OpenStreetMap

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Reported on OpenStreetMap Training (22-24 August, 2016) at Dhaka College

Posted by Sawan Shariar on 26 August 2016 in English (English)

OpenStreetMap(OSM) is the greatest invention of modern mapping system in this digitalized world. It is a great source of open data. It is more helpful and informative from any other online Map. In this MAP we get specific data. It is very helpful for peoples. By this map anyone can get information about his queries. Anyone can upload data and also can use data in their necessity. It is also helpful for the organizations that help vulnerable peoples. In this map we get the information about vulnerable area, way of passing, important point, water point etc. that is necessary for any kind of search and rescue operation in disaster. In this map data is free, so it is granted by everyone. It is easy to upload information in this map so anyone can contribute with the information from anywhere, anytime. OSM data is helpful for Vulnerable Capacity Assessment (VCA). By using this data different NGO, Government and International organizations can continue their relief operation. It is also helpful for research work. After all we can say, OSM data is very helpful for people for society as well as country. OSM data is very helpful for disaster risk management. In this map we get the data about identification of vulnerable places, way of passing, important key indicator as building condition, material, and structure by research this information govt. or relevant authority will identify the vulnerable places and take necessary steps to reduce the vulnerability, to reduce the damage of any kind of disaster. By using this data Bangladesh Red Crescent Society or this type of voluntary organization or Government organization like fire service, civil defense can plan their search & rescue operation.

I am Sawan Shariar from Bangladesh. I am an OpenStreetMap mapper and working since 14th May, 2015. I have done field level Mapping using GPS device, FieldPaper, OSMAnd, OpenMapKit, OSMTracker, Mapillary, MapSwipe and OSM Contributor. I upload data by using ID Editor, JOSM Editor and Potlatch 2. I also work on improving OpenStreetMap data TO-FIX for Mapbox. My complete mapper profile can be found at Sawan Shariar. I have done mapping for "Data for Action Project" and assisted in various OSM training with Bangladesh Red Crescent Society and continuing as a Super Mapper. I successfully completed The "Training for Trainer: OpenStreetMap" Training from Bangladesh Red Crescent Society supported by American Red Cross.

I am a volunteer of Bangladesh Red Crescent Society. And now I am working as Youth Chief of Red Crescent Youth, Dhaka College Unit (RCY,DCU). After successfully completed the Training for Training: OpenStreetMap I realize that, this training will be very useful for the volunteers of RCY,DCU. So I tried to understood my teachers and principal, how it’s effective and importance. And I successfully done that.

Red Crescent Youth, Dhaka College Unit is a voluntary organization, it started its journey in 2 may, 2004. It’s motto is serve the people. In its long journey it conducted many search & Rescue operation in different disaster. When the authority learn about the necessity and importance of OSM in disaster. They decided to arrange Training for the volunteers. In 22-24 August 2016, OpenStreetMap (OSM) training was conducted at Training room, Red Crescent Youth, Dhaka College Unit. This training was started sharp at 09.00am and it solely attended by the 26 volunteers of Red Crescent Youth, Dhaka College Unit.

Opening session was presided by Sheikh Sabbir Ahmed, Associate Professor of Dhaka College and Teacher In Charge of Red Crescent Youth ,Dhaka college Unit. As Chief guest there was present the pioneer of OpenStreetMap in Bangladesh Ahasanul Haque, Geospatial Data Management Consultant in World Bank Bangladesh. He gave his pleasant speech about OSM that inspires the Participants. Then the training Session started. The Training was conducted by Sawan Shariar, Atikur Rahman atik and Manjurul Islam and assisted by Diluar Jahan & Atikul Islam.
On the first day training session was divided into 4 sessions, which were

  1. Introduction of OpenStreetMap

  2. Introduction and use of ID Editor in OSM

  3. Introduction and How to prepare Field Paper.

  4. Field work: Collect data using field papers

The practical session started with opening an account in ID editor, a web-based mapping tool and it always needed to be connected to the internet. Then all the students made their own OSM account using ID editor before the training session. 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.

