Population centrality
[1]:
import pandas as pd
blocks_gdf = pd.read_pickle('./../../data/saint_petersburg/blocks.pickle')[['geometry', 'site_area', 'population']]
blocks_gdf.head()
[1]:
| geometry | site_area | population | |
|---|---|---|---|
| 0 | POLYGON ((349424.859 6631180.891, 349424.751 6... | 0.032316 | 0.0 |
| 1 | POLYGON ((352083.617 6633950.146, 352240.448 6... | 137101.088247 | 0.0 |
| 2 | POLYGON ((346700.642 6618453.176, 346681.107 6... | 95239.084434 | 109.0 |
| 3 | POLYGON ((347043.363 6618261.219, 347042.608 6... | 125919.330984 | 77.0 |
| 4 | POLYGON ((354879.039 6618859.116, 354845.405 6... | 169860.164494 | 431.0 |
[2]:
from blocksnet.relations import generate_adjacency_graph
adjacency_graph = generate_adjacency_graph(blocks_gdf, 10)
2025-09-19 17:13:01.194 | INFO | blocksnet.relations.adjacency.core:_generate_adjacency_nodes:9 - Generating nodes
2025-09-19 17:13:01.202 | INFO | blocksnet.relations.adjacency.core:_generate_adjacency_edges:14 - Generating edges
2025-09-19 17:13:04.334 | SUCCESS | blocksnet.relations.adjacency.core:generate_adjacency_graph:37 - Adjacency graph successfully generated: 9533 nodes, 30567 edges
[3]:
from blocksnet.analysis.centrality import population_centrality
centrality_df = population_centrality(blocks_gdf, adjacency_graph)
centrality_df.head()
[3]:
| population | degree_centrality | population_normalized | degree_centrality_normalized | population_centrality | |
|---|---|---|---|---|---|
| 0 | 0 | 0.000420 | 1.000000 | 1.086957 | 0.060606 |
| 1 | 0 | 0.000734 | 1.000000 | 1.152174 | 0.106061 |
| 2 | 109 | 0.000839 | 1.003471 | 1.173913 | 0.124052 |
| 3 | 77 | 0.000629 | 1.002452 | 1.130435 | 0.092841 |
| 4 | 431 | 0.000839 | 1.013723 | 1.173913 | 0.132440 |
[5]:
blocks_gdf[['geometry']].join(centrality_df).plot('population_centrality', legend=True, figsize=(10, 8)).set_axis_off()
