feat(plotly): implement bubble-map-geographic

[github-actions]

Implementation: bubble-map-geographic - plotly

Implements the plotly version of bubble-map-geographic.

File: plots/bubble-map-geographic/implementations/plotly.py

Parent Issue: #3625

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a world map using the Natural Earth projection with a light gray landmass and pale blue ocean. Thirty major world cities are represented as bubbles sized proportionally to their population (in millions). The bubbles are color-coded by region using a 6-color palette: blue for Asia, orange for South America, green for North America, purple for Africa, yellow for Europe, and cyan for Oceania. The largest bubbles (Tokyo, Delhi, Shanghai) are clearly visible in Asia. A region legend is positioned in the lower-left corner with a white semi-transparent background. Below the legend, a size legend annotation explains "Bubble size = Population (millions)" with example sizes for 5M, 20M, and 35M. The title at the top follows the required format. Country boundaries are shown as subtle gray lines, and the map uses appropriate latitude/longitude constraints to show all populated continents.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title at 32pt is excellent, legend text at 18-20pt is good. Size legend text at 14-16pt is slightly small but readable.
• VQ-02: No Overlap (8/8) - No overlapping text elements. Bubbles intentionally overlap in dense areas (Asia) but this is handled well with transparency.
• VQ-03: Element Visibility (7/8) - Marker sizes scale well from small (Sydney ~5.4M) to large (Tokyo 37.4M). Some smaller cities like Singapore could be slightly more visible.
• VQ-04: Color Accessibility (5/5) - Six-color palette is colorblind-safe with good distinction between blue, orange, green, purple, yellow, and cyan.
• VQ-05: Layout Balance (5/5) - Map fills canvas well, balanced margins, legend positioned appropriately without overlapping data.
• VQ-06: Axis Labels (N/A) - Geographic maps don't use traditional axis labels; this criterion doesn't apply.
• VQ-07: Grid & Legend (2/2) - Region legend is well-formatted with semi-transparent background. Size legend provides helpful context.

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correctly implements a geographic bubble map with Scattergeo.
• SC-02: Data Mapping (5/5) - Latitude/longitude correctly mapped, population correctly encoded as bubble size.
• SC-03: Required Features (5/5) - All spec requirements met: size legend, transparency (0.65), geographic context (countries, coastlines), hover tooltips.
• SC-04: Data Range (3/3) - Map shows all data points from Sydney to Moscow, appropriate lat/lon range.
• SC-05: Legend Accuracy (2/2) - Region legend is accurate; size legend explains the scaling.
• SC-06: Title Format (2/2) - Uses correct format: "World City Populations · bubble-map-geographic · plotly · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows good range of populations (5.0M to 37.4M) across 6 regions. Could include a few more small cities to show the full range.
• DQ-02: Realistic Context (7/7) - Real-world city population data with accurate coordinates and realistic population values.
• DQ-03: Appropriate Scale (4/5) - Population values are realistic and current. Minor: some values slightly outdated but reasonable.

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear structure: imports → data → size calculation → figure → save.
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) even though data is deterministic.
• CQ-03: Clean Imports (2/2) - Only numpy, pandas, and plotly.graph_objects imported.
• CQ-04: No Deprecated API (1/1) - Uses current Plotly API.
• CQ-05: Output Correct (0/1) - Saves as plot.png correctly, but size legend annotation text labels (5M, 20M, 35M) don't have corresponding visual circle indicators.

Library Features (3/5 pts)

• LF-01: Distinctive Features (3/5) - Uses Scattergeo which is Plotly's native geographic plotting. Generates interactive HTML with hover tooltips. Could leverage more Plotly features like animated transitions or zoom controls.

Strengths

• Excellent geographic visualization using Plotly's native Scattergeo with Natural Earth projection
• Proper area scaling using sqrt transform for perceptually accurate bubble sizes
• Well-designed colorblind-safe palette for 6 distinct regions
• Comprehensive hover tooltips showing city name and population
• Clean code structure with clear data preparation
• Both PNG and interactive HTML outputs generated

Weaknesses

• Size legend shows text labels (5M, 20M, 35M) but lacks corresponding visual bubble indicators to demonstrate the actual sizes
• Could leverage more Plotly-specific features like linked hover highlighting or custom zoom controls

Verdict: APPROVED