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. At last participants were paired into 13 team and sent to field for data collection. The first day of the training session was ended with the horizontal development with the shores of knowledge as well as the fervent smiling faces of the participants.

Second Day the training 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 5 sessions, which were-

  1. Review on previous day.

  2. Edit Upload data of previous day’s field work.

  3. Field work: Collect data using field papers

  4. Introduction and the Use of JOSM in OSM

  5. Upload collected data using JOSM Editor by participants.

The Sessions started with review on previous day works and upload data of previous day’s field works. After that, participants went outside again to collect data by using field paper. After the lunch break, the participants were introduced with JAVA Open Street Mapping (JOSM). They learned about interface of JOSM, activate imagery providers and upload data. After that they learned how to use JOSM offline by saving the important files for editing. And finally the second day’s training session ended up with uploading their collected data by using JOSM Editor.

Third Day the training started with its full swing at the same place at the same time like the first day. The Third day of the training sessions divided into 6 sessions, which were-

  1. Review on previous day.

  2. Field work: Collect data using field papers

  3. Edit & Upload their collected data

  4. Introduction in HOT &Teach Tasking Manager.

  5. OSM android apps & how to collect data by using it?

  6. Feedback & closing ceremony.

This day’s session started with Review on previous day works and upload data of previous day’s field works. After that participants sent outside to collect data by using field paper and after lunch participant edit & upload their collected data.

In the second session , the participants have instituted by the Humanitarian OSM Team collaborative mapping tool which is known as Humanitarian OpenStreetMap Team (HOT) Tasking Manager and Teach Tasking Manager. Participants also taught how to edit the maps both in teach and hot tasking manager not only editing by ID editor but also editing by JOSM. After that participants were introduced by many maps editing Mobile apps. The mobile apps are Mapswipe, OSM Tracker for Android, Map ME, Vespucci, Mapillary and OSMand.

The training was successful. The credit goes to The Honorable Principal Professor MD. Muazzom Hossain Mollah of Dhaka College. We are grateful to Sheikh Sabbir Ahmed & Farjana Islam, Associate Professor of Dhaka College and Teacher-in-charge of Red Crescent Youth, Dhaka College Unit. We are also grateful to Ahasanul Haque, The Pioneer of OpenStreetMap in Bangladesh and Geospatial Data Management Consultant in World Bank Bangladesh.

Entradas o accesos al teatro

Posted by Teatro Cadiz on 26 August 2016 in Spanish (Español)

Entrada peatonal: carrera 37 # 24 - 30 portería 4 (junto al supermercado y frente al arco de Corferias)

Parqueadero: carrera 33 # 23 - 51 (residencias universitarias 10 de mayo)

Location: Quinta Paredes, Teusaquillo, Bogota, Bogotá, 111321, Colombia

Adding house numbers of one town from cadastre to OSM and survey verification

Posted by MiroJanosik on 25 August 2016 in English (English)

Why and how

In Slovakia we have electronic cadastre available and it contains buildings in vector format with building conscription numbers (in our country it is conscription number Key:addr:conscriptionnumber ). These information are public and we have some smart OSM users that created a conversion possibility to get it into OSM maps KaporSKAddress#Conscription_numbers_import .

I have decided that I will use available method that takes cadastre house numbers and assign them to OSM map buildings in my town and then I will verify how correct these data are. Goal was:

  1. have houses with numbers searchable in OSM. It was found to be helpful also for ambulances in case of emergency to have searchable map!
  2. to keep OSM map up to date - to reflect reality which is goal of OSM
  3. by curiosity to see how precise are cadastre data

My town has approx. 1600 inhabitants and 632 houses plus some garages, cottages and huts. Town has approx. 10 streets.

Fast part

Building shapes are already imported into OSM for most of Slovakia for some time. Though, importing conscription numbers can't be fully automated and it can be tricky. Jose helped me with import process (thank you!) and it took less than half a hour; This was the first commit.

Slow part

Import process shown that in about 15 cases different houses had the same house number. I decided to resurvey whole village to verify all the house numbers (because of reasons 1-3 above).

