Cityseer API Docs
Network Centrality
Accessible via Processing > Cityseer > Network Centrality. Computes localised closeness and betweenness centrality on a street network using a dual graph representation.
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 2%. | 0.0 |
| Simplest-path tolerance % | Tolerance on angular route cost for near-simplest routes. Keep below 20%. | 0.0 |
| Boundary polygon | Optional polygon layer. Nodes inside the boundary are used as centrality sources; nodes outside provide network context only. | (none) |
| Use adaptive sampling | Experimental. When enabled, a pilot poll measures per-node reach and distances run sampled only when predicted to be faster than exact. | False |
| Error tolerance epsilon | Advanced. Sampling accuracy tolerance. The default 0.05 preserves node rankings; loosen towards 0.1 for exploratory work. | 0.05 |
| Time thresholds | Advanced. Comma-separated minutes; overrides distances when set. Converted to metres using the walking speed. | (none) |
| Walking speed | Advanced. Metres per second, used to convert minutes to distances. | 1.33 |
Metric Selection
The algorithm dialog provides a 2x2 grid of metric categories. Each category can be toggled on or off independently, and individual metrics within each category are selected independently. Enabling a metric in one category does not affect other categories.
| Shortest path | Simplest path (angular) | |
|---|---|---|
| Closeness | harmonic, density, farness, decay, cycles, hillier | harmonic, density, farness, hillier |
| Betweenness | betweenness, betweenness_decay | betweenness |
By default, harmonic closeness and betweenness are enabled for shortest paths. All simplest path categories are off by default; when simplest-path closeness is enabled, Hillier (improved closeness) is the default metric.
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
Adaptive sampling is optional and off by default. When enabled, a pilot poll measures each segment’s network reach at every distance threshold, and per-segment sampling probabilities are derived from the Hoeffding bound so that every catchment accumulates approximately the required number of samples. A work test then decides per distance threshold whether sampling is predicted to be faster than exact computation; distances that would not benefit run exactly. Inverse-probability weighting keeps the resulting estimates unbiased.
The error tolerance epsilon (advanced parameter, default 0.05) is calibrated so that node rankings are preserved relative to exact computation. Speed-ups are largest on dense networks at long distance thresholds. See the sampling module documentation for the methodology.