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Light render (plot-light.png): The plot renders on a warm off-white #FAF8F1 background. A world map with Natural Earth projection is displayed with beige/tan land (#E8E4D8) and soft blue ocean (#D0E4F0). Bubbles represent 30 major world cities, sized proportionally to population, and colored by region using the Okabe-Ito palette: Asia = #009E73 (greenish, largest cluster in East/South Asia), Europe = #D55E00 (vermillion), North America = #0072B2 (blue), South America = #CC79A7 (reddish purple), Africa = #E69F00 (orange), Oceania = #56B4E9 (sky blue). A region legend sits in the upper-left with an elevated-background box. A custom population-scale legend in the lower-left shows three reference circles (5M, 20M, 35M). Title at top reads "World City Populations · bubble-map-geographic · python · plotly · anyplot.ai" in dark ink. All text is clearly readable against the light background — no light-on-light issues detected.
Dark render (plot-dark.png): The same map renders on near-black #1A1A17 background, with dark land (#2E2E28) and dark-navy ocean (#1A2535). Data colors are identical to the light render — Asia bubbles are the same #009E73 green, Europe the same vermillion, etc. — only the chrome has flipped: title, legend text, and scale labels are now light-colored against the dark surface. Map frame is visible with lighter tones. Region legend and population-scale legend backgrounds use the elevated dark token (#242420). No dark-on-dark failures detected — all text is clearly legible against the near-black background. Brand green #009E73 reads well on the dark surface.
Both paragraphs are required. A review that only describes one render is invalid.
Score: 85/100
Category
Score
Max
Visual Quality
26
30
Design Excellence
13
20
Spec Compliance
15
15
Data Quality
14
15
Code Quality
10
10
Library Mastery
7
10
Total
85
100
Visual Quality (26/30)
VQ-01: Text Legibility (7/8) — Title=28, legend title=20, legend=18, scale labels=15 all explicitly set; both themes fully readable. Minor: scale-legend labels at 15 are slightly small.
VQ-02: No Overlap (5/6) — Expected bubble crowding in the Asia cluster handled cleanly with 0.65 opacity; no text overlap anywhere.
VQ-03: Element Visibility (5/6) — Minimum size of 15 keeps small cities visible; transparency prevents the dense Asia cluster from becoming a solid blob.
VQ-04: Color Accessibility (2/2) — Okabe-Ito palette is CVD-safe; color + size dual encoding reduces reliance on hue alone.
VQ-05: Layout & Canvas (3/4) — Map fills canvas well; size-legend circles appear slightly small at display scale.
VQ-06: Axis Labels & Title (2/2) — Descriptive title; geographic maps have no traditional axis labels.
VQ-07: Palette Compliance (2/2) — First series (Asia) = #009E73 ✓; Okabe-Ito order followed for all 6 regions ✓; backgrounds #FAF8F1/#1A1A17 ✓; data colors identical across both renders ✓.
Design Excellence (13/20)
DE-01: Aesthetic Sophistication (5/8) — Above a configured default: theme-adaptive geographic color tokens (land, ocean, coast, country all flip with theme), Okabe-Ito palette applied correctly, and the manually-built population-scale legend shows design thought. Not yet at "clearly above defaults" (6) — typographic hierarchy could be stronger and the overall composition lacks a distinctive focal point.
DE-02: Visual Refinement (4/6) — Clean layout; white bubble edges add definition; transparency is well-chosen; elevated background boxes on legends are polished. Good refinement for a geo map (no traditional spines to remove).
DE-03: Data Storytelling (4/6) — Asia's megacity dominance is immediately apparent through the large clustered bubbles; the dual size+color encoding guides the reader. The population scale legend enables magnitude interpretation. Visual hierarchy through dramatic size variation is effective, though no city-name callouts or annotations anchor specific insights.
Spec Compliance (15/15)
SC-01: Plot Type (5/5) — Correct bubble map using go.Scattergeo with size-encoded geographic markers.
SC-02: Required Features (4/4) — Area-proportional bubble sizing ✓, transparency 0.65 ✓, geographic basemap with country borders and coastlines ✓, size legend ✓, hover tooltips showing city name and population ✓.
SC-03: Data Mapping (3/3) — Lat/lon correctly mapped; population drives marker size; optional category (region) drives color.
