A visual introduction to geospatial visualization, spatial data types, R packages and an example workflow using Düsseldorf OSM data.
Geospatial visualization helps transform raw location-based data into maps, patterns and visual stories. It is used in urban planning, transportation, environmental analysis, real estate, public health and business analytics.
Maps, heatmaps, choropleths, interactive layers and accessibility visuals.
Urban planning, mobility, environment, retail location and decision-making.
R allows reproducible spatial analysis, mapping and data visualization in one workflow.
Spatial data describes position, size and form in space. Geospatial data is a specific type of spatial data connected to real-world locations on Earth.
CAD plans, video game coordinates, 3D models or virtual environments.
GPS coordinates, satellite imagery, land parcels and traffic routing data.
Uses latitude, longitude, elevation and coordinate reference systems.
City boundaries, road networks, parks, rivers and residential areas.
Single coordinate features such as POIs, bus stops or GPS points.
Linear features such as roads, railways, rivers and routes.
Closed shapes such as city boundaries, parks, land use and buildings.
Pixel-based data such as satellite images, elevation and heatmaps.
Reading, cleaning and processing vector spatial data.
Creating clean static maps and visualizations.
Building interactive web maps.
Alternative thematic mapping workflow.
Read shapefiles with st_read().
Check geometries, missing values and spatial validity.
Use a consistent coordinate reference system for all layers.
Build layered maps with ggplot2, leaflet or tmap.
This presentation website transforms a step-by-step R geospatial workflow into a visual data story. Each map adds one analytical layer to understand the urban structure of Düsseldorf.
The analysis focuses on the city boundary as the spatial frame.
Land use, water, roads and residential areas are used.
The maps are produced through a reproducible R workflow.
The final result is a visual sequence of spatial layers.
The maps are ordered as a visual build-up: first the boundary, then green areas, residential areas, water bodies, road network and accessibility to green spaces.
The first map introduces Düsseldorf as the geographic boundary of the analysis.
Parks, forests, grass areas and recreation grounds are added to the city boundary.
Residential areas are added to compare built-up urban zones with green spaces.
The Rhein River and water bodies are included as major spatial elements of Düsseldorf.
The road layer reveals the mobility skeleton and connects residential and green areas.
Residential points are compared by their distance to green areas.
Distance values are grouped into categories to make accessibility patterns easier to read.
The visual sequence shows how different spatial layers gradually reveal the structure of the city.
Green areas are widely distributed, but their intensity changes across the city.
Residential areas and roads create a denser structure around central districts.
Distance to green areas can be visualized as a simple spatial accessibility indicator.
The static maps explain the analytical process. The interactive Leaflet map can be used as an additional exploration layer.
If the interactive map does not load inside the page, open it in a new browser tab .
Geospatial data can be transformed into meaningful insights through reproducible R workflows and clear visual storytelling.