OpenStreetMap

Diary Entries in English

Recent diary entries

The Street Designed to give Taxi Drivers a Nervous Breakdown

Posted by alexkemp on 6 September 2016 in English (English)

An upload has just been made by myself for Marwood Road, Carlton NG4, UK and it has four houses in a row (2 sets of semi-detached) that are each numbered ‘1’ (and two next-door to them that are each numbered ‘375’), and yet they are all correctly numbered. What is going on?

Now yes, I'm being a little bit naughty in my description, because two of those houses are numbered ‘1a’ and ‘1b’, but the other two are each “1 Marwood” and are part of the same semi-detached house. In fact, whilst surveying it was worse, because whilst these four houses are obviously near the beginning of the street there were two houses before them, one of which was positioned on Cavendish Road, but the bungalow was clearly positioned on Marwood Road. That bungalow was residential & occupied (though the owners were out) and no-one else on the street knew what it's number was - and neither did it display one. Truly, this was a street designed to give taxi-drivers a nervous breakdown.

This is the street: Marwood Road, Carlton NG4; the houses concerned are at the eastern end, on the south-side of the street. ‘1a’ and ‘1b’ are part of a semi-detached house that is too new (as is the bungalow) to be shown on the current Google satellite view, although Bing does show them (it is normally the other way around) (Bing metadata capture date of “10/1/2011-3/26/2012”). West of [‘1a’‘1b’] is [1, 1]. The first house is 1 Marwood Road, and the second is 1 Marwood Crescent. Gaah!

I decided that to answer the mystery of the bungalow was going to require some hard snooping; there must be a number somewhere. And there was (carefully hidden in plain sight, next to the door, but much too small to read from the street):

the hidden number

It seems that the Bungalow was originally built as an annex to the house on Cavendish Road.

Location: Arnold and Carlton, Gedling, Nottinghamshire, East Midlands, England, United Kingdom

OpenStreetMap training in Thimphu, Bhutan

Posted by MeghaShrestha on 6 September 2016 in English (English)

It has been three days we returned from Thimphu, Bhutan and I have already started missing the country and the people there. People are very helpful, friendly and motivated. We were invited by Thimphu Thromde through Humanitarian OpenStreetMap Team from a project funded by World Bank. Our main aim in Thimphu was to establish a foundation for OpenStreetMap community in Bhutan. We trained people from various background, government officers, non government officers and also students. We saw almost 81 enthusiastic citizens of Bhutan emerged in the world of OpenStreetMap. They were sensitized, trained and became mappers during our 3 weeks of stay in Thimphu. We had 4 days long training from 9 to 5 in the evening and they stayed there motivated and excited to gain the ability of map and show the identity of their local place. We also trained the participants some basic vector analysis and map creation methodology in QGIS (an open source GIS tool). This helped in better understanding for them about the whole GIS system (data creation to data analysis and output). You can see their activities OSM Bhutan facebook group : https://www.facebook.com/groups/osmbhutan/ I hope that they continue to map and foster this OpenStreetMap community to whole of Bhutan.

OpenStreetMap Carto release v2.43.0

Posted by pnorman on 6 September 2016 in English (English)

Dear all,

Today, v2.43.0 of the openstreetmap-carto stylesheet (the default stylesheet on openstreetmap.org) has been released. It has not yet been rolled out to the openstreetmap.org servers.

Changes include

  • Adjust alotments pattern
  • Whitespace cleanups of code
  • Adjust colours of dog parks and construction sites
  • Increase font size of addresses
  • Fix combination of long names and oneway arrows

Thanks to all the contributors for this release, including Ircama and measad, both new contributors.

For a full list of commits, see https://github.com/gravitystorm/openstreetmap-carto/compare/v2.42.0...v2.43.0

As always, we welcome any bug reports at https://github.com/gravitystorm/openstreetmap-carto/issues.

English Eccentricity #2

Posted by alexkemp on 5 September 2016 in English (English)

English Eccentricity #1

Seen today whilst mapping in Carlton NG4:

fibreglass polar bear

Look, the guy wants a xmassy-sorta fibre-glass polar-bear on the bay-window of his house, and if he does want a sorta-xmassy fibre-glass polar-bear on the bay-window of his house, I say “why not?” (though it could do with a wash).

