PHP Geospatial Libraries: GeoJSON, PostGIS, H3 & Spatial SQL

Quick picks: choose a PHP geospatial library in 60 seconds

If you need a fast decision, start here. Most teams don’t need “one GIS library”. They need a clear split: PHP for format handling and application logic, a spatial database for heavy operations, and an indexing strategy if you’ll scale.

What “geospatial in PHP” really means (and why most pages get it wrong)

Many “PHP GIS library” pages list packages without telling you what they’re actually for. In production, geospatial work has multiple jobs that belong in different layers of your system.

This is why the “best PHP geospatial library” depends on your workload. If you want a page that helps you ship faster, the right output is a stack plus a decision framework.

Recommended stack: PHP + PostGIS + a geometry layer (the pattern that scales)

If you expect growth (more points, more polygons, more users, more queries), the most reliable approach is:

• PostgreSQL + PostGIS for spatial filtering, sorting, joins and index-backed speed.
• A PHP geometry layer for reading/writing formats and keeping your application models clean.
• Optional grid indexing (H3/geohash) for aggregation, clustering and cheap pre-filters.

Example: “near me” search the correct way (database first)

For large datasets, do not compute distances in PHP for every row. Instead, ask PostGIS to: (1) select candidates using an index-friendly predicate, and (2) order by distance.

-- PostGIS idea (simplified)
-- 1) Candidate selection (fast, index-backed)
-- 2) Order by distance (correct)
SELECT id, name
FROM places
WHERE ST_DWithin(
  geom::geography,
  ST_SetSRID(ST_MakePoint(:lon, :lat), 4326)::geography,
  :radius_meters
)
ORDER BY ST_Distance(
  geom::geography,
  ST_SetSRID(ST_MakePoint(:lon, :lat), 4326)::geography
)
LIMIT :limit;

The exact SQL varies by schema and whether you use geometry or geography. The key is the pattern: filter with an index-friendly operation, then compute precise distance on the shortlist.

PHP geospatial libraries by category

The goal of this section is not to overwhelm you with names. It’s to help you create a working stack that: ships quickly, stays correct, and remains maintainable as requirements expand.

1) Geometry & format I/O (GeoJSON, WKT, WKB)

This category is the foundation for API boundaries. Typical needs: parse GeoJSON safely, output GeoJSON consistently, and convert to/from WKT/WKB for databases.

Recommended options

• brick/geo — modern geometry types and format support (great when you want clean domain models).
• GeoPHP — pragmatic pure-PHP library with broad format support (useful for mixed GIS file formats).
• GeoJSON-focused parsers — when your world is “API GeoJSON only” and you want strict validation.

GeoJSON pitfall: coordinate order

In many systems, developers casually write (lat, lon). GeoJSON coordinates are (longitude, latitude). This single mistake creates “it looks close but wrong” bugs that can take hours to debug, especially near coastlines or borders. Make coordinate validation a first-class concern in your PHP layer.

// Defensive PHP idea (pseudo-code)
function assertGeoJsonPoint(array $coords): void {
  // Expect [lon, lat]
  if (count($coords) !== 2) throw new InvalidArgumentException('Point must have 2 numbers.');
  [$lon, $lat] = $coords;

  if (!is_numeric($lon) || !is_numeric($lat)) throw new InvalidArgumentException('Coordinates must be numeric.');
  if ($lon < -180 || $lon > 180) throw new InvalidArgumentException('Longitude out of range.');
  if ($lat < -90  || $lat > 90)  throw new InvalidArgumentException('Latitude out of range.');
}

2) Spatial databases & ORM/framework glue (PostGIS, MySQL spatial)

Your spatial database is where you win on performance and correctness. The question is not “Do I need PostGIS?” but: Which features will my product need in 6–12 months?

• PostGIS is the default choice if you need serious GIS features: spatial joins, large polygon datasets, accurate distance filtering, and robust indexing.
• MySQL/MariaDB spatial can be enough for simpler cases and teams already invested in the MySQL ecosystem.
• Laravel / Doctrine spatial packages matter if your team wants ergonomic spatial columns, casting, migrations, and query helpers.

