Add guided visual evidence story

Applies public data-visualization guidance: front-loaded finding, matched-control anchor, direct-labeled bars, visible uncertainty, and accessible data/table fallbacks.

README.md

@@ -50,6 +50,24 @@ The current evidence surface does **not** support a nuclear-specific proximity s
 - Population-weighted median Census-place distance to a major scheduled airport: `10.764` miles.
 - Missing geocode rows: `17783` of `123013` U.S. rows.
 
+## Visual Presentation Choices
+
+The app is intentionally structured as a guided evidence story before it becomes a data explorer:
+
+- It leads with one public question and one main finding.
+- It uses the matched non-nuclear control comparison as the main anchor.
+- It shows population and airport proximity as visible confounders instead of burying them in tables.
+- It keeps uncertainty visible through a missing-geocode section.
+- It keeps tables and downloads available as accessible fallbacks.
+
+These choices follow public data-visualization guidance from the Office for National Statistics, Datawrapper, the Urban Institute, and the CFPB:
+
+- ONS data visualisation principles: https://service-manual.ons.gov.uk/data-visualisation/guidance/principles
+- ONS chart text guidance: https://service-manual.ons.gov.uk/data-visualisation/guidance/chart-text
+- Datawrapper accessibility guidance: https://www.datawrapper.de/accessibility.html
+- Urban Institute Data Visualization Style Guide: https://urbaninstitute.github.io/graphics-styleguide/
+- CFPB data visualization guidelines: https://cfpb.github.io/design-system/guidelines/data-visualization-guidelines
+
 ## Sources
 
 - DataHerb NUFORC public mirror: https://github.com/DataHerb/nuforc-ufo-records

app.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import html
 import os
 from functools import lru_cache
 from pathlib import Path
@@ -64,6 +65,174 @@ def score_cards():
 """
 
