Cityseer Examples

This repository contains examples for the cityseer-api package.

Use the navigation menu to explore examples.

Getting Started

cityseer is a python package that can be installed with pip:

# !pip install --upgrade cityseer
Warning

The guide and examples are based on cityseer>=4.12.0. Version 4.12.0 uses a new output column naming convention for computed metrics for improved consistency. If using an earlier version, please upgrade to 4.12.0 or else adapt the column names to match the earlier convention.

cityseer revolves around networks (graphs). If you’re comfortable with numpy and abstract data handling, then the underlying data structures can be created and manipulated directly. However, it is generally more convenient to sketch the graph using NetworkX and to let cityseer take care of initialising and converting the graph for you.

# any networkX MultiGraph with 'x' and 'y' node attributes will do
# here we'll use the cityseer mock module to generate an example networkX graph
import networkx as nx
from cityseer.tools import mock, graphs, plot, io

G = mock.mock_graph()
print(G)
# let's plot the network
plot.plot_nx(G, labels=True, node_size=80, dpi=200, figsize=(4, 4))
INFO:cityseer.tools.plot:Preparing graph nodes
INFO:cityseer.tools.plot:Preparing graph edges
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MultiGraph with 57 nodes and 79 edges

Graph Preparation

The tools.graphs module contains a collection of convenience functions for the preparation and conversion of networkX MultiGraphs, i.e. undirected graphs allowing for parallel edges. The tools.graphs module is designed to work with raw shapely Linestring geometries that have been assigned to the graph’s edge (link) geom attributes. The benefit to this approach is that the geometry of the network is decoupled from the topology: the topology is consequently free from distortions which would otherwise confound centrality and other metrics.

There are generally two scenarios when creating a street network graph:

  1. In the ideal case, if you have access to a high-quality street network dataset – which keeps the topology of the network separate from the geometry of the streets – then you would construct the network based on the topology while assigning the roadway geometries to the respective edges spanning the nodes. OS Open Roads is a good example of this type of dataset. Assigning the geometries to an edge involves A) casting the geometry to a shapely LineString, and B) assigning this geometry to the respective edge by adding the LineString geometry as a geom attribute. e.g. G.add_edge(start_node, end_node, geom=a_linestring_geom).

  2. In reality, most data-sources are not this refined and will represent roadway geometries by adding additional nodes to the network. For a variety of reasons, this is not ideal and you may want to follow the Graph Cleaning or Graph Corrections guides.

Here, we’ll walk through a high-level overview showing how to use cityseer. You can provide your own shapely geometries if available; else, you can auto-infer simple geometries from the start to end node of each network edge, which works well for graphs where nodes have been used to inscribe roadway geometries (i.e. OSM).

# use nx_simple_geoms to infer geoms for your edges
G = graphs.nx_simple_geoms(G)
plot.plot_nx(G, labels=True, node_size=80, plot_geoms=True, dpi=200, figsize=(4, 4))
INFO:cityseer.tools.graphs:Generating interpolated edge geometries.
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INFO:cityseer.tools.plot:Preparing graph nodes
INFO:cityseer.tools.plot:Preparing graph edges
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We have now inferred geometries for each edge, meaning that each edge now has an associated LineString geometry. Any further manipulation of the graph using the cityseer.graph module will retain and further manipulate these geometries in-place.

Once the geoms are readied, we can use tools such as nx_decompose for generating granular graph representations and nx_to_dual for casting a primal graph representation to its dual.

# this will (optionally) decompose the graph
G_decomp = graphs.nx_decompose(G, 50)
plot.plot_nx(G_decomp, plot_geoms=True, labels=False, dpi=200, figsize=(4, 4))
INFO:cityseer.tools.graphs:Decomposing graph to maximum edge lengths of 50.
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INFO:cityseer.tools.plot:Preparing graph nodes
INFO:cityseer.tools.plot:Preparing graph edges
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# this will (optionally) cast to a dual network
G_dual = graphs.nx_to_dual(G)
# here we are plotting the newly decomposed graph (blue) against the original graph (red)
plot.plot_nx_primal_or_dual(G, G_dual, plot_geoms=False, dpi=200, figsize=(4, 4))
INFO:cityseer.tools.graphs:Converting graph to dual.
INFO:cityseer.tools.graphs:Preparing dual nodes
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INFO:cityseer.tools.graphs:Preparing dual edges (splitting and welding geoms)
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INFO:cityseer.tools.plot:Preparing graph nodes
INFO:cityseer.tools.plot:Preparing graph edges
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INFO:cityseer.tools.plot:Preparing graph nodes
INFO:cityseer.tools.plot:Preparing graph edges
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Metrics

After graph preparation and cleaning has been completed, the networkX graph can be transformed into data structures for efficiently computing centralities, land-use measures, or statistical aggregations.

