Convert GeoJSON to CSV for Maps, Excel, and GIS Joins

GeoJSON Is Nested; CSV Is Flat

GeoJSON stores geography as Geometry objects and attributes as properties on each Feature. CSV needs one row per record and one column per attribute — plus explicit longitude/latitude (or WKT) columns for points. Converting GeoJSON to CSV unlocks Excel filters, CRM uploads, and BI joins for teams in the US, UK, Canada, Australia, and Europe who receive .geojson from web maps, OpenStreetMap extracts, or internal GIS exports.

What Converts Cleanly

Point features are the sweet spot for tabular CSV.

Step-by-Step: GeoJSON → CSV

Browser path for analyst handoffs.

• Validate the file is a FeatureCollection (or a single Feature) with UTF-8 text.
• Open GeoJSON to CSV on Convert CSV Online.
• Preview rows: confirm property names became headers.
• Check longitude/latitude column order for your destination (see below).
• Download CSV; spot-fix a known city coordinate.
• Optional: convert CSV to Excel for stakeholders, or CSV to KML for another map tool.
• For pipelines, keep the original GeoJSON as the geometric source of truth.

{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": { "type": "Point", "coordinates": [-73.9857, 40.7484] },
      "properties": { "name": "Empire State", "city": "New York", "country": "US" }
    }
  ]
}

name,city,country,longitude,latitude
Empire State,New York,US,-73.9857,40.7484

Longitude, Latitude, and the Excel Swap Bug

GeoJSON coordinates are [longitude, latitude] per RFC 7946. Many US CSV templates and teaching materials use lat,lon order instead.

• Plotting lon/lat reversed puts New York in the Indian Ocean.
• Document column headers clearly: longitude then latitude, or lat then lon — pick one and stick to it.
• UK/EU national projections (BNG, etc.) are not lon/lat — reproject in GIS before assuming WGS84 CSV.
• Australian GDA and US State Plane exports need the same care: know the CRS before flattening.

Flattening Properties

CSV cannot nest.

• Simple keys (name, id, category) become columns.
• Nested objects (address.city) become address.city or address_city.
• Arrays (tags:["a","b"]) become pipe-joined text or a JSON string in one cell.
• Null properties should become empty cells, not the text “null”, unless your importer wants that.
• Inconsistent properties across features create sparse columns — that is normal.

import csv, json

with open("places.geojson", encoding="utf-8") as f:
    data = json.load(f)

rows = []
for feat in data["features"]:
    if feat.get("geometry", {}).get("type") != "Point":
        continue
    lon, lat = feat["geometry"]["coordinates"][:2]
    props = feat.get("properties") or {}
    rows.append({**props, "longitude": lon, "latitude": lat})

fieldnames = sorted({k for r in rows for k in r})
with open("places.csv", "w", newline="", encoding="utf-8") as out:
    writer = csv.DictWriter(out, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerows(rows)

Polygons and Lines: When CSV Is the Wrong Default

For boundaries and routes, prefer keeping GeoJSON/Shapefile/GeoPackage — or add a WKT column.

• Exploding polygon rings to one CSV row per vertex is possible but painful for Excel users.
• Use point-on-surface or centroid columns only when a single pin is an acceptable summary.
• Round-trip risk: CSV → GeoJSON may not restore full topology if you only exported centroids.

id,name,wkt
1,Central Park,"POLYGON((-73.98 40.80, -73.95 40.80, -73.95 40.76, -73.98 40.76, -73.98 40.80))"

KML, CSV, and GeoJSON Together

Google Earth and many CMS tools speak KML; modern web maps speak GeoJSON; ops teams speak CSV.

• KML to CSV for placemark attribute tables.
• GeoJSON to CSV for FeatureCollection properties.
• CSV to GeoJSON / CSV to KML when you only have a spreadsheet of pins.
• Normalize to WGS84 lon/lat before sharing across US and UK teams.

Excel, BI, and Encoding

After conversion, treat coordinates as numbers and ids as text.

• UTF-8 for place names (München, Montréal, São Paulo).
• European Excel users: import comma CSV with the wizard if semicolons are the default.
• Power BI / Tableau: mark lat/lon as geographic roles after load.
• Do not let Excel auto-format long numeric IDs from OSM or census codes.

Common Mistakes

Map-shaped regrets.

• Swapping lat and lon in headers.
• Assuming every Feature is a Point.
• Dropping properties that differ on only some features.
• Reprojecting by renaming columns without transforming values.
• Editing geometry in Excel cell text without validation.

Best Practices

Keep geometry authoritative upstream.

• Use CSV as a distribution format for points and attributes.
• Name columns longitude/latitude explicitly.
• Include a crs/note field or README when not WGS84.
• Validate a few known landmarks after every conversion.
• Version the source GeoJSON alongside exported CSV.

Why Use Convert CSV Online?

Convert CSV Online is free, browser-based, and requires no account for everyday conversions. GeoJSON to CSV flattens features for Excel and BI; CSV to GeoJSON rebuilds point collections from spreadsheets. KML converters cover Earth-style placemarks. Client-side workflows suit US, UK, Canadian, Australian, and EU teams on Windows, macOS, and Linux browsers.

Conclusion

GeoJSON to CSV succeeds for point-heavy datasets when coordinates stay in a documented order and properties flatten deliberately. Use GIS formats for complex geometry; use CSV when the world just needs a spreadsheet of pins.