Geohash shapefile

Posted by Nick Johnson Filed under techcodingdamn-cool-algorithms. Last Thursday night at Oredev, after the sessions, was "Birds of a Feather" - a sort of mini-unconference.

Anyone could write up a topic on the whiteboard; interested individuals added their names, and each group got allocated a room to chat about the topic.

I joined the "Spatial Indexing" group, and we spent a fascinating hour and a half talking about spatial indexing methods, reminding me of several interesting algorithms and techniques. Spatial indexing is increasingly important as more and more data and applications are geospatially-enabled.

Efficiently querying geospatial data, however, is a considerable challenge: because the data is two-dimensional or sometimes, moreyou can't use standard indexing techniques to query on position. Spatial indexes solve this through a variety of techniques.

In this post, we'll cover several - quadtreesgeohashes not to be confused with geohashingand space-filling curves - and reveal how they're all interrelated. Quadtrees are a very straightforward spatial indexing technique. In a Quadtree, each node represents a bounding box covering some part of the space being indexed, with the root node covering the entire area.

Each node is either a leaf node - in which case it contains one or more indexed points, and no children, or it is an internal node, in which case it has exactly four children, one for each quadrant obtained by dividing the area covered in half along both axes - hence the name. A representation of how a quadtree divides an indexed area.

Geohash Converter

Source: Wikipedia. Inserting data into a quadtree is simple: Starting at the root, determine which quadrant your point occupies. Recurse to that node and repeat, until you find a leaf node.

Then, add your point to that node's list of points. If the list exceeds some pre-determined maximum number of elements, split the node, and move the points into the correct subnodes. A representation of how a quadtree is structured internally.

To query a quadtree, starting at the root, examine each child node, and check if it intersects the area being queried for. If it does, recurse into that child node. Whenever you encounter a leaf node, examine each entry to see if it intersects with the query area, and return it if it does. Note that a quadtree is very regular - it is, in fact, a triesince the values of the tree nodes do not depend on the data being inserted. A consequence of this is that we can uniquely number our nodes in a straightforward manner: Simply number each quadrant in binary 00 for the top left, 10 for the top right, and so forthand the number for a node is the concatenation of the quadrant numbers for each of its ancestors, starting at the root.

Using this system, the bottom right node in the sample image would be numbered 11 If we define a maximum depth for our tree, then, we can calculate a point's node number without reference to the tree - simply normalize the node's coordinates to an appropriate integer range for example, 32 bits eachand then interleave the bits from the x and y coordinates -each pair of bits specifies a quadrant in the hypothetical quadtree.

This system might seem familiar: it's a geohash! At this point, you can actually throw out the quadtree itself - the node number, or geohash, contains all the information we need about its location in the tree. Each leaf node in a full-height tree is a complete geohash, and each internal node is represented by the range from its smallest leaf node to its largest one.Generate an array of geohashes that completely contains a polygon.

Return an array of geohashes that completely contains the array of points described by polygon. Shapefile is a. Tile38 is a in-memory geolocation data store, spatial index, and realtime geofence.

It supports spatial index with search methods such as Nearby, Within, and Intersects, Realtime geofencing through persistent sockets or webhooks and lot more. Determine if a point is inside of a polygon. This module casts a ray from the inquiry point and counts intersections, based on this algorithm. It adds support for geographic objects allowing location queries to be run in SQL.

It also adds functions, operators, and index enhancements that apply to these spatial types. A fast algorithm for finding polygon pole of inaccessibility, the most distant internal point from the polygon outline not to be confused with centroidimplemented as a JavaScript library. Useful for optimal placement of a text label on a polygon. This is an iterative grid-based algorithm, which starts by covering the polygon with big square cells and then iteratively splitting them in the order of the most promising ones, while aggressively pruning uninteresting cells.

An easy-to-implement library that can assist Java developers in using the GeoHash algorithm in order to create geocodes based on custom latitude and longitude values.

With the help of jGeohash, Java developers will be able to quickly and easily generate a geohash code using user-defined latitude and longitude values. By using the GeoHash algorithm, the space can be divided into multiple grid shapes.

The focus is based on the realisation of OGC specified web services. GeoUtility is an easy to use coordinate conversion library. Geohash library for nodejs. Encode a pair of latitude and longitude values into a geohash.

