Network

[1]:

import geopandas as gpd

blocks_gdf = gpd.read_parquet('./../data/blocks.parquet')
blocks_gdf.head(2)

[1]:

	geometry	land_use	is_living	build_floor_area	living_demand	living_area	share_living	business_area	share_business	site_area	...	capacity_bus_station	capacity_bus_stop	capacity_pier	capacity_animal_shelter	capacity_military_kom	capacity_prison	capacity_landfill	capacity_plant_nursery	capacity_greenhouse_complex	capacity_warehouse
id
0	POLYGON ((354918.622 6625258.829, 354901.464 6...	None	True	43840.686518	50.641057	30688.480678	2.199357	13152.205840	0.942581	804466.712114	...	0	0	0	0	0	0	0	0	0	0
1	POLYGON ((355412.142 6623378.149, 355411.7 662...	transport	True	10294.395525	53.114392	2177.690063	0.431105	8116.705462	1.606820	23173.129862	...	0	0	0	0	0	0	0	0	0	0

2 rows × 83 columns

Filter blocks getting only local ones for the example

[2]:

import osmnx as ox

local_crs = blocks_gdf.crs
polygon = ox.geocode_to_gdf('R1114252', by_osmid=True).to_crs(local_crs)
blocks_gdf = blocks_gdf[blocks_gdf.intersects(polygon.union_all())]
blocks_gdf.plot().set_axis_off()

../../../_images/examples_analysis_network_od_and_rc_3_0.png

Calculating accessibility matrices

[3]:

from blocksnet.relations import get_accessibility_graph

Getting graphs

[4]:

graph_drive = get_accessibility_graph(blocks_gdf, 'drive')

2025-06-08 15:38:00.477 | WARNING  | blocksnet.relations.accessibility.graph.core:get_accessibility_graph:16 - CRS do not match IDUEDU required crs. Reprojecting
2025-06-08 15:38:00.551 | INFO     | iduedu.modules.drive_walk_builder:get_drive_graph_by_poly:91 - Downloading drive graph from OSM, it may take a while for large territory ...
2025-06-08 15:38:01.897 | WARNING  | iduedu.utils.utils:remove_weakly_connected_nodes:37 - Removing 24 nodes that form 23 trap components. These are groups where you can enter but can't exit (or vice versa). Keeping the largest strongly connected component (663 nodes).

[5]:

graph_walk = get_accessibility_graph(blocks_gdf, 'walk')

2025-06-08 15:38:01.927 | WARNING  | blocksnet.relations.accessibility.graph.core:get_accessibility_graph:16 - CRS do not match IDUEDU required crs. Reprojecting
2025-06-08 15:38:01.969 | INFO     | iduedu.modules.drive_walk_builder:get_walk_graph:217 - Downloading walk graph from OSM, it may take a while for large territory ...

Calculating distances from `blocks` to `nodes`

[6]:

from blocksnet.relations import accessibility_graph_to_gdfs

nodes_gdf, _ = accessibility_graph_to_gdfs(graph_drive)
nodes_gdf.plot().set_axis_off()

../../../_images/examples_analysis_network_od_and_rc_10_0.png

[7]:

from iduedu import get_adj_matrix_gdf_to_gdf

blocks_to_nodes = get_adj_matrix_gdf_to_gdf(blocks_gdf, nodes_gdf, graph_walk)

[8]:

blocks_to_nodes.head()

/home/vasilstar/masterplanning/.venv/lib/python3.10/site-packages/pandas/io/formats/format.py:1458: RuntimeWarning: overflow encountered in cast
  has_large_values = (abs_vals > 1e6).any()

[8]:

	0	1	2	3	4	5	6	7	8	9	...	653	654	655	656	657	658	659	660	661	662
id
112	3410.0	3602.0	3526.0	3528.0	3408.0	3850.0	3856.0	3586.0	2840.0	3434.0	...	2690.0	3186.0	4052.0	3222.0	4356.0	4544.0	4156.0	4572.0	4952.0	4972.0
113	2096.0	2288.0	2212.0	2212.0	2094.0	2348.0	2354.0	2270.0	1179.0	1774.0	...	3318.0	3806.0	4708.0	3880.0	5212.0	5340.0	5012.0	5364.0	4676.0	5200.0
114	3920.0	4116.0	4036.0	4038.0	3918.0	4364.0	4368.0	4096.0	3352.0	3944.0	...	3200.0	3698.0	4564.0	3734.0	4864.0	5056.0	4668.0	5080.0	5464.0	5484.0
115	3046.0	3238.0	3160.0	3160.0	3040.0	3298.0	3302.0	3218.0	2128.0	2724.0	...	2974.0	3470.0	4368.0	3540.0	4732.0	4920.0	4532.0	4944.0	5160.0	5284.0
215	4116.0	4312.0	4232.0	4232.0	4112.0	4556.0	4560.0	4292.0	3758.0	4352.0	...	3124.0	3620.0	4452.0	3658.0	4372.0	4728.0	4148.0	4752.0	5392.0	5380.0