Use network_structure_from_nx to convert a networkX graph into a GeoPandas GeoDataFrame and a rustalgos.NetworkStructure, which is used by Cityseer for efficiently computing over the network.

Network Centralities

The networks.node_centrality_shortest, networks.node_centrality_simplest, and networks.segment_centrality methods wrap underlying rust functions that compute the centrality methods. All selected measures and distance thresholds are computed simultaneously to reduce the amount of time required for multi-variable and multi-scalar workflows. The results of the computations will be written to the GeoDataFrame.

from cityseer.metrics import networks

# create a Network layer from the networkX graph
# use a CRS EPSG code matching the projected coordinate reference system for your data
nodes_gdf, edges_gdf, network_structure = io.network_structure_from_nx(
    G_decomp, crs=3395
)
# the underlying method allows the computation of various centralities simultaneously, e.g.
nodes_gdf = networks.segment_centrality(
    network_structure=network_structure,  # the network structure for which to compute the measures
    nodes_gdf=nodes_gdf,  # the nodes GeoDataFrame, to which the results will be written
    distances=[
        200,
        400,
        800,
        1600,
    ],  # the distance thresholds for which to compute centralities
)
nodes_gdf.head()  # the results are now in the GeoDataFrame
INFO:cityseer.tools.io:Preparing node and edge arrays from networkX graph.
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INFO:cityseer.metrics.networks:Computing shortest path segment centrality.
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ns_node_idx x y live weight geom cc_seg_density_200 cc_seg_density_400 cc_seg_density_800 cc_seg_density_1600 ... cc_seg_harmonic_800 cc_seg_harmonic_1600 cc_seg_beta_200 cc_seg_beta_400 cc_seg_beta_800 cc_seg_beta_1600 cc_seg_betweenness_200 cc_seg_betweenness_400 cc_seg_betweenness_800 cc_seg_betweenness_1600
0 0 700700.0 5719700.0 True 1 POINT (700700.000 5719700.000) 857.327026 3042.260254 10088.987305 13137.789062 ... 36.822689 40.173214 159.837616 421.984436 1365.011597 3582.891113 590.865845 2805.017822 23575.015625 159337.765625
1 1 700610.0 5719780.0 True 1 POINT (700610.000 5719780.000) 784.184753 2527.174805 9059.986328 13137.791992 ... 34.135193 38.450062 154.743927 388.553589 1221.976562 3301.651611 534.708435 2270.786865 15957.039062 103352.648438
2 2 700460.0 5719700.0 True 1 POINT (700460.000 5719700.000) 695.835876 2062.288330 6928.821289 13137.790039 ... 29.375729 35.568359 150.506683 353.928864 999.668030 2788.973877 484.306824 1700.836914 7541.833008 36259.546875
3 3 700520.0 5719820.0 True 1 POINT (700520.000 5719820.000) 817.972900 2366.633057 5945.978027 13137.791992 ... 28.526949 35.446590 156.397873 392.560852 1018.365723 2680.288574 538.021912 2131.172119 7549.368652 24907.978516
4 4 700620.0 5719905.0 True 1 POINT (700620.000 5719905.000) 812.112549 2419.865234 7675.241699 13137.791992 ... 31.385777 36.887234 155.821732 388.663147 1108.671631 2981.365723 528.050293 2153.490479 11441.318359 66363.156250

5 rows × 22 columns

# plot centrality
from matplotlib import colors

# custom colourmap
cmap = colors.LinearSegmentedColormap.from_list("cityseer", ["#64c1ff", "#d32f2f"])
# normalise the values
segment_harmonic_vals = nodes_gdf["cc_seg_harmonic_800"]
segment_harmonic_vals = colors.Normalize()(segment_harmonic_vals)
# cast against the colour map
segment_harmonic_cols = cmap(segment_harmonic_vals)
# plot segment_harmonic
# cityseer's plot methods are used here and in tests for convenience
# that said, rather use plotting methods directly from networkX or GeoPandas where possible
plot.plot_nx(
    G_decomp, labels=False, node_colour=segment_harmonic_cols, dpi=200, figsize=(4, 4)
)
INFO:cityseer.tools.plot:Preparing graph nodes
INFO:cityseer.tools.plot:Preparing graph edges
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Land-use and statistical measures

Landuse and statistical measures require a GeoPandas GeoDataFrame consisting of Point geometries. Columns representing categorical landuse information (“pub”, “shop”, “school”) can be passed to landuse methods, whereas columns representing numerical information can be used for statistical methods.

