skip the selection-edge query where nothing reads it

The temporal timeline summarises every snapshot in a series. Each summary now
also counts how many features the selection edge cuts, but a timeline point
renders values only, so that was one database round trip per snapshot for a
disclosure nobody sees. Make it opt-out and have the timeline opt out.

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

backend/app/services/temporal_analysis_service.py

@@ -171,11 +171,15 @@ class TemporalAnalysisService:
 
         summaries: dict[UUID, dict[str, Any]] = {}
 
-        def summarize(dataset: Dataset) -> dict[str, Any]:
+        def summarize(dataset: Dataset, *, disclose_selection_edge: bool = True) -> dict[str, Any]:
             cached = summaries.get(dataset.id)
             if cached is not None:
                 return cached
-            kwargs: dict[str, Any] = {"dataset": dataset, "bbox": bbox}
+            kwargs: dict[str, Any] = {
+                "dataset": dataset,
+                "bbox": bbox,
+                "disclose_selection_edge": disclose_selection_edge,
+            }
             if selection_area is not None:
                 dataset_is_preclipped = is_preclipped_to_selection_area(dataset)
                 kwargs["selection_geometry"] = None if dataset_is_preclipped else selection_geometry
@@ -234,7 +238,9 @@ class TemporalAnalysisService:
             project_id=project_id,
             series_key=earlier.temporal_series_key,
             fallback_datasets=[earlier, later],
-            summarize=summarize,
+            # A timeline point shows values only, so the per-snapshot
+            # selection-edge query would be a round trip nobody reads.
+            summarize=lambda dataset: summarize(dataset, disclose_selection_edge=False),
         )
 
         return TemporalComparisonResponse(

backend/app/services/vector_feature_service.py

@@ -811,6 +811,7 @@ class VectorFeatureService:
         selection_geometry: Any | None = None,
         full_dataset_area: bool = False,
         preclipped_partition_filter: tuple[str, str] | None = None,
+        disclose_selection_edge: bool = True,
     ) -> dict[str, Any]:
         normalized_bbox = VectorFeatureService._normalize_selection_bbox(bbox)
         selection_shape = selection_geometry
@@ -842,7 +843,7 @@ class VectorFeatureService:
         # How many of the counted features the selection edge cuts. Skipped for
         # a pre-clipped whole-area selection, which has no edge to cut against.
         fully_covered_feature_count: int | None = None
-        if not full_dataset_area and feature_count:
+        if disclose_selection_edge and not full_dataset_area and feature_count:
             try:
                 fully_covered_feature_count = int(
                     db.query(func.count(VectorFeature.id))