5 rows × 663 columns

Calculating distances from nodes to nodes

[9]:

nodes_to_nodes = get_adj_matrix_gdf_to_gdf(nodes_gdf, nodes_gdf, graph_drive)

[10]:

nodes_to_nodes.head()

/home/vasilstar/masterplanning/.venv/lib/python3.10/site-packages/pandas/io/formats/format.py:1458: RuntimeWarning: overflow encountered in cast
  has_large_values = (abs_vals > 1e6).any()

[10]:

	0	1	2	3	4	5	6	7	8	9	...	653	654	655	656	657	658	659	660	661	662
0	0.000	17.375	371.50000	315.250000	215.0000	664.0	659.5	349.000	1181.0	1254.0	...	3626.0	3888.0	4696.0	3860.0	5392.0	5592.0	5080.0	5576.0	4276.0	5016.0
1	37.750	0.000	354.00000	297.750000	197.6250	647.0	642.0	331.750	1163.0	1237.0	...	3610.0	3870.0	4680.0	3842.0	5376.0	5576.0	5060.0	5556.0	4256.0	4996.0
2	267.500	285.000	0.00000	5.519531	107.1875	354.5	350.0	39.375	1281.0	944.5	...	3894.0	4156.0	4964.0	4128.0	5660.0	5860.0	5344.0	5844.0	4544.0	5284.0
3	323.750	341.000	56.15625	0.000000	163.3750	349.0	344.5	33.875	1275.0	939.0	...	3950.0	4212.0	5020.0	4184.0	5716.0	5916.0	5400.0	5900.0	4600.0	5340.0
4	160.375	177.750	532.00000	475.500000	0.0000	824.5	820.0	509.500	1341.0	1415.0	...	3788.0	4048.0	4856.0	4020.0	5552.0	5752.0	5240.0	5736.0	4436.0	5176.0

5 rows × 663 columns

Calculating OD matrix

[11]:

services_dfs = []
for column in [c for c in blocks_gdf.columns if 'capacity_' in c]:
    services_df = blocks_gdf[[column]].rename(columns={column:'capacity'})
    services_dfs.append(services_df)

[12]:

from blocksnet.analysis.network import origin_destination_matrix

od_mx = origin_destination_matrix(blocks_gdf, blocks_to_nodes, nodes_to_nodes, services_dfs)

2025-06-08 15:38:19.243 | INFO     | blocksnet.analysis.network.origin_destination.core:_validate_input:81 - Validating input data
2025-06-08 15:38:19.244 | INFO     | blocksnet.analysis.network.origin_destination.core:_calculate_diversity:51 - Calculating diversity and density
2025-06-08 15:38:19.633 | INFO     | blocksnet.analysis.network.origin_destination.core:_calculate_attractiveness:59 - Calculating attractiveness
2025-06-08 15:38:19.640 | INFO     | blocksnet.analysis.network.origin_destination.core:_calculate_nodes_weights:32 - Identifying nearest nodes to blocks
2025-06-08 15:38:19.646 | INFO     | blocksnet.analysis.network.origin_destination.core:_calculate_nodes_weights:37 - Calculating weights
2025-06-08 15:38:19.655 | INFO     | blocksnet.analysis.network.origin_destination.core:_calculate_nodes_weights:43 - Distributing
2025-06-08 15:38:19.659 | INFO     | blocksnet.analysis.network.origin_destination.core:_calculate_od_mx:72 - Calculating origin destination matrix

Calculating roads congestion

[16]:

from blocksnet.analysis.network import road_congestion

graph_congestion = road_congestion(od_mx, graph_drive)

2025-06-08 15:41:45.987 | INFO     | blocksnet.analysis.network.road_congestion.core:road_congestion:18 - Calculating shortest path
2025-06-08 15:41:47.616 | INFO     | blocksnet.analysis.network.road_congestion.core:road_congestion:21 - Evaluating congestion
100%|██████████| 663/663 [00:44<00:00, 15.04it/s]

[17]:

_, edges_gdf = accessibility_graph_to_gdfs(graph_congestion)
edges_gdf.head()

[17]:

		length_meter	time_min	geometry	congestion
0	1	17.379	0.017	LINESTRING (348610.46 6648540.586, 348595.032 ...	0.058759
1	339	8.024	0.008	LINESTRING (348595.032 6648548.588, 348588.653...	0.019130
1	340	245.966	0.246	LINESTRING (348595.032 6648548.588, 348581.331...	0.040963
2	3	5.515	0.008	LINESTRING (348859.015 6648442.978, 348857.583...	0.000718
2	4	107.189	0.107	LINESTRING (348859.015 6648442.978, 348847.224...	0.048565

[18]:

edges_gdf.plot('congestion', legend=True, figsize=(8,5)).set_axis_off()

../../../_images/examples_analysis_network_od_and_rc_22_0.png