3) Distance & geodesy (Haversine, Vincenty, tracks)

There are two typical “distance” needs:

• One-off distances (two points, a single route segment, a track length) → PHP libraries are fine.
• Distance filtering/sorting on many rows (“near me”, “closest 50”, “within 2km”) → PostGIS wins.

If you’re building tracking features (GPX-style tracks, length-by-segment, bounding boxes), a dedicated geodesy library keeps your code readable and tested.

4) CRS / projection transforms (EPSG strategy)

CRS problems are the hidden cost center of geospatial projects. Most bugs are not “math bugs”, they’re “SRID mismatch” bugs: one dataset is EPSG:4326, another is EPSG:3857, and suddenly polygons don’t contain points that obviously sit inside them.

If you ingest GIS datasets or receive coordinates from multiple sources, use a library like proj4php to transform coordinates, and decide early:

• Which SRID is canonical in your database
• How you store SRID metadata
• Where transforms happen (import pipeline vs application requests)

5) Spatial indexing for scale (geohash and H3)

Indexing is not a substitute for spatial correctness. Think of geohash/H3 as fast bucketing: useful for analytics, clustering, caching, and cheap pre-filters.

• Geohash: simple prefix-based bucketing, easy to implement, great for “rough area” grouping.
• H3: hex-based global grid; excellent for aggregations (heatmaps, counts by cell, zoom-friendly summaries).
• PostGIS indexes: still your core “exact query” engine for containment and precise distance logic.

A strong pattern is: grid pre-filter → PostGIS exact predicate. This reduces load without sacrificing correctness.

6) GIS file formats (Shapefile, KML, GPX)

Real-world geospatial data often arrives as files: city boundaries (Shapefile), overlays (KML), GPS tracks (GPX). Your job is to ingest them safely and consistently.

• Shapefile: common for official datasets; expect CRS surprises and geometry validity issues.
• KML/KMZ: often used for overlays and “shareable polygons”.
• GPX: activity and tracking data (routes, tracks, timestamps).

Comparison table: what each library category is best for

Use this table as a high-level decision guide. Your final choice should follow your requirements: dataset size, geometry complexity, accuracy needs, and deployment constraints.

Production checklist: correctness + performance for geospatial PHP systems

If you only copy one section from this page into your internal docs, copy this checklist. Most GIS incidents come from a small set of repeating mistakes.

Data model and storage

• Pick a canonical SRID (often 4326) and enforce it everywhere (imports, APIs, DB).
• Store SRID explicitly and reject unknown/mismatched SRIDs at write time.
• Index geometry (GiST indexes in PostGIS; appropriate spatial indexes in MySQL/MariaDB).
• Track provenance: where the geometry came from, when it was imported, what CRS it used.

Query design

• Pre-filter cheaply (bounding boxes, grid buckets), then apply exact predicates.
• For near‑me, use index-backed filtering + distance ordering on the reduced candidate set.
• Inspect query plans when spatial queries slow down (indexes may not be used as you think).

Geometry validity and edge cases

• Validate geometry on ingestion (self-intersections and invalid rings are common).
• Be explicit about coordinate order at every boundary (GeoJSON is lon/lat).
• Test edge cases: antimeridian/dateline, poles, and very large polygons.

Scaling and maintainability

• Use geohash/H3 for aggregation and caching, not as a replacement for geometry checks.
• Keep the PHP layer focused on I/O + validation + domain modeling.
• Keep spatial operations centralized and documented: a “spatial query layer” is often worth it.

Work with PHPTrends

If you build a geospatial product, API, database tool, mapping platform, or a PHP library in this space, the hard part is not just “being good”—it’s being discoverable by the developers who need you.

PHPTrends can help you turn technical capability into qualified conversations through developer-first content, clear positioning, and distribution where PHP engineers actually pay attention.