When computing these measures, cityseer will assign each data point to the two closest network nodes — one in either direction — based on the closest adjacent street edge. This enables cityseer to use dynamic spatial aggregation methods that more accurately describe distances from the perspective of pedestrians travelling over the network, and relative to the direction of approach.

layers.compute_landuses and layers.compute_mixed_uses methods are used for the calculation of land-use accessibility and mixed-use measures whereas layers.compute_stats can be used for statistical aggregations. As with the centrality methods, the measures are computed over the network and are computed simultaneously for all measures and distances.

from cityseer.metrics import layers

# a mock data dictionary representing categorical landuse data
# here randomly generated letters represent fictitious landuse categories
data_gdf = mock.mock_landuse_categorical_data(G_decomp, random_seed=25)
data_gdf.head()
geometry data_id categorical_landuses
uid
0 POINT (701144.149 5719228.311) 0 h
1 POINT (700798.732 5719938.464) 1 f
2 POINT (700434.607 5719165.951) 2 h
3 POINT (700323.093 5719450.769) 3 a
4 POINT (700593.320 5719841.434) 4 h 
# example easy-wrapper method for computing mixed-uses
# this is a distance weighted form of hill diversity
nodes_gdf, data_gdf = layers.compute_mixed_uses(
    data_gdf,  # the source data
    landuse_column_label="categorical_landuses",  # column in the dataframe which contains the landuse labels
    nodes_gdf=nodes_gdf,  # nodes GeoDataFrame - the results are written here
    network_structure=network_structure,  # measures will be computed relative to pedestrian distances over the network
    distances=[
        200,
        400,
        800,
        1600,
    ],  # distance thresholds for which you want to compute the measures
)
print(
    nodes_gdf.columns
)  # the GeoDataFrame will contain the results of the calculations
print(nodes_gdf["cc_hill_q0_800_nw"])  # which can be retrieved as needed
INFO:cityseer.metrics.layers:Computing mixed-use measures.
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Index(['ns_node_idx', 'x', 'y', 'live', 'weight', 'geom', 'cc_seg_density_200',
       'cc_seg_density_400', 'cc_seg_density_800', 'cc_seg_density_1600',
       'cc_seg_harmonic_200', 'cc_seg_harmonic_400', 'cc_seg_harmonic_800',
       'cc_seg_harmonic_1600', 'cc_seg_beta_200', 'cc_seg_beta_400',
       'cc_seg_beta_800', 'cc_seg_beta_1600', 'cc_seg_betweenness_200',
       'cc_seg_betweenness_400', 'cc_seg_betweenness_800',
       'cc_seg_betweenness_1600', 'cc_hill_q0_200_nw', 'cc_hill_q0_200_wt',
       'cc_hill_q1_200_nw', 'cc_hill_q1_200_wt', 'cc_hill_q2_200_nw',
       'cc_hill_q2_200_wt', 'cc_hill_q0_400_nw', 'cc_hill_q0_400_wt',
       'cc_hill_q1_400_nw', 'cc_hill_q1_400_wt', 'cc_hill_q2_400_nw',
       'cc_hill_q2_400_wt', 'cc_hill_q0_800_nw', 'cc_hill_q0_800_wt',
       'cc_hill_q1_800_nw', 'cc_hill_q1_800_wt', 'cc_hill_q2_800_nw',
       'cc_hill_q2_800_wt', 'cc_hill_q0_1600_nw', 'cc_hill_q0_1600_wt',
       'cc_hill_q1_1600_nw', 'cc_hill_q1_1600_wt', 'cc_hill_q2_1600_nw',
       'cc_hill_q2_1600_wt'],
      dtype='object')
0          9.0
1          9.0
2          9.0
3          9.0
4          9.0
          ... 
53±0±54    2.0
53±1±54    2.0
53±2±54    2.0
54±0±55    2.0
54±1±55    2.0
Name: cc_hill_q0_800_nw, Length: 294, dtype: float32
# for curiosity's sake - plot the assignments to see which edges the data points were assigned to
plot.plot_assignment(network_structure, G_decomp, data_gdf, dpi=200, figsize=(4, 4))
/opt/hostedtoolcache/Python/3.11.8/x64/lib/python3.11/site-packages/cityseer/tools/plot.py:560: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  plt.tight_layout()