SC-04: Title & Legend (3/3) — Title: "World City Populations · bubble-map-geographic · python · plotly · anyplot.ai" matches required format ✓; legend labels match region data ✓.
Data Quality (14/15)
DQ-01: Feature Coverage (5/6) — 30 cities across all 6 inhabited continents with a wide population range (5.0M–37.4M); good size variation showcases the bubble map's encoding strength.
DQ-02: Realistic Context (5/5) — Real-world major-city populations; neutral, factual topic with no controversial elements.
DQ-03: Appropriate Scale (4/4) — Population values are factually accurate and proportionally correct for the chosen cities.
Code Quality (10/10)
CQ-01: KISS Structure (3/3) — Flat linear flow: theme tokens → data → figure → style → save. No functions or classes.
CQ-04: Code Elegance (2/2) — Clean region loop; manual size legend via paper-coordinate shapes is appropriately complex for the task.
CQ-05: Output & API (1/1) — Saves plot-{THEME}.png + plot-{THEME}.html using current plotly API ✓.
Library Mastery (7/10)
LM-01: Idiomatic Usage (4/5) — Correct use of go.Scattergeo with comprehensive geo layout dict; idiomatic hovertemplate with <extra></extra> to suppress trace name box.
LM-02: Distinctive Features (3/5) — Uses geographic projection (natural earth), showland/showocean/showcoastlines/showcountries, and paper-coordinate shapes for the size legend. These are distinctly plotly geo features, but the HTML interactivity is not fully leveraged (e.g., no custom click events or animation).
Score Caps Applied
None — no caps triggered.
Strengths
Complete spec compliance: all required features (size encoding, transparency, basemap, size legend, tooltips) are present and working.
Excellent theme adaptation: geographic chrome tokens (land, ocean, coast, country borders) flip correctly between light and dark themes, which is non-trivial for geo maps.
Clean, readable code with proper flat structure; size legend implementation using paper-coordinate shapes is elegant.
Okabe-Ito palette applied correctly with Asia as first series (#009E73); correct background tokens in both renders.
Population data is real-world accurate and the 30-city dataset shows clear size variation across continents.
Weaknesses
DE-01 (5/8): Design sophistication stops at "correctly configured" — the composition lacks a strong typographic focal point or any city-name labels for the largest bubbles (Tokyo, Delhi, Shanghai) that would elevate it toward publication quality.
LM-02 (3/5): Plotly's interactive distinctiveness is underused — the hover tooltip only shows city name and population; adding formatted multi-line tooltips (e.g., rank, region) or custom marker symbol variation would better showcase plotly's interactive strengths.
VQ-01 (7/8): Population-scale legend labels at font size 15 are slightly below the recommended 18px for secondary text at this canvas resolution.
Issues Found
DE-01 LOW (5/8): Composition lacks a dominant focal point beyond size variation. The largest cities (Tokyo cluster) don't have explicit callouts or city-name labels.
Fix: Add city-name text labels for the top 5 most-populous cities positioned near their bubbles; this would both improve storytelling and push DE-01 toward 6–7.
LM-02 MODERATE (3/5): Hover tooltips are minimal; plotly's interactive geo features are not showcased beyond basic hover.
Fix: Enrich hovertemplate to include population rank and region; consider using customdata for richer tooltip content.
VQ-01 MINOR (7/8): Population scale label font size 15 is below the 18px recommended secondary-text size.
Fix: Increase scale legend annotation font from 15 → 18.
AI Feedback for Next Attempt
Add city-name text labels for the top 5–6 most-populous cities (Tokyo, Delhi, Shanghai, Dhaka, São Paulo, Mumbai) positioned adjacent to their bubbles — this is the single highest-impact improvement for both DE-01 and DE-03. Enrich the hovertemplate with population rank and a formatted population string (e.g., "#1 globally · 37.4M") to better leverage plotly's interactive strength (LM-02). Increase population-scale annotation font size from 15 → 18 (VQ-01). These three targeted fixes should push the score above 90.