So I have printed out zoomed OSM map with visible building numbers on paper. I walked around the town and noted down if something was not correct (I have done this in 4 separated sessions). After each session I started JOSM and corrected houses that needed correction, removed 'import notes' from houses, added source:conscriptionnumber.

My findings

  • I had 4 walking sessions around the town, each took me about 1,5 hour and fixing the data in JOSM was another 30-45 minutes. That sums up to 8-9 hours for my small town!
  • 73/632 of houses did not have visible numbers on the house
  • 10/632 houses were not reachable to verify their number - they were inside locked yards, and number was not on the gate either
  • 26 houses look like a real house with families living for some time inside, but they have no number in cadastre or on the house
  • ~5% of houses in cadastre data were somehow wrong - incorrect number, two houses had the same number (either completely different houses or two joined buildings, sometimes one of them is garage)
  • it is not good to do paper-walk mapping in cold weather, your fingers freeze off :)
  • people stare at you if you are looking on buildings and note something into a paper sheet :-)

I have realised that after 4 walking sessions I am still missing some data and had to make 5th, and yesterday I was checking all the data if they all have a proper source:conscriptionnumber and I realised that I'm still missing that on 30 buildings and I need to make 6th walk.

What would I do differently next time

  • do walking sessions while it is summer and you can feel your fingers, otherwise you will need to wait for next warm season
  • print out walking papers in good detail and think if there will be enough space to write comments on walking papers
  • write real comments on walking papers, not just abbreviations, you forget those abbreviations if you upload the data after few days
Location: Sološnica, District of Malacky, Region of Bratislava, Slovakia

Projeto Boa Esperança

Posted by GugaMap1248 on 25 August 2016 in Brazilian Portuguese (Português do Brasil)

Estou começando a editar mais no meu bairro, meus planos saem do papel hoje, ireicolocar os números das casas no mapa, terminar ruas (colocar o nome) e outras coisas. Abaixo, olocal principal de uma área que irei editar bastante

Location: Boa Esperança, Sombrio, Microrregião de Araranguá, Mesorregião do Sul Catarinense, Santa Catarina, Região Sul, Brasil

The future needs better maps...

Posted by mtc on 25 August 2016 in English (English)

I enjoy following news about selfdriving cars. I have been watching the progress since the first DARPA GC. So I enjoyed reading this article especially the comments, about their need for better maps.

iD with modules 🔜

Posted by tmcw on 24 August 2016 in English (English)

There's long been a desire & incoming wish for iD to be modular. Modular is a pretty general term, so I'll narrow it down into three general goals:

  1. Modules as a way of building a system. Using rollup, browserify, etc., in order to structure code and separate internal components in a nice, predictable way.
  2. Modules as a way of letting other systems include yours. Generally, in JavaScript-land, means publishing to npm.
  3. Modules as ways to include new code in your project. This is more in the vein of iD plugins. Supporting plugins would let us support contentious or rare needs without bikeshedding their inclusion to the main project.

With the help of Bryan, Kushan, David, Beau and Martin, we've made significant progress toward the first goal and are nearly at the finish line. iD will be switching from a system where we concatenate source files and use GNU make as a build process - to one that uses rollupjs to build a bundle from JavaScript modules. The expected benefits are big, for maintainers:

  • iD no longer relies on the global namespace for dependencies like d3, so there's less chance for conflict.
  • misnamed requires or invalid requires will be caught early
  • we can use npm modules for our dependencies, rather than keeping them in the project's source tree
  • we'll be able to build a faster and more reliable environment for development
  • we're upgrading d3 to v4, keeping it in line with the changing software world.

This doesn't immediately win us (2) and (3) but it pulls them closer. Soon, a new editor could reuse iD's data model, and iD could load new functionality from a plugin.

And there likely won't be any user-facing change as part of this port. This is the stage where the developers throw tens of hours into the low-level guts so that plugins can be possible and long-term maintenance can be less painful.


Bryan and I are working on the final steps of this modularization. I'll take some time to land: this is a major refactoring of a large project with a lot of functionality, so it involves a huge amount of work and has many opportunities to introduce regressions. We ask for your patience and/or support during this time.