Location: Arnold and Carlton, Gedling, Nottinghamshire, East Midlands, England, United Kingdom

301 E. Cevallos #353

Posted by Dekon on 5 September 2016 in English (English)

G

Location: Arsenal, San Antonio, Bexar County, Texas, 78205, United States of America

Visualizing OSM.org's Map Views

Posted by tyr_asd on 4 September 2016 in English (English)

OpenStreetMap's standard map layer is used by many people each day. OSM even provides a dump to the access logs from which one can see which parts of the world is viewed how many times for each day. Inspired by Lukas Martinelli's work on Parsing and Visualizing OSM Access Logs, I've worked on a web app to visualize OSM's tile access logs. Contrary on Lukas' approach, I wanted to focus on an accurate representation of the data and wanted to make something works for the whole (Mercator-projected) world.

I've ended up with a viewer that takes an (uncompressed) log file from planet.osm.org and produces a two-dimensional histogram for each zoom level: For example, at zoom level 6 in the viewer each pixel on the viewer represents the number of accesses of the corresponding osm.org-tile at zoom level 14. That's 8 zoom levels further in – or, put another way, each 256x256px² osm.org-tile is represented by a single pixel in the visualization.

The number of accesses of each tile is represented by a logarithmic color palette:

screenshot osm-tile-access-log-viewer

You can play with the tool at http://osm-tile-access-log-viewer.raifer.tech, the source code is found on github and openly licenced (MIT), of course.

With this one can for example compare map views before and after a specific event, for example the recent earthquake in central Italy:

map of map views before and after the recent earthquake in central Italy

But one can also see some interesting artifacts in the data, for example the large amount of tile views around null island or those (to me inexplicable) "comet tail" shaped patterns at some Russian cities. Do you have an idea where these artifacts stem from?

Making-of

warning: the rest of this post will be a bit more technical

What bugged me a bit at first was that my initial implementation was quite slow and made the webapp unresponsive. On my machine the first version took about 40 seconds for the initial processing step (between dropping the log-file onto the page and the first displayed results), which is quite a lot! Meanwhile those calculations were blocking the main UI thread and even causing this nasty browser-popup to appear:

page unresponsive warning

So, what can we do about that? As always, optimizing this kind of stuff starts with some profiling and goes through multiple iterations of optimizing and refactoring with more profiling in between. In the end, I managed to cut the time down from 40 seconds to a mere 9 seconds in the current version:

  • 40 s – initial version
  • 24 s – low hanging fruit
  • 15 s – optimized parser (ArrayBuffer)
  • 13 s – default rbush accessors
  • 14 s – web worker to render tiles (1 worker)
  • 14 s – web worker to render tiles (4 workers)
  • 9 s – parsing in own (single) thread (4+1 workers)

Let's go though each of these steps, but let's start with a short overview of the code structure:

Code overview

The code of the visualization isn't very intricate, it basically just parses the tile log files (which are txt files containing pairs of tile-coordinates z/x/y and their respective view counts, see below), put's them into a spatial index (I'm using rbush) and finally grabs the data from the index whenever a tile is requested to be rendered. (Then, the rendering of the tiles is just some pixel-pushing onto a canvas which is quick and wasn't an issue that I had to look much into here.)

Here's what the access logs look like: (this goes on for ~6,000,000 lines, or about 100MB of data)

13/4316/2511 20
13/4316/2512 18
13/4316/2513 16
13/4316/2514 14

low hanging fruit

flame graph of first profiling session

That's the flame graph of the first profiling session. There are clearly two distinct processing steps, one relatively flat calculation, talking about 25 seconds and another more recursive which took about 10 seconds. The second portion is quickly identified as rbush building up it's indexes (which is already pretty much fully optimized, I'd say). But what really stoked me was that the other part corresponding to the following few lines of code took up much more CPU:

    data = data.split("\n")
    data = data.map(function(line) {
        return line.split(/[ \/]/).map(Number)
    })

Pretty basic string operations, as it looks at first. Looking at the profiler again reveals that, of course, there's a regular expression (/[ \/]/) in a hot loop and converting strings to integers using the Number constructor isn't the fastest one can do either.

Getting rid of those two results in the first performance win:

line = line.split(' ')
var coords = line[0].split('/')
return { x: +coords[1], y: +coords[2], zoom: +coords[0], count: +line[1] }

optimized parser (ArrayBuffer)

Now, we're still opening a function scope for each line in the input data and are working with relatively costly string-operations such as split and the + operator (to convert strings to numbers). Getting rid of that was quite fun and resulted in the biggest performance gain after which parsing was 90% faster than at the beginning!