 
+def fmt_int(value):
+    try:
+        return f"{int(float(value)):,}"
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def fmt_float(value, digits=2):
+    try:
+        return f"{float(value):,.{digits}f}"
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def pct(value, digits=1):
+    try:
+        return f"{float(value) * 100:.{digits}f}%"
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def bar_row(label, value, max_value, color_class, note=""):
+    width = 0 if max_value == 0 else max(2, min(100, (float(value) / float(max_value)) * 100))
+    safe_label = html.escape(str(label))
+    safe_note = html.escape(str(note))
+    return f"""
+    <div class="bar-row" role="img" aria-label="{safe_label}: {fmt_float(value, 2)} {safe_note}">
+      <div class="bar-label"><span>{safe_label}</span><strong>{fmt_float(value, 2)}</strong></div>
+      <div class="bar-track"><div class="bar-fill {color_class}" style="width:{width:.1f}%"></div></div>
+      <div class="bar-note">{safe_note}</div>
+    </div>
+    """
+
+
+def share_bar(label, value, color_class, note=""):
+    safe_label = html.escape(str(label))
+    safe_note = html.escape(str(note))
+    try:
+        width = max(2, min(100, float(value) * 100))
+    except (TypeError, ValueError):
+        width = 0
+    return f"""
+    <div class="share-row" role="img" aria-label="{safe_label}: {pct(value)} {safe_note}">
+      <div class="share-label"><span>{safe_label}</span><strong>{pct(value)}</strong></div>
+      <div class="bar-track"><div class="bar-fill {color_class}" style="width:{width:.1f}%"></div></div>
+      <div class="bar-note">{safe_note}</div>
+    </div>
+    """
+
+
+def visual_story_html():
+    m = metrics()
+    assessment = m["assessment"]
+    stats = m["nuclear_statistical_tests"]
+    primary = stats["tests_by_radius"]["50"]
+    population = m["population_summary"]
+    airport = m["airport_summary"]
+    missing = m["missing_geocode_summary"]
+
+    nuclear_mean = float(primary["nuclear_mean_reports_per_site"])
+    control_mean = float(primary["control_mean_reports_per_site"])
+    comparison_max = max(nuclear_mean, control_mean)
+    nuclear_bars = (
+        bar_row("Nuclear power-plant sites", nuclear_mean, comparison_max, "fill-nuclear", "mean reports per site within 50 miles")
+        + bar_row("Matched non-nuclear power controls", control_mean, comparison_max, "fill-control", "mean reports per site within 50 miles")
+    )
+
+    bin_labels = {
+        "lt_10k": "Places under 10k people",
+        "10k_50k": "10k to 50k people",
+        "50k_250k": "50k to 250k people",
+        "250k_1m": "250k to 1M people",
+        "gte_1m": "1M+ people",
+        "unknown_population": "Unknown population",
+    }
+    population_bins = population["population_bin_event_distribution"]
+    pop_rows = []
+    for key in ["lt_10k", "10k_50k", "50k_250k", "250k_1m", "gte_1m", "unknown_population"]:
+        value = population_bins.get(key, {}).get("event_share", 0)
+        pop_rows.append(share_bar(bin_labels[key], value, "fill-population", f"{fmt_int(population_bins.get(key, {}).get('event_count', 0))} rows"))
+    population_bars = "".join(pop_rows)
+
+    airport_rows = []
+    for radius in (10, 25, 50):
+        event_key = f"event_share_within_{radius}_miles_major_airport"
+        pop_key = f"population_weighted_share_within_{radius}_miles_major_airport"
+        airport_rows.append(f"""
+        <div class="paired-block">
+          <div class="paired-title">Within {radius} miles of a major scheduled airport</div>
+          {share_bar("Public report rows", airport[event_key], "fill-airport", "event-place centroids")}
+          {share_bar("Population-weighted places", airport[pop_key], "fill-baseline", "baseline geography")}
+        </div>
+        """)
+    airport_bars = "".join(airport_rows)
+
+    missing_max = max(missing["reason_bucket_counts"].values()) if missing.get("reason_bucket_counts") else 1
+    missing_bars = "".join(
+        bar_row(label.replace("_", " ").title(), count, missing_max, "fill-gap", "rows needing recovery")
+        for label, count in missing.get("reason_bucket_counts", {}).items()
+    )
+
+    return f"""
+<section class="hero-panel">
+  <div class="eyebrow">Public-source evidence surface</div>
+  <h1>The nuclear-specific UAP proximity claim does not hold in this dataset</h1>
+  <p class="lede">We tested public geocoded report rows against nuclear power-plant sites and matched non-nuclear power-plant controls. The stronger visible pattern is reporting geography: where people live, report, and see the sky.</p>
+  <div class="claim-path" aria-label="Question, test, result">
+    <div><span>Question</span><strong>Do reports cluster near nuclear plants?</strong></div>
+    <div><span>Control</span><strong>Compare with similar non-nuclear power sites</strong></div>
+    <div><span>Result</span><strong>No nuclear-specific lift observed</strong></div>
+  </div>
+</section>
+
+<section class="visual-grid">
+  <article class="viz-card wide">
+    <h2>Matched controls are the comparison anchor</h2>
+    <p>The 50-mile test compares nuclear power-plant sites with matched non-nuclear power-plant controls. If the nuclear claim were visible here, the nuclear bar would be clearly higher.</p>
+    {nuclear_bars}
+    <div class="takeaway">Nuclear/control ratio: <strong>{primary["nuclear_to_control_mean_ratio"]}</strong>. One-sided p-value for nuclear greater than controls: <strong>{primary["p_value_one_sided_nuclear_greater"]}</strong>.</div>
+  </article>
+
+  <article class="viz-card">
+    <h2>Population is the first pattern to notice</h2>
+    <p>Report rows are not evenly distributed across places. Place population and report count move together on a log scale.</p>
+    <div class="big-number">{population["log_population_log_event_count_pearson"]}</div>
+    <div class="big-number-label">log population vs. log report-count correlation</div>
+    <div class="takeaway">{fmt_int(population["matched_population_event_count"])} of {fmt_int(population["event_count"])} rows matched to Census population places.</div>
+  </article>
+
+  <article class="viz-card">
+    <h2>Airport proximity mostly follows people</h2>
+    <p>Major airports are close to many reports, but they are also close to much of the U.S. population.</p>
+    <div class="big-number">{airport["event_nearest_major_airport_distance_median_miles"]} mi</div>
+    <div class="big-number-label">median report-row distance to a major scheduled airport</div>
+    <div class="takeaway">Population-weighted Census-place median: <strong>{airport["population_weighted_nearest_major_airport_distance_median_miles"]} mi</strong>.</div>
+  </article>
+
+  <article class="viz-card wide">
+    <h2>Reports span place sizes, with large places carrying much of the volume</h2>
+    <p>These bars show the share of report rows by Census-place population bin. The table tabs remain available for exact rows.</p>
+    {population_bars}
+  </article>
+
+  <article class="viz-card wide">
+    <h2>Airport proximity should be read against the population baseline</h2>
+    <p>If public report rows and the population baseline are close, airport proximity is a confounder rather than a standalone explanation.</p>
+    {airport_bars}
+  </article>
+
+  <article class="viz-card wide">
+    <h2>Uncertainty is visible, not hidden</h2>
+    <p>{fmt_int(missing["missing_geocode_rows"])} U.S. rows did not resolve to Census place centroids. They are excluded from spatial tests until recovered by a public geocoder path.</p>
+    {missing_bars}
+  </article>
+</section>
+
+<section class="claim-box">
+  <h2>How to read the evidence</h2>
+  <div class="claim-grid">
+    <div><strong>Supported</strong><span>Population/reporting geography is a strong observable factor.</span></div>
+    <div><strong>Confounder</strong><span>Airport proximity is measurable, but close to the population baseline.</span></div>
+    <div><strong>Not supported here</strong><span>A nuclear-specific proximity lift after matched power-plant controls.</span></div>
+    <div><strong>Still outside scope</strong><span>Classified activity, exact witness GPS, and case-level truth claims.</span></div>
+  </div>
+</section>
+"""
+
+
 def overview_markdown():
     m = metrics()
     assessment = m["assessment"]
@@ -179,18 +348,52 @@ def download_files():
 
