How densely built is one neighborhood compared to another? How many square meters have been added in a neighborhood? Has the number of homes increased or decreased, or has the surface area of the homes increased? And if there is an ambition to increase the density of a neighborhood, which existing neighborhoods with that higher density are good comparison material? How do those neighborhoods function? The GIS dataset RUDIFUN developed by PBL can be helpful with such questions.
A high building density allows the landscape around cities and villages to remain undeveloped and ensures that distances within cities are relatively short. This creates favorable conditions for active mobility and economic interaction. However, high densities are also associated with livability issues and congestion. Knowledge about spatial densities is therefore very relevant for area development. The RUDIFUN dataset can help analyze density and densification issues.
For the special issue on densification of the trade journal for social housing Renda, Arjan Harbers, Hans van Amsterdam, and Martijn Spoon from PBL wrote an article about the RUDIFUN dataset and its application for densification.
Authors
Specifications
- Publication Title
- RUDIFUN
- Publication Subtitle
- Spatial indicators for densification tasks
- Publication Date
- April 6, 2025
- Publication Type
- Article
- Publication Language
- Dutch
- Product Number
- 5876