Distance relations

Straight forward way to obtain euclidean distance matrix

import pandas as pd

blocks_gdf = pd.read_pickle('./../data/saint_petersburg/blocks.pickle')

Distance matrix

from blocksnet.relations import calculate_distance_matrix

distance_matrix = calculate_distance_matrix(blocks_gdf)

distance_matrix.head()

	0	1	2	3	4	5	6	7	8	9	...	9523	9524	9525	9526	9527	9528	9529	9530	9531	9532
0	0	3671	12961	13090	13595	15265	14353	9158	9098	15711	...	17578	16948	16749	16670	16924	16681	16784	18689	18670	18856
1	3671	0	16211	16307	15388	13265	16733	11665	11595	13218	...	15133	14921	14756	14718	15004	14742	14913	17132	16709	16841
2	12961	16211	0	296	8238	27812	5769	5855	5922	23053	...	30366	29468	29249	29139	29359	29139	29181	30714	31092	31321
3	13090	16307	296	0	8010	27986	5499	5793	5861	22975	...	30525	29645	29426	29318	29540	29319	29364	30912	31275	31501
4	13595	15388	8238	8010	0	28569	3100	5055	5079	17022	...	30518	30243	30065	30010	30284	30029	30170	32214	32013	32165

5 rows × 9533 columns

Visualization example

from blocksnet.analysis.network import median_accessibility

med_dist_df = median_accessibility(distance_matrix)

blocks_gdf.join(med_dist_df).plot('median_accessibility', legend=True).set_axis_off()