoration:none;\"\u003eDownload\u003c\/a\u003e\u003c\/li\u003e\n\u003cli style=\"display:inline; margin-right:12px;\"\u003e\u003ca href=\"#regional-distribution\" style=\"font-size:0.85em; color:#2c6fad; text-decoration:none;\"\u003eRegional Distribution\u003c\/a\u003e\u003c\/li\u003e\n\u003cli style=\"display:inline; margin-right:12px;\"\u003e\u003ca href=\"#brand-bundle\" style=\"font-size:0.85em; color:#2c6fad; text-decoration:none;\"\u003eBrand Bundle\u003c\/a\u003e\u003c\/li\u003e\n\u003cli style=\"display:inline; margin-right:12px;\"\u003e\u003ca href=\"#related-datasets\" style=\"font-size:0.85em; color:#2c6fad; text-decoration:none;\"\u003eRelated Datasets\u003c\/a\u003e\u003c\/li\u003e\n\u003cli style=\"display:inline; margin-right:12px;\"\u003e\u003ca href=\"#use-cases\" style=\"font-size:0.85em; color:#2c6fad; text-decoration:none;\"\u003eUse Cases\u003c\/a\u003e\u003c\/li\u003e\n\u003cli style=\"display:inline; margin-right:12px;\"\u003e\u003ca href=\"#faq\" style=\"font-size:0.85em; color:#2c6fad; text-decoration:none;\"\u003eFAQ\u003c\/a\u003e\u003c\/li\u003e\n\u003cli style=\"display:inline; margin-right:12px;\"\u003e\u003ca href=\"#ai-prompts\" style=\"font-size:0.85em; color:#2c6fad; text-decoration:none;\"\u003eAnalyze with AI\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/nav\u003e\u003cp\u003e The Danish arm of E.ON, a major German multinational energy company. Its public EV charging network operates under the E.ON Drive brand, run by its infrastructure unit E.ON Drive Infrastructure (EDRI), alongside separate home-charging and business subscription products.\u003c\/p\u003e\u003cp\u003eThere are \u003cstrong\u003e978 E.ON EV Charging Stations\u003c\/strong\u003e as of \u003cstrong\u003e15 July 2026\u003c\/strong\u003e in Denmark. This dataset is compiled and maintained by \u003ca href=\"https:\/\/geolocet.com\" rel=\"noopener noreferrer\"\u003eGeolocet\u003c\/a\u003e and provides a complete, geocoded list of all E.ON locations, including full address details, administrative divisions, and precise WGS84 latitude\/longitude coordinates - structured for GIS, retail analytics, mapping, and AI\/RAG workflows.\u003c\/p\u003e\n        \u003cdiv class=\"dataset-summary\"\u003e\n            \u003ch2 id=\"dataset-summary\"\u003eDataset Summary\u003c\/h2\u003e\n            \u003cul\u003e\n            \u003cli\u003e\n\u003cstrong\u003eDataset Coverage:\u003c\/strong\u003e 978 E.ON ev charging stations in Denmark\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eContents:\u003c\/strong\u003e Coordinates, addresses, postal codes, and administrative divisions\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eFile Format:\u003c\/strong\u003e Fully geocoded CSV dataset (UTF-8)\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eFree Sample:\u003c\/strong\u003e Instantly accessible dataset to verify structure and data quality\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eUse Cases:\u003c\/strong\u003e Suitable for GIS, retail analytics, site selection, and AI\/RAG workflows\u003c\/li\u003e\n            \u003cli\u003e\n\u003cstrong\u003eLast Updated:\u003c\/strong\u003e 15 July 2026\u003c\/li\u003e\n        \u003c\/ul\u003e\n    \u003c\/div\u003e\n    \u003ch2 id=\"methodology\"\u003eDataset Methodology:\u003c\/h2\u003e\u003cp\u003e This dataset is compiled from publicly available business listings, official company sources, and geospatial validation workflows. Automated quality checks and manual analyst reviews are applied to improve coordinate precision, address standardisation, duplicate detection, and overall analytical consistency.\u003c\/p\u003e\u003cp\u003eIt is periodically reviewed and updated to reflect known network changes, closures, relocations, and newly identified locations.