Light render (plot-light.png): The plot renders on a warm off-white #FAF8F1 surface — correct. A world map using the Natural Earth projection spans the canvas with beige land masses (#E8E4D8) and soft light-blue ocean (#D0E4F0). Bubble markers sized by city population appear across all continents: large green clusters dominate East/South Asia (Tokyo, Delhi, Shanghai, Mumbai in #009E73), several orange/vermillion dots mark Europe (Istanbul, Moscow) in #D55E00, blue markers denote North America in #0072B2, pink marks South America in #CC79A7, yellow-orange Africa (Cairo, Lagos) in #E69F00, and two small sky-blue dots represent Oceania in #56B4E9. All bubble colors follow Okabe-Ito order. The title "World City Populations · bubble-map-geographic · python · plotly · anyplot.ai" appears at top center in dark #1A1A17 ink at 28px, fully readable. The Region legend sits in the upper-left map area with dark text. A "Population scale" box with three reference circles (5M, 20M, 35M) occupies the lower-left corner. All text is clearly readable against the light background. Legibility verdict: PASS
Dark render (plot-dark.png): The plot renders on a near-black #1A1A17 surface — correct. Land masses render in dark charcoal (#2E2E28), ocean in dark navy (#1A2535), providing clear geographic distinction. The title appears in light #F0EFE8 text — readable. Data bubble colors are identical to the light render (same green Asia cluster, orange Europe, blue North America, etc.) — only the chrome flips. The Region legend text renders in lighter #B8B7B0 ink against the dark #242420 elevated background. The Population scale box also uses themed colors. No dark-on-dark text failures observed; all labels remain legible against the dark background. Legibility verdict: PASS
Both renders are theme-correct. Okabe-Ito data colors are identical across both themes.
Score: 86/100
Category
Score
Max
Visual Quality
28
30
Design Excellence
11
20
Spec Compliance
15
15
Data Quality
15
15
Code Quality
10
10
Library Mastery
7
10
Total
86
100
Visual Quality (28/30)
VQ-01: Text Legibility (7/8) — All rendered text has explicitly set font sizes (title 28px, legend title 20px, legend text 18px, size annotations 15px). The 15px size-legend annotations fall just below the 16px minimum, minor deduction.
VQ-02: No Overlap (5/6) — East Asian cities physically overlap (Tokyo, Osaka, Seoul, Beijing, Shanghai all clustered), but this is geographically inherent. The 0.65 opacity and white borders handle it gracefully. No text collision observed.
VQ-03: Element Visibility (6/6) — All bubbles clearly visible, including the smallest Oceania cities (Sydney, Melbourne). The min_size=15 ensures no value disappears.
VQ-04: Color Accessibility (2/2) — Okabe-Ito palette is CVD-safe. Adequate contrast between all six region colors and both map surfaces.
VQ-05: Layout & Canvas (4/4) — Map fills the landscape canvas well. Legends are well-placed in map corners. Margins appropriate.
VQ-06: Axis Labels & Title (2/2) — Descriptive title present. No axis labels needed for a geographic map — N/A.
VQ-07: Palette Compliance (2/2) — First series (Asia) = #009E73 ✓. Multi-series follows Okabe-Ito canonical order ✓. Background #FAF8F1 (light) / #1A1A17 (dark) ✓. Theme-adaptive chrome correct in both renders ✓.
Design Excellence (11/20)
DE-01: Aesthetic Sophistication (5/8) — Above the default 4: custom theme-adaptive geographic colors (land, ocean, coastline, country borders all themed for both surfaces), custom size legend with styled annotation box, proper Okabe-Ito implementation. However, falls short of "FiveThirtyEight-level" — no subtitle, no focal annotations, no visual emphasis on standout data points.
DE-02: Visual Refinement (3/6) — Slightly above default 2: white marker edges provide definition against both map surfaces, 0.65 opacity for overlap, themed legend boxes with ELEVATED_BG borders. Geographic maps offer limited chrome reduction opportunities (no axis spines), so refinement options are constrained.
DE-03: Data Storytelling (3/6) — Slightly above default 2: bubble size variation immediately conveys Asia's population dominance, and six distinct region colors allow instant multi-dimensional comparison. However, no annotations highlight the largest cities, no callout draws attention to the Asia cluster insight, and viewers must extract the narrative themselves.
Spec Compliance (15/15)
SC-01: Plot Type (5/5) — Correct: geographic bubble map with go.Scattergeo using Natural Earth projection.
