Ingekleed vraagstuk: dakdekkerslift huren en plaatsen

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

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.

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Voorbeeld 1

This example shows interactive maps of the Netherlands, centered on Groningen, with a marker and popup identifying the Martinitoren. It demonstrates three versions of Leaflet-based mapping: the first displays a standard tile layer; the second adds a layer control to switch between five Dutch base maps (standaard, grijs, pastel, water, luchtfoto); the third uses a custom projected coordinate system (EPSG:28992) via Proj4Leaflet to load an OpenTopo background tile layer. The code uses Leaflet’s tile layer, marker, popup, and control layers, with tile sources from PDOK and OpenTopo.

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Document

This visualization uses a treemap layout to display monthly household expenses, organized by spending category (vaste lasten, reserves, and huishoudelijke uitgaven) with numeric values from a CSV file. The code employs d3.stratify to build a hierarchy from the flat data, then d3.treemap to compute rectangle positions and sizes. Each leaf is rendered as a green SVG rectangle with a white text label showing the category name, using D3 v5 within a React framework. The chart uses a fixed 960×500 SVG canvas.

MManoukK

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Voorbeeld van het gebruik van de BRT Achtergrondkaart in Leaflet in combinatie met Shiny

This example demonstrates how to combine the Dutch national map service BRT Achtergrondkaart with Leaflet within a Shiny application, offering four selectable background layers: standard, pastel, grey, and aerial photography. The code uses the `leaflet` package in R, with tile layers fetched from the PDOK geodata service via WMTS URLs. A single marker with a popup is placed at the Martinitoren landmark. The Shiny UI and server components define an interactive map where users can switch base layers through a layers control.

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.

New code structure

This interactive map visualizes where parking facilities are located relative to city centers across the Netherlands, with buttons to filter parking spots for Amsterdam, Den Haag, Rotterdam, and Utrecht. The visualization uses d3.geoMercator and d3.geoPath to project Dutch township boundaries from TopoJSON, while parking locations are fetched from the RDW Open Data API, cleaned, and drawn as blue circles around a red city center marker. The code relies on d3.select, d3.json, d3.zoom, and TopoJSON's feature extraction, with SVG rendering and transition-based animations for zoom resets.

Mmbergevoet

<title>Begrooting Standaard en Heraut

This visualization presents the 1897 budget of De Standaard and De Heraut, distinguishing between projected revenues and expenditures. It uses a network diagram where nodes represent transactions and edges map the flow of funds between categories. The code employs d3.forceSimulation for layout and transitions to animate the graph. Data comes from a TEI-encoded XML source, with each transaction’s note and monetary measure parsed via the d3.hierarchy API. The visualization highlights subscription and advertising income against printing costs, rendered through SVG paths and dynamic node positioning.

CCliffordAnderson

Oefenen met scale

This example demonstrates how to load and display geographic boundary data from a Web Feature Service (WFS) endpoint using D3.js. It shows a map of Dutch arrondissementsgebieden (judicial districts) for 2019, projected with a Mercator projection. The code uses `d3.json` to fetch GeoJSON from the national georegister WFS, then renders it as SVG paths via `d3.geo.path` with a `d3.geo.mercator` projection. A simple alert greets viewers before the visualization loads, and the SVG canvas is sized to 960 by 500 pixels.