GeoJSON to CSV Converter

Transform GeoJSON map data into Excel-ready CSV files instantly. Convert coordinates, flatten properties, and export geographic features with full geometry support.

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Free GeoJSON to CSV Converter: Transform Geographic Data for Excel & Analysis

Convert complex GeoJSON map files into spreadsheet-ready CSV format in seconds. Extract coordinates, flatten nested properties, and analyze geographic data in Excel, Google Sheets, or any database—no coding required.

What Is GeoJSON and Why Convert It to CSV?

GeoJSON is the standard format for encoding geographic features like points, lines, and polygons with their properties. While perfect for web maps and Leaflet or Mapbox applications, GeoJSON's nested JSON structure makes it impossible to open in Excel or import into most databases without conversion.

Converting GeoJSON to CSV transforms hierarchical geographic data into flat tabular rows that Excel, Google Sheets, SQL databases, and data analysis tools can process directly. According to OGC standards, GeoJSON can contain unlimited nested properties—our converter flattens these automatically while preserving all geographic information.

Common GeoJSON to CSV Use Cases:

Data Analysis & Reporting
  • • Excel pivot tables: Analyze location patterns and demographics
  • • SQL imports: Load geographic data into PostgreSQL/PostGIS
  • • Business intelligence: Feed Tableau, Power BI, or Looker
  • • Statistical analysis: Process in R, Python pandas, or SPSS
  • • Geospatial joins: Merge with existing datasets by coordinates
Practical Workflows
  • • OpenStreetMap exports: Convert OSM extracts to spreadsheets
  • • API data cleaning: Process Mapbox or Google Maps API responses
  • • Survey results: Analyze field data from mobile mapping apps
  • • Property listings: Extract real estate coordinates for CRM
  • • Delivery routes: Plan logistics from GPS waypoint collections

Real Example: GeoJSON vs CSV

📍 GeoJSON Input (nested, complex): { "type": "Feature", "geometry": { "type": "Point", "coordinates": [-122.42, 37.77] }, "properties": { "name": "Café", "address": { "city": "SF" } } }
✓ CSV Output (flat, Excel-ready): geometry,latitude,longitude,name,address.city "POINT(-122.42 37.77)",37.77,-122.42,"Café","SF" Opens directly in Excel with all data preserved

How to Convert GeoJSON Files to CSV in 3 Steps

1
Paste or upload your GeoJSON: Copy GeoJSON from geojson.io, API responses, QGIS exports, or downloaded OpenStreetMap data. Supports FeatureCollections with up to 1 million features—handles city-scale datasets that crash online converters limited to 10,000 rows.
2
Choose geometry format: Select how coordinates appear in your CSV:
  • • WKT (Well-Known Text): Industry standard for PostGIS, QGIS, ArcGIS imports
  • • Lat/Lng columns: Separate latitude/longitude for Google Sheets mapping
  • • Centroid: Reduces polygons to center points for simplified analysis
  • • GeoJSON string: Preserves full geometry for re-import to mapping tools
  • • Skip geometry: Properties-only export for attribute-focused work
3
Download and use: Get instant CSV ready for Excel, import to SQL databases, or load into Tableau for visualization. Works with any tool expecting tabular data—no geographic software required after conversion.

💡 Pro Tip: Nested Properties Flattening

Our converter automatically flattens deeply nested JSON properties using dot notation. For example, properties.demographics.population becomes the CSV column header demographics.population. Configure nesting depth (1-10 levels) in advanced options to control how deep the flattening goes—perfect for complex datasets from census APIs or business directories.

Complete Guide to CSV Geometry Formats

1
WKT (Well-Known Text) - Best for GIS Software:

The universal format recognized by PostGIS, QGIS, ArcGIS, and every professional mapping tool. Represents any geometry type in human-readable text: POINT(-122.4194 37.7749) for points, LINESTRING(...) for routes, POLYGON(...) for boundaries. According to OGC Simple Features specification, WKT is the interchange standard for spatial data across platforms.

