R-script dat aan de hand van de gegevens uit het objectenportaal van de provincie Groningen, de BAG en de Zonneatlas bepaald welke panden van de provincie Groningen wel/niet geschikt zijn voor het opwekken van zonne-energie

100%

R-script dat aan de hand van de gegevens uit het objectenportaal van de provincie Groningen, de BAG en de Zonneatlas bepaald welke panden van de provincie Groningen wel/niet geschikt zijn voor het opwekken van zonne-energie

✓ Published0🌍 Public

Last edited Feb 7, 2016

Created on Feb 7, 2016

This R Markdown analysis determines which buildings in the province of Groningen are suitable for solar energy generation, based on open data from the provincial object portal, the BAG (Basisregistratie Adressen en Gebouwen), and the Zonatlas. It shows the workflow from downloading building and address data via WFS APIs to converting address objects into building polygons using the `httr`, `sp`, and `rgdal` packages. The script also integrates municipal boundaries to apply the correct Zonatlas layer, producing a reproducible geospatial assessment.

AI-generated description

Similar vizzes

Loading thumbnail…

R-script voor het downloaden en georefereren van een OpenStreetMap tile

This R script downloads a single OpenStreetMap tile centered on the Martinitoren in Groningen and georeferences it by generating a World file. It first converts latitude and longitude coordinates to tile coordinates using the slippy map tilenames algorithm, then fetches the PNG from the `tile.openstreetmap.org` server. The script calculates the tile’s bounding box from the zoom level and writes a six-line `.pgw` file with pixel-to-world transformation parameters, enabling the image to be overlaid with geographic data in GIS software. The code uses base R functions like `download.file`, `paste0`, and `writeLines` without external libraries.

FFrieseWoudloper

67% match

Loading thumbnail…

R script om te demonstreren hoe je WFS paging kunt implementeren in R

This example shows how to retrieve all Dutch address objects (verblijfsobjecten) in the town of Zuidhorn from the BAG Web Feature Service (WFS) using paging to handle large result sets. The code first queries the server’s maximum page size via GetCapabilities, then counts matching features, and finally fetches them page by page using `startindex` while sorting by identifier. It uses the `httr`, `xml2`, and `sf` R packages to parse the WFS responses and convert the resulting GeoJSON into simple features, which are then mapped with the `tmap` package. The data source is the national georegister’s BAG WFS endpoint.

FFrieseWoudloper

63% match

Loading thumbnail…

R-script voor het downloaden en converteren van een JSON-bestand met locaties van jachthavens converteren naar shape-bestand

This example demonstrates how to download JSON data containing marina locations from a Dutch tourism website and convert it into a shapefile. The script shows the process of fetching data via an HTTP POST request with a custom header, parsing the JSON response, extracting attributes like address and contact details from nested HTML, and then transforming geographic coordinates from WGS84 to the Dutch RD coordinate system using the `sp` and `rgdal` packages. It uses `httr` for the API request, `jsonlite` for JSON parsing, and `XML` for handling HTML content within the JSON. The final shapefile is written with `writeOGR`.

FFrieseWoudloper

62% match

Loading thumbnail…

Groninger gemeenten

This example visualizes the municipalities of the Dutch province of Groningen using D3.js. The map is generated from multiple TopoJSON files (with and without bounding boxes, and with different quantization parameters) that were created with FME Desktop 2019. Due to mixed winding orders of the polygons, the initial rendering shows a rectangle instead of the correct municipality boundaries. The visualization demonstrates the impact of winding order and TopoJSON preprocessing on geographic data rendering. By comparing the different file versions, viewers can observe how the bounding box and the right-hand rule (Orientator transformer) affect the final output. The map displays the municipalities in the northern Netherlands, with the D3 code provided in index.html. A key challenge highlighted is the need for consistent polygon winding order to ensure correct D3.js visualization.**Title:** Groninger gemeenten **Description:** This visualization demonstrates a common pitfall in geographic data processing: winding order in polygon coordinates. The map displays the municipalities in the northern Netherlands, but due to mixed winding orders in the polygon definitions, D3.js renders a solid rectangle instead of the expected municipal boundaries. The author created four TopoJSON variants using FME Desktop 2019—with and without bounding boxes, and with different quantization parameters—to isolate the winding order issue. The accompanying `index.html` file contains the D3.js code used to visualize the files and reproduce the problem. This example is particularly useful for developers working with TopoJSON and D3.js, illustrating how winding order can affect rendering and how data preprocessing choices influence the final output. Source: [gist by FrieseWoudloper](https://gist.github.com/FrieseWoudloper)

FFrieseWoudloper

60% match

Loading thumbnail…

Gist 3d7c0da4efc292e95700cda51537747d

This map visualizes the twelve municipalities of the Dutch province of Groningen, showing their boundaries as empty polygons with no fill. It uses a D3 v4 geoMercator projection centered on the province, with each municipality rendered as an SVG path element. The geographic data comes from the PDOK WFS service, requested as GeoJSON via d3.json. The map applies a gray stroke to outline each municipality, while intentionally leaving the polygons unfilled to emphasize the administrative borders over any thematic data.

FFrieseWoudloper

59% match

Loading thumbnail…

Groninger gemeenten

The visualization displays the municipalities of the Dutch province of Groningen as an interactive SVG map. It uses D3.js v4 to render geographic paths from GeoJSON data, while Turf.js rewinds the polygon coordinates to ensure correct orientation. The map is projected with `d3.geoMercator` and fitted to the viewport using `projection.fitSize`. Hovering over a municipality triggers a tooltip with the municipality name, and the `path:hover` CSS rule highlights the area in orange. Boundary data originates from the PDOK WFS service, fetched via a URL with a CQL filter for selected gemeenten.

FFrieseWoudloper

59% match

Loading thumbnail…

Groninger gemeenten

The visualization displays the municipalities of the Dutch province of Groningen as a static choropleth-free map. It shows the boundaries and spatial arrangement of twelve named gemeenten, including Groningen, Delfzijl, and Oldambt. The map uses the d3.geoMercator projection and d3.geoPath to render GeoJSON features fetched from an ArcGIS REST service (ESRI’s Levende Atlas), with the projection fitted to the viewport via projection.fitSize. The SVG is sized responsively with a viewBox attribute. No interactive elements or color encodings are present beyond the default black vector paths.

FFrieseWoudloper

58% match

Loading thumbnail…

Power BI query om onvolledige en foute adressen van provinciehuizen te verbeteren en verrijken met LocatieServer

This Power Query example cleans and enriches incomplete or incorrect addresses of provincial government buildings (provinciehuizen) by geocoding them through the LocatieServer API. It first transforms the input address list, then constructs a URL query for the Nationaal Georegister’s free geocoding endpoint, fetching JSON attributes like postcode, street name, and coordinates. Using Power Query’s table functions (`Table.AddColumn`, `Table.ReplaceValue`, `Table.SplitColumn`), it extracts longitude and latitude, then removes intermediate columns. The data source is a hardcoded gist list, and the output includes structured address components and map-ready coordinates.

FFrieseWoudloper

58% match