What does decoding a geohash return?

Decoding gives the cell that the geohash represents. The tool reports the geometric center as a single lat, lng and the four bounds that enclose the cell.

Why is there a bounding box rather than one point?

A geohash always describes an area whose size depends on the string length. Shorter geohashes cover larger cells, so the bounding box tells you how much spatial uncertainty the code carries.

Which characters are valid?

Geohash uses base-32 with the symbols 0-9 and b, c, d, e, f, g, h, j, k, m, n, p, q, r, s, t, u, v, w, x, y, z. The letters a, i, l and o are excluded, and any other character is rejected.

Does longer input give a more precise location?

Yes. Each additional character shrinks the cell by roughly a factor of eight, so an 11 character geohash pins a location to a few metres while a 5 character one spans a few kilometres.

How accurate is the center coordinate?

The center is exact for the decoded cell. The real point that produced the geohash lies somewhere inside the bounding box, so the center is the best single estimate.

What is the Geohash to Lat/Lng Converter?

Decode a geohash string back to the center latitude and longitude plus the full bounding box of its cell. Implements the standard base-32 interleaved-bit decoding used for spatial indexing. It runs free in your browser on Gera Tools, with nothing uploaded.

Geohash to Lat/Lng Converter

Name: Geohash to Lat/Lng Converter
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

What geohashes are and why they need decoding

A geohash is a compact base-32 string that encodes a geographic location as a hierarchically nested cell. The longer the string, the smaller and more precise the cell it describes. Geohashes are widely used in databases and search engines because they let you find nearby locations using simple string prefix matching — a spatial property that B-tree indexes understand natively.

You typically encounter a geohash when a database or API returns stored location data in this format. Decoding turns it back into human-readable latitude and longitude coordinates, plus the bounding box that shows how precise the original encoding was.

How it works

Each character in a geohash maps to a 5-bit value using a specific base-32 alphabet (the digits 0–9 and the letters b, c, d, e, f, g, h, j, k, m, n, p, q, r, s, t, u, v, w, x, y, z — skipping a, i, l, and o to avoid visual confusion). The bits from all characters are read in sequence, alternating between refining longitude (odd bit positions, starting from the first) and latitude (even bit positions).

Starting from the full longitude range [−180, 180] and latitude range [−90, 90], each bit halves the relevant range:

A 1 bit selects the upper half of the current range
A 0 bit selects the lower half

After all bits are consumed, the remaining longitude and latitude ranges form the bounding box of the cell. The center of that box is the single reported coordinate.

gbsuv7ztq  ->  center near 50.998, -1.499 (with a small bounding box)

Precision and cell sizes

The length of a geohash directly controls its precision:

Characters	Example	Approximate cell size
1	g	~5,000 km × 5,000 km
3	gcp	~156 km × 156 km
5	gcpvj	~4.9 km × 4.9 km
7	gcpvj0d	~153 m × 153 m
9	gcpvj0duq	~4.8 m × 4.8 m
11	gcpvj0duqk9	~15 cm × 15 cm

The bounding box this tool returns tells you exactly how much spatial uncertainty is encoded in the string you are decoding. A short geohash carries significant uncertainty; a long one pins a location to metres.

Prefix matching and proximity search

The most useful property of geohashes for developers is that a shared prefix means shared geography. Two geohashes that start with the same characters represent cells that are neighbours or nested within each other. Most proximity searches work by generating the geohash of a center point at a given precision, listing the 8 neighbouring cells (the same prefix hash plus its 8 adjacent cells), and querying a database WHERE geohash LIKE ‘prefix%’. This tool helps you verify whether a stored geohash actually represents the location you expect, and to understand the precision level before writing a proximity query.

Common uses

Debugging a spatial database: paste a stored geohash to verify it decodes to the right location and has the expected precision.
Understanding storage trade-offs: shorter geohashes use less storage but encode a larger area; use this tool to see the bounding box at different lengths before choosing your precision.
Converting API responses: some APIs return locations as geohashes; decode them here before passing coordinates to other tools or map libraries.