GPS Coordinates (Decimal Degrees)

Name: GPS Coordinates (Decimal Degrees)
Creator: TestPattern Community
License: https://opensource.org/licenses/MIT

Detects GPS Coordinates (Decimal Degrees) patterns. Requires location/GPS context keywords

Type: regex
Engine: universal
Confidence: medium
Confidence justification: Medium confidence: pattern has structural constraints but corroborative keywords are recommended to reduce false positive rates. Added context gating and exclusion rules improve precision and reduce incidental matches.
Detection quality: Verified
Jurisdictions: global
Frameworks: ISO 27001, ISO 27701
Data categories: location, pii
Scope: wide
Platform compatibility: Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Compatible, Netskope: Compatible

Pattern

-?\d{1,3}\.\d{4,}\s*,\s*-?\d{1,3}\.\d{4,}

Corroborative evidence keywords

identifier, number, ID, geolocation, GPS data, GPS coordinates, location data, location tracking, cell tower, triangulation, latitude, longitude, geo-fence, geofence, location history, field, column, row, entry, record (+21 more)

Proximity: 300 characters

Should match

-33.8688, 151.2093 — Sydney GPS coordinates
40.7128, -74.0060 — New York GPS coordinates
-37.8136, 144.9631 — Melbourne GPS coordinates

Should not match

-33.86, 151.20 — Too few decimal places (2 instead of minimum 4)
40.7128, -74.006 — Only 3 decimal places in longitude (below minimum 4)
-33.8688 151.2093 — Missing comma separator between coordinates
template example placeholder record identifier — Template/sample context should be excluded even when anchor words are present

Known false positives

Common words and phrases related to gps coordinates (decimal degrees) appearing in policy documents, training materials, HR templates, or compliance guidelines without actual personal data. Mitigation: Require corroborative evidence keywords within the proximity window to confirm sensitive data context rather than general discussion.
In English (as the primary international business language), similar terminology used in formal or administrative contexts (education, professional documentation) that does not constitute sensitive data collection. Mitigation: Layer with additional contextual signals such as structured identifiers, form fields, or database column headers to distinguish sensitive records from general references.
High-frequency pattern matches in large document corpora due to broad regex anchors. Expected match rate is significantly higher than specific identifier patterns. Mitigation: Tune confidence thresholds for bulk scanning. Consider using this pattern primarily as a pre-filter with secondary validation.