# plot distance-weighted hill mixed uses
mixed_uses_vals = nodes_gdf["cc_hill_q0_800_wt"]
mixed_uses_vals = colors.Normalize()(mixed_uses_vals)
mixed_uses_cols = cmap(mixed_uses_vals)
plot.plot_assignment(
    network_structure,
    G_decomp,
    data_gdf,
    node_colour=mixed_uses_cols,
    data_labels=data_gdf["categorical_landuses"].values,
    dpi=200,
    figsize=(4, 4),
)
/opt/hostedtoolcache/Python/3.11.8/x64/lib/python3.11/site-packages/cityseer/tools/plot.py:560: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  plt.tight_layout()

# compute landuse accessibilities for land-use types a, b, c
nodes_gdf, data_gdf = layers.compute_accessibilities(
    data_gdf,  # the source data
    landuse_column_label="categorical_landuses",  # column in the dataframe which contains the landuse labels
    accessibility_keys=[
        "a",
        "b",
        "c",
    ],  # the landuse categories for which to compute accessibilities
    nodes_gdf=nodes_gdf,  # nodes GeoDataFrame - the results are written here
    network_structure=network_structure,  # measures will be computed relative to pedestrian distances over the network
    distances=[
        200,
        400,
        800,
        1600,
    ],  # distance thresholds for which you want to compute the measures
)
# accessibilities are computed in both weighted and unweighted forms, e.g. for "a" and "b" landuse codes
print(
    nodes_gdf[["cc_a_800_wt", "cc_b_1600_nw"]]
)  # and can be retrieved as needed
INFO:cityseer.metrics.layers:Computing land-use accessibility for: a, b, c
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         cc_a_800_wt  cc_b_1600_nw
0           0.316222           2.0
1           0.173186           2.0
2           0.074022           2.0
3           0.047997           2.0
4           0.092515           2.0
...              ...           ...
53±0±54     0.000000           0.0
53±1±54     0.000000           0.0
53±2±54     0.000000           0.0
54±0±55     0.000000           0.0
54±1±55     0.000000           0.0

[294 rows x 2 columns]

Aggregations can likewise be computed for numerical data. Let’s generate some mock numerical data:

numerical_data_gdf = mock.mock_numerical_data(G_decomp, num_arrs=3)
numerical_data_gdf.head()
# compute stats for column mock_numerical_1
nodes_gdf, numerical_data_gdf = layers.compute_stats(
    numerical_data_gdf,  # the source data
    stats_column_label="mock_numerical_1",  # numerical column to compute stats for
    nodes_gdf=nodes_gdf,  # nodes GeoDataFrame - the results are written here
    network_structure=network_structure,  # measures will be computed relative to pedestrian distances over the network
    distances=[
        800,
        1600,
    ],  # distance thresholds for which you want to compute the measures
)
# statistical aggregations are calculated for each requested column, and in the following forms:
# max, min, sum, sum_weighted, mean, mean_weighted, variance, variance_weighted
print(nodes_gdf["cc_mock_numerical_1_max_800"])
print(nodes_gdf["cc_mock_numerical_1_mean_800_wt"])
INFO:cityseer.metrics.layers:Computing statistics.
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0          99.438004
1          99.438004
2          99.438004
3          90.749001
4          99.438004
             ...    
53±0±54     1.913000
53±1±54     1.913000
53±2±54     1.913000
54±0±55     1.913000
54±1±55     1.913000
Name: cc_mock_numerical_1_max_800, Length: 294, dtype: float32
0          51.426273
1          53.213058
2          51.837540
3          49.952995
4          53.090736
             ...    
53±0±54     1.913000
53±1±54     1.913000
53±2±54     1.913000
54±0±55     1.913000
54±1±55     1.913000
Name: cc_mock_numerical_1_mean_800_wt, Length: 294, dtype: float32

The landuse metrics and statistical aggregations are computed over the street network relative to the network, with results written to each node. The mixed-use, accessibility, and statistical aggregations can therefore be compared directly to centrality computations from the same locations, and can be correlated or otherwise compared.

Data derived from metrics can be converted back into a NetworkX graph using the nx_from_cityseer_geopandas method.

nx_multigraph_round_trip = io.nx_from_cityseer_geopandas(
    nodes_gdf,
    edges_gdf,
)
nx_multigraph_round_trip.nodes["0"]
INFO:cityseer.tools.io:Populating node and edge map data to a networkX graph.
INFO:cityseer.tools.io:Unpacking node data.
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INFO:cityseer.tools.io:Unpacking edge data.
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INFO:cityseer.tools.io:Unpacking metrics to nodes.
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{'x': 700700.0,
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