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Geohash is a latitude/longitude geocode system invented by Gustavo Niemeyer when writing the web service at geohash.org, and put into the public domain. It is a hierarchical spatial data structure which subdivides space into buckets of grid shape.

Geohashes offer properties like arbitrary precision and the possibility of gradually removing characters from the end of the code to reduce its size (and gradually lose precision).

As a consequence of the gradual precision degradation, nearby places will often (but not always) present similar prefixes. Conversely, the longer a shared prefix is, the closer the two places are.

Service

The purpose of the geohash.org service, launched in February 2008, is to offer short URLs which uniquely identify positions on the Earth, so that referencing them in emails, forums, and websites is more convenient.

To obtain the Geohash, the user provides an address to be geocoded, or latitude and longitude coordinates, in a single input box (most commonly used formats for latitude and longitude pairs are accepted), and performs the request.

Besides showing the latitude and longitude corresponding to the given Geohash, users who navigate to a Geohash at geohash.org are also presented with an embedded map, and may download a GPX file, or transfer the waypoint directly to certain GPS receivers. Links are also provided to external sites that may provide further details around the specified location.

For example, the coordinate pair 57.64911,10.40744 (near the tip of the peninsula of Jutland, in Denmark) produces a slightly shorter hash of u4pruydqqvj, which can be used in the URL http://geohash.org/u4pruydqqvj

Uses

The main usages of Geohashes are

• as a unique identifier.
• represent point data e.g. in databases.

Geohashes have also been proposed to be used for geotagging.

When used in a database, the structure of geohashed data has two advantages. First, data indexed by geohash will have all points for a given rectangular area in contiguous slices (the number of slices depends on the precision required and the presence of geohash "fault lines"). This is especially useful in database systems where queries on a single index are much easier or faster than multiple-index queries. Second, this index structure can be used for a quick-and-dirty proximity search - the closest points are often among the closest geohashes.

Example

Using the hash ezs42 as an example, here is how it is decoded into a decimal latitude and longitude

Decode from base 32

The first step is decoding it from base 32 using the following character map:

Decimal	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
Base 32	0	1	2	3	4	5	6	7	8	9	b	c	d	e	f	g
Decimal	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31
Base 32	h	j	k	m	n	p	q	r	s	t	u	v	w	x	y	z

This operation results in the bits 01101 11111 11000 00100 00010. Assuming that counting starts at 0 in the left side, the even bits are taken for the longitude code (0111110000000), while the odd bits are taken for the latitude code (101111001001).

Decode binary to decimal

Each binary code is then used in a series of divisions, considering one bit at a time, again from the left to the right side. For the latitude value, the interval -90 to +90 is divided by 2, producing two intervals: -90 to 0, and 0 to +90. Since the first bit is 1, the higher interval is chosen, and becomes the current interval. The procedure is repeated for all bits in the code. Finally, the latitude value is the center of the resulting interval. Longitudes are processed in an equivalent way, keeping in mind that the initial interval is -180 to +180.

Finishing the procedure should yield approximately latitude 42.6 and longitude -5.6.

Worked example

Here's a worked example decoding 101111001001 into 42.6. To start with, we know the latitude is somewhere in the range −90 to 90. With no bits, we'd have to guess the latitude was 0, giving us an error of ±90. With one bit, we can decide whether its in the range −90 to 0, or 0 to 90. The first bit is high, so we know our latitude is somewhere between 0 and 90. Without any more bits, we'd guess the latitude was 45, giving us an error of ±45.

Each subsequent bit halves this error. This table shows the effect of each bit. At each stage, the relevant half of the range is highlighted in green; a low bit selects the lower range, a high bit the upper range.

