Calculate mixed land-uses.

Calculate mixed landuses from a geopandas GeoDataFrame.

import geopandas as gpd
import matplotlib.pyplot as plt
from cityseer.metrics import layers
from cityseer.tools import graphs, io

To start, follow the same approach as shown in the network examples to create the network.

streets_gpd = gpd.read_file("data/madrid_streets/street_network.gpkg")
streets_gpd = streets_gpd.explode(reset_index=True)
G = io.nx_from_generic_geopandas(streets_gpd)
G_dual = graphs.nx_to_dual(G)
nodes_gdf, _edges_gdf, network_structure = io.network_structure_from_nx(G_dual)

INFO:cityseer.tools.graphs:Merging parallel edges within buffer of 1.
INFO:cityseer.tools.graphs:Converting graph to dual.
INFO:cityseer.tools.graphs:Preparing dual nodes
INFO:cityseer.tools.graphs:Preparing dual edges (splitting and welding geoms)
INFO:cityseer.tools.io:Preparing node and edge arrays from networkX graph.

Read-in the dataset from the source Geopackage or Shapefile Geopandas.

prems_gpd = gpd.read_file("data/madrid_premises/madrid_premises.gpkg")
prems_gpd.head()

	index	local_id	local_distr_id	local_distr_desc	local_neighb_id	local_neighb_desc	local_neighb_code	local_census_section_id	local_census_section_desc	section_id	section_desc	division_id	division_desc	epigraph_id	epigraph_desc	easting	northing	geometry
0	0	10003324	1	CENTRO	105	UNIVERSIDAD	5	1091	91	I	hospitality	56	food_bev	561001	RESTAURANTE	440181.6	4475586.5	POINT (440181.6 4475586.5)
1	1	10003330	1	CENTRO	105	UNIVERSIDAD	5	1115	115	R	art_rec_entert	90	creat_entert	900003	TEATRO Y ACTIVIDADES ESCENICAS REALIZADAS EN D...	440000.6	4474761.5	POINT (440000.6 4474761.5)
2	2	10003356	1	CENTRO	104	JUSTICIA	4	1074	74	I	hospitality	56	food_bev	561004	BAR RESTAURANTE	440618.6	4474692.5	POINT (440618.6 4474692.5)
3	3	10003364	1	CENTRO	104	JUSTICIA	4	1075	75	G	wholesale_retail_motor	47	retail	472401	COMERCIO AL POR MENOR DE PAN Y PRODUCTOS DE PA...	440666.6	4474909.5	POINT (440666.6 4474909.5)
4	4	10003367	1	CENTRO	106	SOL	6	1119	119	G	wholesale_retail_motor	47	retail	477701	COMERCIO AL POR MENOR DE JOYAS, RELOJERIA Y BI...	440378.6	4474380.5	POINT (440378.6 4474380.5)

Identify or prepare any columns and land uses of interest, for which you want to compute accessibilities.

prems_gpd.division_desc.unique()

