.

pipeline/transform/tree_density.py

@@ -45,7 +45,7 @@ POSTCODE_DENSITY_PERCENTILE_COL = "Tree canopy density percentile within {radius
 POSTCODE_AREA_COL = "Tree canopy area within {radius}m (sqm)"
 POSTCODE_HEIGHT_COL = "Mean TOW height within {radius}m (m)"
 
-# National Forest Inventory (NFI) woodland — the geometric complement of TOW.
+# National Forest Inventory (NFI) woodland: the geometric complement of TOW.
 # NFI ships as a zipped shapefile of woodland parcels (>=0.5 ha) in EPSG:27700.
 # Field names are from the NFI Woodland England 2022 release; re-check on bumps.
 NFI_CATEGORY_COL = "CATEGORY"
@@ -263,7 +263,7 @@ def _postcode_buffers(
     return circles, shapely.STRtree(circles)
 
 
-# 0.1 mm in the BNG working CRS (EPSG:27700) — far below survey resolution; the
+# 0.1 mm in the BNG working CRS (EPSG:27700), far below survey resolution; the
 # same grid the postcode_boundaries overlay uses.
 _OVERLAY_GRID_M = 1e-4
 
@@ -274,13 +274,13 @@ def _robust_intersection_area(a: np.ndarray, b: np.ndarray) -> np.ndarray:
     External Forest Research TOW/NFI polygons are occasionally invalid
     (self-intersections), and a single bad polygon makes the batched
     ``shapely.intersection`` raise ``TopologyException: side location conflict``,
-    aborting the whole run. The fast path is the raw batched overlay — unchanged,
-    full-speed, when the data is clean — and only a failure triggers repair.
+    aborting the whole run. The fast path is the raw batched overlay (unchanged,
+    full-speed, when the data is clean), and only a failure triggers repair.
 
     The repair deliberately uses a *plain* overlay rather than the fixed-precision
     (``grid_size``) one: ``make_valid`` can emit a mixed-dimension
     ``GeometryCollection`` (a polygon plus a dangling line), which OverlayNG
-    rejects with ``Overlay input is mixed-dimension`` — whereas a plain overlay
+    rejects with ``Overlay input is mixed-dimension``, whereas a plain overlay
     accepts it, and its non-polygonal debris has zero area and is dropped by the
     ``clipped_area > 0`` filter downstream anyway. A final pointwise coordinate
     snap (which never raises) collapses the near-coincident edges behind any
@@ -496,7 +496,7 @@ def _finalize_metrics(
     if over_count:
         print(
             f"  note: {over_count:,} postcode(s) exceeded 100% raw canopy and were "
-            "capped — indicates overlapping TOW/NFI canopy within the buffer"
+            "capped, indicating overlapping TOW/NFI canopy within the buffer"
         )
 
     mean_height = np.divide(