Exporting GeoJSON Vector Data to CSV Format
GeoJSON and CSV are two of the most popular data formats used in GIS, data science, and spatial analysis. While GeoJSON is ideal for nesting complex nested attributes and geometry collections in web mapping APIs, CSV (Comma-Separated Values) remains the standard for tabular data manipulation, spreadsheet modeling, and loading into traditional database systems.
Why Convert GeoJSON to CSV?
Tabular processing engines like Microsoft Excel, Google Sheets, or Python's Pandas library cannot natively parse nested JSON structures easily. By flattening a GeoJSON file into a CSV structure, each spatial feature becomes a single row. Feature properties are mapped directly to CSV column headers, and geometry coordinates are extracted as distinct columns or standardized geometric definitions, making it straightforward to build charts, calculate averages, or run SQL queries.
Handling Geometric Structures in CSV (WKT vs. Columns)
Unlike GeoJSON, CSV files do not have a native representation for geographic shapes. To overcome this, our converter supports two main geometry export structures:
- Coordinate Columns (Point Geometries): For Point features, the converter extracts coordinates directly into
latitudeandlongitudecolumns, which is ideal for mapping store locations, coordinates, or GPS logs. - Well-Known Text (WKT): For complex lines or polygons, the converter formats geometries into WKT (Well-Known Text) string representations (e.g.,
POLYGON ((...))). This is the standard open format used to import vector geometries into QGIS, Esri ArcGIS, or PostGIS databases.
Preserving Feature Attributes
During conversion, the nested properties object of each GeoJSON feature is parsed dynamically. All top-level keys inside the properties dictionary are exported as independent columns in the CSV. Any missing values or sparse datasets are automatically padded with empty strings to guarantee uniform row alignment, preserving your database schema structure.