report what an area selection actually measured

Four ways a selection produced a confident number about a different area than
the operator drew:

Flood hazard divided the inundated cells by every cell in the drawn rectangle,
including cells the VMM raster does not model at all. A selection reaching
past the modelled extent therefore reported a diluted risk share, turning
missing data into an implied absence of risk. Terrain, bathymetry and thematic
raster already divided by valid cells; flood hazard was the outlier. It now
reports the three populations separately, states model coverage next to the
drawn area, and returns a null fraction rather than a zero when nothing was
modelled.

geometry_mask selects a cell when its centre falls inside the geometry, so a
rectangle smaller than one cell — or one landing between four centres —
selected nothing and the analysis returned zeros indistinguishable on screen
from "we looked and there is nothing here". On a 100 m population raster a
40 m rectangle over a city block reported no inhabitants. Selection now falls
back to the touched cells and says that it did, since the answer then covers
more ground than was requested. rasterio.mask applies the same centre rule
when cropping, so that call is widened too; the cells that count are still
decided by the centre rule wherever it selects anything.

The object count treated any feature touching the selection as whole, while
intersection_area clipped it — two headline numbers on one panel describing
different populations. The count stays whole-feature, which is what "objecten"
means to an operator, but now reports how many the edge cuts and is marked an
estimate when it does. The area_weighted_sum branch reuses that same count
instead of issuing its own near-identical query.

Partitioned selection de-duplicated the count on source_feature_id but
returned the raw rows, so a building on a municipal boundary was counted once
and drawn twice.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

backend/app/schemas/bathymetry.py

@@ -160,6 +160,10 @@ class BathymetryRasterSelectionResponse(BaseModel):
     selected_cell_count: int = Field(ge=1)
     valid_cell_count: int = Field(ge=1)
     coverage_ratio: float = Field(ge=0, le=1)
+    # Set when the drawn selection is smaller than one source cell and the
+    # analysis was widened to the cells it touches, so the value covers more
+    # ground than was requested.
+    cell_selection_warning: str | None = None
     resolution_m: float = Field(gt=0)
     vertical_reference: str
     survey_period: str

backend/app/schemas/dhmv.py

@@ -90,6 +90,10 @@ class TerrainSelectionResponse(BaseModel):
     sample_count: int
     slope_sample_count: int
     coverage_ratio: float
+    # Set when the drawn selection is smaller than one source cell and the
+    # analysis was widened to the cells it touches, so the value covers more
+    # ground than was requested.
+    cell_selection_warning: str | None = None
     resolution_m: float
     vertical_reference: str
     summary: TerrainSelectionSummary

backend/app/schemas/flood_hazard.py

@@ -93,9 +93,17 @@ class FloodHazardSelectionResponse(BaseModel):
     return_period_years: int
     selection_bbox: VectorSelectionBBox
     selection_area_id: UUID | None = None
+    # Three populations kept apart: cells drawn, cells the model covers, and
+    # cells with a positive modelled depth. ``inundated_fraction`` is a share
+    # of the modelled cells, and is null when nothing was modelled — absence
+    # of a model is not evidence of zero risk.
     selected_cell_count: int
+    valid_cell_count: int = 0
+    no_data_cell_count: int = 0
+    data_coverage_ratio: float = 1.0
     inundated_cell_count: int
-    inundated_fraction: float
+    inundated_fraction: float | None = None
+    coverage_warning: str | None = None
     resolution_m: float
     summary: FloodHazardSelectionSummary
     unsupported_metrics: list[str]

backend/app/schemas/operations.py

@@ -236,7 +236,13 @@ class VectorSelectionSummary(BaseModel):
     metric_unit: str
     aggregation_method: str
     primary_metric_key: str | None = None
+    # ``feature_count`` counts whole features that touch the selection, while
+    # area and length metrics clip to it. These fields say how far the two
+    # populations diverge, so the numbers on one panel can be read together.
     feature_count: int
+    fully_covered_feature_count: int | None = None
+    partially_covered_feature_count: int | None = None
+    selection_edge_warning: str | None = None
     is_estimate: bool = False
     warning: str | None = None
     metrics: list[VectorSelectionMetric] = Field(default_factory=list)

backend/app/schemas/thematic_raster.py

@@ -113,6 +113,10 @@ class ThematicRasterSelectionResponse(BaseModel):
     selected_cell_count: int
     valid_cell_count: int
     coverage_ratio: float
+    # Set when the drawn selection is smaller than one source cell and the
+    # analysis was widened to the cells it touches, so the value covers more
+    # ground than was requested.
+    cell_selection_warning: str | None = None
     resolution_m: float
     observation_year: int
     summary: ThematicRasterSelectionSummary