What I ended up doing was to implement a custom parser that works on the raw byte-data (using ArrayBuffers), presuming that the log files are well structured. In its heart is a for loop that walks over all bytes of the data and manually constructs the data:

for (var i = 0; i<view.length; i++) {
    switch (view[i]) {
    default:
        currentInt = currentInt*10 + (view[i] - 48 /*'0'*/)
    break;
    case 10: // '\n'
        data.push({ x: currentCoords[1], y: currentCoords[2], zoom: currentCoords[0], count: currentInt })
        currentCoords = []
        currentInt = 0
    break;
    case 32: // ' '
    case 47: // '/'
        currentCoords.push(currentInt)
        currentInt = 0
    break;
    }
}

One interesting line to note is currentInt = currentInt*10 + (view[i] - 48 /*'0'*/): whenever we don't see a separating character (newline, space or /), we assume that it must be a numeral, whose value we can get by subtracting the ascii code of 0 from.

default rbush accessors

The next optimization is a rather small one, but one I came across after the recent 2.0.0 release of rbush: apparently, it's faster to access named attributes of a javascript object rather than elements of a javascript array. Changing the parsed data output to something that can be digested easily by rbush shaved off a few more seconds of the preprocessing.

web workers!

Even after all those optimizations, the calculations (even though they are relatively quick by now), are still blocking the UI. That isn't a big deal during the initial processing, but the main-thread implementation means that rendering of the histogram tiles also blocks the browser. And even though each rendering is quick (typically only a couple up to a few tens of milliseconds), these small interruptions can add up significantly especially in situations when one pans around quickly or zooms in or out. It's a bit hard to see in the gif below, but trust me: it feels quite laggy!

laggy zooming/panning

The only solution to this issue is to do rendering in a separate web worker thread. The implementation is a matter of refactoring the data parsing plus rendering code into a web worker and making sure that the returned data is a transferable buffer object. Using a single web worker, this is a bit slower than the non-threaded version, but not too much.

multi threading, first try

When we run a web worker anyway, why not multiple in parallel? That should make rendering of the tiles even faster, right? Well, not really in a naive approach: As every worker needs to have its own spatial index and there's no way to effectively split the input data into distinct chunks that can be rendered independently later on, the total time with 4 workers is basically the same as with a single one (the overhead of having to duplicate the input data eats up any later gains in faster building up of the indices).

multi threading, second try

Doing multi threading properly in this case is a bit of a larger refactoring, but the effort is worth it in the end with additional ~30% faster processing and even smoother map panning and zooming.

Here, I've split the data parsing into a separate web worker which runs single-threadedly (this could in principle also be parallelized, but it's not worth the effort in my opinion, as this step is quite quick with 2 seconds already – but, potentially one could shave of another second or so). The results of this parsing are then divided up into buckets of transferable ArrayBuffers (which are always important when working with web workers) and distributed among the rendering workers.

That's how deep I dared to explore this rabbit hole of code optimization this time. I hope you liked my adventure. ;)

Rain Coming Down Like Stair-Rods

Posted by alexkemp on 4 September 2016 in English (English)

Today's Weather Alert from weather.com said: “CHANCE OF RAIN: 100%” (they weren't kidding). In typical macho style I put on my rain-proof jacket & went out mapping anyway. I stayed out too long.

Surprisingly, the rain doesn't penetrate the smartphone — a credit to the design — but it does interfere with the capacitance on the glass surface, and that interferes with it's touch-sensitive design, which becomes a real pain after a short while. In spite of it all I managed whilst mapping to get a couple of photos of interesting house numbers to show here...

twee maybe

The first (above) comes from very close to last week's MPG. I think that some may think this a little twee, and the mixture of Japanese-inspired artwork & renaissance-inspired cherubs is certainly odd, but I don't care - the owners like them & so do I.

For a complete contrast (below) how about this black cat (you can possibly detect traces of rain on the lens here):

black cat

Coda: “Surprisingly, the rain doesn't penetrate the smartphone”

I believe that the Army has a small mantra for it's officers to learn from:—

“Spoken in haste, regretted at leisure”.

Sunday 4 Sep (next day): This morning the SmartFirst6 smartphone would not start up, nor be powered from it's charger. It turned out to have small pools (yes! pools) of water in the rear, where there are slots in the plastic to allow the sound out, and the battery & electronics are all congregated. Very poor design, Vodaphone.

My so-called rain-proof jacket was twice the weight when I got home yesterday, the rubberised-coating is abrading & it seems that quantities of water dripped into the pockets, which is where the smartphone was placed. Very dumb decisions, Alex.

It seems that I am learning the difference between “water-resistant” and “water-proof”. I've opened the phone up as much as possible & will leave it to dry & see if it can be rescued, or will I be pleased that it was a “cheap” smartphone?

