How do I…?

Short answers to common questions, each pointing to the part of the API that handles it. In every case the measure is computed over the street network: distances are shortest-path distances along the streets and aggregations run over network catchments, not straight-line buffers. The examples use the CityNetwork class; every answer also works through the functional API, and most through the QGIS plugin without code.

Count the schools reachable within 800 m of each street?

Use accessibility. Pass a GeoDataFrame of points or polygons with a category column and specify the required categories: cn.compute_accessibilities(data_gdf, landuse_column_label="type", accessibility_keys=["school"], distances=[800]). Each street segment receives count columns (cc_school_800_nw unweighted, cc_school_800_wt decay-weighted) and a cc_school_nearest_max_800 distance to the closest feature. In QGIS, use the Accessibility algorithm.

Find the distance to the nearest supermarket?

The same accessibility call computes it: the cc_<category>_nearest_max_<distance> column holds the network distance from each street to the nearest feature of that category. See compute_accessibilities.

Aggregate building areas around each street?

Use statistics. Give each building polygon an area column (gdf["area"] = gdf.geometry.area), then cn.compute_stats(gdf, stats_column_labels=["area"], distances=[400, 800]). The cc_area_sum_400_nw column is the total built area reachable within 400 m of each street (pass decay_fn="1" for a single unsuffixed column). In QGIS, use the Statistics algorithm.

Get the average income around each street?

Use statistics with the mean: attach the income value to census points or polygon centroids and read cc_income_mean_<distance>_nw. Pass measures=["mean"] to compute only the required statistics. See compute_stats.

Aggregate several columns at once?

Pass them together: stats_column_labels=["income", "age", "density"] computes all three in a single network pass, which is faster than three separate runs. The QGIS Statistics dialog also accepts multiple numeric fields.

Measure how mixed the land uses are around each street?

Use mixed uses: cn.compute_mixed_uses(data_gdf, landuse_column_label="type", distances=[800]). Hill diversity at q = 0 (cc_hill_q0_800_nw) counts the distinct land uses reachable within the threshold. See the land-use guide.

Find the streets likely to carry the most movement?

Betweenness centrality: cn.centrality_shortest(distances=[800, 4000]) writes cc_betweenness_<distance> columns counting the shortest paths passing through each street. Shorter thresholds correspond to local pedestrian movement and longer thresholds to larger-scale through-movement. See the centrality guide.

Find the streets most central to their local area?

Closeness centrality from the same call: cc_harmonic_<distance> measures how easily each street reaches its surroundings, and cc_hillier_<distance> (density squared over farness) is an improved-closeness variant. Closeness identifies local centres, whereas betweenness identifies through-routes.

Map the major routes between homes and shops?

Use demand betweenness: cn.betweenness_demand(origins_gdf=homes, destinations_gdf=shops, origin_weight_col="population", destination_weight_col="floorspace", distances=[1600]). Trips are allocated by a spatial interaction model and routed along the network; the cc_demand_1600 value on a segment is the modelled trip volume passing through it. See the flows guide or the QGIS Demand Betweenness algorithm.

Route an observed commuting matrix onto streets?

If you have explicit origin-destination counts (for example census travel-to-work data), build a matrix with cn.build_od_matrix(od_df, zones_gdf, ...) and route it with cn.betweenness_od(od_matrix, distances=[...]). See the flows guide.

See how flows would change if a street were added or removed?

Run the analysis twice: once on the current network and once on the edited network (add, remove, or reshape the line geometries), then subtract the result columns. The difference map shows which streets gain or lose flow under the change. CityNetwork.update applies edits incrementally without rebuilding the whole network.

Decide between metric and angular analysis?

Metric (shortest-path) analysis models distance-minimising movement. Angular (simplest-path) analysis, via cn.centrality_simplest, models route choice by minimising turns, which suits questions of legibility and wayfinding. See the centrality guide.

Use walking time instead of distance?

Every metric accepts minutes in place of distances, converted with a configurable walking speed (speed_m_s, default 1.33). Output columns are keyed by the converted metre distances so results stay comparable across runs. The QGIS dialogs expose minutes and walking speed under the advanced parameters.

Avoid edge effects at the boundary of my study area?

Build the network over a buffered extent so that catchments near the boundary remain complete, then restrict analysis sources to the study area itself with cn.set_boundary(polygon). Streets inside the boundary are used as analysis sources and the buffered surround completes their catchments. See fundamentals on edge rolloff.

Get a network from OpenStreetMap?

CityNetwork.from_osm(poly) downloads, cleans, and builds a network for a boundary polygon in one step. For finer control over the download and cleaning stages, see tools.io.

Clean a messy network?

Automated cleaning handles most OSM input; for configurable control over consolidation and simplification see the network cleaning guide and tools.graphs.

Speed up long-distance computations?

Pass sample=True to the centrality methods (experimental). A pilot poll measures each node’s reach and only samples where that is predicted to be faster; inverse-probability weighting keeps estimates unbiased and rankings are preserved at the default epsilon=0.05. See the sampling module.

Export results to QGIS or a figure?

cn.to_geopandas() returns the street segments with all computed columns. Write them with to_file("results.gpkg") for styling in QGIS, or plot with GeoPandas and matplotlib. The From Results to Maps recipe covers both.

Do all of this without writing Python?

The QGIS plugin exposes centrality, demand betweenness, accessibility, mixed uses, and statistics as Processing algorithms over ordinary QGIS layers.