Ehren- und Denkmale
View allAll Photos Tagged Openstreetmap,
sur la ligne 1 à destination de Ténério (Vannes), rue du Port, Vannes (Morbihan, Bretagne, France, FR)
sur la ligne 31 à destination de Saint-Sulpice – Venoge Sud, Renens – Gare Sud, place de la Gare, Chavannes-près-Renens (District Ouest Lausannois, Canton de Vaud, Suisse, CH)
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)
Blue pictures are by locals. Red pictures are by tourists. Yellow pictures might be by either.
Base map © OpenStreetMap, CC-BY-SA
sur la ligne 64 à destination de Gambetta (Paris 20e), terminus Place d’Italie, place d’Italie, Paris (13ème arrondissement, Île-de-France, France, FR)
sur la ligne 84 à destination de Panthéon (Paris 5ème arrondissement), arrêt Luxembourg, place Edmond Rostand, Paris (6ème arrondissement, Île-de-France, France, FR)
Blue pictures are by locals. Red pictures are by tourists. Yellow pictures might be by either.
Base map © OpenStreetMap, CC-BY-SA
sur la ligne 25 à destination de Beaumont-lès-Valence, Gare Routière, Valence (Drôme (26), Auvergne-Rhône-Alpes, France, FR)
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.
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
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.
In France the word arrondissement best signals the spiral of urban districts that turn out from the centre of Paris before the start of the sprawling 'banlieue'. A lone village in the south west of France could only become an 'arrondissement' by the application of the science of pataphysics, or by a sudden surge of semantic freelib behind the backs of the Academies.
With belts of countryside and multiple intervening towns, there is simply none of the urban attachment associated with a major metropol that the word arrondissement requires, and the concrete image of sprawling urbanisation is about as fitting as calling a tree a forest.
This is another example of individuals playing with the second address line in Open street maps to create effects: publicity for towns (?) historical game (see below for a second example), or a problem with the AI translation?
Fairbanks a district of Anchorage, Panguitch a district of Salt Lake City, Passau a vorort of Munich, Teruel a suburbio of Zaragoza. Returning to a regions dominant city has less meaning than simply registering the region: Fairbanks, Alaska. Panguitch, Utah. Passau, Bavaria. Teruel, Aragon. Many photographers want to know about regions with all of their rural and landscape features and not to be returned to the big city. Those who shoot the street can find regional centres via the main term: Tarn, Perthshire, Holland, Western Australia and so on. Substituting this line of detail for 'Roman province' or 'arrondissement' is reductionist at best.
I have placed the screen cap in the very same 'box' next to the photo that created the 'arrondissement' address and the address is quite different.
AJ
* The word "arrondissement" dissapeared here 11.06.20
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.
International Organization on Migration (IOM) showing an UN representative a large OSM print of Guiuan, Eastern Samar. This photo was taken in the DSWD Operations Center in Tacloban Airport. The map will be used to coordinate the relief and rescue efforts for the victims/survivors of Typhoon Yolanda/Haiyan. Data is from OpenStreetMap printed using MapOSmatic.org contributed by thousands of OSM volunteers for HOT's Typhoon Ylanda/Haiyan remote mapping activation. wiki.openstreetmap.org/wiki/Typhoon_Haiyan. Photo by Joe Lowry, Senior Communications Officer and Spokesperson at IOM.
A year of edits on OpenStreetMap. This image will be updated from time to time with a new current view.
The intensity of white and yellow shows areas of considerable recent activity.
Created using OSM Mapper from ITO World Ltd with support from Ideas in Transit.
This shows urban areas derived from residential roads, before (various polygons in pastel colours), and after a review of many residential roads and correction to unclassified.
Actual urban areas are now much more prominent, and roads which got missed in the reclassification stand out more.
heat map of OpenStreetMap tile usage for 25 May 2015.
Data copyright OpenStreetmap contributors (data from here).
Only shows tiles at zoom level 9... higher zoom levels (like 15-17) would give a better indication of possible editing activity.
Darker areas are more requested tiles. Done using deciles of the natural logarithm of number of tile requests
Used a short (<50 line) python script to convert the tile log (which is essentially a csv file) into another csv file with wkt for each tile's geometry added as a field. This was then brought into QGIS as a wkt delimited file. Simpler to do this than to mess around with creating shapefiles :)
Can see HOT humanitarian mapping hotspots in Nepal and the Phillipines.
There's a hotspot around (0,0), and an interesting great circle fragment over Russia, which might be someone panning on a globe?
Interesting to see that most of the inhabited parts of the world are being served
Map of south Mumbai's local train network with tourist attractions.
Made completely using Openstreetmap Data
Mapping the Grande Synthe refugee camp in Dunkirk. Read more about our trip here: mapfugees.wordpress.com/2016/04/20/mappers-from-london/