PopulationCentrality

blocksnet/method/centrality/__init__.py

@@ -2,3 +2,4 @@
 Connectivity method is located here.
 """
 from .centrality import *
+from .population_centrality import *

blocksnet/method/centrality/population_centrality.py

@@ -0,0 +1,79 @@
+import geopandas as gpd
+import networkx as nx
+import numpy as np
+from scipy.spatial import cKDTree
+from shapely import Point
+from sklearn.preprocessing import MinMaxScaler
+from ..base_method import BaseMethod
+
+
+POPULATION_CENTRALITY_COLUMN = "population_centrality"
+
+PLOT_KWARGS = {"column": POPULATION_CENTRALITY_COLUMN, "legend": True, "cmap": "RdYlGn", "vmin": 0}
+
+
+class PopulationCentrality(BaseMethod):
+ """
+ This class provides methods to calculate and visualize the population centrality of urban blocks.
+ Population centrality is determined based on connectivity and population density within a specified radius.
+ """
+
+ @staticmethod
+ def plot(gdf: gpd.GeoDataFrame, figsize=(10, 10)):
+ ax = gdf.plot(color="#ddd", figsize=(10, 10))
+ gdf.plot(ax=ax, **PLOT_KWARGS)
+ ax.set_axis_off()
+
+ def calculate(self, connectivity_radius: float = 1000) -> gpd.GeoDataFrame:
+ """
+ Calculate population centrality for urban blocks based on connectivity and population density.
+
+ Parameters:
+ - connectivity_radius: The radius within which to consider connectivity between blocks (default is 1000 meters).
+
+ Returns:
+ - A GeoDataFrame of urban blocks with an added column for population centrality.
+
+ This method calculates the connectivity of urban blocks within the specified radius,
+ computes the degree centrality of each block, normalizes the values, and combines them with
+ normalized population density to determine the final population centrality.
+ """
+ # get blocks and find neighbors in radius
+ blocks = self.city_model.get_blocks_gdf()
+ points = np.array([(point.x, point.y) for point in blocks.geometry.centroid])
+ tree = cKDTree(points)
+ pairs = tree.query_pairs(r=connectivity_radius)
+ # graph creation and centrality calc
+ connectivity_graph = nx.Graph()
+ for idx, point in enumerate(points):
+ connectivity_graph.add_node(idx, pos=(point[0], point[1]))
+ for i, j in pairs:
+ connectivity_graph.add_edge(i, j)
+ centrality = nx.degree_centrality(connectivity_graph)
+
+ # connect graph and blocks
+ nodes = gpd.GeoDataFrame(
+ geometry=[Point(position["pos"]) for _, position in connectivity_graph.nodes(data=True)], crs=32636
+ )
+ nodes["centrality"] = centrality
+ blocks_out = gpd.sjoin(blocks, nodes, how="left", predicate="intersects")
+
+ # normalize values
+ data = blocks_out["centrality"].values.astype(float).reshape(-1, 1)
+ minmax_scaler = MinMaxScaler(feature_range=(1, 2))
+ normalized_data = minmax_scaler.fit_transform(data)
+ blocks_out["centrality"] = normalized_data
+
+ data = blocks_out["population"].values.astype(float).reshape(-1, 1)
+ normalized_data = minmax_scaler.fit_transform(data)
+ blocks_out["population"] = normalized_data
+
+ minmax_scaler = MinMaxScaler(feature_range=(0, 10))
+ blocks_out[POPULATION_CENTRALITY_COLUMN] = np.round(
+ minmax_scaler.fit_transform(
+ (blocks_out["centrality"] * blocks_out["population"]).values.astype(float).reshape(-1, 1)
+ ),
+ 2,
+ )
+
+ return blocks_out[["geometry", POPULATION_CENTRALITY_COLUMN]]