2
Latitude/Longitude Columns - Best for Excel & Spreadsheets:

Creates separate latitude and longitude columns that Excel, Google Sheets, and business intelligence tools recognize immediately. Perfect for store locators, customer mapping, or any point-based analysis. Limitation: Only works with Point geometry—polygons and lines need WKT or centroid conversion. Import these columns directly into Google My Maps for instant visualization.

3
Centroid Coordinates - Best for Simplifying Complex Shapes:

Calculates the geometric center of any polygon or linestring, outputting a single representative point. Use when you need one location per feature for distance calculations, proximity analysis, or simplified mapping. Example: convert building footprint polygons to address points, or reduce country boundaries to capital coordinates for clean charts. Mathematically computed using standard centroid algorithms—more accurate than simple bounding box centers.

4
GeoJSON String - Best for Preserving Full Geometry:

Stores the complete geometry object as JSON text in a single CSV column. Maintains exact coordinate precision and geometry complexity for later re-import to mapping applications. Ideal when you need CSV portability but can't lose geographic detail—parse the JSON column programmatically when needed. Larger file sizes than WKT but guarantees perfect round-trip conversion back to GeoJSON.

5
Skip Geometry - Best for Attribute-Only Analysis:

Exports only property data without any coordinate information—smallest file size, fastest processing. Perfect when location is irrelevant and you only need the attributes (names, IDs, categories). Use for extracting metadata from geographic datasets, generating address lists, or feeding non-spatial analytics tools. Cuts file sizes by 40-60% compared to full geometry exports.

7 Real-World Scenarios for GeoJSON to CSV Conversion

1. OpenStreetMap Data Extraction

Export POIs (points of interest) from Overpass Turbo as GeoJSON, then convert to CSV for business analysis. Extract all restaurants in a city, coffee shops along a route, or ATM locations for market research. OSM GeoJSON contains nested tags—our flattening handles amenity, cuisine, opening_hours automatically.

Example workflow: Overpass query → GeoJSON download → CSV conversion → Excel pivot table → Business insights

2. Real Estate Property Mapping

Convert property listings from APIs (Zillow, Rightmove, Redfin) that return GeoJSON. Generate spreadsheets with addresses, prices, coordinates for CRM import or mail merge. Lat/lng columns let sales teams plot territories in Google Maps without technical skills. Common property fields like price.amount and address.postcode flatten to price.amount and address.postcode columns.

3. Field Survey Data Processing

Mobile data collection apps (Fulcrum, Survey123, KoBoToolbox) export survey results as GeoJSON. Convert to CSV for statistical analysis in SPSS, R, or Python. Researchers collecting environmental data, infrastructure inspections, or public health surveys need flat tables for regression analysis and reporting—nested survey responses become individual columns ready for data cleaning.

4. Delivery Route Optimization

GPS tracking and route planning APIs (Mapbox Directions, OSRM) output GeoJSON polylines. Extract waypoints to CSV for logistics software that expects tabular data. Calculate distances in Excel, join with delivery schedules, or import to fleet management systems. LineString geometries convert to WKT for PostGIS distance calculations or centroid lat/lng for approximate locations.

5. Government Open Data Analysis

Census data, electoral boundaries, and administrative zones often publish in GeoJSON following Data.gov standards. Convert to CSV for demographic analysis, policy research, or data journalism. Flatten census variables like demographics.age.median and economics.income.household for comparative studies across regions. Essential for journalists without GIS training who need geographic context in Excel.

6. IoT and Sensor Network Data

Environmental sensors, weather stations, and smart city infrastructure broadcast readings as GeoJSON with temporal properties. Convert to time-series CSV for trend analysis, anomaly detection, or machine learning. Sensor metadata like device.id, timestamp, and nested measurement arrays flatten to training data for predictive models. Critical for researchers analyzing air quality, traffic flow, or climate patterns.

