Accessibilities

Accessibility metrics quantify how easily different land uses (such as shops, parks, or restaurants) can be reached from each point in the street network. These metrics are fundamental to understanding the distribution and proximity of urban amenities.

The following examples use the layers.compute_accessibilities method. The same network pre-processing steps can be followed as shown for the network preparation or centrality examples. A dataset identifying the locations of different land uses is then needed to compute accessibilities over the network.

This method requires:

• A geopandas GeoDataFrame containing landuse data;
• A landuse_column_label identifying the column with the landuse labels;
• The target accessibility_keys indicating which landuse types to compute accessibilities for.

The output GeoDataFrame will contain new columns for each landuse in three forms:

• A count of reachable instances of a given landuse within the specified distance thresholds;
• A distance-weighted count of reachable locations, where nearer locations contribute more;
• The distance to the nearest instance of a given landuse.

The last example computes mixed land-uses using the layers.compute_mixed_uses method, which measures the diversity of land uses reachable from each node.

Tip

These examples assume you have a prepared network. See Network Preparation for how to create one. For background on geopandas, see the GeoPandas chapter of the Python 101 section.

Landuse accessibility from geopandas data

Calculate landuse accessibilities from a geopandas GeoDataFrame.

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Landuse accessibility from OSM data

Calculate landuse accessibilities from OpenStreetMap.

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Parks accessibility from OSM data

Calculate park accessibilities from OSM Polygon data.

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Calculating mixed-uses

Calculate mixed land-uses.

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Adding GTFS transport data

GTFS transport data support is still experimental.

Add GTFS transportation data to the network.

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