Cartonama 2012 - Geohash System and \

The third argument is optional, you can specify a length of this hash string, which also affects the precision of the geohash. The purpose of this project is to build a. NET library of tools that simplifies GeoCoding addresses, Polygon hit-testing and conversion of custom GeoSpatial data to popular formats e. A binary shapefile loader and canvas-based renderer, for javascript. Many caveats. The mapshaper command line program supports essential map making tasks like simplifying shapes, editing attribute data, clipping, erasing, dissolving, filtering and more.

For a live example, see bl. See Command-Line Cartography for a longer introduction. See text-encoding for a browser polyfill. Much of the data in government coffers is contained in spatial databases. A large percentage of government spatial data is created and managed using ESRI software.

While the common interchange format, the ESRI Shapefile, is easily exported and imported by many other softwares, this data file format the Shapefile is not intrinsically part of the www ecology.There are different ways of creating choropleth maps in Python.

In a previous notebookI showed how you can use the Basemap library to accomplish this. More than 2 years have passed since publication and the available tools have evolved a lot. In this notebook I use the GeoPandas library to create a choropleth map.

As you'll see the code is more concise and easier to follow along. Load the necessary modules and specify the files for input and output, set the number of colors to use, the size of the figure in inches width, height and meta information about what is displayed. Next read the datafile downloaded from the World Bank Open Data site and create a pandas DataFrame that contains values for Country CodeCountry Name and the percentages of Internet users in the year Next we merge the data frames on the columns containing the 3-letter country codes and show summary statistics as returned from the describe method.

The merge operation above returned a GeoDataFrame. From this data structure it is very easy to create a choropleth map by invoking the plot method.

We also set the size of the figure and show a legend in the plot. This is pretty nice already, but before publishing this map, there remains some work to be done. As is often the case, some data is missing. You may or may not have noticed it, but the corresponding countries are not shown at all, look for North Korea. The call to dropna right before the plot call removed these records from the plotted GeoDataFrame.

We could just leave it like that, because we simply don't know the values, but I'm sure that would put off some people. So let's draw these countries and fill them with a light gray and a striped pattern as in this D3. Moreover, the image taken by itself provides no clue about what is shown, so we'll add a title and an annotation. Also we to turn off the axes, cut off some space in the far west and east, and move the legend to the lower left of the figure, because there is more empty space.

I think this map is fine for publication and the code is pretty easy to follow, but there is some room for improvement as far as I'm concerned.While Redshift does not offer native support for spatial data, indexes and functions, there exists a partial workaround.

Out of the box, Redshift has numpy, scipy, pandas and many other useful Python libraries. For spatial functionality, one saving grace is the high quality spatial libraries that exist for Python, such as shapely.

Of course, the alternative is to simply implement useful spatial functions in Python directly, which we will do here. The drawback is that this does not provide the means for spatial indexes or native spatial types in Redshift. As long as you are working mainly with point data, this should not be a huge obstacle.

While polygons and operations on them are useful in many cases, a properly utilized GeoHash can usually do the trick. So, let's get into it! Connect to your Redshift cluster using a client of your choosing. Properly connected, attempt to create the following UDF in Python, which implements the haversine formula using NumPy thanks to jterrace for the solution.

One very big drawback is that it is incredibly slow an understatement. The following query computes the function just times, which on my cluster took over Because the speed is so slowI will investigate another way to achieve this goal with Redshift.

Expect updates to this post. You must be logged in to post a comment. This site uses Akismet to reduce spam. Learn how your comment data is processed. Skip to content While Redshift does not offer native support for spatial data, indexes and functions, there exists a partial workaround. What kind of Machine Learning person are you? Leave a Reply Cancel reply You must be logged in to post a comment.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. For example, Parque Nacional Tayrona in Colombia is located at roughly This can be expressed more compactly as:.

H3: Uber’s Hexagonal Hierarchical Spatial Index

These 6 characters identify this point on the globe to within 1. We can use this as a simple, regular level of spatial aggregation for spatial points data, e. This is pretty impractical per se where is dp3wm? The reverse of encoding geohashes is of course decoding them β€” taking a given geohash string and converting it into global coordinates. For example, the Ethiopian coffee growing region of Yirgacheffe is roughly at sc54v :. One unfortunate consequence of the geohash system is that, while geohashes that are lexicographically similar e.

Put another way, small movements on the globe occasionally have visually huge jumps in the geohash-encoded output. For example, the Merlion statue in Singapore is roughly at w21z74nzbut this level of precision zooms in a bit too far. The geohash neighborhood thereof can be found with:. This will facilitate the best part of working with GIS data β€” the visualizations! Returning to public art locations in Chicago, we can visualize the spatial aggregations carried out above by converting to spcombining with a shapefile of Chicago, and plotting:.