Monday 5 Sep: Rice seems to have saved the 'phone (or at least, that is the early experience - time will show).

Capillary action (wicking) is the great danger with electronic equipment, and therefore the question is how to wick the moisture in the opposite direction? What is needed is a hygroscopic (water-loving) material, and the easiest available is (uncooked) rice. I love boxes and therefore have lots around. The clear-plastic container for a former gift of Ferrero Rocher (are they ever bought not as a gift?) was the perfect size to create a cm of rice as a bed with smartphone on top in a sealed compartment. 36 hours seems to have fixed it. The one surprise is that the battery was at 100% (and, at this moment, is at 95%).

Trees mostly wick water from their roots to the crown. The tallest tree is reported at 379.7 feet (115.7 m), which gives some feeling for the power of water to penetrate across thin films.

Two of the greatest dangers in the phone are SIM + SD-Card (both offer ideal thin-film surfaces and have active electronic connections). After checking it was powered down, the first thing that I did was to remove:

  1. phone-back
  2. battery
  3. SIM card
  4. SD-Card

The above were also all left off during the 36 hours & re-assembled last. I already had the PDF on disk, and it had full diagrams & instructions. In addition, the SmartFirst6 is able to have both SIM & SDCard removed (I would not have bought it otherwise).

final words: I mapped today & the smartphone has worked OK, so I seem to have dodged the bullet this time.

Location: Arnold and Carlton, Gedling, Nottinghamshire, East Midlands, England, United Kingdom

TMS layer of Comparative Map of Brazilian Roads (IBGE x OSM)

Posted by smaprs on 3 September 2016 in English (English)

Purpose: to help on easy identification and zooming in undermapped areas, mainly for roads still missing on OSM map, on basis of IBGE data (Brazilian Institute of Geography and Statistics - Data publicly available according to legal statements).

Documented at (in portuguese): "Situação do Mapeamento de Estradas no Brasil".

Feeling the lack of some data that could easy identify and help to improve on basic mapping in undermapped areas of Brazil, regarding accessibility, I've firstly decided to try to do a single georeferenced PNG image showing where there could be these areas, generated with QGis to use in JOSM with Piclayer plugin. Then tried a TMS layer.

Our community keeps doing great efforts on mapping. Mostly metropolitan and urban areas are well mapped for general accesibility. Also are rural and inner country roads linking main urban zones. Many data recently delivered for legal public use by IBGE is helping on the mapping on OSM. These data lead to identify many other roads that area still missing on OSM. Almost no data from GPS layers in the huge areas of inner country, as expected too.
So I've started trying to do a map that could help to identify roads from IBGE that are missing in OSM.

These two sources of data were used on this Comparative Map:
* roads from the .shp of IBGE (compiled untill 2015);
* roads from the current state of OSM map (2nd half of 2016), obtained from geofabrik.de (file "roads.shp").

The methodology adopted was as simple as I could attempt to do in QGIS: just overlaying IBGE ways (in orange and yellow) with OSM ways (in dark blue), so to highlight IBGE roads, no need for overlapping analysis. Just colors:

Zoom 5: Identifying and zooming in to undermapped areas (next example highlighted)
Zoom 5: Identifying and zooming in to undermapped areas (next example highlighted)

I've started to generate this map in TMS tiles, with transparent background, using QTiles plugin in QGis, with precious technical help from user:naoliv. Decided to do it from zoom 3 to 9: it took 5 hours of processing and 110MB to generate 4850 TMS tiles in PNG 256px. User:wille is helping hosting the 110MB tiles on his server, and many others are helping on improvements and using the TMS layer to map. Maybe another day I'll generate zoom 10, it's expected it'll take around 20 hours and 400MB. Further zooms would take additional 4x time and size each zoom, so up to 80 hours and almost 2GB if zoom 11, with only 2x gain in resolution from 10 to 11. The aim was to help on identifying location of official roads still not mapped. It was achieved, so that's enough for now. From zoom 9 to closer zooms the purpose is, as expected, to trace the identifyied missing roads from hi-res satellite imagery.

Zoom 9: road labels and zoom limit
Zoom 9: road labels and zoom limit

Zoom 11 and 14: road detected in hi-res image to map in closer zooms
Zooms 11 to 14: road detected to map in closer zooms

So now we have another tool to help on improving road mapping in Brazil. We expect to update this TMS layer as the road mapping increases, perhaps once a year.