Once we've ensured that the refactored iD is just as good, then it'll be the future, and we'll be excited to keep moving toward a more interchangeable stack of tools for OSM.

Location: Logan Circle/Shaw, Washington, District of Columbia, 20005, United States of America

5.000 changesets

Posted by Tomio on 24 August 2016 in Brazilian Portuguese (Português do Brasil)

Iniciado no Openstreetmap em Janeiro de 2014, hoje cheguei aos 5000 changesets. Mais que um simples número, ele carrega muitos e muitos Kms de vias que adicionei ao OSM deste gigante Brasil.

Continuo empenhado e motivado em mapear nosso país e no trabalho de sensibilizar e conquistar novos mapeadores. Ainda há muito o que fazer. Que venham outros 5000, 10000, 15000,... :)

[edited]

Posted by guineu on 24 August 2016 in English (English)

[edited]

Location: 0.000, 0.000

Singasandra, Bengaluru — a Mapping disaster

Posted by Ov3r10rd on 24 August 2016 in English (English)

I recently moved to Singasandra and realised that being a new colony, this area was very badly mapped. The creation of AECS(Aeronautical Engineers Cooperative society) Layout involved the area being carved out of Chikka Begur village, Singasandra. Not the problem is that the area is not not mapped correctly on any official or non-official platform. Let me Illustrate

The highlighted area is designated as AECS B Block(Verified on location) Given that its a new area, there are very few location markers placed by authorities- precisely 3 in the area(highlighted below) (1) Marked as 2nd Main Road (2) Marked as 3rd Cross Road (3) Marked as 2nd Cross Road

(1) Marked as 2nd Main Road (2) Marked as 3rd Cross Road (3) Marked as 2nd Cross Road

These are the only 3 physical markers in the area and the rest of the area just has to guess its own address. All the roads have been marked out on the basis of these 3 reference points. But there are some major issues here.

Physical markers

Point 1–2nd Main Road is a north-south running road as per the convention. But, Where is the 1st Main Road? It cannot be the one as labelled because that is an east-west road and should ideally be a Cross Road Points 2 & 3–2nd and 3rd Cross roads are marked by physical markers. These are north-south roads and should not ideally be Cross Roads, they should be mail roads. This has led to a complete breakdown of the addressing system.

localities

Approximate locality boundaries The addresses in AECS B Block area identify themselves with Begur, Singasandra and Kudlu whereas they all belong in Singasandra. This may seem like a trivial issue but there are many real-world implications in this hyper-local world we live in. It is almost impossible to accurately call a cab because this area is not correctly mapped o google maps(the service that is used by Ola ad Uber) and hence cab drivers go in circles around the house without actually being led to the correct location. Same goes for grocery deliveries and courier deliveries. Sharing a location with a friend marking the location of the house is pointless since the navigation does not understand how to direct them here. Calling upon fellow mappers to help resolve this issue.

Location: AECS Layout, A block, Kudlu, Bengaluru, Bangalore Urban, Karnataka, 560001, India

Map Assumptions

Posted by mtc on 24 August 2016 in English (English)

Among the things that have changed, since I was previously an active in 2010, is the way parking lots are defined. When there were none on the map, a parking lot area appeared to be a Public Parking Area, such as one that you might see on a road sign. The information seemed to be directing you toward a widely acceptable place for parking.

Today, every flat, paved place that one might find a few vehicles left turned off has a parking polygon. So, the meaning of the map has changed, but the data structure has had trouble keeping pace.

I have learned about how parking lots are used in today's OSM. I have attempted to add the "access" tag to the parking lots in my area, including the data that I entered many years ago. The vast majority of parking lots are privately maintained, but without the "access" tag defined, the (unofficial) default is public since they are accessed through public roads. Certainly many renderers put a (P) symbol which is commonly used for Public Parking Areas. That means more work for the mappers. I spent many hours adding "access=customers" to commercial locations or the key "access=private" to more remote locations.

