Urban Spatial Analysis Using Voronoi Diagrams
Summary
Voronoi diagrams provide a spatial partitioning technique that assigns every location within an urban area to its nearest feature, such as a transport hub, facility or service centre. By constructing a tessellation of cells around seed points, this method reveals patterns of accessibility, influence and territorial demarcation. In urban studies, Voronoi analyses inform the delineation of service areas for schools, hospitals and public amenities, support site‐selection models and facilitate assessments of spatial equity. Extensions to the basic framework—such as order k Voronoi diagrams, network‐constrained Voronoi on street graphs and weighted tessellations—have enhanced its adaptability to complex city infrastructures. Recent algorithmic advances address the computational challenges of large‐scale networks and high‐resolution urban datasets, enabling dynamic analyses of pedestrian catchments, emergency response zones and resource allocation in both monocentric and polycentric metropolitan regions.
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Urban Spatial Analysis Using Voronoi Diagrams publication trend
The graph below shows the total number of articles in urban spatial analysis using voronoi diagrams across all publications each year (not limited to Nature Index journals).
Technical terms
Voronoi diagram: A planar partition that assigns each location to its nearest seed point, forming non‐overlapping cells.
Network Voronoi diagram: A tessellation constrained to a graph (e.g. road network), where distances follow link weights rather than Euclidean metric.
Order k Voronoi diagram: A generalisation in which each cell comprises points sharing the same set of k nearest seed points.
Point of interest (POI): A spatial feature such as a facility or landmark used as a seed in Voronoi analyses.
References
- Efficient Algorithm for Constructing Order K Voronoi Diagrams in Road Networks. ISPRS International Journal of Geo-Information (2023).
- An Algorithm to Generate a Weighted Network Voronoi Diagram Based on Improved PCNN. Applied Sciences (2022).
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