array(['food_bev', 'creat_entert', 'retail', 'services', 'education',
       'accommod', 'Real Estate Activities',
       'Residential Care Activities',
       'Social Services Without Accommodation',
       'Graphic Arts and Reproduction of Recorded Media',
       'Sale and Repair of Motor Vehicles and Motorcycles',
       'Decontamination and Other Waste Management Services',
       'Gambling and Betting Activities', 'sports_rec', 'health',
       'Wholesale Trade and Middlemen, Except for Motor Vehicles and Motorcycles',
       'Telecommunications',
       'Financial Services, Except Insurance and Pension Funds',
       'Associative Activities', 'Veterinary Activities',
       'Auxiliary Services to Financial Services and Insurance',
       'Insurance, Reinsurance, and Pension Funds, Except Compulsory Social Security',
       'Rental Activities',
       'Office Administrative and Other Business Support Activities',
       'Technical Services of Architecture and Engineering; Technical Testing and Analysis',
       'Other Manufacturing Industries',
       'Storage and Activities Related to Transport',
       'Specialized Construction Activities and Building Finishing',
       'Clothing Manufacturing',
       'Activities of Travel Agencies, Tour Operators, Reservation Services, and Related Activities',
       'Land Transport and Pipeline Transport',
       'Other Professional, Scientific, and Technical Activities',
       'Computer Repair, Personal Effects, and Household Items',
       'Publishing (Includes Software)', 'Postal and Courier Activities',
       'Food Industry', 'Advertising and Market Research',
       'Public Administration and Defense; Compulsory Social Security',
       'Film, Video, and TV Program Activities, Sound Recording, and Music Publishing, Sound Recording and Music Publishing',
       'Legal and Accounting Activities',
       'Building Promotion and Construction',
       'Supply of Electricity, Gas, Steam, and Air Conditioning',
       'Activities of Libraries, Archives, Museums, and Other Cultural Activities',
       'Paper Industry',
       'Manufacturing of Computer, Electronic, and Optical Products',
       'Security and Investigation Activities',
       'Leather and Footwear Industry',
       'Repair and Installation of Machinery and Equipment',
       'Manufacturing of Metal Products, Except Machinery and Equipment',
       'Furniture Manufacturing',
       'Building Services and Gardening Activities', 'Chemical Industry',
       'Programming, Consultancy, and Other Computer-Related Activities',
       'Manufacturing of Other Non-Metallic Mineral Products',
       'Textile Industry', 'Research and Development',
       'Activities of Head Offices; Management Consultancy Activities',
       'Pharmaceutical Product Manufacturing',
       'Manufacturing of Machinery and Equipment N.E.C.',
       'Manufacturing of Motor Vehicles, Trailers, and Semi-Trailers',
       'Information Services',
       'Manufacturing of Rubber and Plastic Products',
       'Manufacturing of Electrical Equipment',
       'Manufacturing of Other Transport Equipment', 'Civil Engineering',
       'Wood and Cork Industry, Except Furniture; Basketry and Basketwork',
       'Waste Collection, Treatment, and Disposal; Materials Recovery',
       'Activities of Extraterritorial Organizations and Bodies',
       'Air Transport', 'Employment Activities',
       'Agriculture, Livestock, Hunting, and Related Service Activities',
       'Metallurgy; Manufacturing of Iron, Steel, and Ferro-Alloy Products',
       'Broadcasting and Television Programming and Broadcasting Activities',
       'Wastewater Collection and Treatment', 'Beverage Manufacturing',
       'Water Collection, Treatment, and Supply',
       'Support Activities for Extractive Industries',
       'Other Extractive Industries', 'Forestry and Logging',
       'Tobacco Industry'], dtype=object)

Once the land use and network data has been prepared, use the layers.compute_mixed_uses method to compute accessibilities to land uses. The landuse_column_label should correspond to the data in the input GeoDataFrame.

# compute pub accessibility
distances = [100, 200, 400, 800]
nodes_gdf, prems_gpd = layers.compute_mixed_uses(
    prems_gpd,
    landuse_column_label="division_desc",
    nodes_gdf=nodes_gdf,
    network_structure=network_structure,
    distances=distances,
)

INFO:cityseer.metrics.layers:Computing mixed-use measures.
INFO:cityseer.metrics.layers:Assigning data to network.
INFO:cityseer.config:Metrics computed for:
INFO:cityseer.config:Distance: 100m, Beta: 0.04, Walking Time: 1.25 minutes.
INFO:cityseer.config:Distance: 200m, Beta: 0.02, Walking Time: 2.5 minutes.
INFO:cityseer.config:Distance: 400m, Beta: 0.01, Walking Time: 5.0 minutes.
INFO:cityseer.config:Distance: 800m, Beta: 0.005, Walking Time: 10.0 minutes.

The output columns are named cc_{key}_{distance}_{type}. Where the keys will correspond to the form of mixed-use measure and the distances will correspond to the input distances. The measures are available in non-weighted nw or distance weighted wt form.

Standard GeoPandas functionality can be used to explore, visualise, or save the results.

nodes_gdf.columns

Index(['ns_node_idx', 'x', 'y', 'live', 'weight', 'primal_edge',
       'primal_edge_node_a', 'primal_edge_node_b', 'primal_edge_idx',
       'dual_node', 'cc_hill_q0_100_nw', 'cc_hill_q0_100_wt',
       'cc_hill_q1_100_nw', 'cc_hill_q1_100_wt', 'cc_hill_q2_100_nw',
       'cc_hill_q2_100_wt', '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'],
      dtype='object')

fig, ax = plt.subplots(1, 1, figsize=(8, 8), facecolor="#1d1d1d")
nodes_gdf.plot(
    column="cc_hill_q0_400_wt",
    cmap="hot",
    legend=False,
    ax=ax,
)
prems_gpd.plot(
    column="division_desc",
    cmap="tab20",
    markersize=1,
    edgecolor=None,
    legend=False,
    ax=ax,
)
ax.set_xlim(439000, 439000 + 2500)
ax.set_ylim(4473000, 4473000 + 2500)
ax.axis(False)

(np.float64(439000.0),
 np.float64(441500.0),
 np.float64(4473000.0),
 np.float64(4475500.0))