The examination of social structures is one thing that gets me so excited about mapping. And nothing looks at the public-private social contracts more closely than the "access" tag. We can talk about whether the data or the renderer should be defaulting to public or private, but it comes down to this basic question: "Who is permitted to use that place?" Some of the information in OSM is the very complicated answer to that question. Little wonder it is hard to keep that data correlated with our assumptions.

Bing contra Wirklichkeit

Posted by kreuzschnabel on 24 August 2016 in German (Deutsch)

Die Faszination des Bing-Luftbildes als Mapping-Quelle fällt besonders dann auf, wenn man eine Gegend bearbeitet, die in der Vor-Bing-Ära gemappt wurde, meist nach einmaligem Abtracken per GPS, wo schnurgerade Straßen Schlangenlinien machen oder um 50 m versetzt sind. Man lächelt milde und stellt das alles „richtig“. Nach dem Bing-Luftbild, versteht sich, denn das hat ja recht.

Oder?

Auch 170 Jahre Fotografie und fast ebenso viele Jahre Fototricks haben uns offenbar noch nicht abgewöhnen können, das, was wir realitätsnah auf einem Bild sehen, als Wahrheit zu betrachten. Auch Bing verführt uns in dieser Weise. Dabei wäre Vorsicht angebracht. Schaun wir mal auf die B 54 nördlich Bad Schwalbach: Fehlerhafte Straßendarstellung

Hat es sich hier nicht ein Mapper zu einfach gemacht und die Straße als geraden Strich gezeichnet? Komm, das müssen wir gleich mal korrigieren!

Ich kenne diesen Reflex. Aber in diesem Fall widerstehe ich ihm, denn ich kenne auch die Straße persönlich. Und die verläuft da genau so, wie sie gemappt ist – der Bogen nach Osten ist schlicht ein Fehler im Luftbild. Man könnte es anhand der relativ scharfen Knicke an den Enden schon ahnen, kein vernünftiger Mensch baut so was in eine Bundesstraße ein.

Tröstlicherweise ist auch die Vereinigung Hessischer GPX-Tracks mit überwältigender Mehrheit dieser Meinung: Gerade GPX-Tracks

Wie kommt es zu diesen Fehlern? Nun bin ich kein Fachmann im Geoingenieurwesen, aber ich reime es mir etwa so zusammen:

Nicht jeder Punkt des Luftbildes ist senkrecht von oben aufgenommen. Das geht ja gar nicht. Das Luftbild besteht aus vielen Einzelaufnahmen, und alle Punkte, die zufällig am Rand des Bildfeldes einer Einzelaufnahme lagen, wurden schräg fotografiert.

Aber das macht ja nichts – wenn da nicht das Gelände wäre.

Nimm zur Verdeutlichung mal an, rechts außen im Bild steht ein Aussichtsturm auf einem Berg. Dessen Turmspitze sollte einklich mittig über seiner Standfläche sein, von der Geoposition her, alles andere wäre für den Turm gar nicht gut. Ist sie aber nicht, weil der Turm schräg fotografiert wurde, er scheint auf dem Bild zu kippen, die Spitze ist weiter außen als die Grundfläche. Welcher Punkt des Turmes ist jetzt seine „richtige“ Position, wo setzt du den Node hin? Und jetzt kommt’s: Das gilt nicht nur für den Turm, sondern auch für den Berg, auf dem er steht. Auch der wurde schräg fotografiert und kippt deshalb nach außen (das fällt nur nicht so auf wie beim Turm).

Deshalb müssen Luftbilder aufwendig entzerrt werden: Man legt ein Höhenmodell des Geländes darüber, berechnet für ein Raster von Referenzpunkten den seitlichen Versatz, der sich aus Höhe und Aufnahmewinkel ergibt, und verbiegt das Bild entsprechend in die andere Richtung. Hoffentlich halbwegs zutreffend. Denn erstens kann dieses Referenzpunktraster nicht beliebig engmaschig werden, deshalb stimmt die Entzerrung immer nur im Mittel, und zweitens ist auch das Höhenmodell nicht metergenau und löst keine feinen Strukturen auf.