7. E-commerce Store Locator Management

Maintain retail locations, pickup points, or service areas in GeoJSON for web maps, but export to CSV for inventory systems, marketing campaigns, and partner dashboards. Non-technical teams update spreadsheets with store hours, contact info, photos—then re-import to mapping platforms. The two-way workflow (CSV ↔ GeoJSON) lets business users own location data without developer bottlenecks.

Technical Specifications & Limits

Supported Input Formats

  • • RFC 7946 compliant GeoJSON (standard)
  • • FeatureCollection with unlimited features
  • • Single Feature objects
  • • All geometry types: Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, GeometryCollection
  • • Nested properties up to 10 levels deep
  • • CRS transformations (coordinate reference systems)

Performance Benchmarks

  • • Max file size: 50MB input (handles city-scale data)
  • • Max features: 1 million geographic features
  • • Processing speed: ~10,000 features/second
  • • Property columns: Up to 1,000 unique columns
  • • Character encoding: UTF-8, Windows-1252, ISO-8859-1

CSV Output Options

  • • Delimiters: Comma, semicolon, tab, pipe
  • • Geometry formats: WKT, lat/lng, centroid, GeoJSON, skip
  • • Null handling: Empty strings, "NULL", custom values
  • • Array separator: Customizable for multi-value fields
  • • Column ordering: Geometry-first or properties-first
  • • Encoding: Cross-platform compatibility

Data Validation

  • • Real-time JSON syntax validation
  • • GeoJSON schema compliance checking
  • • Coordinate range validation (-180 to 180, -90 to 90)
  • • Property type inference (string, number, boolean)
  • • Duplicate column name detection
  • • Warning system for data quality issues

6 Common GeoJSON to CSV Conversion Mistakes

1. Losing Coordinate Precision

Excel defaults to 6 decimal places, losing the last 5 digits of GPS precision (accurate to ~10cm). This matters for surveying, autonomous vehicles, or parcel boundaries. Solution: Format latitude/longitude columns as "Number" with 15 decimal places immediately after import, or use WKT format which Excel treats as text (preserving full precision).

2. Mixing Geometry Types in One Dataset

A FeatureCollection with both Points and Polygons creates inconsistent CSV rows—Points have lat/lng, Polygons need WKT or centroid. Solution: Filter by geometry type before conversion, or use WKT format (works for all types). PostGIS and QGIS handle mixed geometries, but Excel analysis requires consistent column structure.

3. Ignoring Nested Array Properties

Properties like tags: [cafĂŠ, wifi, outdoor] become "cafĂŠ, wifi, outdoor" in CSV by default. This breaks filtering and pivot tables. Solution: Configure array separator (default comma) or flatten arrays to separate boolean columns (tags.cafĂŠ, tags.wifi) for proper analysis.

4. Using Wrong Delimiter for Your Region

European Excel uses semicolons as default CSV delimiter (commas are decimal separators). US coordinates like -122.4, 37.7 break European imports. Solution: Match your Excel regional settings—semicolon for Europe, comma for US/UK. Our converter auto-detects and suggests the right delimiter based on your system locale.

5. Exceeding Excel's Row Limits

Excel maxes out at 1,048,576 rows. Converting a full-city OSM extract (2M+ features) creates an unloadable file. Solution: Filter features before conversion using jq or geographic bounds, split into multiple CSV files, or use database imports (PostgreSQL handles billions of rows).

6. Not Validating CRS (Coordinate Reference System)

GeoJSON spec requires WGS84 (EPSG:4326) coordinates, but some tools export in local projections. Coordinates like 500000, 4000000 (UTM) look wrong in lat/lng columns. Solution: Verify your GeoJSON uses standard WGS84 before conversion. If not, reproject in QGIS first.

Frequently Asked Questions

Can I convert CSV back to GeoJSON after editing?

Yes, if you preserve geometry columns (WKT or lat/lng). Tools like Mapshaper, QGIS, or Python's geopandas read CSV with coordinates and reconstruct GeoJSON. The round-trip workflow lets teams edit attributes in Excel while maintaining geographic accuracy—critical for collaborative mapping projects.

