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pipeline/transform/postcode_boundaries/voronoi.py

@@ -1,8 +1,7 @@
 from collections import defaultdict
 
 import numpy as np
-from scipy.spatial import Voronoi
-from scipy.spatial.qhull import QhullError
+from scipy.spatial import QhullError, Voronoi
 from shapely import make_valid
 from shapely.geometry import MultiPolygon, Polygon
 from shapely.ops import unary_union
@@ -24,12 +23,15 @@ def compute_voronoi_regions(
     # Deduplicate points, keeping one per (location, postcode) pair.
     # Multiple postcodes at the same coordinate each get their own point,
     # jittered by a tiny offset (0.01m) so Voronoi can distinguish them.
+    # Coords are rounded to mm precision for stable hashing — UPRN inputs are
+    # already integer metres, but the float64 cast can introduce ULP noise.
+    GOLDEN_ANGLE = np.pi * (3.0 - np.sqrt(5.0))
     seen: dict[tuple[float, float, str], bool] = {}
     unique_pts = []
     unique_pcs = []
     coord_counts: dict[tuple[float, float], int] = defaultdict(int)
     for i in range(len(points)):
-        coord = (points[i, 0], points[i, 1])
+        coord = (round(float(points[i, 0]), 3), round(float(points[i, 1]), 3))
         key = (coord[0], coord[1], postcodes[i])
         if key not in seen:
             seen[key] = True
@@ -38,11 +40,10 @@ def compute_voronoi_regions(
             if jitter_idx == 0:
                 unique_pts.append(points[i].copy())
             else:
-                # Tiny jitter so Voronoi sees distinct points (0.01m per step)
+                # Golden-angle spacing distributes any number of jittered
+                # points evenly around (and outward from) the original coord.
                 jittered = points[i].copy()
-                angle = (
-                    2 * np.pi * jitter_idx / max(coord_counts[coord], jitter_idx + 1)
-                )
+                angle = jitter_idx * GOLDEN_ANGLE
                 jittered[0] += 0.01 * np.cos(angle)
                 jittered[1] += 0.01 * np.sin(angle)
                 unique_pts.append(jittered)