 css = """
 .gradio-container { max-width: 1180px !important; }
+.hero-panel { border: 1px solid #d7dde8; border-radius: 6px; padding: 24px; background: #f7fbfd; margin-bottom: 18px; }
+.eyebrow { color: #48606f; font-size: 13px; font-weight: 700; text-transform: uppercase; letter-spacing: .04em; }
+.hero-panel h1 { font-size: 32px; line-height: 1.14; margin: 8px 0 10px; color: #18232f; }
+.lede { font-size: 17px; max-width: 860px; color: #344554; }
+.claim-path { display: grid; grid-template-columns: repeat(auto-fit, minmax(210px, 1fr)); gap: 10px; margin-top: 18px; }
+.claim-path div { border-left: 4px solid #12719e; background: white; padding: 12px; }
+.claim-path span { display: block; color: #5b6975; font-size: 13px; font-weight: 700; }
+.claim-path strong { display: block; margin-top: 4px; color: #1d2b36; }
 .score-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(210px, 1fr)); gap: 12px; margin: 16px 0; }
-.score-card { border: 1px solid #d7dde8; border-radius: 6px; padding: 14px; background: #fbfcff; }
+.score-card, .viz-card, .claim-box { border: 1px solid #d7dde8; border-radius: 6px; padding: 16px; background: #fff; }
 .label { font-weight: 700; color: #24324a; }
 .score { font-size: 30px; font-weight: 800; margin: 6px 0; }
-.small { font-size: 13px; color: #536070; }
+.small, .bar-note, .big-number-label { font-size: 13px; color: #536070; }
+.visual-grid { display: grid; grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 14px; }
+.viz-card h2, .claim-box h2 { font-size: 18px; margin: 0 0 8px; color: #18232f; }
+.viz-card p { color: #415160; margin-top: 0; }
+.wide { grid-column: 1 / -1; }
+.bar-row, .share-row { margin: 12px 0; }
+.bar-label, .share-label { display: flex; justify-content: space-between; gap: 12px; font-size: 14px; margin-bottom: 4px; }
+.bar-track { height: 14px; background: #eceff3; border-radius: 4px; overflow: hidden; }
+.bar-fill { height: 100%; border-radius: 4px; }
+.fill-nuclear { background: #12719e; }
+.fill-control { background: #ca5800; }
+.fill-population { background: #408941; }
+.fill-airport { background: #af1f6b; }
+.fill-baseline { background: #696969; }
+.fill-gap { background: #e88e2d; }
+.takeaway { margin-top: 12px; padding: 10px 12px; background: #f4f6f8; border-left: 4px solid #696969; color: #25313c; }
+.big-number { font-size: 42px; font-weight: 850; color: #12719e; line-height: 1; margin-top: 12px; }
+.paired-block { margin: 14px 0 18px; }
+.paired-title { font-weight: 750; margin-bottom: 8px; color: #25313c; }
+.claim-box { margin-top: 16px; background: #fbfcff; }
+.claim-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(230px, 1fr)); gap: 10px; }
+.claim-grid div { background: white; border: 1px solid #d7dde8; border-radius: 6px; padding: 12px; }
+.claim-grid strong { display: block; color: #18232f; margin-bottom: 5px; }
+.claim-grid span { color: #40515f; }
+@media (max-width: 760px) { .visual-grid { grid-template-columns: 1fr; } .hero-panel h1 { font-size: 26px; } }
 """
 
 
 with gr.Blocks(css=css, title="Nuclear UAP Evidence Surface") as demo:
-    gr.Markdown(overview_markdown())
+    gr.HTML(visual_story_html())
     gr.HTML(score_cards())
     with gr.Tabs():
+        with gr.Tab("Claim Boundaries"):
+            gr.Markdown(overview_markdown())
         with gr.Tab("Explore Places"):
             with gr.Row():
                 place_state = gr.Dropdown(choices=state_choices(), value="All", label="State")

data/artifact_manifest.json

@@ -2,160 +2,154 @@
   "manifest_version": "nuclear_uap_hf_space_package_v0",
   "repo_id": "cjc0013/nuclear-uap-evidence-surface",
   "space_url": "https://huggingface.co/spaces/cjc0013/nuclear-uap-evidence-surface",
-  "generated_utc": "2026-04-29T01:53:15+00:00",
+  "generated_utc": "2026-04-29T02:04:04+00:00",
   "source_correlation_run_dir": "C:\\scraper\\runs\\nuclear_uap_correlation_index\\nuclear_uap_correlation_index_private_preview_final_20260429T010304Z",
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+      "path": "C:\\scraper\\runs\\huggingface_spaces\\nuclear-uap-evidence-surface_visual_20260429T021500Z\\README.md",
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