This TMS layer "Comparative Map of Brazilian Roads (IBGE x OSM)" is available here:
http://tms.openstreetmap.com.br/ibgeXosm/{z}/{x}/{-y}.png

Location: Aquidauana, Microrregião de Aquidauana, Mesorregião Pantanal Sul Matro-Grossense, Mato Grosso do Sul, Central-West Region, Brazil

Training on OpenStreetMap in BRAC UNIVERSITY 2-3 September,2016

Posted by Manjurul Islam on 3 September 2016 in English (English)

OpenStreetMap is a great source of information related to the necessity of people. It is one of best source of data in digital format. . In this MAP we get specific data. It is very helpful for the people .By this map anyone can get information about their queries and export data according to their purpose. Anyone can upload data and also use data to their necessity .It is the only digital map in the world which data is free. GIS specialist export data in GIS format , Architect use data in another format . It is very helpful for Disaster Risk Management . It is the great source of information that is very helpful to conduct any search and rescue operation . Dhaka City Red Crescent Unit a sister concern of Bangladesh Red Crescent Society conduct many search and rescue operation . OpenStreetMap is very helpful for their work . Bangladesh Red Crescent Society arranged training on Openstreetmap for volunteers in collaboration with International Federation of Red Cross &Red Crescent Societies and American Red Cross . In this map, data is uploaded by the Community . So the authority decided to train the Community . In this concern Dhaka City Red Crescent Unit organized training on OpenstreetMap under Data4Action Project .

In 2-3 September,2016 Dhaka City Red Crescent Unit organized training on OpenStreetMap for the student of BRAC University, one of the renowned Private university in Bangladesh.This training was held at Architecture Department of BRAC University. This training was started sharp at 09.30 am and it solely attended by 25 student and teacher of BRAC Unibersity. The main vision of this OSM training for the students was enriching their skills by using the open geographical data to identify the hazard, disaster , the vulnerability of any area , navigational and other humanitarian purposes .

Opening session was started with the pleasant voice of Ahasanul Hoque pioneer of OpenStreetMap in Bangladesh and Geospatial data management Consultant in World Bank Bangladesh. He gave his pleasant speech about openstreetmap that inspires the Participant . Then the training Session started . The Training was conducted by Ahasanul Hoque and assisted by Sawan Shariar , Atikur Rahman atik and Manjurul Islam.

On the first day training included the following session. # Introduction of Map & it’s component #Introduction of OpenStreetMap & relation in Disaster Preparedness and Response #Sign Up in Osm # Introduction and use of ID Editor # Introducing OSM field data collection tools #Use of Field paper in Osm This practical session started with opening an account in iD editor , this web-based mapping tool always needed to be connected to the internet . Then all the students made their own OSM account by iD editor before the training began with its own enthusiastic wave . The participants were learned how to configure the background layer and how to do basic edit with iD editor along with knowing additional information and custom tags. Data that used in editing in this session was specially the home address of the individual participants .Then they were informed about OSM field data collection tools. After the lunch break , the participants were introduced to the Field Papers website . The participants were taught how to create a printable map atlas for anywhere in the world . They were learned how to print and add notes to the field papers. Then the first day of the training session was ended with the improvement & development of knowledge as well as the cheerful mind and smiling faces of the participants .

On the Second day the training was started with its full swing at the same place at the same time like the day before . The Second day of the training , the practical session divided into following sessions #Introduction and the Use of JOSM #Trace Imagery #Editing by JOSM #Tagging Convention #How to upload data from field paper #Add data collected from field # Introducing of OSM apps #Introduction of Tasking Manager #MapSwipe and it’s Use

The training started with the basic knowledge of JOSM and then continued it with the basic editor and tags in JOSM . The participants were learned to use plugs in JOSM , activate imagery providers and upload data by using JOSM. After understanding how to run JOSM quickly , the participants were learned how to use JOSM offline by saving the important files for editing . Then the participant were informed how to upload data from Field Papers. Then participant were divided into 13 pairs and go to field with field paper. After collecting data they came back and uploaded their collected data. After lunch break they inform about Tasking Manager and how to contribute in Humanitarian work. They were also informed about MapSwipe &it’s use.

It's time for closing . Participants were share their feelings about training .The participants also said that OSM is a great platform for them and it has some extraordinary useful concepts in the context of Architecture & Disaster Management . They included that they will use this map data in different research . Finally, the training was closed by the photo session between trainers and the participants.

