Location data adds geographic context to records, enabling use cases like proximity targeting, foot traffic analysis, and location-based audience building. At its core, location data maps coordinates to meaningful places—transforming raw latitude/longitude pairs into actionable insights.
Why location context matters
Raw location data typically consists of latitude, longitude, and timestamp—coordinates that have limited meaning in isolation. The latitude 40.74576 and longitude -73.9848 tells you little on its own, but when mapped to “Narrative Headquarters in New York City,” the data becomes actionable.
Location context enables:
- Proximity targeting: Reach users near specific locations
- Foot traffic analysis: Understand visitation patterns
- Competitive intelligence: Analyze visits to competitor locations
- Attribution: Connect ad exposure to physical store visits
Location polygons
A location polygon is a shape that represents a place, point of interest, or geographic boundary using geospatial coordinates. Polygons are defined by the coordinates of their vertices (corner points), connected by edges.
What polygons represent
Polygons can represent locations at any scale:
- Individual buildings or storefronts
- City blocks or neighborhoods
- Entire cities or regions
- Country borders
Coordinate systems
Most location data uses the WGS84 (World Geodetic System 1984) coordinate reference system, which represents locations as latitude/longitude pairs:
- Latitude: -90 to 90 (north/south of equator)
- Longitude: -180 to 180 (east/west of prime meridian)
Coordinate order varies by format. GeoJSON uses longitude-first ([lon, lat]), while many other systems use latitude-first (lat, lon). Always verify the expected order when working with location data.
GeoJSON
GeoJSON is a JSON-based standard for encoding geographic data. It’s human-readable, easy to parse in most programming languages, and widely supported.
{
"type": "Polygon",
"coordinates": [[
[-122.389946, 37.7786],
[-122.389095, 37.7786],
[-122.389095, 37.7793],
[-122.389946, 37.7793],
[-122.389946, 37.7786]
]]
}
.json, .geojson, and .parquet filesPolygon and MultiPolygon geometries- Does not accept GeoJSON embedded in CSV
Well-Known Text (WKT)
WKT is a text-based format for representing geometry. It’s more compact than GeoJSON but less human-readable.
POLYGON((-122.389946 37.7786, -122.389095 37.7786, -122.389095 37.7793, -122.389946 37.7793, -122.389946 37.7786))
- WKT in CSV format
- Does not accept WKT in JSON format
Shapefile
Shapefiles are a common GIS format, but they require specialized libraries to read and write. They consist of multiple files (.shp, .shx, .dbf) that must be kept together.
Point-radius
For locations where exact boundaries aren’t known, point-radius defines a center point and a radius (typically in meters). While not technically a polygon, many systems convert point-radius to polygons by calculating vertices around the circle.
-122.389521, 37.7789, 100
- Point-radius in
.txt, .json, .parquet, and .csv formats
| Format | File Types | Notes |
|---|
| GeoJSON | .json, .geojson, .parquet | Polygon and MultiPolygon only |
| WKT | .csv | WKT column with polygon geometry |
| Point-radius | .txt, .json, .parquet, .csv | lon, lat, radius |
Coordinate order
A common source of errors is mixing up longitude and latitude order. Different formats use different conventions:
| Format | Order | Example |
|---|
| GeoJSON | [longitude, latitude] | [-122.389, 37.778] |
| WKT | longitude latitude | POINT(-122.389 37.778) |
| Google Maps | latitude, longitude | 37.778, -122.389 |
Creating location data
If you need to define custom polygons:
- Drawing tools: Use Bounding Box Tool to draw shapes and export in various formats
- GIS software: Tools like QGIS (free) can create and edit complex geometries
- Existing datasets: Many POI datasets are available commercially or through open sources
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