Skip to main content
skill CX Codex

cartography-geoviz — Claude Code skill: maps and geovisualization

Skill for building thematic maps — choropleths, proportional symbols, bivariate schemes, and interactive layers. Teaches the agent to pick classification, palette, projection, and normalization so the map tells the truth.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add muend/geoai-skills --skill cartography-geoviz -a claude-code
Open on GitHub

What cartography-geoviz does

Triggers on any request to create, compare, or review a user-facing map. Distinguishes map types by data: choropleth for normalized rates, proportional symbols for raw counts, bivariate for two related variables, heatmap or dot density for individual events. Checks normalization: raw-count polygons turn into population maps, and the agent will suggest rates or density instead.

Inside are tables for projection choice, classification (equal interval, quantile, Jenks, manual), and ColorBrewer palettes with colorblind checks. For map series it locks class breaks so panels stay comparable. Without GeoPandas or QGIS the skill fails: it orchestrates plot() arguments, scheme=, cmap=, and legend rendering. For interactive maps it configures Folium or deck.gl and requires tiling layers above fifty thousand features.

Five-step protocol: squint test at thumbnail size, colorblind simulator pass, legend audit (units, rounding, non-overlapping class edges), cross-check three features against the attribute table, and verify identical class breaks for series. Without denominator, CRS, or units the agent qualifies the result as unfinished.

What it helps with

  • select map type and classification from data semantics
  • normalize counts to density or rate for choropleth encoding
  • lock class breaks across map series for comparison
  • render interactive map with legend and formatted tooltips
  • check palette for colorblind safety and NoData for separate color

Example prompts for the agent

  • Build a disease-rate choropleth by district with quantile classification and YlGnBu palette
  • Compare two dates on maps with identical class breaks using scheme='userdefined'
  • Make a Folium interactive map with legend, tooltips, and formatted numbers

How to install cartography-geoviz

1Skills CLI (Vercel)

npx skills add muend/geoai-skills --skill cartography-geoviz -a claude-code

2Manually: put the files in place

~/.claude/skills/cartography-geoviz/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/cartography-geoviz/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about cartography-geoviz

How to install cartography-geoviz in Claude Code?

Run npx skills add muend/geoai-skills --skill cartography-geoviz -a claude-code, and the skill lands in ~/.claude/skills/cartography-geoviz/SKILL.md.

What does cartography-geoviz do?

Orchestrates thematic map creation: picks type (choropleth, proportional symbols, bivariate), classification, palette, and projection; checks normalization, legend, and colorblind accessibility; locks class breaks in map series.

What libraries does cartography-geoviz require?

GeoPandas for static maps (plot() calls, classification, PNG/PDF export), Folium or MapLibre for interactive layers, deck.gl or Kepler.gl for large point datasets. QGIS is mentioned as an alternative for layout.

Source text

SKILL.md · MIT
---
name: cartography-geoviz
description: >-
  Always invoke before answering any request to create, compare, design, or
  review a user-facing map, even if the request is terse or underspecified.
  Covers publication maps, choropleths, map series and small multiples,
  comparable multi-date panels, proportional/bivariate/flow maps, raster
  rendering, and interactive web maps. Includes classification, color,
  legends, projections, accessibility, and large-data aggregation. Do not
  trigger for a temporary diagnostic plot inside another analysis.
license: MIT
metadata:
  author: Muhammed Enes Duran
---

# 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.
2. **Normalized?** Choropleths of raw counts are population maps in
   disguise. Rates, densities, or per-capita for area-based color; raw
   magnitudes → proportional symbols instead.
3. **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.

```python
# 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).
5. 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.

© Muhammed Enes Duran · MIT · muend/geoai-skills

Similar skills

All skills for Data and databases →

From the same set18

mcda-suitability-analysis Guides spatial multi-criteria suitability analysis, AHP factor weighting, and model sensitivity testing across GIS rasters. CX Codex +1 ★ 35repo stars google-earth-engine This skill guides Google Earth Engine workflows for satellite imagery, from filtering collections to computing zonal statistics and exporting results. CX Codex +1 ★ 35repo stars geostatistics-interpolation A skill that turns sparse point measurements into a continuous surface and ships an uncertainty map alongside it. Handy when values between stations, wells or soundings have to be estimated. CX Codex +1 ★ 35repo stars geoai-orchestrator Routes complex multi-stage geospatial workflows across dedicated specialist skills while enforcing strict coordinate reference system and geometry rules. CX Codex +1 ★ 35repo stars geo-deep-learning Guides neural methods for geospatial imagery, from choosing a model to checking mapped predictions. Useful for segmentation, object detection, and spatial validation. CX Codex +1 ★ 35repo stars geo-data-engineering Keeps an agent on track when spatial data has to be fetched, cleaned or pushed through a pipeline that has to run again next month. CX Codex +1 ★ 35repo stars change-detection Guides satellite and vector change detection workflows on comparable observations. Separates true land surface dynamics from phenology, registration jitter, and sensor drift. CX Codex +1 ★ 35repo stars arcgis-pro-automation A skill that drives saved ArcGIS Pro projects and local datasets through arcgis-mcp-bridge: geoprocessing, projections, rasters, networks, layout export. CX Codex +1 ★ 35repo stars

Project sponsors

Companies that keep this analytics open
Gold sponsor

Impressions, clicks and code copies — open statistics. We keep the numbers public for the same reason NeBlask itself is public. Become a sponsor