Training on OpenStreetMap in BRAC UNIVERSITY 2-3 September,2016

Posted by Manjurul Islam on 3 September 2016 in English (English)

OpenStreetMap is a great source of information related to the necessity of people. It is one of best source of data in digital format. . In this MAP we get specific data. It is very helpful for the people .By this map anyone can get information about their queries and export data according to their purpose. Anyone can upload data and also use data to their necessity .It is the only digital map in the world which data is free. GIS specialist export data in GIS format , Architect use data in another format . It is very helpful for Disaster Risk Management . It is the great source of information that is very helpful to conduct any search and rescue operation . Dhaka City Red Crescent Unit a sister concern of Bangladesh Red Crescent Society conduct many search and rescue operation . OpenStreetMap is very helpful for their work . Bangladesh Red Crescent Society arranged training on Openstreetmap for volunteers in collaboration with International Federation of Red Cross &Red Crescent Societies and American Red Cross . In this map data is uploaded by the Community . So the authority decided to train the Community . In this concern Dhaka City Red Crescent Unit organized training on OpenstreetMap under Data4Action Project . In 2-3 September,2016 Dhaka City Red Crescent Unit organized training on OpenStreetMap for the student of BRAC University, one of the renowned Private university in Bangladesh.This training was held at Architecture Department of BRAC University. This training was started sharp at 09.30 am and it solely attended by 25 student and teacher of BRAC Unibersity. The main vision of this OSM training for the students was enriching their skills by using the open geographical data to identify the hazard, disaster , the vulnerability of any area , navigational and other humanitarian purposes . Opening session was started with the pleasant voice of Ahasanul Hoque pioneer of OpenStreetMap in Bangladesh and Geospatial data management Consultant in World Bank Bangladesh. He gave his pleasant speech about openstreetmap that inspires the Participant . Then the training Session started . The Training was conducted by Ahasanul Hoque and assisted by Sawan Shariar , Atikur Rahman atik and Manjurul Islam.

On the first day training included the following session. # Introduction of Map & it’s component #Introduction of OpenStreetMap & relation in Disaster Preparedness and Response #Sign Up in Osm # Introduction and use of ID Editor # Introducing OSM field data collection tools #Use of Field paper in Osm This practical session started with opening an account in iD editor , this web-based mapping tool always needed to be connected to the internet . Then all the students made their own OSM account by iD editor before the training began with its own enthusiastic wave . The participants were learned how to configure the background layer and how to do basic edit with iD editor along with knowing additional information and custom tags. Data that used in editing in this session was specially the home address of the individual participants .Then they were informed about OSM field data collection tools. After the lunch break , the participants were introduced to the Field Papers website . The participants were taught how to create a printable map atlas for anywhere in the world . They were learned how to print and add notes to the field papers. Then the first day of the training session was ended with the improvement & development of knowledge as well as the cheerful mind and smiling faces of the participants .

On the Second day the training was started with its full swing at the same place at the same time like the day before . The Second day of the training , the practical session divided into following sessions #Introduction and the Use of JOSM #Trace Imagery #Editing by JOSM #Tagging Convention #How to upload data from field paper #Add data collected from field # Introducing of OSM apps #Introduction of Tasking Manager #MapSwipe and it’s Use

The training started with the basic knowledge of JOSM and then continued it with the basic editor and tags in JOSM . The participants were learned to use plugs in JOSM , activate imagery providers and upload data by using JOSM. After understanding how to run JOSM quickly , the participants were learned how to use JOSM offline by saving the important files for editing . Then the participant were informed how to upload data from Field Papers. Then participant were divided into 13 pairs and go to field with field paper. After collecting data they came back and uploaded their collected data. After lunch break they inform about Tasking Manager and how to contribute in Humanitarian work. They were also informed about MapSwipe &it’s use Its time for closing . Participant were share their feelings about training .The participants also said that OSM is a great platform for them and it has some extraordinary useful concepts in the context of Architecture & Disaster Management . They included that they will use this map data in different research . Finally, the training was closed by the photo session between trainers and the participants.

Manjurul Islam

Strange OSM Sighting

Posted by mtc on 3 September 2016 in English (English)

Strange occurrence happened today. While waiting for the shops to open, I recorded the names of the stores in this strip mall. One office appeared to be a computer themed consulting firm, with GPS systems in their window. When I researched the company website, I found they specialized in GPS systems for sports, and they used openstreetmap in their sport device solution! This was a pleasant coincidence, but how odd that they had not even entered their own office into OSM. I will not link to them in this post, in case the situation makes them appear poorly, but you could find them in my edit history easily enough. Good seeing people using OSM data in a variety of ways. I think these people are focused on real-time data from their sensors, but perhaps not very interested in the completeness of the street maps.

Towards Creating General Melchett's Map

Posted by SomeoneElse on 2 September 2016 in English (English)

I've written before about the changes needed to render more zoom levels than 18 with a "Switch2Osm-style" tile server.