The last column shows the latitude, simply the mean value of the range. Each subsequent bit makes this value more precise.

bit	min	mid	max	val	err
1	-90.000	0.000	90.000	45.000	45.000
0	0.000	45.000	90.000	22.500	22.500
1	0.000	22.500	45.000	33.750	11.250
1	22.500	33.750	45.000	39.375	5.625
1	33.750	39.375	45.000	42.188	2.813
1	39.375	42.188	45.000	43.594	1.406
0	42.188	43.594	45.000	42.891	0.703
0	42.188	42.891	43.594	42.539	0.352
1	42.188	42.539	42.891	42.715	0.176
0	42.539	42.715	42.891	42.627	0.088
0	42.539	42.627	42.715	42.583	0.044
1	42.539	42.583	42.627	42.605	0.022

(The numbers in the above table have been rounded to 3 decimal places for clarity)

Final rounding should be done carefully in a way that

$\min \leq \mathrm {round} (value)\leq \max$

So if rounding 42.605 to 42.61 or 42.6 is correct, rounding to 43 it is not.

geohash length	lat bits	lng bits	lat error	lng error	km error
1	2	3	±23	±23	±2500
2	5	5	± 2.8	± 5.6	±630
3	7	8	± 0.70	± 0.7	±78
4	10	10	± 0.087	± 0.18	±20
5	12	13	± 0.022	± 0.022	±2.4
6	15	15	± 0.0027	± 0.0055	±0.61
7	17	18	±0.00068	±0.00068	±0.076
8	20	20	±0.000085	±0.00017	±0.019

Limitations

One limitation of the Geohash algorithm is in attempting to utilize it to find points in proximity to each other based on a common prefix. Edge case locations close to each other but on opposite sides of the Equator or a meridian can result in Geohash codes with no common prefix.

Secondly a geohash essentially defines a bounding box within which a location lies, therefore two locations may be spatially very close but have different geohashes. In order to be useful to proximity searches, the surrounding eight geohashes of a geohash must be calculated and the locations matching these pulled out, therefore complicating potential usage in proximity searches.

Despite those issues, there are possible workarounds, and the algorithm has been successfully used in MongoDB and HBase to implement proximity searches.

An alternative to storing Geohashes as strings in a database are Locational codes, which are also called spatial keys and similar to QuadTiles.

License and patents

The Geohash geocode has been put in the public domain by its inventor in the public announcement date, on February 26, 2008.

While comparable algorithms have been successfully patented and had copyright claimed upon, GeoHash is based on an entirely different algorithm and approach.

See also

• Geohash-36
• Grid (spatial index)
• Morton number (number theory)
• Natural Area Code
• Maidenhead Locator System
• Military grid reference system

External links

• geohash.org
• Visualizing Geohash
• Clojure library for encoding & decoding geohashes without calling geohash.org
• Perl module to interact with geohash.org
• Java classes for encoding and decoding geohashes: geospatialweb, jgeohash, geohash-java, and geo
• Javascript module for encoding and decoding geohashes without interacting with geohash.org and Demo
• MySQL Function for encoding and decoding geohash
• PostGIS function that returns a geohash representation of a geometry
• Ruby gem for encoding and decoding geohashes without interacting with geohash.org
• Perl module for encoding and decoding geohashes without interacting with geohash.org
• PHP class for encoding and decoding geohashes without interacting with geohash.org
• Python module for encoding and decoding geohashes without interacting with geohash.org
• Haskell library for encoding and decoding geohashes without interacting with geohash.org
• C library for encoding and decoding geohashes without interacting with geohash.org
• Go package for encoding / decoding geohashes to UTM or lat / long
• Objective-C library for encoding and decoding geohashes without interacting with geohash.org
• Ocaml module for encoding and decoding geohashes without interacting with geohash.org
• Proposal for a similar encoding and associated 'geo' URL scheme
• http://loc.is - a geohash designed for analytics.
• kml file for Google Earth displaying geohash grid
• ngeohash, nodejs module for encoding and decoding geohash
• geoplace.it: a free service to reference positions using an approach different from geohash.org and with a more accurate service
• A geohash implementation in pure ruby (~50 sloc)
• Area Check Tool

References

43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro.