Generating intermodal graph and calculating accessibility matrix

BlocksNet uses IduEdu network library to generate city intermodal graph and calculate the accessibility matrix.

[1]:

import os
import geopandas as gpd

data_path = "./data"

Read blocks and initialize processor instance

[2]:

from blocksnet import AccessibilityProcessor

blocks = gpd.read_parquet(os.path.join(data_path, 'blocks.parquet'))
ap = AccessibilityProcessor(blocks)

Generate an intermodal city graph

[3]:

graph = ap.get_intermodal_graph()

/home/vasilstar/masterplanning/.venv/lib/python3.10/site-packages/shapely/linear.py:90: RuntimeWarning: invalid value encountered in line_locate_point
  return lib.line_locate_point(line, other)

[4]:

ap.plot(blocks, graph)

../_images/examples_accessibility_processor_6_0.png

Calculate the accessibility matrix

[5]:

acc_mx = ap.get_accessibility_matrix(graph)
acc_mx.head()

[5]:

	0	1	2	3	4	5	6	7	8	9	...	16310	16311	16312	16313	16314	16315	16316	16317	16318	16319
0	0.000000	18.718750	9.234375	17.21875	30.296875	27.390625	30.671875	23.281250	78.6875	81.2500	...	76.5625	102.5625	97.5000	132.250	130.8750	142.125	247.625	228.625	247.625	124.6250
1	17.859375	0.000000	19.015625	7.65625	20.281250	25.250000	25.234375	15.765625	76.8750	79.4375	...	74.6875	100.7500	95.6875	130.500	129.0000	140.250	245.875	226.875	245.875	112.5000
2	9.234375	20.796875	0.000000	20.21875	27.968750	25.062500	28.343750	20.953125	71.8750	74.4375	...	69.7500	95.8125	90.6875	125.500	124.0625	135.375	240.875	221.875	240.875	124.6250
3	15.937500	7.449219	18.421875	0.00000	21.109375	24.984375	27.781250	16.593750	76.3125	78.8125	...	74.1250	100.1875	95.0625	129.875	128.5000	139.750	245.250	226.250	245.250	113.3750
4	31.906250	19.765625	31.734375	21.28125	0.000000	21.781250	23.125000	15.429688	89.5625	92.1250	...	87.4375	113.5000	108.3750	143.125	141.7500	153.000	258.500	239.500	258.500	108.3125

5 rows × 16320 columns

[6]:

acc_mx.to_pickle(os.path.join(data_path, 'acc_mx.pickle'))