However, sometimes zoom level 20 isn't enough. Here:

nott_ajt_20.png

is part of Nottingham at zoom level 20. At least one of the office names doesn't appear (it corresponds to here in OSM). The problem is that the way that the "standard" renderd stores metatiles means that only a certain number of tiles can be stored for each zoom level (see this list post for the details). In order to store more I changed renderd slightly so that more zoom levels can be stored - see here and here for the details.

Rendering works fine at higher zoom levels (up to 28 in my example) so that all of those office names now appear. Here's the same area at zoom level 21:

nott_ajt_21.png

The principle could be extended to an eventual goal of 1:1 to keep Melchett and Darling happy (roughly zoom level 32 at this latitude) but that seems unnecessary even to me currently.

Find total highway length per type of road / per Country in PostGIS using OSM PostGIS Script Repository

Posted by baditaflorin on 2 September 2016 in English (English)

How to find out the total length of highway per type of road / per Country in PostGIS

using the OSM postgis Script Repository This is a part of the final result.

  • Step 1. - Download the North-America osm.pbf file from Geofabrik (7.4 Gb)

The link is here You can use this command line to download the file in linux.

wget http://download.geofabrik.de/north-america-latest.osm.pbf
  • Step 2. - Load the data into PostGIS using the scope.sh utility tool

You can find the script here https://github.com/baditaflorin/osm-postgis-scripts/ The aim of this tool is to simplify the process of importing a osm.pbf file into PostGIS. Now the procedure is complicated and you first have to create the database, then to enable postGIS and Hstore on that database, etc

Here, you have a very simple process where you need to tell the name of the postgres user, the name of the database that you will create and the name of the osm.pbf file that you want to import, without the extension. At the end you will see something like this :

INFO: Total execution time: 93403048 milliseconds.

This
     script
            is
               done

                     Cluj
                          Map Analyst Team
                                           Telenav

 Find Postgis Scripts and snippents of code that you can use here
 https://github.com/baditaflorin/osm-postgis-scripts/
 It is a Open Source Project so you can also contribuite
 with you PostGIS Code to make the repository more complete
  • Step 3. - Wait 25 hours for the file to import.

In the end we will have a 280 Gb database, that is composed of over 59M ways, 855M nodes, and 500k relations

  • Step 4. - Find out if a node is from USA,Canada or Mexico.

Now, all of or nodes and ways have GPS coordinates, but we do not know if they are in Canada or Mexico. Fortunately, there is a great guy in Germany that goes by the name of Walter Nordmann that have a server where he hosts the OSM Boundaries Map using the data from OSM. He is doing this as a volunteer, so if yoi can help with any donations this will definitely help him to keep the server running. The website is https://osm.wno-edv-service.de/boundaries/ To do this we will download the admin_level=2 (al_2) boundaries of Canada.Mexico and USA.

  • Step 5. - Load the al_2 boundaries into the same postGIS database

To do this i used QGIS, a free and open source GIS tool, where you first need to connect to the database by doing :

Layer -> Add Layer -> Add PostGIS Layer.

In the new windows, under Connections click New and setup the connection.

After this, click OK and close the windows

We then go to

Database -> DB Manager -> DB Manager

We find and select our database from the left part of the screen.

We click on the Table Menu and we select Import vector layer

We put the name of the new table to be al_2 and we also select the index and we click ok

  • Step 6. - Calculate the total road length per each type of road and for each Country

This is the simple part, because we already have a OSM github script page with PostGIS scripts that you can run it for a city, a country or the planet, and they will work the same. The link is this OSM PostGIS script repository and the script that we are interested can be found here highway_length_per_type_different_attr.sql

  • Step 7. - Run the script

If you would run the script from the osm PostGIS script repo, you will get the total value of all of the roads in North America. This is why we need to see where each of the roads are located, and we do this using a inner join and ST_contains

The modified script is available as a gist here

I have not used the admin_level=2 borders that i have imported because the geometry of them was to complicated and it would have taken to much to get the result.

Instead i over-simplified the geometry until i got something really cartoon-ish compared to the first example. I first used the plugin SimpliPy developed by Albert Ferràs. You can download the plugin from Qgis Web link

  • Step 8 - Test and optimize the script, the polygon for the st_contains

I put a limit of 500 ways and tested to see how it will compare if i try to run the 500 examples using the bulk admin_level=2 boundaries, if i simplify and if i simplify and then also delete a lot of nodes so that i will speed up the process of Geocoding.

| admin_level=2 no simplification. | al_2_simplipy | al_2_simplipy_extreme | |---------| | 16 sec | 0.7 sec| 0.3 sec |

You have to have in mind that at the borders you will get some mixed results, where some parts of a town in Canada can become counted as part of USA, because of the over-simplification, but this is a useful when we have 59M ways, from where 22M ways have highway informations.

