Simplify drone map UI and link each point to exact public row data

README.md

@@ -13,8 +13,8 @@ python_version: 3.11
 
 # Drone Sightings Near Covered U.S. Military and Civilian Areas
 
-This private preview shows reported drone sightings from news stories over official covered military and civilian areas.
+This private preview maps reported drone sightings from news stories over official covered military and civilian areas.
 
-Use the built-in time slider and play button in the map to watch red dots appear across time.
+Use the built-in time slider and play button in the map to watch red dots appear across time, then match the `event_id` on the map to the row data directly below it.
 
-This release tracks reported drone sightings and does not prove intent, threat, wrongdoing, or a verified security breach.
+Each point is tied to one public event row and a linked source list. This release tracks reported drone sightings and does not prove intent, threat, wrongdoing, or a verified security breach.

dataset_bundle/public_release_manifest.json

@@ -1,7 +1,7 @@
 {
   "public_version": "drone-sightings-slice-2026-04-v1-smoke",
   "title": "Drone Sightings Near Covered U.S. Military and Civilian Areas",
-  "release_date": "2026-04-20T17:54:30.455865+00:00",
+  "release_date": "2026-04-20T18:04:25.903466+00:00",
   "source_run_name": "drone_sightings_smoke_20260420",
   "slice_description": "A small, review-oriented slice of U.S. news-reported drone sightings mapped against official covered-area registries.",
   "source_window": {

public_copy.json

@@ -1,5 +1,5 @@
 {
   "title": "Drone Sightings Near Covered U.S. Military and Civilian Areas",
   "dataset_bundle_prefix": "dataset_bundle",
-  "landing_markdown": "# Drone Sightings Near Covered U.S. Military and Civilian Areas\n\nThis private preview shows reported drone sightings from news stories over official covered military and civilian areas.\n\nUse the built-in time slider and play button in the map to watch red dots appear across time.\n\nThis release tracks reported drone sightings and does not prove intent, threat, wrongdoing, or a verified security breach."
+  "landing_markdown": "# Drone Sightings Near Covered U.S. Military and Civilian Areas\n\nThis private preview maps reported drone sightings from news stories over official covered military and civilian areas.\n\nUse the built-in time slider and play button in the map to watch red dots appear across time, then match the `event_id` on the map to the row data directly below it.\n\nEach point is tied to one public event row and a linked source list. This release tracks reported drone sightings and does not prove intent, threat, wrongdoing, or a verified security breach."
 }

public_space_app.py

@@ -20,32 +20,36 @@ def _dataset_root(public_copy_path: Path) -> Path:
 def load_release_data(public_copy_path: Path) -> dict:
     dataset_root = _dataset_root(public_copy_path)
     events = pd.read_csv(dataset_root / "events.csv")
-    areas = pd.read_csv(dataset_root / "monitored_areas.csv")
     event_sources = pd.read_csv(dataset_root / "event_sources.csv")
-    daily_rollup = pd.read_csv(dataset_root / "daily_area_rollup.csv")
     manifest = json.loads((dataset_root / "public_release_manifest.json").read_text(encoding="utf-8"))
     return {
         "events": events.fillna(""),
-        "areas": areas.fillna(""),
         "event_sources": event_sources.fillna(""),
-        "daily_rollup": daily_rollup.fillna(""),
         "manifest": manifest,
     }
 
 
-def _filter_events(events: pd.DataFrame, area_type: str, civ_mil_class: str, state: str, confidence: str, review_status: str) -> pd.DataFrame:
-    filtered = events.copy()
-    if area_type != "all":
-        filtered = filtered[filtered["area_type"] == area_type]
-    if civ_mil_class != "all":
-        filtered = filtered[filtered["civ_mil_class"] == civ_mil_class]
-    if state != "all":
-        filtered = filtered[filtered["state"] == state]
-    if confidence != "all":
-        filtered = filtered[filtered["confidence_label"] == confidence]
-    if review_status != "all":
-        filtered = filtered[filtered["review_status"] == review_status]
-    return filtered
+def _point_table(events: pd.DataFrame) -> pd.DataFrame:
+    ordered = events.copy()
+    ordered["event_date"] = ordered["event_date"].astype(str)
+    ordered = ordered.sort_values(["event_date", "event_id", "headline"], ascending=[True, True, True]).reset_index(drop=True)
+    return ordered[
+        [
+            "event_id",
+            "event_date",
+            "headline",
+            "area_name",
+            "area_type",
+            "civ_mil_class",
+            "state",
+            "lat",
+            "lon",
+            "source_count",
+            "confidence_label",
+            "review_status",
+            "primary_source_url",
+        ]
+    ]
 
 
 def _build_map(events: pd.DataFrame):
@@ -61,14 +65,15 @@ def _build_map(events: pd.DataFrame):
         color_discrete_sequence=["#d62728"],
         hover_name="headline",
         hover_data={
+            "event_id": True,
             "area_name": True,
             "state": True,
             "event_date": True,
             "source_count": True,
             "confidence_label": True,
+            "primary_source_url": True,
             "lat": False,
             "lon": False,
-            "event_id": True,
         },
         custom_data=["event_id"],
         animation_frame="animation_frame",
@@ -100,7 +105,7 @@ def _build_map(events: pd.DataFrame):
 