What's the difference between GeoJSON and Shapefile?

GeoJSON is text-based JSON (human-readable, web-friendly), while Shapefile is binary (requires specialized software). Both store geographic features, but GeoJSON integrates directly with web APIs and JavaScript. For CSV conversion: GeoJSON's JSON structure makes property flattening straightforward—Shapefiles need intermediate conversion to GeoJSON first using ogr2ogr.

How do I handle very large GeoJSON files (100MB+)?

Files over 50MB may timeout in browsers. Solutions: (1) Use streaming conversion via command-line tools like ndjson-cli, (2) Split large files into chunks by geographic bounds, (3) Filter features before conversion using jq, (4) Import directly to PostGIS using ogr2ogr then export database query results as CSV.

Why are some property columns missing in my CSV?

GeoJSON features can have heterogeneous properties—not all features share the same fields. Our converter creates columns for ALL unique properties found across features, filling missing values with nulls (or your configured null representation). This is standard behavior— check the "Unique Properties" statistic to see total column count. Use our JSON formatter to inspect property structure.

Can I preserve custom CRS or projection information?

GeoJSON specification requires WGS84 (EPSG:4326) coordinates—custom CRS isn't officially supported per RFC 7946. If your data uses a different projection, reproject to WGS84 in QGIS before conversion. CSV format doesn't inherently store projection metadata— document CRS separately or use GeoPackage/Shapefile for projection-aware exports.

What happens to 3D coordinates (elevation data)?

GeoJSON supports 3D coordinates [longitude, latitude, elevation]. In WKT format, these become POINT Z (-122.42 37.77 100). In lat/lng mode, we create a third elevation column. This preserves altitude data from drone surveys, hiking trails, or building heights—essential for 3D urban modeling and terrain analysis.

Advanced Integration Workflows

PostGIS Database Import Pipeline

Convert GeoJSON → CSV with WKT geometry → Import to PostgreSQL → Cast WKT to PostGIS geometry type using ST_GeomFromText(). This bypasses GeoJSON parsing overhead for bulk imports, achieving 10x faster loading than ogr2ogr for massive datasets.

Power BI Geographic Visualization

Export lat/lng columns → Import to Power BI → Use "Map" visualization with latitude/longitude fields. Instantly create interactive dashboards from OpenStreetMap extracts, census data, or IoT sensor networks without ArcGIS licenses. Joins with business data by location enable sales territory analysis and market penetration maps.

Python Data Science Integration

Use CSV as intermediate format between web APIs and pandas DataFrames. GeoJSON from Mapbox/Google → CSV → pandas with WKT geometry column → Spatial joins using geopandas. Enables geospatial machine learning workflows without maintaining JSON parsers in production code.

Tableau Map Layer Creation

Convert custom geographic boundaries (sales territories, delivery zones) from GeoJSON to CSV with WKT → Import to Tableau → Create calculated fields using MAKEPOINT() for Points or spatial joins for Polygons. Combines business metrics with geography without Tableau's limited built-in map data.

Google Sheets Geocoding Workflow

Import lat/lng CSV → Use Google Sheets add-ons like "Geocode by Awesome Table" to reverse geocode (coordinates → addresses) or calculate distances. Non-technical teams manage location data in familiar spreadsheets while leveraging Google Maps APIs through formula-based automation.

ArcGIS Online Data Publishing

CSV with lat/lng uploads directly to ArcGIS Online as hosted feature layers. Enables field teams to collect data in mobile apps, export GeoJSON, convert to CSV for quality control in Excel, then republish to live web maps—closing the collaborative editing loop without desktop GIS software.

Related Geospatial Data Tools

Complete your geographic data processing workflow with these complementary utilities:

JSON Formatter URL Encoder Coordinate Converter Distance Calculator Bearing Calculator View All Tools

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Transform map data into Excel-ready spreadsheets in seconds. No software installation, no file uploads to cloud servers— 100% browser-based conversion that keeps your geographic data private.

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