Encoding a geometry, date-time pair is only useful when those encoded entries can be found quickly. That is the job of the query planner. In this post, we present one challenge the query planner in GeoMesa has to overcome: How to identify the Geohash prefixes that cover a query polygon. Indexing geo-time data is a matter of finding a sensible way to flatten three dimensions of data — longitude, latitude, and time — into a single dimension: the list of Accumulo keys.
This encoding is a simplified version of what GeoMesa uses as the default index-schema format. The two ribbons in front of the space-time cube represent the single, logical list of cell identifiers in the foreground ; and the same list in which each cell has been randomly assigned to one of four partitions in the background, with each partition identified by a gray or black stripe next to the ribbon.
If you keep the image of the preceding space-time cube in mind, then you might intuit that a query plan is really a list of contiguous cell ranges that fall inside the desired geographic area and time interval. As an example, assume we had this query:. This corresponds to just over half of the lowest values in each of the three dimensions.
The visual selection of cells and their locations in both the global list of keys as well as the sharded lists of keys are depicted in this image:. This example uses a bit encoding, so there are only cells in the cube.
The default GeoMesa index-schema format uses more than 55 bits in the encoding, resulting in more than 36 quadrillion possible cells, so an exhaustive search would take far too long to be practical. Dates work similarly, because includes all twelve months as well as all days. To simplify the discussion, we will omit the date portion, and focus on how the query planner identifies the ranges of contiguous Geohash values. Here is an animated GIF that shows how, given a query polygon that roughly approximates the continental USA, this algorithm steps through identifying Geohash prefixes whose precision is less than or equal to 10 bits:.
The query polygon is colored yellow. Each of the non-terminal Geohashes that intersects the query polygon is shaded white with a red square indicating which of its two children will be the first candidate considered.
The Geohashes that are uncolored are ones that have not yet been explicitly visited; those that are shaded red are Geohashes that constitute prefixes that remain in the final result set; Geohashes that are shaded dark gray are those that have been rejected, because they do not intersect the target polygon at all. This discussion of the algorithm is coarse, in part because its role in the overall query process has been simplified to streamline the explanation in this short note.
The full version as used within the query planner can be found nested inside the getUniqueGeohashSubstringsInPolygon method, where it enables GeoMesa to enumerate the unique substrings of larger Geohashes quickly and efficiently, expediting the entire query-planning process.
I would like the code could be manipulated by humans, ie. Have a look at Georaptor. Creates a compressed set of geohashes, i. The most robust implementation is in the s2 geometry library with its Polygon Cover implementation. Infortunately, I didn't see something about polygons Geohash. A very simple implementation would be to perform BFS filling the polygon with the geohashes. Then you can recursively breakdown the edges into higher precision. Here's an example Example BFS. Of course, any geometry other than a Point would result in lower precision.
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Asked 5 years, 9 months ago. Active 5 months ago.
Viewed 8k times. I'm looking for an equivalent method to code polygons, not points. I saw two ways to do it: geohash all polygon points, then concatenate them, and then compress the resulting string using classical text compression algorithms.
TomazicM 6, 5 5 gold badges 16 16 silver badges 23 23 bronze badges. Sylvain Lesage Sylvain Lesage 1 1 silver badge 8 8 bronze badges. Clearly yes. But the question is more about concepts and algorithms than implementations. Active Oldest Votes. Ashwin Nair Ashwin Nair 1 1 silver badge 7 7 bronze badges. Note that ElasticSearch is using the same strategy for polygons indexing: elastic.
ThomasG77 ThomasG77 Carlos Carlos 11 1 1 bronze badge.A unique feature of the S2 library is that unlike traditional geographic information systems, which represent data as flat two-dimensional projections similar to an atlasthe S2 library represents all data on a three-dimensional sphere similar to a globe. This makes it possible to build a worldwide geographic database with no seams or singularities, using a single coordinate system, and with low distortion everywhere compared to the true shape of the Earth.
