Versatile Map Maker

Versatile Map Maker

Create real, editable SVG maps from either built-in public map data or user-supplied geometry.

Use the fastest honest path:

1. Known admin0/admin1 map: fetch a ready SVG + GeoJSON from Highcharts.
2. County, municipality, custom, historical, fantasy, or unusual regions: ask for or use a GeoJSON/TopoJSON/shapefile-derived file, then generate the SVG locally.
3. Boundary overlay: draw one or more polygon/line overlays on top of either map source, using real feature geometry when available.

Always be explicit about precision: region boundaries are as exact as the source geometry; hand-entered lines are schematic; coarse datasets must not be described as fine-grained maps.

Working Rules

• Work in a scratch directory under the current project or system temp directory.
• Keep source data, generated SVGs, previews, and notes together.
• Preserve originals. Write new outputs rather than overwriting user-provided data.
• Prefer editable SVG as the deliverable; render PNG previews only for verification.
• Preview every final SVG before reporting completion.
• If a requested granularity is unavailable, say so before making a coarser substitute.

Dependencies

Install only what is needed for the chosen path:

python -m pip install numpy cairosvg

Optional:

• npm for the Highcharts fast path.
• geopandas, shapely, or ogr2ogr only when converting shapefiles/TopoJSON outside the bundled scripts.
• matplotlib only if you extend palettes beyond the built-in script palettes.

Path A: Highcharts Fast Path

Use this for world, continent, country, state, or province maps when admin1 granularity is enough.

python scripts/fetch_base_map.py --list serbia
python scripts/fetch_base_map.py countries/rs/rs-all ./mapwork
python scripts/fit_transform.py ./mapwork/rs-all.svg ./mapwork/rs-all.geo.json ./mapwork/transform.json

Highcharts keys commonly look like:

• custom/world
• custom/europe
• countries/us/us-all
• countries/fr/fr-all
• countries/rs/rs-all

If npm is missing or the key is unavailable, switch to Path B.

Path B: Bring Your Own Geometry

Use this for counties, municipalities, custom regions, historical maps, fictional regions, or any project where the built-in map is too coarse.

Input should be GeoJSON when possible. If the user has a shapefile, convert it to GeoJSON with GIS tooling first.

python scripts/geojson_to_svg.py regions.geojson base.svg \
  --id-field GEOID --name-field NAME --metadata-out regions-index.json \
  --transform-out transform.json

Then color the generated SVG just like a Highcharts map:

python scripts/recolor_choropleth.py base.svg data.json choropleth.svg \
  --title "..." --subtitle "..." --legend-label "..."

The generated SVG path IDs come from --id-field; if omitted, the script tries common fields such as id, GEOID, ISO_A2, hc-key, hasc, and name.

Joining User Data

For JSON data already keyed by region id, use it directly:

{"US.CA": 39.5, "US.TX": 30.0}

For CSV tables, create the JSON mapping:

python scripts/join_data.py data.csv data.json --id-col region_id --value-col value --numeric

If labels do not match geometry IDs, inspect regions-index.json or the GeoJSON properties and build a clean crosswalk. Do not guess ambiguous matches.

Choropleths

Numeric data uses a continuous color scale; text data uses categorical swatches.

python scripts/recolor_choropleth.py base.svg data.json out.svg \
  --title "Population by Region" \
  --subtitle "Source: ..." \
  --legend-label "People" \
  --cmap YlOrRd \
  --missing-fill "#F2F2F2" \
  --style style.json

Use a style JSON when the user needs brand colors or a different visual tone:

{
  "font_family": "Arial, sans-serif",
  "neutral_fill": "#F2F2F2",
  "neutral_stroke": "#BDBDBD",
  "qualitative": ["#4E79A7", "#F28E2B", "#59A14F"],
  "title_fill": "#222222"
}

Boundary And Feature Overlays

Use overlays for historical borders, custom territories, service areas, disputed/uncertain lines, routes, and highlighted regions.

Boundary JSON accepts either a single ring or a FeatureCollection-like object:

{
  "features": [
    {"type": "polygon", "coordinates": [[[20.4,44.8],[21.0,44.6],[20.4,44.8]]], "label": "Inside"},
    {"type": "line", "coordinates": [[20.4,44.8],[21.0,44.6]], "dash": true, "label": "Approximate"},
    {"type": "point", "coordinates": [20.46,44.81], "label": "Capital"}
  ]
}

Draw it:

python scripts/overlay_boundary.py base.svg transform.json boundary.json out.svg \
  --title "Historical Boundary" \
  --subtitle "Modern base, historical overlay" \
  --legend-inside "Historical area" \
  --legend-outside "Modern reference"

When a boundary follows a river/coast/administrative border, prefer real geometry. For rivers:

python scripts/fetch_rivers.py "Danube" "Sava" --out rivers.json
python scripts/fetch_rivers.py --slice rivers.json "Danube" 20.455,44.840 22.545,44.226 --out danube-segment.json

If a real feature cannot be found, draw a schematic line and label the uncertainty.

Preview

Render a PNG preview before delivery:

python - <<'PY'
import cairosvg
cairosvg.svg2png(url="out.svg", write_to="preview.png", output_width=1000)
PY

Inspect the preview for:

• missing colored regions
• mismatched IDs
• clipped titles or legends
• illegible labels
• boundaries shifted away from the base map
• misleading precision

Deliver

Return the final SVG and any useful preview PNG. Include a short note naming:

• map source and granularity
• data source
• whether geometry was exact, generalized, or schematic
• any unmatched regions or missing data

Read More When Needed

• For source choice and granularity: references/data-sources.md
• For geometry conversion and ID matching: references/geometry-inputs.md
• For styling and accessibility: references/styling.md
• For the original Highcharts/Natural Earth technique notes: references/technique.md