\u003c\/p\u003e\u003cdiv style=\"overflow: auto;\"\u003e\n\u003cdiv style=\"float: right; margin: 0 0 15px 20px; width: 45%; max-width: 560px;\"\u003e\u003cimg src=\"https:\/\/cdn.shopify.com\/s\/files\/1\/0727\/3853\/7806\/files\/Denmark_EON_EV_Charging_Stations_Locations_Map.png\" alt=\"Map showing the geographical distribution of E.ON locations in Denmark\" style=\"width: 100%; height: 100%; object-fit: contain;\"\u003e\u003c\/div\u003e\n\u003ch3\u003eDataset fields included in the CSV:\u003c\/h3\u003e\n\u003cul style=\"columns: 150px 2; margin-left: 20px;\"\u003e\n\u003cli\u003eGUID\u003c\/li\u003e\n\u003cli\u003eTitle\u003c\/li\u003e\n\u003cli\u003eLatitude\u003c\/li\u003e\n\u003cli\u003eLongitude\u003c\/li\u003e\n\u003cli\u003eStreet No\u003c\/li\u003e\n\u003cli\u003eStreet\u003c\/li\u003e\n\u003cli\u003eCity\u003c\/li\u003e\n\u003cli\u003eKommune\u003c\/li\u003e\n\u003cli\u003eRegion\u003c\/li\u003e\n\u003cli\u003ePopulation\u003c\/li\u003e\n\u003cli\u003ePostal Code\u003c\/li\u003e\n\u003cli\u003eAddress\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp style=\"margin-top: 15px;\"\u003e\u003cem\u003eRequire additional attributes such as charging points, charger types, or charging speed? \u003ca href=\"\/pages\/contact-us\"\u003eContact us\u003c\/a\u003e to request a custom data enrichment.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003ch2\u003eData Preview: Sample geospatial records from the E.ON dataset in Denmark\u003c\/h2\u003e\n\u003ctable class=\"data-preview-table\" aria-describedby=\"table-note\"\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth scope=\"col\"\u003eID\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eLocation Title\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eLatitude\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eLongitude\u003c\/th\u003e\n\u003cth scope=\"col\"\u003ePostal Code\u003c\/th\u003e\n\u003cth scope=\"col\"\u003eFull Address\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e8dfe16e...\u003c\/td\u003e\n\u003ctd\u003eE.ON Drive Charging Station\u003c\/td\u003e\n\u003ctd\u003e55.304863\u003c\/td\u003e\n\u003ctd\u003e12.192975\u003c\/td\u003e\n\u003ctd\u003e4653\u003c\/td\u003e\n\u003ctd\u003e26 Solagervej, Karise, 4653, Denmark\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e5b758cd...\u003c\/td\u003e\n\u003ctd\u003eE.ON Drive Charging Station\u003c\/td\u003e\n\u003ctd\u003e56.882011\u003c\/td\u003e\n\u003ctd\u003e9.830323\u003c\/td\u003e\n\u003ctd\u003e9530\u003c\/td\u003e\n\u003ctd\u003e75 Mastrupvej, Støvring, 9530, Denmark\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eab3a487...\u003c\/td\u003e\n\u003ctd\u003eE.ON Drive Charging Station\u003c\/td\u003e\n\u003ctd\u003e54.948966\u003c\/td\u003e\n\u003ctd\u003e9.694157\u003c\/td\u003e\n\u003ctd\u003e6400\u003c\/td\u003e\n\u003ctd\u003eVester Sottrup, 6400, Denmark\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e8f81879...\u003c\/td\u003e\n\u003ctd\u003eE.ON Drive Charging Station\u003c\/td\u003e\n\u003ctd\u003e54.993523\u003c\/td\u003e\n\u003ctd\u003e15.077335\u003c\/td\u003e\n\u003ctd\u003e3730\u003c\/td\u003e\n\u003ctd\u003e2A Fyrvejen, Nexø, 3730, Denmark\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e1f035ba...\u003c\/td\u003e\n\u003ctd\u003eE.ON Drive Charging Station\u003c\/td\u003e\n\u003ctd\u003e57.457459\u003c\/td\u003e\n\u003ctd\u003e9.831185\u003c\/td\u003e\n\u003ctd\u003e9800\u003c\/td\u003e\n\u003ctd\u003e23 Klangshøjvej, Hjørring, 9800, Denmark\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp id=\"table-note\" class=\"data-preview-note\" style=\"font-style: italic; font-size: 0.9em;\"\u003eNote: Only a subset of the full dataset fields are displayed here. Download the \u003cstrong\u003efree sample\u003c\/strong\u003e (option above) to view all fields and verify the data structure.\u003c\/p\u003e\u003cdiv style=\"background:#f8fff8; border:1px solid #c3e6cb; border-radius:6px; padding:16px 20px; margin:24px 0;\"\u003e\n\u003cp style=\"margin:0 0 10px 0; font-weight:bold; font-size:1em;\"\u003eWhy download from Geolocet?\u003c\/p\u003e\n\u003cul style=\"margin:0; padding-left:18px; font-size:0.95em; line-height:1.8;\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eInstant download\u003c\/strong\u003e - full dataset available immediately after purchase, no waiting, no manual fulfilment\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFree sample first\u003c\/strong\u003e - verify structure, fields, and coordinate precision before you commit\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAnalysis-ready CSV\u003c\/strong\u003e - clean, standardised, and compatible with Excel, Python, QGIS, Power BI, and PostgreSQL out of the box\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRegularly updated\u003c\/strong\u003e - last updated \u003cstrong\u003e15 July 2026\u003c\/strong\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cp style=\"text-align:center; margin:16px 0; font-size:0.95em;\"\u003e✅ Data looks right? \u003ca href=\"#\" onclick=\"window.scrollTo({top:0,behavior:'smooth'});return false;\" style=\"color:#2c6fad; font-weight:bold;\"\u003eAdd to cart ↑\u003c\/a\u003e - or download the \u003cstrong\u003efree sample\u003c\/strong\u003e first.