MapConverter — Convert Shapefiles, GeoJSON & KML Easily

MapConverter — Convert Shapefiles, GeoJSON & KML EasilyMap data comes in many formats — Shapefiles, GeoJSON, KML, and more — each with their own strengths, uses, and quirks. MapConverter is a tool designed to simplify working across these formats: fast, reliable conversions that preserve geometry, attributes, and coordinate reference systems. This article explains why conversion matters, common challenges, how MapConverter handles them, practical workflows, and tips to get accurate results.

Why converting map formats matters

Different mapping and GIS tools prefer different formats. Web maps commonly use GeoJSON; Google Earth and many consumer tools accept KML; traditional desktop GIS workflows still rely on Shapefiles. Converting between formats enables data sharing, web publishing, and interoperability across platforms without re-creating or manually reformatting datasets.

Key formats: brief overview

• Shapefile (SHP + DBF + SHX + .prj)

  • Widely supported by desktop GIS (ArcGIS, QGIS).
  • Splits geometry, attributes, and projection into separate files.
  • Attribute names limited to ~10 characters in older implementations; non-ASCII handling can vary.

• GeoJSON

  • JSON-based, human-readable, ideal for web mapping (Leaflet, Mapbox).
  • Stores geometry and properties in a single file; supports nested structures.
  • Uses WGS84 coordinates by default (longitude, latitude).

• KML (Keyhole Markup Language)

  • XML-based format used by Google Earth and other viewers.
  • Supports rich styling, popups, and hierarchical organization.
  • Coordinates use longitude, latitude altitude; styling semantics differ from GeoJSON.

Common conversion challenges

• Coordinate Reference Systems (CRS)

  • Not all formats embed CRS. GeoJSON assumes WGS84; Shapefiles often include a .prj but may be missing or incorrect.
  • Misaligned CRS causes layers to appear in the wrong place.

• Attribute names and types

  • DBF (Shapefile attributes) has limits on name length and data types. Converting to GeoJSON/KML may require renaming or type coercion.
  • Nulls, dates, and special characters can be lost or changed.

• Geometry differences

  • Multi-part vs. single-part geometries may need normalization.
  • Invalid geometries (self-intersections, duplicate vertices) can break conversions or downstream rendering.

• File size and streaming

  • Large Shapefiles or GeoJSON files can be slow to read/write. Proper handling (compression, tiling, streaming) is needed for web use.

• Styling and metadata

  • KML supports rich styling; GeoJSON typically leaves styling to the renderer. Converting styles is lossy unless explicitly mapped.

How MapConverter addresses these issues

• Automatic CRS detection and re-projection

  • Reads .prj files or inspects data to infer CRS.
  • Offers safe re-projection to target CRS (default: WGS84 for GeoJSON/KML) with on-the-fly transformations to preserve spatial integrity.

• Attribute handling

  • Preserves attribute names and values when possible.
  • Provides configurable rules for DBF name truncation, field type coercion, and character encoding (UTF-8 support).
  • Option to export attribute metadata and schema for auditing.

• Geometry validation and repair

  • Runs geometry checks during conversion; attempts auto-repair (e.g., buffering, polygonizing) and reports unrecoverable features.
  • Normalizes single-part/multi-part geometries according to target format expectations.

• Streaming and chunked output

  • For large datasets, MapConverter can stream features into newline-delimited GeoJSON (NDJSON) or split outputs into tiled GeoJSON/KML sets to improve memory use and web performance.

• Style mapping (basic)

  • Translates simple styling (colors, line widths, icons) between KML and GeoJSON-compatible style hints; complex symbology is exported as metadata.

Typical workflows

1. Prepare and inspect

   • Load source (Shapefile, GeoJSON, KML).
   • Check CRS; open attribute schema; run geometry validation.
   • Example MapConverter CLI check:

     
     mapconverter inspect input.shp 

2. Convert to web-friendly GeoJSON (WGS84)

   • Reproject to EPSG:4326, normalize attributes, stream to minimize memory:

     
     mapconverter convert input.shp output.geojson --target-crs EPSG:4326 --stream 

3. Convert GeoJSON to KML for Google Earth sharing

   • Preserve placemark names and basic styling:

     
     mapconverter convert input.geojson output.kml --style-map style.json 

4. Split large datasets into tiles

   • Produce tiled GeoJSON for faster web delivery:

     
     mapconverter tile input.shp outdir/ --tile-size 4096 

5. Batch automation (CI/CD)

   • Integrate MapConverter into an automated pipeline to convert nightly exports into web-ready formats.

Practical tips for accurate results

• Always verify CRS before converting. If .prj is missing, check source metadata or ask the data provider.
• For Shapefiles, watch DBF attribute limits. Use field-mapping to preserve meaningful names.
• Run geometry validation and fix invalid features before publishing.
• For web maps, prefer TopoJSON or simplified GeoJSON for large polygon datasets to reduce size.
• Use compression (gzip) for serving GeoJSON over the web; many browsers and services accept .geojson.gz.
• Keep a reproducible conversion script or config (MapConverter CLI args or JSON) for transparency and repeatability.

Troubleshooting common problems

• Misplaced features after conversion

  • Likely CRS mismatch. Re-check source CRS and force correct input CRS during conversion.

• Missing attributes or truncated names

  • DBF field name length limits; use explicit field mapping or export to a format with longer field support.

• Invalid KML in Google Earth

  • Check for unsupported geometry types (e.g., complex multipart geometries). Convert or simplify geometries first.

• Very large output files

  • Simplify geometry (reduce vertex count), tile outputs, or export to a binary format (e.g., FlatGeobuf) that supports efficient indexing.

Example conversion scenarios

• Publish municipal parcel data to the web

  • Source: local Shapefile (EPSG:27700). Steps: reproject to EPSG:4326, simplify polygons, export as tiled GeoJSON, gzip tiles.

• Share hiking trails with KML users

  • Source: GeoJSON track logs. Steps: validate LineString geometries, convert to KML with placemarks and style icons for trailheads.

• Migrate legacy GIS datasets

  • Source: multiple Shapefiles with inconsistent schemas. Steps: batch-convert to GeoPackage (single-file container), normalize schemas, then export targeted layers to GeoJSON/KML as needed.

Advanced features (for power users)

• Command-line scripting and batch conversion with parallel processing.
• Custom field-mapping and transformation pipelines (rename, calculate new fields, drop unused columns).
• Plugin hooks for custom geometry processing (snap, dissolve, union).
• Output to efficient spatial formats: GeoPackage, FlatGeobuf, MBTiles for vector tiles.

When to choose which format

Summary

MapConverter streamlines the messy, detail-heavy work of moving spatial data between formats while addressing CRS, attribute, and geometry pitfalls. Whether you need one-off conversions for sharing or a repeatable pipeline for publishing, careful preparation (CRS checks, attribute mapping, geometry validation) combined with MapConverter’s automated safeguards will save time and reduce errors.

If you want, tell me the formats and dataset size you’re working with and I’ll suggest exact MapConverter commands and settings.