Make drone Space map-first

Replace chart-first triage surface with a map-first Space plotting all 149 cases, including coordinate-quality labels and source-detail table.

README.md

@@ -11,6 +11,6 @@ python_version: 3.11
 
 # Mystery Drone Reports Around Sensitive Sites
 
-Interactive review surface for public-source reports about mystery, unidentified, suspicious, or unauthorized drone activity around sensitive sites.
+Map-first review surface for public-source reports about mystery, unidentified, suspicious, or unauthorized drone activity around sensitive sites.
 
-This Space presents evidence tiers, source links, date signals, country/site filters, and row-level claim boundaries. It does not claim that any row proves threat, attribution, anomalous origin, or hostile intent.
+This Space plots the expanded case set on a world map, with evidence tiers, source links, coordinate-quality labels, country/site filters, and row-level claim boundaries. It does not claim that any row proves threat, attribution, anomalous origin, or hostile intent.

data/quality_report.json

@@ -7,6 +7,8 @@
   "missing_source_url_count": 0,
   "duplicate_source_url_count": 0,
   "generic_headline_count": 0,
+  "mappable_case_count": 149,
+  "unresolved_global_fallback_count": 0,
   "forbidden_public_term_hits": [],
   "drone_signal_count": 149,
   "sensitive_signal_count": 148,

data/release_manifest.json

@@ -1,7 +1,7 @@
 {
   "release_version": "mystery-drone-sensitive-site-cases-2026-05-v1",
   "title": "Mystery Drone Reports Around Sensitive Sites",
-  "generated_utc": "2026-05-01T20:32:52-04:00",
+  "generated_utc": "2026-05-01T20:52:07-04:00",
   "source_lead_count": 150,
   "case_count": 149,
   "resolved_sensitive_site_report_count": 9,
@@ -28,21 +28,21 @@
     "source_discovered_report": 129
   },
   "claim_boundary": "Public-source report index; not a verified finding of threat, attribution, or anomalous origin.",
-  "summary_hash": "5285f25aeaff138958731d3eda485b925682ed50a2742c3c319cb5349cd92c47",
+  "summary_hash": "5b77a3c7a1a911bbfe43907912230479392387942073ed57f029f33df1a48e79",
   "release_grade": true,
   "quality_failed_checks": [],
   "artifacts": [
     {
       "artifact_role": "cases_csv",
       "artifact_path": "data/mystery_drone_sensitive_site_cases.csv",
-      "content_sha256": "62c2aba1b5708d7223ac6c6b3dcb94a777d4289dab73f521f93fa6baa6480aa6",
-      "byte_count": 105504
+      "content_sha256": "c4c2ccd98afa495171a2dd30563e2f8812941c646090e6e930fa9c06285219f9",
+      "byte_count": 114576
     },
     {
       "artifact_role": "cases_jsonl",
       "artifact_path": "data/mystery_drone_sensitive_site_cases.jsonl",
-      "content_sha256": "53546245b4b553861f30d9565a20645d7828a348b8e6e9280d26c327432f024d",
-      "byte_count": 164513
+      "content_sha256": "50f5f9837ec9832e5c5f380bdd45d4e0176cae94f1f0aa346390a3eb7ab33fa0",
+      "byte_count": 183665
     },
     {
       "artifact_role": "readme",
@@ -71,9 +71,9 @@
     {
       "artifact_role": "quality_report",
       "artifact_path": "quality_report.json",
-      "content_sha256": "a8f2e692f08bd8d5fef88d00691d018f6fa0b28b2f550da995d3b2eb1f9072b3",
-      "byte_count": 935
+      "content_sha256": "b17691a650a7a913224143e660f6049502c4d6077c5a5d06cc32b83ddb12b0ad",
+      "byte_count": 1008
     }
   ],
-  "manifest_hash": "cd861520adba5e1ead213fd6389208eeea2d5d1041c1590bbdd7ddca1525cfbb"
+  "manifest_hash": "f6b93171f75323da37d79c9d18fe48ee8dca5ae066c5b14c689d3450b9bd63b4"
 }

public_space_app.py

@@ -15,6 +15,7 @@ DISPLAY_COLUMNS = [
     "country",
     "site_name",
     "site_type",
+    "coordinate_quality",
     "headline",
     "source_domain",
     "followup_status",
@@ -35,7 +36,7 @@ def _markdown_header(manifest: dict, quality: dict) -> str:
     top_countries = ", ".join(f"{key}: {value}" for key, value in list(countries.items())[:7])
     return f"""# Mystery Drone Reports Around Sensitive Sites
 
-Public-source review surface for mystery, unidentified, suspicious, or unauthorized drone reports around military, airport, maritime, emergency-service, and critical-infrastructure contexts.
+Map-first public review surface for mystery, unidentified, suspicious, or unauthorized drone reports around military, airport, maritime, emergency-service, and critical-infrastructure contexts.
 
