GPS Coordinates (Decimal Degrees — High Precision) Regex for Python

What this pattern does

This page provides a comprehensive, battle-tested regular expression for matching gps coordinates (decimal degrees — high precision), ported and verified for Python. Identity and credential patterns need both correctness and safety, since they're frequent targets for adversarial input. The snippet below is ready to drop into your Python project — whether you're validating in a Django view, a FastAPI endpoint, or a standalone data processing script.

Python Implementation

# GPS Coordinates (Decimal Degrees — High Precision)
# ReDoS-safe | RegexVault — Identity & PII > Location PII

import re

gps_coordinates_decimal_degrees_high_precision_pattern = re.compile(r'^(-?(?:90(?:\.0{1,6})?|(?:[0-8][0-9]|[0-9])(?:\.[0-9]{1,8})?)),\s?(-?(?:180(?:\.0{1,6})?|(?:1[0-7][0-9]|[0-9]{1,2})(?:\.[0-9]{1,8})?))$')

def validate_gps_coordinates_decimal_degrees_high_precision(value: str) -> bool:
    return bool(gps_coordinates_decimal_degrees_high_precision_pattern.fullmatch(value))

# Example
print(validate_gps_coordinates_decimal_degrees_high_precision("1.3521,103.8198"))  # True

Test Cases

When to use this pattern

This pattern is drawn from the Identity & PII > Location PII category and carries a ReDoS-safe certification. That matters for Python developers because particularly important in Python web servers where CPU-bound regex operations can stall concurrent request handling. RegexVault audits patterns against known backtracking attack vectors, ensuring you have the necessary context before using this regex in a high-stakes production environment.

Common Pitfalls

Technical Notes

High-precision GPS (>4 decimal places) can identify a specific building or room. 4 decimal places ≈ 11m accuracy. 6 decimal places ≈ 0.1m. GPS coordinates of a home address or frequent location are personal data under GDPR.