If for 500 we had to wait 16 seconds, for 5.000.000 ways, or a quarter of the total number of ways that at the worst case scenario we will have to wait 160.000 seconds, or
44 hours until the task will be completed.
For the complete 22M ways, this would mean around one week until we get the results.! dsds

By doing the simplification we reduce the amount to 0.7 seconds * 10000 = 7000 seconds or 2 hours for 25 % of the dataset. In 8 hours it should give us the results.

The last simplification meant that we start to deleting almost everything, trying to get a square, at least where i know that there is only water.

  • Step 9 - Run the script and get the results

In the end the script completed in 2861 seconds, or around 50 minutes.

The table with the results for North America can be seen here

The table was generated using this link

House Art

Posted by alexkemp on 2 September 2016 in English (English)

I try to feature the best stuff I come across whilst mapping. This was the latest:

owl + hedgehog

Others have been: Ladybirds + squirrel; Flowerpot man + Gargoyles; No Riff-Raff; Floral Abundance; Tweety Pie, Kenya Art + Plaster Dolls; Leaded Light door + another high-class door-sign. That last link also contains links to previous street art featured in these posts. I do spoil you, y'know.

Location: Arnold and Carlton, Gedling, Nottinghamshire, East Midlands, England, United Kingdom

Oscar & Leo – the Nottingham Lions – Together

Posted by alexkemp on 1 September 2016 in English (English)

Oscar – the southern lion in Slab Square – was first featured in Stone Lions of England. Here he is again:

Oscar

I've long wanted a photo of his northern companion, Leo (although do not bother to ask why I did not photograph both on the first occasion, as I cannot recall), but I do not get into town often, nor want to stay when I get there. I managed it today, although the bright sun possibly defeated my smartphone's circuitry:

Leo

Location: Lace Market, St Ann's, Nottingham, East Midlands, England, United Kingdom

Venice, Florida Wildlife Map

Posted by Luis Linares on 1 September 2016 in English (English)

Green Anole Lizrd

Location: Jacaranda Boulevard, Venice Groves, Sarasota County, Florida, 34293, United States of America

Unexpected Mapping Side-effect

Posted by mtc on 1 September 2016 in English (English)

Now that I am more aware of all the numerous POIs around me, I find that I am driving much more slowly. People in the car behind me must get slightly agitated, but that is only because they are not seeing all the things happening on the road. I drive much more safely, when I am concerned about each crosswalk, parking space, driveway, alleyway, and bikepath that must be navigated. I am mentally checking all of them and even see the street signs in a whole new way.

Germany navigation data review update

Posted by nammala on 1 September 2016 in English (English)

Following the previous diary post on improving navigation data in Germany, around 1000 turn restrictions detected on Mapillary were reviewed and 72 notes were added on OSM where there was a mismatch with the data for community mappers to verify with local knowledge. The community response has been extremely quick to verify an resolve these notes almost within a day of them being added. The whole exercise resulted in adding 27 new turn restrictions to the map which is already highly detailed and up to date.

screen shot 2016-09-01 at 8 42 44 pm (Blue=added to OSM; Orange=not required to add to OSM; Red=Invalid detection or location)_

Exit and destination signs on motorways

Now that we have completed with turn restrictions in 3 test locations in Germany, we would like to continue working with community to review exit numbers and destinations in these 3 cities. We plan to follow the wiki to review and we intend to add OSM notes for the missing exit numbers and destinations for the community to verify and map. For this process, we will be using tasking manager and checkautopista2 for identifying the missing exit numbers and destinations. Here is the guide and detailed workflow of the task.

Happy to hear your feedback on the process and if anything could be improved.

Grüße,

From Mapbox Data Team

Salt Lake City as trainigs area for "OSM go"

Posted by -karlos- on 1 September 2016 in English (English)

Funny: The Apple Store had one corner with doubble Nodes, causing an error in my software.

Location: Marmalade District, Capitol Hill, Salt Lake City, Salt Lake County, Utah, 84103, United States of America

OSM editor for mapping on iOS mobile devices?

Posted by mfroessl on 1 September 2016 in English (English)

Dear all,

I was trying to find an OSM editor for mapping directly on an iOS mobile device, but couldn't find anything on openstreetmap.org. Does anyone have a suggestion?

Regards, Matthias

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