 **{manifest.get("case_count", 0)} cases** | **{manifest.get("probable_cluster_count", 0)} probable clusters** | **release gate: {"pass" if quality.get("release_grade") else "review"}**
 
@@ -43,7 +44,7 @@ Evidence tiers: resolved sensitive-site reports `{tiers.get("resolved_sensitive_
 
 Country coverage: {top_countries}
 
-Rows are source-indexed report cases, not verified findings of threat, attribution, anomalous origin, or hostile intent.
+Rows are source-indexed report cases, not verified findings of threat, attribution, anomalous origin, or hostile intent. Map points use the best public coordinate available: site centroid, city/region centroid, country centroid, or a clearly labeled fallback.
 """
 
 
@@ -90,41 +91,39 @@ def _summary_text(filtered: pd.DataFrame) -> str:
     return f"Showing {len(filtered)} cases. Evidence tiers: {tiers}. Top countries: {countries}."
 
 
-def _timeline(filtered: pd.DataFrame):
+def _map(filtered: pd.DataFrame):
     if filtered.empty:
-        return px.bar(pd.DataFrame({"report_year": [], "count": []}), x="report_year", y="count", height=320)
-    chart_data = (
-        filtered.groupby(["report_year", "evidence_tier"], dropna=False)
-        .size()
-        .reset_index(name="count")
-        .sort_values(["report_year", "evidence_tier"])
-    )
-    fig = px.bar(
-        chart_data,
-        x="report_year",
-        y="count",
+        fig = px.scatter_geo(pd.DataFrame({"plot_lat": [], "plot_lon": []}), lat="plot_lat", lon="plot_lon", height=620)
+        fig.update_layout(margin={"l": 0, "r": 0, "t": 20, "b": 0})
+        return fig
+    plot_rows = filtered.copy()
+    plot_rows["plot_lat"] = pd.to_numeric(plot_rows["plot_lat"], errors="coerce")
+    plot_rows["plot_lon"] = pd.to_numeric(plot_rows["plot_lon"], errors="coerce")
+    plot_rows = plot_rows.dropna(subset=["plot_lat", "plot_lon"])
+    fig = px.scatter_geo(
+        plot_rows,
+        lat="plot_lat",
+        lon="plot_lon",
         color="evidence_tier",
-        height=320,
-        labels={"report_year": "Report year", "count": "Cases", "evidence_tier": "Evidence tier"},
+        symbol="coordinate_quality",
+        hover_name="headline",
+        hover_data={
+            "case_rank": True,
+            "site_name": True,
+            "plot_label": True,
+            "country": True,
+            "report_date": True,
+            "source_domain": True,
+            "coordinate_quality": True,
+            "plot_lat": False,
+            "plot_lon": False,
+        },
+        projection="natural earth",
+        height=660,
     )
-    fig.update_layout(margin={"l": 10, "r": 10, "t": 24, "b": 10}, legend_orientation="h")
-    return fig
-
-
-def _country_chart(filtered: pd.DataFrame):
-    if filtered.empty:
-        return px.bar(pd.DataFrame({"country": [], "count": []}), x="count", y="country", height=320)
-    chart_data = filtered["country"].replace("", "unknown").value_counts().head(15).reset_index()
-    chart_data.columns = ["country", "count"]
-    fig = px.bar(
-        chart_data,
-        x="count",
-        y="country",
-        orientation="h",
-        height=320,
-        labels={"country": "Country", "count": "Cases"},
-    )
-    fig.update_layout(margin={"l": 10, "r": 10, "t": 24, "b": 10}, yaxis={"categoryorder": "total ascending"})
+    fig.update_traces(marker={"size": 9, "opacity": 0.78, "line": {"width": 0.4, "color": "white"}})
+    fig.update_geos(showland=True, landcolor="#eef2f5", showocean=True, oceancolor="#dfeaf2", showcountries=True)
+    fig.update_layout(margin={"l": 0, "r": 0, "t": 24, "b": 0}, legend_orientation="h")
     return fig
 