\u003c\/p\u003e\u003ch2 id=\"regional-distribution\"\u003eRegional Distribution Breakdown\u003c\/h2\u003e\u003cp\u003eLooking at the geographic distribution, the highest concentration of E.ON locations in Denmark is found in \u003cstrong\u003eRegion Hovedstaden\u003c\/strong\u003e (410 sites, equivalent to \u003cstrong\u003e21.13 E.ON ev charging stations per 100,000 residents\u003c\/strong\u003e). This is followed by \u003cstrong\u003eRegion Syddanmark\u003c\/strong\u003e (208 sites; 16.77 per 100,000) and \u003cstrong\u003eRegion Nordjylland\u003c\/strong\u003e (145 sites; 24.58 per 100,000). From a market-penetration perspective, \u003cstrong\u003eRegion Nordjylland\u003c\/strong\u003e has the highest brand density at \u003cstrong\u003e24.58 locations per 100,000 people\u003c\/strong\u003e (population: 590,000), making it the most saturated region for E.ON in Denmark. By contrast, \u003cstrong\u003eRegion Sjælland\u003c\/strong\u003e records only 9.12 locations per 100,000 residents (population: 855,000), indicating a potential white-space opportunity for network expansion or competitor analysis.\u003c\/p\u003e\u003ch2 id=\"brand-bundle\" style=\"margin-bottom:12px;\"\u003eAlso available for Denmark\u003c\/h2\u003e\u003cdiv style=\"border:1px solid #2c6fad; border-left:4px solid #2c6fad; border-radius:0 6px 6px 0; padding:16px 20px; margin-bottom:12px; background:#f5f9ff;\"\u003e\n\u003cp style=\"margin:0 0 4px 0; font-size:0.8em; text-transform:uppercase; letter-spacing:0.05em; color:#2c6fad; font-weight:bold;\"\u003eBrand bundle\u003c\/p\u003e\n\u003cp style=\"margin:0 0 8px 0; font-weight:bold; font-size:1.05em;\"\u003eTop 14 EV Charging Stations Brands in Denmark - €380\u003c\/p\u003e\n\u003cp style=\"margin:0 0 12px 0; font-size:0.9em; color:#444;\"\u003eAll major chains in one standardised dataset. Best for competitive benchmarking, network analysis, and market sizing across the leading brands.\u003c\/p\u003e\n\u003ca href=\"https:\/\/geolocet.com\/products\/denmark-ev-brands\" style=\"display:inline-block; background:#2c6fad; color:#fff; padding:7px 14px; border-radius:4px; text-decoration:none; font-size:0.88em; font-weight:bold;\"\u003eView Top Brands dataset →\u003c\/a\u003e\n\u003c\/div\u003e\u003cdiv style=\"border:1px solid #6c757d; border-left:4px solid #6c757d; border-radius:0 6px 6px 0; padding:16px 20px; margin-bottom:12px; background:#f9f9f9;\"\u003e\n\u003cp style=\"margin:0 0 4px 0; font-size:0.8em; text-transform:uppercase; letter-spacing:0.05em; color:#6c757d; font-weight:bold;\"\u003eFull market coverage\u003c\/p\u003e\n\u003cp style=\"margin:0 0 8px 0; font-weight:bold; font-size:1.05em;\"\u003eAll EV Charging Stations Locations in Denmark - complete POI dataset\u003c\/p\u003e\n\u003cp style=\"margin:0 0 12px 0; font-size:0.9em; color:#444;\"\u003eIncludes everything in the brand bundle \u003cem\u003eplus\u003c\/em\u003e independent operators, smaller chains, and local businesses not covered by the top brands. Best for full market mapping, territory planning, and white-space analysis.\u003c\/p\u003e\n\u003ca href=\"https:\/\/geolocet.com\/products\/denmark-ev-charging-stations-poi-data\" style=\"display:inline-block; background:#6c757d; color:#fff; padding:7px 14px; border-radius:4px; text-decoration:none; font-size:0.88em; font-weight:bold;\"\u003eView full POI dataset →\u003c\/a\u003e\n\u003c\/div\u003e\u003ch2 id=\"related-datasets\"\u003eRelated geospatial datasets\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"https:\/\/geolocet.com\/products\/denmark-all-available-administrative-boundaries\" rel=\"noopener noreferrer\"\u003eAdministrative boundaries and full polygon dataset for Denmark\u003c\/a\u003e: Map these E.ON locations against highly precise administrative polygons for territory analysis and spatial structuring.\u003c\/li\u003e\n\u003cli\u003e\n\u003ca href=\"https:\/\/geolocet.com\/products\/denmark-geodemographics-dataset-with-boundaries\" rel=\"noopener noreferrer\"\u003eDemographics dataset for Denmark\u003c\/a\u003e: Overlay demographic indicators to deeply understand the population structures and household types surrounding these E.ON locations.