Weighted 3D visibility graph
A distance-based extension of visibility graph analysis
Bibliographic Data
| ID | 21247725 |
|---|---|
| Authors | Sara Omrani Azizabad (0000-0002-3385-2424, Tarbiat Modares University), Mohammadjavad Mahdavinejad (0000-0002-6454-6518, University of Nizwa, corresponding author) |
| Year | 2026 |
| Publication date | 2026-07-04 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/23998083261465711 |
| OpenAlex | W7167334640 |
| Language | EN |
| References cited | 19 |
The focus of visibility analysis is the relationship between people’s perception and the environment. Visibility Graph Analysis (VGA) is one of the important spatial concepts in visibility analysis, which, in architecture, is first represented in two dimensions. Recently, computing the 3D VGA in a three-dimensional environment has become more applicable. Since environmental perception is a complex concept influenced by multiple parameters, limiting the relationship of visible nodes in the visibility graph overlooks the depth of visibility perception. The distance between two intervisible nodes should not be neglected. In this paper, we take a step forward by adding a computational layer to the 3D VGA, developing it into a weighted 3D VGA. In this model, each edge is assigned a weight based on the distance between nodes; two additional metrics—sum of distance and average distance—are defined to support multi-scale visibility interpretation, providing a more detailed representation of visibility relationships
Graph · Perception · Power graph analysis · Visibility · Visibility graph · 3D Modeling in Geospatial Applications · Data Visualization and Analytics · Urban Design and Spatial Analysis
To Take Hold of Space
From Isovists to Visibility Graphs
Exploring Isovist Fields
Collective dynamics of ‘small-world’ networks
Three-dimensional visibility graph analysis and its application
Integrating ‘weighted views’ to quantitative 3D visibility analysis as a predictive tool for perception of space
Unpacking isovists
Convolutional neural networks for predicting the perceived density of large urban fabrics
| Citation velocity | historical |
|---|---|
| Highly cited | No |