SC-02: Required Features (4/4) — Area-proportional sizing (sqrt normalization + diameter mode) ✓, transparency (0.65) ✓, geographic context with country boundaries and coastlines ✓, size legend ✓, hover tooltips with city + population ✓.
SC-03: Data Mapping (3/3) — lat/lon correctly mapped to geographic coordinates, population mapped to bubble size, region mapped to color ✓.
SC-04: Title & Legend (3/3) — Title "World City Populations · bubble-map-geographic · python · plotly · anyplot.ai" matches required format ✓. Region legend labels match data ✓.
Data Quality (15/15)
DQ-01: Feature Coverage (6/6) — 30 real world cities spanning all inhabited continents, covering a wide population range (5M–37.4M). Both small and large cities present. Six regions with multiple members each demonstrate the categorical dimension.
DQ-02: Realistic Context (5/5) — Real-world world city populations — neutral, comprehensible, science/geography topic.
DQ-03: Appropriate Scale (4/4) — Population values (Tokyo 37.4M, Delhi 31.2M, Sydney 5.4M, etc.) match real-world data closely. Coordinates are accurate.
Code Quality (10/10)
CQ-01: KISS Structure (3/3) — Flat script: theme tokens → data → sizing calculation → plot → layout → size legend → save. No functions or classes.
CQ-02: Reproducibility (2/2) — np.random.seed(42) set (defensive; data is hardcoded). Fully deterministic output.
CQ-04: Code Elegance (2/2) — Clean, readable. sizemode="diameter" with sqrt-normalized values is the correct approach for area-proportional encoding.
CQ-05: Output & API (1/1) — Saves plot-{THEME}.png and plot-{THEME}.html ✓.
Library Mastery (7/10)
LM-01: Idiomatic Usage (4/5) — Correct use of go.Scattergeo with hovertemplate, fig.update_layout() for geo configuration, fig.add_shape() and fig.add_annotation() for the custom size legend in paper coordinates. Idiomatic throughout.
LM-02: Distinctive Features (3/5) — Natural Earth geographic projection (Plotly-specific), interactive hover with formatted hovertemplate, HTML export alongside PNG, and the custom paper-coordinate size legend using fig.add_shape() circles. These features are Plotly-distinctive, though the usage is not exceptionally advanced.
Score Caps Applied
None — No caps apply. DE-01=5 > 2, DE-02=3 > 2, no VQ/SC/DQ zeros.
Strengths
Full spec compliance: all required features (size encoding, transparency, geographic context, size legend, hover tooltips) implemented correctly
Excellent theme-adaptive implementation: both renders pass legibility and color checks; geographic chrome (land, ocean, coast, country) is also theme-adaptive
Real, factually accurate world city data with good geographic distribution and population range
DE-01/DE-03: No visual emphasis or callout on notable data points — the Asia population dominance is visually obvious but not reinforced by any annotation or focal treatment
DE-03: No subtitle or caption providing geographic insight (e.g., "Asia hosts 10 of the world's 15 largest cities")
VQ-01: Size legend annotation text at 15px is just below the 16px minimum for legend-level text
Issues Found
DE-01/DE-03 MODERATE: No annotations or emphasis on standout cities (Tokyo/Delhi as the two largest). The plot displays data but doesn't guide the viewer to the key insight.
Fix: Add a subtle annotation callout for Tokyo and/or Delhi as the largest cities, or add a subtitle like "Asia accounts for 10 of the 15 largest cities"
VQ-01 MINOR: Population scale annotation text at 15px is slightly below the 16px threshold.
Fix: Increase size legend annotation font size from 15 to 16px
AI Feedback for Next Attempt
This is a strong implementation that meets all spec requirements with correct theming. To push further, add visual storytelling: (1) annotate Tokyo and Delhi as the two standout data points with brief callouts, or add a subtitle summarizing the Asia dominance insight; (2) increase size legend annotation font to 16px. These are targeted, low-risk changes that would boost DE-03 from 3 to 4 and VQ-01 from 7 to 8.
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quality:85
Implementation:
bubble-map-geographic- python/plotlyImplements the python/plotly version of
bubble-map-geographic.File:
plots/bubble-map-geographic/implementations/python/plotly.pyParent Issue: #3625
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