\u003c\/li\u003e\n\u003cli\u003e\n\u003ca href=\"https:\/\/geolocet.com\/pages\/denmark#DemographicsData\" rel=\"noopener noreferrer\"\u003eExplore demographics data insights for Denmark\u003c\/a\u003e: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.\u003c\/li\u003e\n\u003cli\u003e\n\u003ca href=\"https:\/\/geolocet.com\/products\/denmark-income-indicators-parish\" rel=\"noopener noreferrer\" title=\"Download small-area income dataset for Denmark: income distribution, household earnings, socio-economic segmentation indicators.\"\u003eIncome indicators dataset for Denmark (small-area income analysis)\u003c\/a\u003e: Download an analysis-ready dataset on income distribution across small geographic units - ideal for segmentation, customer analytics, location planning, and socio-economic scoring.\u003c\/li\u003e\n\u003cli\u003e\n\u003ca href=\"https:\/\/geolocet.com\/blogs\/news\/income-distribution-across-danish-parishes\" rel=\"noopener noreferrer\" title=\"Read an expert analysis of income distribution and socio-economic patterns in Denmark, including practical use cases for location intelligence.\"\u003eIncome distribution and socio-economic patterns in Denmark - expert analysis\u003c\/a\u003e: A detailed blog post explaining income geography, disparities, and how income-based indicators can support location intelligence and market analytics.\u003c\/li\u003e\n\u003cli\u003eExplore a rich library of Denmark-specific datasets on our dedicated country page: detailed demographics, wealth indicators, multi-level boundaries, and a broad spectrum of retail POIs. \u003ca href=\"https:\/\/geolocet.com\/pages\/denmark\" rel=\"noopener noreferrer\"\u003eView demographics, retail POI, and administrative boundary datasets for Denmark\u003c\/a\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2 id=\"formats\"\u003eNeed the data in another format?\u003c\/h2\u003e\u003cp\u003eWe can deliver this dataset in alternative formats upon request (GeoJSON, Shapefile, Excel, PostgreSQL import files, etc.). Contact us at \u003ca href=\"mailto:contact@geolocet.com\"\u003econtact@geolocet.com\u003c\/a\u003e.\u003c\/p\u003e\u003ch2 id=\"use-cases\"\u003eWho uses this data?\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eStore Closure \u0026amp; Relocation Strategy:\u003c\/strong\u003e Corporate teams optimizing existing footprints by analyzing underperforming regions.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMobility Analysis:\u003c\/strong\u003e Transport consultants evaluating retail proximity to major transit corridors and parking infrastructure.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLast-Mile Delivery Routing:\u003c\/strong\u003e E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVendor Distribution:\u003c\/strong\u003e FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eB2B Telemarketing \u0026amp; Outreach:\u003c\/strong\u003e Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSmart City Research:\u003c\/strong\u003e Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUrban Planning:\u003c\/strong\u003e City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConsumer Behavior Analytics:\u003c\/strong\u003e Researchers correlating local demographics, foot traffic data, and proximity to physical stores.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGeofencing \u0026amp; Targeted Advertising:\u003c\/strong\u003e Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2 id=\"faq\" style=\"margin-top: 30px;\"\u003eFrequently Asked Questions\u003c\/h2\u003e\u003cdiv class=\"faq-section\"\u003e\n\u003cdiv style=\"border-bottom:1px solid #eee; padding:12px 0;\"\u003e\n\u003cp style=\"font-weight:bold; margin:0 0 6px 0;\"\u003eQ: What coordinate reference system is used?\u003c\/p\u003e\n\u003cp style=\"margin:0; color:#444;\"\u003eA: Coordinates are provided in the global WGS84 geographic coordinate system (EPSG:4326).\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"border-bottom:1px solid #eee; padding:12px 0;\"\u003e\n\u003cp style=\"font-weight:bold; margin:0 0 6px 0;\"\u003eQ: Does the dataset include unique identifiers?\u003c\/p\u003e\n\u003cp style=\"margin:0; color:#444;\"\u003eA: Yes. Each record includes a GUID field to support deduplication, joins, and downstream database operations.