feat(altair): implement bubble-map-geographic

plots/bubble-map-geographic/implementations/altair.py

@@ -0,0 +1,255 @@
+""" pyplots.ai
+bubble-map-geographic: Bubble Map with Sized Geographic Markers
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 93/100 | Created: 2026-01-10
+"""
+
+import altair as alt
+import pandas as pd
+
+
+# Data: Major world cities with population (millions)
+cities_data = {
+    "city": [
+        "Tokyo",
+        "Delhi",
+        "Shanghai",
+        "Sao Paulo",
+        "Mexico City",
+        "Cairo",
+        "Mumbai",
+        "Beijing",
+        "Dhaka",
+        "Osaka",
+        "New York",
+        "Karachi",
+        "Buenos Aires",
+        "Istanbul",
+        "Lagos",
+        "Los Angeles",
+        "Kolkata",
+        "Manila",
+        "Rio de Janeiro",
+        "Guangzhou",
+        "Moscow",
+        "Shenzhen",
+        "Paris",
+        "Jakarta",
+        "Lima",
+        "Bangkok",
+        "London",
+        "Chicago",
+        "Bogota",
+        "Sydney",
+    ],
+    "latitude": [
+        35.68,
+        28.61,
+        31.23,
+        -23.55,
+        19.43,
+        30.04,
+        19.08,
+        39.90,
+        23.81,
+        34.69,
+        40.71,
+        24.86,
+        -34.60,
+        41.01,
+        6.52,
+        34.05,
+        22.57,
+        14.60,
+        -22.91,
+        23.13,
+        55.76,
+        22.54,
+        48.86,
+        -6.21,
+        -12.05,
+        13.76,
+        51.51,
+        41.88,
+        4.71,
+        -33.87,
+    ],
+    "longitude": [
+        139.69,
+        77.21,
+        121.47,
+        -46.63,
+        -99.13,
+        31.24,
+        72.88,
+        116.41,
+        90.41,
+        135.50,
+        -74.01,
+        67.01,
+        -58.38,
+        28.98,
+        3.38,
+        -118.24,
+        88.36,
+        120.98,
+        -43.17,
+        113.26,
+        37.62,
+        114.06,
+        2.35,
+        106.85,
+        -77.04,
+        100.50,
+        -0.13,
+        -87.63,
+        -74.07,
+        151.21,
+    ],
+    "population": [
+        37.4,
+        32.9,
+        29.2,
+        22.4,
+        21.8,
+        21.3,
+        21.0,
+        20.9,
+        22.5,
+        19.1,
+        18.8,
+        16.8,
+        15.4,
+        15.6,
+        15.3,
+        12.5,
+        15.1,
+        14.4,
+        13.5,
+        14.3,
+        12.5,
+        13.4,
+        11.0,
+        11.2,
+        11.0,
+        10.7,
+        9.5,
+        8.9,
+        11.3,
+        5.4,
+    ],
+    "region": [
+        "Asia",
+        "Asia",
+        "Asia",
+        "South America",
+        "North America",
+        "Africa",
+        "Asia",
+        "Asia",
+        "Asia",
+        "Asia",
+        "North America",
+        "Asia",
+        "South America",
+        "Europe",
+        "Africa",
+        "North America",
+        "Asia",
+        "Asia",
+        "South America",
+        "Asia",
+        "Europe",
+        "Asia",
+        "Europe",
+        "Asia",
+        "South America",
+        "Asia",
+        "Europe",
+        "North America",
+        "South America",
+        "Oceania",
+    ],
+}
+
+df = pd.DataFrame(cities_data)
+
+# Load world map from vega-datasets URL (works without vega_datasets package)
+world_url = "https://cdn.jsdelivr.net/npm/vega-datasets@2/data/world-110m.json"
+countries = alt.topo_feature(world_url, "countries")
+
+# Region color mapping (colorblind-safe)
+region_colors = {
+    "Asia": "#306998",
+    "Europe": "#FFD43B",
+    "North America": "#2CA02C",
+    "South America": "#D62728",
+    "Africa": "#9467BD",
+    "Oceania": "#17BECF",
+}
+
+# Create base map with country boundaries
+base_map = (
+    alt.Chart(countries)
+    .mark_geoshape(fill="#E8E8E0", stroke="#B0B0B0", strokeWidth=0.5)
+    .project(type="equirectangular", scale=280, translate=[800, 480])
+    .properties(width=1600, height=900)
+)
+
+# Create bubble layer with sized markers
+bubbles = (
+    alt.Chart(df)
+    .mark_circle(opacity=0.7, stroke="#FFFFFF", strokeWidth=1.5)
+    .encode(
+        longitude="longitude:Q",
+        latitude="latitude:Q",
+        size=alt.Size(
+            "population:Q",
+            scale=alt.Scale(domain=[5, 40], range=[100, 2500]),
+            legend=alt.Legend(
+                title="Population (millions)",
+                titleFontSize=16,
+                labelFontSize=14,
+                symbolFillColor="#306998",
+                orient="bottom-left",
+                offset=20,
+            ),
+        ),
+        color=alt.Color(
+            "region:N",
+            scale=alt.Scale(domain=list(region_colors.keys()), range=list(region_colors.values())),
+            legend=alt.Legend(title="Region", titleFontSize=16, labelFontSize=14, orient="bottom-right", offset=20),
+        ),
+        tooltip=[
+            alt.Tooltip("city:N", title="City"),
+            alt.Tooltip("population:Q", title="Population (M)", format=".1f"),
+            alt.Tooltip("region:N", title="Region"),
+            alt.Tooltip("latitude:Q", title="Latitude", format=".2f"),
+            alt.Tooltip("longitude:Q", title="Longitude", format=".2f"),
+        ],
+    )
+    .project(type="equirectangular", scale=280, translate=[800, 480])
+)
+
+# Combine base map and bubbles
+chart = (
+    (base_map + bubbles)
+    .properties(
+        title=alt.Title(
+            text="World City Populations · bubble-map-geographic · altair · pyplots.ai",
+            fontSize=28,
+            anchor="middle",
+            color="#333333",
+        ),
+        width=1600,
+        height=900,
+    )
+    .configure_view(stroke=None)
+    .configure_legend(titleColor="#333333", labelColor="#555555", padding=15, cornerRadius=5)
+)
+
+# Save as PNG (scale 3x for 4800x2700)
+chart.save("plot.png", scale_factor=3.0)
+
+# Save interactive HTML version
+chart.save("plot.html")