While the Earth is not quite spherical, it is much closer to being a sphere than it is to being flat! If you want to learn more about the library, start by reading the Overview and QuickStart documentthen read the introduction to the basic types.
Get S2 on GitHub. If you want to learn more about the library, start by reading the Overview and QuickStart documentthen read the introduction to the basic types Get S2 on GitHub S2 Features Notable features of the library include: Flexible support for spatial indexing, including the ability to approximate arbitrary regions as collections of discrete S2 cells.
This feature makes it easy to build large distributed spatial indexes. Fast in-memory spatial indexing of collections of points, polylines, and polygons. Robust constructive operations such as intersection, union, and simplification and boolean predicates such as testing for containment.
Efficient query operations for finding nearby objects, measuring distances, computing centroids, etc. A collection of efficient yet exact mathematical predicates for testing relationships among geometric primitives. Flexible Apache 2.When dealing with geospatial data it is sometimes useful to have a grid at hand that represents the given data. One way to create a grid like this is to use Geohashes. GeoHashes are a hierarchical spatial data structure which subdivides space into buckets of grid shape, which is one of the many applications of what is known as a Z-order curve, and generally space-filling curves.
A Geohash is an encoded character string that is computed from geographic coordinates. The longer the Geohash the smaller is the rectangle it represents. The following table shows the relationship between Geohash length and the size of the rectangle represented by that Geohash at the equator. Let's say we want to populate a grid over Berlin at level 5 4. The algorithm to populate the given bounding box works as follows:.
Sometimes a bounding box is not enought and you want a more specific shape for the grid. I downloaded a polygon of Berlin from OpenDataLab. The algorithm is almost the same as above. The only difference is that we test if a point is inside the given polygon. Therefor, we use the shapely library.Search with Polygons: Another Approach to Solr Geospatial Search
If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Polygon Geohasher is an open source Python package for converting Shapely's polygons into a set of geohashes. It obtains the set of geohashes inside a polygon or geohashes that touch intersect the polygon. This library uses python-geohash and shapely. This function receives a Shapely's Polygon and the precision of geohashes to be used to create a polygon and returns a set of geohashes strings that covers said polygon.
It also receives an optional parameter inner that defines the way in which those polygons will be created. If an inner parameter is given, then only contained geohashes will be used; otherwise, intersected geohashes will be used. See geohashed polygons resulting from both options with and without inner in the following example:.
Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Converts a polygon into a set of geohashes with arbitrary precision. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.
Latest commit. Latest commit 63f27f4 Jan 9, Polygon [ - You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Improved documentation.Released: Jan 10, Wrapper over Shapely that returns the set of geohashes that form a Polygon. View statistics for this project via Libraries. Tags polygon, geohashes.
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Maintainers Bonsanto. Project description Project details Release history Download files Project description polygon-geohasher Polygon Geohasher is a Open source Python package which converts a polygon into a set of geohashes with arbitrary precision. More conversely, it can obtain the set of geohashes inside a polygon or geohashes that touch intersects the polygon. Polygon [ If inner only contained geohashes will be used, otherwise intersected will. For instance you can see resulted geohashed polygons with both options.
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Creating a grid based on Geohashes
It only takes a minute to sign up. GeoHash is an excellent method to code the latitude and longitude of a point into a string. I would like the code could be manipulated by humans, ie. Have a look at Georaptor. Creates a compressed set of geohashes, i. The most robust implementation is in the s2 geometry library with its Polygon Cover implementation.
Infortunately, I didn't see something about polygons Geohash. A very simple implementation would be to perform BFS filling the polygon with the geohashes. Then you can recursively breakdown the edges into higher precision. Here's an example Example BFS.
Of course, any geometry other than a Point would result in lower precision. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Methods for geohash of polygon? Ask Question. Asked 5 years, 9 months ago. Active 5 months ago.
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