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"border-bottom:1px solid #eee; padding:12px 0;\"\u003e\n\u003cp style=\"font-weight:bold; margin:0 0 6px 0;\"\u003eQ: Can I request the data in GeoJSON or Shapefile format?\u003c\/p\u003e\n\u003cp style=\"margin:0; color:#444;\"\u003eA: Yes. Alternative delivery formats such as GeoJSON, Shapefile, Excel, and PostgreSQL imports are available upon request.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"border-bottom:1px solid #eee; padding:12px 0;\"\u003e\n\u003cp style=\"font-weight:bold; margin:0 0 6px 0;\"\u003eQ: Are the datasets suitable for machine learning workflows?\u003c\/p\u003e\n\u003cp style=\"margin:0; color:#444;\"\u003eA: Yes. The structured tabular format and standardized coordinates make the datasets suitable for machine learning and predictive analytics applications.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"border-bottom:1px solid #eee; padding:12px 0;\"\u003e\n\u003cp style=\"font-weight:bold; margin:0 0 6px 0;\"\u003eQ: Can I use this dataset for competitor benchmarking?\u003c\/p\u003e\n\u003cp style=\"margin:0; color:#444;\"\u003eA: Yes. The dataset is frequently used to compare retail footprints, market density, and regional presence against competing brands.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What coordinate reference system is used?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Coordinates are provided in the global WGS84 geographic coordinate system (EPSG:4326).\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Does the dataset include unique identifiers?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. Each record includes a GUID field to support deduplication, joins, and downstream database operations.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I request the data in GeoJSON or Shapefile format?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. Alternative delivery formats such as GeoJSON, Shapefile, Excel, and PostgreSQL imports are available upon request.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Are the datasets suitable for machine learning workflows?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. The structured tabular format and standardized coordinates make the datasets suitable for machine learning and predictive analytics applications.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I use this dataset for competitor benchmarking?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. The dataset is frequently used to compare retail footprints, market density, and regional presence against competing brands.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\u003ch2 id=\"ai-prompts\"\u003eAnalyze this data with AI\u003c\/h2\u003e\u003cp\u003eUse these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:\u003c\/p\u003e\u003cul style=\"margin-left: 20px;\"\u003e\n\u003cli class=\"ai-prompt-item\"\u003e\u003ccode\u003e\"Analyze this E.ON dataset to identify underserved regions in Denmark for potential market expansion.\"\u003c\/code\u003e\u003c\/li\u003e\n\u003cli class=\"ai-prompt-item\"\u003e\u003ccode\u003e\"Detect clusters where E.ON sites are in close proximity to analyze potential self-cannibalization in Denmark.\"\u003c\/code\u003e\u003c\/li\u003e\n\u003cli class=\"ai-prompt-item\"\u003e\u003ccode\u003e\"Calculate the total population coverage for E.ON in Denmark using a 10km catchment radius around each coordinate.\"\u003c\/code\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp style=\"font-size: 0.8em; color: #888; margin-top: 20px; border-top: 1px solid #eee; padding-top: 10px;\"\u003e\u003cem\u003e\u003cstrong\u003eDisclaimer:\u003c\/strong\u003e All brand logos and trademarks displayed are the property of their respective owners and are used strictly for identification purposes. This product consists of geospatial location data only; no images, logos, or trademark rights are included in the downloadable files.\u003c\/em\u003e\u003c\/p\u003e","brand":"Denmark","offers":[{"title":"Full dataset","offer_id":60213549826382,"sku":"denmark-e-on-ev-full","price":60.0,"currency_code":"EUR","in_stock":true},{"title":"Free sample","offer_id":60213549859150,"sku":"denmark-e-on-ev-sample","price":0.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0727\/3853\/7806\/files\/Locations_EON.png?v=1780310702","url":"https:\/\/geolocet.com\/products\/denmark-e-on-ev","provider":"Geolocet","version":"1.0","type":"link"}