Cityseer API Docs
QGIS Plugin
The cityseer QGIS plugin provides a Processing algorithm for computing localised network centrality metrics (closeness and betweenness) directly within QGIS. It uses a dual graph representation where each road segment becomes a node connected to its neighbours, with support for multiple distance thresholds and deterministic distance-based sampling.
The plugin is experimental and requires QGIS 4.0+.
Installation
Install the plugin from the QGIS plugin repository: go to Plugins > Manage and Install Plugins, search for “Cityseer”, and click Install. Enable the “Show also experimental plugins” option in the Settings tab if the plugin is not visible.
On first load, the plugin will prompt to install the cityseer Python library if it is not already available in the QGIS Python environment.
Usage
The algorithm is accessible via Processing > Cityseer > Network Centrality.
Input Parameters
| Parameter | Description | Default |
|---|---|---|
| Street network line layer | A line layer in a projected metre-based CRS | (required) |
| Distance thresholds | Comma-separated distances in metres | 400,800 |
| Betweenness tolerance % | Controls betweenness spread across near-shortest paths. 0 = exact shortest paths only. Keep below 1%. | 0.0 |
| Boundary polygon | Optional polygon layer. Nodes inside the boundary are used as centrality sources; nodes outside provide network context only. | (none) |
| Use deterministic distance-based sampling | Experimental. When enabled, sampling probability is computed per distance threshold. Distances where the probability is 1.0 are computed exactly; larger distances are sampled for speed. | True |
The input layer must be in a projected metre-based CRS (not geographic/degrees).
Metric Selection
The algorithm dialog provides a 2x2 grid of metric categories:
| Shortest path | Simplest path (angular) | |
|---|---|---|
| Closeness | harmonic, density, farness, beta, cycles, hillier | harmonic, density, farness, hillier |
| Betweenness | betweenness, betweenness_beta | betweenness, betweenness_beta |
Each category can be toggled on or off, and individual metrics within each category can be selected independently. By default, harmonic closeness and betweenness are enabled for shortest paths.
Output
The output is a line layer with the original street segments and computed centrality values as attributes. Output fields follow the naming convention:
cc_<metric>_<distance>[_ang]For example, with distances 400,800:
cc_harmonic_400,cc_harmonic_800cc_betweenness_400,cc_betweenness_800cc_harmonic_400_ang(if simplest path closeness is enabled)
Sampling
Deterministic distance-based sampling is enabled by default. Sampling probability depends only on the distance threshold: smaller distances run exactly while larger distances are sampled for a speed-up. The following table shows approximate sampling rates:
| Distance (m) | Sampled sources | Approx. speed-up |
|---|---|---|
| 400 | 100% | 1x |
| 800 | 100% | 1x |
| 1600 | 100% | 1x |
| 3200 | 100% | 1x |
| 5000 | 58.7% | 1.7x |
| 8000 | 24.9% | 4.0x |
| 10000 | 16.6% | 6.0x |
| 15000 | 7.8% | 12.7x |
| 20000 | 4.6% | 21.7x |
Disable sampling for exact computation at all distances.