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Geohash is a public domain geocode system invented in 2008 by Gustavo Niemeyer [2] which encodes a geographic location into a short string of letters and digits. Similar ideas were introduced by G.M. Morton in 1966. [ 3 ]
The Z-ordering can be used to efficiently build a quadtree (2D) or octree (3D) for a set of points. [4] [5] The basic idea is to sort the input set according to Z-order.Once sorted, the points can either be stored in a binary search tree and used directly, which is called a linear quadtree, [6] or they can be used to build a pointer based quadtree.
Base32 is an encoding method based on the base-32 numeral system.It uses an alphabet of 32 digits, each of which represents a different combination of 5 bits (2 5).Since base32 is not very widely adopted, the question of notation—which characters to use to represent the 32 digits—is not as settled as in the case of more well-known numeral systems (such as hexadecimal), though RFCs and ...
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Geohashing / ˈ dʒ iː oʊ ˌ h æ ʃ ɪ ŋ / is an outdoor recreational activity inspired by the webcomic xkcd, in which participants have to reach a random location (chosen by a computer algorithm), prove their achievement by taking a picture of a Global Positioning System (GPS) receiver or another mobile device and then tell the story of their trip online.
Plus Codes logo. The Open Location Code (OLC) is a geocode based on a system of regular grids for identifying an area anywhere on the Earth. [1] It was developed at Google's Zürich engineering office, [2] and released late October 2014. [3]
Geocode cells of Geohash, with 8 (blue) and 9 (yellow) digits, a typical hierarchical grid, comparing with latitude-longitude (12 or more digits). A museum is a typical location to be pointed by a geocode, its gate need ~20 meters of precision.
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.