feat: add cross-domain Mol data profile

backend/app/services/geo_assistant_service.py

@@ -22,8 +22,11 @@ from app.schemas.assistant import (
     AssistantTemporalSeries,
 )
 from app.schemas.flood_hazard import FloodHazardSelectionRequest
+from app.schemas.thematic_raster import ThematicRasterSelectionRequest
 from app.services.flood_hazard_acquisition_service import FloodHazardAcquisitionService
 from app.services.flood_hazard_analysis_service import FloodHazardAnalysisService
+from app.services.thematic_raster_acquisition_service import ThematicRasterAcquisitionService
+from app.services.thematic_raster_analysis_service import ThematicRasterAnalysisService
 from app.services.vector_feature_service import VectorFeatureService
 
 
@@ -42,9 +45,17 @@ class GeoAssistantService:
     )
     ESTIMATE_TOPIC_TERMS = {
         "population": ("bevolk", "inwoner"),
+        "space_occupation": ("ruimtebeslag",),
+        "open_space": ("open ruimte",),
+        "accessibility": ("bereikbaar", "knooppunt"),
+        "services": ("voorziening",),
     }
     ESTIMATE_TOPIC_LABELS = {
         "population": "bevolkingswaarden",
+        "space_occupation": "ruimtebeslagoppervlakten",
+        "open_space": "openruimte-oppervlakten",
+        "accessibility": "bereikbaarheidsscores",
+        "services": "voorzieningenscores",
     }
 
     @classmethod
@@ -264,6 +275,19 @@ class GeoAssistantService:
             if dataset.dataset_type == "raster" and dataset.source_name == FloodHazardAcquisitionService.PROVIDER
             and (area is None or dataset.area_id is None or dataset.area_id == area.id)
         ]
+        thematic_candidates = [
+            dataset
+            for dataset in datasets
+            if dataset.dataset_type == "raster" and dataset.source_name == ThematicRasterAcquisitionService.PROVIDER
+            and (area is None or dataset.area_id is None or dataset.area_id == area.id)
+        ]
+        thematic_by_product: dict[str, Dataset] = {}
+        for dataset in thematic_candidates:
+            product_key = str((dataset.source_metadata or {}).get("product_key") or "")
+            current = thematic_by_product.get(product_key)
+            if product_key and (current is None or (dataset.imported_at or datetime.min.replace(tzinfo=timezone.utc)) > (current.imported_at or datetime.min.replace(tzinfo=timezone.utc))):
+                thematic_by_product[product_key] = dataset
+        thematic_datasets = list(thematic_by_product.values())
         warnings: list[str] = []
         context_metrics: list[AssistantContextMetric] = []
         source_dataset_ids: list[UUID] = []
@@ -323,6 +347,47 @@ class GeoAssistantService:
                     }
                 )
 
+            for dataset in sorted(thematic_datasets, key=lambda item: str((item.source_metadata or {}).get("product_key") or item.name)):
+                try:
+                    result = ThematicRasterAnalysisService.analyze(
+                        db,
+                        project_id,
+                        dataset.id,
+                        ThematicRasterSelectionRequest(bbox=bbox, area_id=area.id if area is not None else None),
+                        settings=self.settings,
+                    )
+                except AppError as exc:
+                    warnings.append(f"{dataset.name}: {exc.message}")
+                    continue
+                serialized_metrics: list[dict[str, Any]] = []
+                for metric in result["summary"]["metrics"]:
+                    item = AssistantContextMetric(
+                        theme=result["theme"],
+                        label=str(metric["metric_label"]),
+                        value=float(metric["metric_value"]),
+                        unit=str(metric["metric_unit"]),
+                        source=ThematicRasterAcquisitionService.ATTRIBUTION,
+                        dataset_id=dataset.id,
+                        observed_at=dataset.observed_at,
+                        is_estimate=bool(metric.get("is_estimate", True)),
+                    )
+                    context_metrics.append(item)
+                    serialized_metrics.append(item.model_dump(mode="json"))
+                    serialized_metrics[-1]["measurement_quality"] = "resolutiegebonden_bronmeting"
+                source_dataset_ids.append(dataset.id)
+                current_context.append(
+                    {
+                        "dataset_name": dataset.name,
+                        "dataset_id": str(dataset.id),
+                        "theme": result["theme"],
+                        "source": ThematicRasterAcquisitionService.ATTRIBUTION,
+                        "observed_at": dataset.observed_at.isoformat() if dataset.observed_at else None,
+                        "metrics": serialized_metrics,
+                        "unsupported_metrics": result["unsupported_metrics"],
+                        "warning": result["limitation_message"],
+                    }
+                )
+
             for dataset in sorted(
                 flood_hazard_datasets,
                 key=lambda item: str((item.source_metadata or {}).get("product_key") or item.name),
@@ -425,6 +490,7 @@ class GeoAssistantService:
                 "water_volume_available": False,
                 "water_volume_reason": "Geen bathymetrie gekoppeld voor de permanente inhoud van waterlichamen.",
                 "flood_hazard_scenarios_available": bool(flood_hazard_datasets),
+                "thematic_policy_rasters_available": bool(thematic_datasets),
                 "flood_depth_area_integral_is_concurrent_volume": False,
                 "object_counts_are_supporting_metrics": True,
                 "causal_explanations_available": False,