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Mapping administrative boundaries in Indonesia can tricky especially when dealing with overlapping names. Here is my simplified workflow for preparing this data:

1. Data Sourcing


First, download the official spatial data from Peta Rupa Bumi by Badan Informasi Geospasial. This serves as the primary geometry source.

2. Extracting Place Nodes


Since the source data is in polygon format, I use QGIS to extract the centroids (points). These points are essential for creating the place=* tags that represent the center of each administrative area.

3. The Importance of Kemendagri Codes


The polygons include Kemendagri reference codes. These are vital for:

  • Conflation: Ensuring data matches across different sets.

  • Identification: Many villages (admin_level 7 or 8) share the same name. The code helps distinguish them within a Regency or Province.

4. Enriching Metadata


Using spreadsheet tools and conflation techniques, I cross-reference the data to add:

  • wikidata and wikipedia tags.

  • Multilingual names (name:en, etc.).

5. Geometry Processing


To follow OSM best practices, I convert the polygons into independent ways (polylines).

  • This allows adjacent areas to share a single boundary line via a multipolygon relation.

  • Once converted, I export the result as a .geojson file.

6. Final Tagging


Finally, I use the previously extracted place nodes to quickly copy and paste the relevant tags into the new multipolygon relations in my OSM editor.
















Es macht besonders Spaß, draußen an der frischen Luft zu kartieren. Gerade jetzt, wo es wieder wärmer wird, ist das durchaus eine angenehme Art zu mappen. Doch das wäre ohne bestimmte Tools gar nicht möglich.



🗺️ Entry 1 — Setting up JOSM & Plugins


Mapping Banjë, Albania

I started mapping the Banjë region in Albania by setting up my editing environment in JOSM.

⚙️ Setup


I configured JOSM with a set of plugins to support structured mapping and validation:
  • utilsplugin2 – general productivity tools
  • reltoolbox – relation and multipolygon editing
  • waydownloader – working with connected geometries
  • merge-overlap – cleaning overlapping features
  • Relation Validation Plugin – checking data consistency
  • FastDraw – faster geometry digitizing
I also explored additional plugins like contour-related tools for terrain-based mapping.

🗺️ Mapping Context


The focus area is Banjë (central Albania) — a landscape with: - Complex terrain (valleys, rivers, slopes)
- Mixed land use (forests, agriculture, settlements)
- Incomplete or inconsistent OSM coverage

🌱 Initial Observations

  • Landuse classification is often fragmented or overlapping
  • Boundaries between forest, farmland, and settlements are not always clear
  • Many features require clean multipolygon structures
  • Validation tools already highlight conflicts in relations

🎯 Next Steps

  • Clean and structure landuse polygons (forest, farmland, residential)
  • Resolve relation conflicts and validation errors
  • Improve consistency of tagging using presets
  • Start refining settlement structures and road connectivity





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