CSV to GeoJSON Converter

CSV to GeoJSON is reliable when coordinate columns are explicit and clean

Rows with latitude and longitude can be converted into GeoJSON Point features while the remaining columns become properties. This is a useful bridge from spreadsheets into web maps and GIS tools.

Check axis order, numeric ranges and missing values before trusting the result. Latitude must be between -90 and 90 and longitude between -180 and 180; swapped columns are one of the most common mapping errors.

When this tool is useful

Use CSV to GeoJSON Converter when the question matches the method the page actually uses. Typical workflows include:

• Run the same workflow for locations in different countries while keeping units and assumptions consistent.
• Answer a one-off question about csv to geojson converter and keep the result tied to a visible map or explicit input.
• Compare two or more scenarios involving csv to geojson converter online without creating an account or installing desktop software.

Worked examples

These examples are meant to show how to interpret this kind of output, not to replace the values produced from your own inputs.

• GPX track — A GPX file can carry track points, timestamps and elevation. Local browser tools can inspect those fields without sending the file to a server.
• KML/KMZ — KML is XML-based; KMZ is a ZIP container that commonly contains a KML document plus assets.
• CSV coordinates — CSV mapping works best when latitude and longitude columns are explicit and invalid rows are reported instead of silently ignored.

How to interpret the result

The most common errors come from using a correct result to answer the wrong geographic question. Keep these points in mind for CSV to GeoJSON Converter:

• For big CSVs, fix the coordinate columns first and map a sample before mapping everything: auto-detection is good but not magical, and one swapped column renders a beautiful map of the wrong ocean.
• Keep the original file even after converting: GPX→GeoJSON drops timestamps' context, KML→GeoJSON drops styling, and every pipeline has a lossy edge. Originals are cheap; re-acquisition is not.

Limits & appropriate use

The file tools' limits are format-inherent and honestly named. GPX carries time and elevation but not polygons or rich properties; KML carries presentation but ages into XML quirks; GeoJSON carries data but no styling; CSV carries anything and guarantees nothing. Conversions therefore always have a named loss, and the professional move — keep the original, export the derivative — is recommended on every export panel. A second boundary is source quality: a track's elevation is only as calm as the receiver that logged it, and a CSV's coordinates are only as sane as the column that birthed them; the viewers show counts, boxes and skipped-row tallies so those truths surface immediately.

What remains after those limits is a genuinely complete local workbench: validation, inspection, measurement, conversion and export, with zero upload. The escalation path is about scale and authority rather than privacy — national cadastral formats, laser-scan point clouds and billion-row rasters belong to GIS workstations; everything a laptop holds is welcome here, and the honesty labels travel with it.

• Cadastral/legal formats → jurisdictional land-registry exports.
• Point clouds and rasters → desktop GIS with proper spatial indexes.
• Certified tracks (evidence, insurance) → chain-of-custody originals, untouched.

Data & methodology

Recognizes latitude and longitude headers and maps remaining CSV columns to GeoJSON feature properties. Parsing uses browser-native readers (FileReader, DOMParser, JSON.parse); files never leave the device. Format fidelity notes accompany every export button.

For source details, licences and model limitations, see Data Sources and Methodology.

Key terms worth knowing

Bounding box

The minimal rectangle enclosing a dataset; the instant sanity check.

GeoJSON

JSON geographic format; the web-mapping native dialect.

Go deeper: guides & original studies

Use these resources when you need the reasoning behind the calculation, a comparison with another geographic method, or an example of the same approach applied across a dataset.

Related tools

If this page answers only part of your question, these tools use a nearby but different method or dataset.