Use the free GeoJSON to CSV Converter below: your data never leaves your browser — all work happens on your device, and nothing is uploaded to any server.
Map Data Wants to Be a Spreadsheet Sometimes
GeoJSON is the standard way to describe map features on the web: points, lines, and polygons, each with properties like a name, a population, or a category. It is perfect for maps. It is less perfect for analysis, because spreadsheets and databases want rows and columns, not nested geometry. This converter flattens GeoJSON into CSV: one row per feature, one column per property, plus columns for the geometry type and coordinates.
The flattening is straightforward and predictable. Every property becomes a column, and the tool collects every property key used across all features, so features with different properties still share one table, with blanks where a feature lacks a property. Nested property objects are flattened with dotted names, so an address object with a city becomes an address.city column. Point coordinates become separate longitude and latitude columns, which are the two numbers most tools actually need. Other geometries keep their coordinates as JSON text in one column, so nothing is lost.
How to Use It
Paste a GeoJSON FeatureCollection, or a single Feature if you only have one. Click convert, and the CSV appears below with a header row naming every column. Copy it into your spreadsheet or download it as a .csv file. Open it in your spreadsheet app and you will see your map features as ordinary rows, ready to sort, filter, and chart. If you need the data back on a map later, keep the original GeoJSON: CSV is a working copy, and the round trip is not always exact for complex shapes.
Coordinates deserve care. GeoJSON stores coordinates as longitude first, then latitude, which is the opposite of the order most people say aloud. The tool preserves that order in its longitude and latitude columns, and labels them clearly, so there is no guessing. If your spreadsheet expects latitude first, swap the two columns after import. Getting this backwards puts your points in the wrong hemisphere, which is an error that looks plausible until someone checks a map.
What Flattens Well, and What Does Not
Points flatten beautifully: a list of places with names and values becomes a clean table, and that table is often all an analyst needs. Lines and polygons flatten less cleanly, because their coordinates are long lists of numbers. The tool keeps those coordinates as JSON text in a single column, which preserves them exactly but does not make them spreadsheet-friendly. If you need line lengths or polygon areas, compute them in a GIS tool before converting, and add them as properties: then they arrive as ordinary numeric columns.
Property types matter too. Numbers stay numbers, text stays text, and boolean values arrive as true and false. Arrays inside properties become JSON text, because a spreadsheet cell cannot hold a list without a convention. Null properties become empty cells, which is the honest representation. If a property holds inconsistent types across features, like a number in one feature and text in another, the CSV keeps both as written, and your spreadsheet will treat the column as mixed, which is worth knowing before you sort it.
Real Uses for the CSV
The most common use is analysis: a set of survey points, shop locations, or sensor readings becomes a table you can pivot, filter, and chart without any mapping software. Another is joining: a CSV of places joins easily to a table of sales or population figures on a shared key like a city name or an ID. A third is cleaning: property values are easier to spot-check in a spreadsheet than inside nested JSON, and typos in names or categories jump out when the column is sorted.
Fieldwork is another home for this conversion. A team collecting points on a map can export GeoJSON, convert to CSV, and hand the table to anyone with a spreadsheet, no GIS training required. The same table feeds reports, slides, and dashboards. And because the conversion is local, sensitive location data, like survey respondents or private sites, never travels to a server in the process.
Privacy and Practical Tips
All work happens in your browser. Your GeoJSON is never uploaded, stored, or logged. That matters for location data, which can be sensitive even when it looks anonymous: a set of points can reveal homes, workplaces, and routines. Paste, convert, copy, close. Two practical tips: check the row count after conversion against your feature count, because a mismatch means a feature was malformed and skipped. And before sharing the CSV, scan it for properties you did not mean to share, like internal IDs or notes fields that rode along inside the GeoJSON properties.