View allAll Photos Tagged OpenStreetMap.

20101113-062

 

"The most beautiful thing we can experience is the mysterious. It is the source of all true art and science." - Albert Einstein

 

@ Klek (1753m), Karavanke, Slovenia, Europe, Earth. map

 

Thanks for looking... :)

 

Do not use this image on any media without my permission. All rights reserved.

südlich Bernshof (so steht es sogar in der OpenStreetMap www.openstreetmap.org/#map=17/51.50033/6.64385)

Inland sea oats (Chasmanthium latifolium) blowing in the wind.

 

On Cecilia Creek, alongside the...

East Decatur Greenway

DeKalb County (Forrest Hills), Georgia, USA.

17 July 2024.

 

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▶ Photo by: YFGF.

▶ For a larger image, type 'L' (without the quotation marks).

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▶ Camera: Olympus OM-D E-M10 II.

— Lens: Olympus M.40-150mm F4.0-5.6 R.

— Edit: Photoshop Elements 15, Nik Collection (2016).

▶ Commercial use requires explicit permission, as per Creative Commons.

A map of railways in France according to OpenSteetMap as of 10th Jan 2009. Do add notes on the image in places where you are aware of gaps in the network. We can update this image from time to time.

mapping Edinburgh in the style of Ordnance Survey maps from the early 20th century, using contemporary OpenStreetMap data.

 

Inspired by the wonderful georeferenced maps from the National Library of Scotland, in particular the early 1900s Ordnance Survey Maps.

 

Using QGIS 2.18. The hardest part is the street labelling and typography. Edinburgh is mapped in great detail in OSM; I had to reduce the detail and shrink the building outlines to get the feel of the originals.

 

Added a bit of grunge using blending mode, transparency, and a texture (a photo of a plaster wall I took during house renovation)

 

OpenStreetMap recently made a bulk dump of GPS points available as a massive 55Gb csv file.

 

This heatmap shows a random sample of 1% of the points and their distribution, to show where GPS is used to upload data to the map. There are just short of 2.8 billion points, so the sample is nearly 28 million points. Red cells have the most points, blue cells have the fewest.

 

Points were given a geohash, and the first 3 characters of the geohash were used to bin the points into a regular grid.

 

Using a couple of python scripts, and tidied up the SVG in Inkscape. Geohashing code here.

 

You can see some interesting patterns:

- some europe-carribean flights/boat journeys

- flights from US west coast to NZ

- a hotspot over Germany, UK and central/eastern Europe

- an odd delineated band between 30N and 30S in the oceans - this may be a result of the sampling

 

Data copyright OpenStreetMap and its contributors, CC-BY-SA.

  

Blue pictures are by locals. Red pictures are by tourists. Yellow pictures might be by either.

 

Base map © OpenStreetMap, CC-BY-SA

height and colour of each road in proportion to number of cafes and restaurants within 30m of each road

 

uses map and data copyright openstreetmap contributors

map of road accidents using the police Stats19 data and OpenStreetMap.

 

#EDIT: replaced original monochrome with colour version.

 

Mapped and rendered in QGIS 2.4 using the Print Composer.

 

Method: buffered each accident site by 20m, and used "points in polygon" to count how many accidents happened within 20m of each site. Then used data driven properties to scale each accident site using log10("PNTCNT")*3, and Symbol Levels to make sure the blackspots are shown on top.

 

End result is similar to Heatmap, but looks better when zoomed in IMO. Best viewed Large or Original sizes.

  

This is a debugging image for helping to sort out the lakes tagged as coastline in OpenStreetMap. There are various lakes and areas of water that are not sea that are currently marked with natural=coastline. They should actually be marker natural=water as per guidance on the OSM wiki.

State of the Map US 2017, Boulder, CO, October 20-22

 

University of Colorado

 

Photo copyright Justin R. Miller.

OpenStreetMap data for Great Britain were downloaded from Geofabrik on 22 Jan. 2011, uploaded to a PostGIS database using osm2pgsql. Nodes and Ways tagged with amenity=pub were assigned to centroids of 5 km square grid based on the Ordnance Survey National Grid and the number of pubs in each square counted.

 

The grid was generated, pubs counted, and output generated using Quantum GIS.

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