 def _detail_markdown(event_sources: pd.DataFrame, events: pd.DataFrame, event_id: str) -> str:
     if not event_id:
-        return "Select a table row or click a point to inspect supporting sources."
+        return "Select a row in the point-data table to inspect the exact public data and source URLs used for that plotted point."
     matched = events[events["event_id"] == event_id]
     if matched.empty:
         return "No event details available."
@@ -109,17 +114,33 @@ def _detail_markdown(event_sources: pd.DataFrame, events: pd.DataFrame, event_id
     lines = [
         f"### {event_row['headline']}",
         "",
-        f"- Date: `{event_row['event_date']}` (`{event_row['date_quality']}`)",
-        f"- Area: `{event_row['area_name']}` ({event_row['area_type']}, {event_row['civ_mil_class']})",
-        f"- State: `{event_row['state']}`",
-        f"- Supporting stories: `{len(sources)}`",
+        "This is the exact public row used to draw the point on the map.",
+        "",
+        f"- `event_id`: `{event_row['event_id']}`",
+        f"- `event_date`: `{event_row['event_date']}`",
+        f"- `date_quality`: `{event_row['date_quality']}`",
+        f"- `area_name`: `{event_row['area_name']}`",
+        f"- `area_type`: `{event_row['area_type']}`",
+        f"- `civ_mil_class`: `{event_row['civ_mil_class']}`",
+        f"- `state`: `{event_row['state']}`",
+        f"- `lat`: `{event_row['lat']}`",
+        f"- `lon`: `{event_row['lon']}`",
+        f"- `source_count`: `{event_row['source_count']}`",
+        f"- `confidence_label`: `{event_row['confidence_label']}`",
+        f"- `review_status`: `{event_row['review_status']}`",
+        f"- `primary_source_url`: {event_row['primary_source_url']}",
+        "",
+        "#### Summary",
         "",
         event_row.get("summary", ""),
         "",
-        "#### Supporting URLs",
+        "#### Source stories behind this point",
     ]
     for _, source_row in sources.iterrows():
-        lines.append(f"- [{source_row['publisher']} | {source_row['title']}]({source_row['canonical_url']})")
+        lines.append(
+            f"- [{source_row['publisher']} | {source_row['title']}]({source_row['canonical_url']})"
+            f" | `published_at={source_row['published_at']}`"
+        )
     return "\n".join(lines)
 
 
@@ -129,42 +150,24 @@ def build_app(public_copy_path: str | Path):
     data = load_release_data(public_copy_path)
     events = data["events"]
     event_sources = data["event_sources"]
-    states = ["all"] + sorted(str(value) for value in events["state"].astype(str).unique() if str(value))
-    area_types = ["all"] + sorted(str(value) for value in events["area_type"].astype(str).unique() if str(value))
-    classes = ["all"] + sorted(str(value) for value in events["civ_mil_class"].astype(str).unique() if str(value))
-    confidences = ["all"] + sorted(str(value) for value in events["confidence_label"].astype(str).unique() if str(value))
-    review_statuses = ["all"] + sorted(str(value) for value in events["review_status"].astype(str).unique() if str(value))
-
-    def render(area_type: str, civ_mil_class: str, state: str, confidence: str, review_status: str):
-        filtered = _filter_events(events, area_type, civ_mil_class, state, confidence, review_status)
-        table = filtered[["event_id", "event_date", "area_name", "area_type", "state", "confidence_label", "source_count", "headline"]].head(250)
-        return _build_map(filtered), table
+    point_table = _point_table(events)
 
     with gr.Blocks(title=payload["title"]) as app:
         gr.Markdown(payload["landing_markdown"])
-        with gr.Row():
-            area_type = gr.Dropdown(choices=area_types, value="all", label="Area Type")
-            civ_mil_class = gr.Dropdown(choices=classes, value="all", label="Military / Civilian")
-            state = gr.Dropdown(choices=states, value="all", label="State")
-            confidence = gr.Dropdown(choices=confidences, value="all", label="Confidence")
-            review_status = gr.Dropdown(choices=review_statuses, value="all", label="Review Status")
         plot = gr.Plot(label="Drone sightings over time")
-        table = gr.Dataframe(label="Filtered events", interactive=False)
-        detail = gr.Markdown("Select a table row or click a point to inspect supporting sources.")
+        gr.Markdown("## Point data")
+        gr.Markdown("Every row below is one plotted point. The `event_id` shown on hover in the map matches the `event_id` in the table and detail panel.")
+        table = gr.Dataframe(label="Point data used to draw the map", interactive=False)
+        detail = gr.Markdown("Select a row in the point-data table to inspect the exact public data and source URLs used for that plotted point.")
 
         def _table_detail(evt: gr.SelectData):
             if not evt or evt.index is None:
-                return "Select a table row or click a point to inspect supporting sources."
+                return "Select a row in the point-data table to inspect the exact public data and source URLs used for that plotted point."
             row_index = evt.index[0] if isinstance(evt.index, (list, tuple)) else evt.index
-            filtered = _filter_events(events, area_type.value, civ_mil_class.value, state.value, confidence.value, review_status.value)
-            filtered = filtered.reset_index(drop=True)
-            if row_index >= len(filtered):
+            if row_index >= len(point_table):
                 return "No event details available."
-            return _detail_markdown(event_sources, events, str(filtered.iloc[row_index]["event_id"]))
+            return _detail_markdown(event_sources, events, str(point_table.iloc[row_index]["event_id"]))
 
-        inputs = [area_type, civ_mil_class, state, confidence, review_status]
-        for component in inputs:
-            component.change(render, inputs=inputs, outputs=[plot, table])
         table.select(_table_detail, outputs=detail)
-        app.load(render, inputs=inputs, outputs=[plot, table])
+        app.load(lambda: (_build_map(events), point_table), outputs=[plot, table])
     return app