 
@@ -134,7 +133,8 @@ def _table(filtered: pd.DataFrame) -> pd.DataFrame:
 
 def _render(cases: pd.DataFrame, evidence_tiers, countries, site_types, query):
     filtered = _filter_cases(cases, evidence_tiers, countries, site_types, query)
-    return _summary_text(filtered), _timeline(filtered), _country_chart(filtered), _table(filtered), filtered.to_dict("records"), _detail(filtered.to_dict("records"), 0)
+    rows = filtered.to_dict("records")
+    return _summary_text(filtered), _map(filtered), _table(filtered), rows, _detail(rows, 0)
 
 
 def _detail(rows: list[dict], index: int | None) -> str:
@@ -150,6 +150,7 @@ def _detail(rows: list[dict], index: int | None) -> str:
 - Follow-up status: `{row.get("followup_status", "")}`
 - Report date: `{row.get("report_date", "")}` (`{row.get("date_quality", "")}`)
 - Site signal: `{row.get("site_name", "")}` / `{row.get("site_type", "")}`
+- Map point: `{row.get("plot_label", "")}` / `{row.get("coordinate_quality", "")}`
 - Location signal: `{row.get("country", "")}` `{row.get("state_region", "")}`
 - Source: [{row.get("publisher", "") or row.get("source_domain", "")}]({row.get("source_url", "")})
 - Boundary: {row.get("claim_boundary", "")}
@@ -182,9 +183,7 @@ def build_app(data_dir: str | Path):
             )
         query = gr.Textbox(label="Search", placeholder="Try Langley, Copenhagen, airport, military base")
         summary = gr.Markdown()
-        with gr.Row():
-            timeline = gr.Plot(label="Cases by report year")
-            country_chart = gr.Plot(label="Cases by country")
+        map_plot = gr.Plot(label="Case map")
         table = gr.Dataframe(label="Filtered cases", interactive=False)
         rows_state = gr.State([])
         detail = gr.Markdown()
@@ -196,7 +195,7 @@ def build_app(data_dir: str | Path):
             control.change(
                 render,
                 inputs=[evidence_filter, country_filter, site_filter, query],
-                outputs=[summary, timeline, country_chart, table, rows_state, detail],
+                outputs=[summary, map_plot, table, rows_state, detail],
             )
 
         def select_detail(rows, evt: gr.SelectData):
@@ -209,6 +208,6 @@ def build_app(data_dir: str | Path):
         app.load(
             render,
             inputs=[evidence_filter, country_filter, site_filter, query],
-            outputs=[summary, timeline, country_chart, table, rows_state, detail],
+            outputs=[summary, map_plot, table, rows_state, detail],
         )
     return app

space_manifest.json

@@ -13,14 +13,14 @@
     {
       "artifact_role": "space_public_app",
       "artifact_path": "public_space_app.py",
-      "content_sha256": "7e3e75670d058329a171ad3c8a5e94a7f16180648ff42a14fc2fdae73db0d44c",
-      "byte_count": 8719
+      "content_sha256": "fa5bdda74630f425a0389dbb89ba43ab1bc81d9302a5a0feeb657d5146ccd172",
+      "byte_count": 8877
     },
     {
       "artifact_role": "readme",
       "artifact_path": "README.md",
-      "content_sha256": "7b777472f3186a1897a48733a178809b4b7c31bc3c7232ed485beef2322a893b",
-      "byte_count": 575
+      "content_sha256": "3dc884fb6fe90ae55d6494c38e365e0660dc68efb2519d8e54f7c14fc19e2669",
+      "byte_count": 626
     },
     {
       "artifact_role": "requirements",
@@ -31,21 +31,21 @@
     {
       "artifact_role": "cases_csv",
       "artifact_path": "data/mystery_drone_sensitive_site_cases.csv",
-      "content_sha256": "62c2aba1b5708d7223ac6c6b3dcb94a777d4289dab73f521f93fa6baa6480aa6",
-      "byte_count": 105504
+      "content_sha256": "c4c2ccd98afa495171a2dd30563e2f8812941c646090e6e930fa9c06285219f9",
+      "byte_count": 114576
     },
     {
       "artifact_role": "release_manifest",
       "artifact_path": "data/release_manifest.json",
-      "content_sha256": "7949299f4fc596f268adcf4f97834c0aa8f2ef90c00342b2b2c8d28143aff276",
-      "byte_count": 2772
+      "content_sha256": "6e44306e314c2b3f7350cf53a65fc42b3ac0a92fd1d2f21b95b812544dcf85ee",
+      "byte_count": 2773
     },
     {
       "artifact_role": "quality_report",
       "artifact_path": "data/quality_report.json",
-      "content_sha256": "a8f2e692f08bd8d5fef88d00691d018f6fa0b28b2f550da995d3b2eb1f9072b3",
-      "byte_count": 935
+      "content_sha256": "b17691a650a7a913224143e660f6049502c4d6077c5a5d06cc32b83ddb12b0ad",
+      "byte_count": 1008
     }
   ],
-  "bundle_hash": "7643bacb21b3784e4117c48f4858374d174e480e416569c7913a9553af226619"
+  "bundle_hash": "89aeb842010180e667afc7a238b888daaf1480ac29ed298735b5f454c67e107b"
 }