Cartography Geoviz

Cartography & Geovisualization

Purpose: maps that communicate honestly. Cartographic choices (class breaks, ramps, normalization, projection) can manufacture or hide patterns; this skill treats them as analytical decisions with stated rationale, not styling.

The first three questions

1. What's the message? One map = one message. If two variables

compete, consider small multiples or a bivariate scheme — not twelve legend classes.

1. Normalized? Choropleths of raw counts are population maps in

disguise. Rates, densities, or per-capita for area-based color; raw magnitudes → proportional symbols instead.

1. Static or interactive? Print/PDF/paper → matplotlib/QGIS layout;

exploration/stakeholders → Folium/MapLibre; big point data → Kepler.gl/deck.gl (GPU).

Thematic map type selection

| Data | Map type | |---|---| | Rate/ratio by polygon | Choropleth | | Count/magnitude by place | Proportional/graduated symbols | | Two related rates | Bivariate choropleth (3×3 max) | | Individual-level density | Dot density or KDE surface (label bandwidth) | | Continuous field (raster) | Classified or stretched render + hillshade context | | Movement/OD | Flow map (width∝volume), aggregate to avoid hairballs | | Change over time | Small multiples > animation for analysis; animation for outreach |

Classification — the honesty lever

• Natural breaks (Jenks): default for skewed data; breaks are

data-specific, so NOT comparable across maps/dates.

• Quantiles: guaranteed color balance; can split near-identical values.
• Equal interval: comparable and intuitive; fails on skew.
• Manual/defined: the ONLY correct choice for map series (same breaks

across all dates/regions) and for domain thresholds (WHO limits, slope classes).

• 5±2 classes; show the histogram with breaks in the workflow; state the

scheme in the caption/metadata. Try two schemes — if the story changes materially, the story is the classification, and the reader must be told.

Color

• Ramps from ColorBrewer/cmcrameri/viridis family: sequential (ordered),

diverging (meaningful midpoint — zero, mean, threshold), qualitative (categories, ≤ 8).

• Colorblind-safe by default (~8% of male readers); never red-green

diverging without checking a CVD simulator.

• NoData ≠ zero: render as neutral gray with its own legend entry, never

the ramp's low end.

• Muted basemaps (CartoDB Positron) under thematic layers — the basemap

must never win.

Projection for display

• Web tiles = Web Mercator: fine for city scale; area comparisons at

continental scale on Mercator are visual lies — use equal-area projections (Albers, Mollweide, Equal Earth) for static thematic maps of large extents.

• National mapping → the national grid; polar work → polar stereographic.
• Label the projection on publication maps.

Required furniture (publication static maps)

Title (the message, not the filename), legend (units!, sensible number formatting), scale bar (projected CRS only — degrees have no fixed scale), north arrow (only when north isn't up or the audience expects it), data source + date + projection + author, and an inset locator map for unfamiliar regions.

# GeoPandas static map core
ax = gdf.plot(column="rate_per_1k", scheme="naturalbreaks", k=5,
              cmap="YlGnBu", legend=True, edgecolor="white", linewidth=0.3,
              missing_kwds={"color": "#d9d9d9", "label": "No data"})
ax.set_axis_off()

Export: 300 dpi PNG/PDF for print; SVG when editors will touch it; COG + style for GIS handoff.

Interactive maps

• Folium/MapLibre: tooltips with formatted values, layer control, sensible

initial bounds (fit_bounds), legend included (Folium needs a manual HTML/branca legend — don't ship without one).

• Performance: >~50k vector features → tile it (tippecanoe → PMTiles) or

switch to deck.gl/Kepler; never dump 500k GeoJSON features into Leaflet.

• Every popup number formatted (thousands separators, units, rounding

matched to precision honesty).

Verification protocol

1. Squint test: does the message survive at thumbnail size?
2. CVD simulation pass.
3. Legend audit: units, rounding, class edges non-overlapping.
4. Cross-check 3 features' rendered values against the attribute table

(classification bugs are silent).

1. For map series: identical breaks, ramp, and extent across panels.

Pitfalls checklist

• Raw-count choropleth (population in disguise).
• Jenks breaks compared across two dates.
• Red-green diverging ramp, unlabeled midpoint.
• NoData painted as the lowest class.
• Scale bar on an unprojected (degree) map.
• Continental-area comparisons on Web Mercator.
• Interactive map with no legend or units.

Execution contract

• Workflow: inspect audience, data semantics, scale, and output medium; select projection, normalization, classification, and visual hierarchy; render; verify; export.
• Decision rules: choose map type from the analytical question, normalize counts when exposure differs, and keep breaks fixed for comparisons.
• Verification protocol: run the five checks above and reconcile rendered values, units, class edges, and missing-data treatment against the source.
• Failure modes: stop or qualify delivery when denominators, CRS, units, accessibility, or cross-panel comparability are unresolved.
• Deliverables: final map, legend and units, data/source note, projection and classification rationale, accessibility note, and reproducible style or code.
• Source freshness: consult [the authoritative source registry](references/authoritative-sources.md) before using version-sensitive APIs and record the checked date.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: muend
• Source: muend/geoai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.