Skip to content. Fast, accurate geohash encoding MPL Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Creating a Choropleth Map of the World in Python using GeoPandas

Latest commit. Git stats commits 1 branch 13 tags. Failed to load latest commit information. View code. About Fast, accurate geohash encoding Resources Readme. Releases 13 tags. Contributors 2. You signed in with another tab or window. Reload to refresh your session.

geohash shapefile

You signed out in another tab or window.Grid systems are critical to analyzing large spatial data sets, partitioning areas of the Earth into identifiable grid cells. With this in mind, Uber developed H3our grid system for efficiently optimizing ride pricing and dispatch, for visualizing and exploring spatial data. H3 enables us to analyze geographic information to set dynamic prices and make other decisions on a city-wide level. We use H3 as the grid system for analysis and optimization throughout our marketplaces.

H3 was designed for this purpose, and led us to make some choices such as using hexagonal, hierarchical indexes. Earlier this year, we open sourced H3 on Github, giving others access to this powerful solution, and last week, we open sourced our H3 JavaScript bindings. In this article, we discuss why we use a grid system, some of the unique properties of H3, and how you can get started using H3.

Every day, millions of events occur in the Uber marketplace. Every minute, riders request rides, driver-partners start trips, and hungry users request food, among other actions on the platform.

Each event happens at a specific location, for example, a rider requests a ride from home, and a driver accepts that request in their car just a few miles away. These events empower Uber to better understand and optimize the marketplace for users across our services.

For instance, these events might tell us that there is more demand than supply in a certain part of a city and adjust pricing in response, or inform the platform that there are two ride requests within close proximity to a specific driver on uberPOOL.

Deriving information and insights from data in the Uber marketplace requires analyzing data across an entire city. Because cities are geographically diverse, this analysis needs to happen at a fine granularity.

Analysis at the finest granularity, the exact location where an event happens, is very difficult and expensive. Analysis on areas, such as neighborhoods within a city, is much more practical.

geohash shapefile

We use a grid system to bucket events into hexagonal areas, in other words, cells. Data points are bucketed into hexagons and can be written using the hexagonally bucketed data. For example, we calculate surge pricing by measuring supply and demand in hexagons in each city that we serve.

These hexagons form the basis for our analysis of the Uber marketplace. Hexagons were an important choice because people in a city are often in motion, and hexagons minimize the quantization error introduced when users move through a city. Hexagons also allow us to approximate radiuses easily, such as in this example using Elasticsearch. There are other choices we could use for bucketing events into areas, for instance polygonal zones around areas. These could be postal code areas, but such areas have unusual shapes and sizes which are not helpful for analysis, and are subject to change for reasons entirely unrelated to what we would use them for.

Zones could also be drawn by Uber operations teams based on their knowledge of the city, but such zones require frequent updating as cities change and often define the edges of areas arbitrarily. Grid systems can have comparable shapes and sizes across the cities that Uber operates in and are not subject to arbitrary changes.

While grid systems do not align to streets and neighborhoods in cities, they can be used to efficiently represent neighborhoods by clustering grid cells.

Clustering can be done using objective functions, producing shapes much more useful for analysis. Determining membership of a cluster is as efficient as a set lookup operation. We decided to create H3 to combine the benefits of a hexagonal global grid system with a hierarchical indexing system.For example, Parque Nacional Tayrona in Colombia is located at roughly This can be expressed more compactly as:.

These 6 characters identify this point on the globe to within 1. We can use this as a simple, regular level of spatial aggregation for spatial points data, e. This is pretty impractical per se where is dp3wm? The reverse of encoding geohashes is of course decoding them β€” taking a given geohash string and converting it into global coordinates. For example, the Ethiopian coffee growing region of Yirgacheffe is roughly at sc54v :. One unfortunate consequence of the geohash system is that, while geohashes that are lexicographically similar e.

Put another way, small movements on the globe occasionally have visually huge jumps in the geohash-encoded output. The geohash neighborhood thereof can be found with:. This will facilitate the best part of working with GIS data β€” the visualizations! Returning to public art locations in Chicago, we can visualize the spatial aggregations carried out above by converting to spcombining with a shapefile of Chicago, and plotting:.

geohash shapefile

For more information on customizing the embed code, read Embedding Snippets. Functions Source code Man pages 5. Decoding geohashes The reverse of encoding geohashes is of course decoding them β€” taking a given geohash string and converting it into global coordinates.

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geohash shapefile

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