Wenn das klappt, steht das untere Ende unseres Turmes auf der richtigen Geoposition. Aber in einem engen Flusstal wie dem Aartal oben im Bild kann es da schnell passieren, dass entweder das Höhenmodell nicht genau genug ist oder die Referenzpunkte zufällig auf den Hängen statt im Talgrund liegen. Die Punkte dazwischen werden ausgemittelt, und schon haben wir den Salat – eine gerade Struktur wird ans Gelände angepasst. Weil die Software zufällig auf die Hänge korrigierte, nicht aufs Tal.

TL;DR: Auch Bing verkündet kein Evangelium. Die Luftbilder müssen entzerrt werden, um einigermaßen lagegenau zu sein, und diese Entzerrung hat ihre technischen Grenzen. Die genaueste Quelle ist immer noch Ortskenntnis und eine Vielzahl von GPS-Tracks, deren Messfehler sich ausmittelt.

STL;SDR: Bing ist keine Quelle, Bing ist ein Werkzeug.

--ks

Location: Erlenhof, Mappershain, Heidenrod, Rheingau-Taunus-Kreis, Regierungsbezirk Darmstadt, Hessen, Deutschland

Reviewing Turn restrictions in Germany using Mapillary

Posted by nammala on 24 August 2016 in English (English)

This diary post last week outlined an overarching goal of making OpenStreetMap navigable, and our intent to investigate and add missing turn-restrictions in Berlin, Stuttgart, and Wolfsburg using Mapillary as a source. We received some good questions from the local OpenStreetMap community about using Mapillary as a source. Considering the concerns of the community, we did a preliminary research on the Mapillary imagery to ascertain their recency. We want to share our findings here.
Here is an animation showing Mapillary traces in Berlin, Stuttgart, and Wolfsburg spanning the last six months (March - August 2016). Not surprisingly, Berlin was found to have the best Mapillary coverage. The coverage in Stuttgart and Wolfsburg was sparse in comparison.

Berlin2 Berlin Mapillary Coverage

Stuttgart2 Stuttgart Mapillary Coverage

Wolfsburg Wolfsburg Mapillary Coverage

Recency of Imagery

When adding data on OpenStreetMap using any type of source, recency is an important aspect to consider. The plan is to add data on OpenStreetMap only if Mapillary imagery is more recent than when the turn-restriction was added. If this is not the case, or there was a recent change in the area, the intent is to add a note, and rely on the knowledge of the local community to verify accuracy. Based on this approach, here are a few cases where turn-restrictions could be added. To the best of our knowledge, these are valid turn-restrictions that are not added on OpenStreetMap. To do this, we're using the OSM Navigation Map to detect turn-restrictions, and their absolute positions using Mapillary. We'd appreciate if anyone with knowledge of these areas can verify if these detected signs are in fact correct or not.

screen shot 2016-08-23 at 11 48 50 am

We had carried out our preliminary research in reviewing Mapillary detected turn restrictions in these 3 cities, using OSM navigation map. We time boxed this task to 2 hours with two people working on Berlin and one each working on Stuttgart & Wolfsburg. We were able to find 25 missing turn restrictions out of the 110 we reviewed. Turn restrictions are categorised as valid/invalid/redundant based on the Mapillary detected signboards and by the OSM data present.

Case 1:

screen shot 2016-08-23 at 12 03 25 pm Missing No Left Turn (Link to OSM note)

Case 2:

screen shot 2016-08-23 at 12 08 56 pm Missing No U Turn (Link to OSM note)

Case 3:

image Missing only_straight_on (Link to OSM note)

Case4:

screen shot 2016-08-23 at 12 45 20 pm

Missing only_straight_on (Link to OSM note)

Case5:

screen shot 2016-08-23 at 12 54 41 pm Missing only_right_on (Link to OSM map)

Also, some other missing cases are listed here.

While we reviewed turn restrictions that can be mapped using Mapillary, we also verified some of the existing turn restrictions and encountered few cases where community feedback would help

Case 1:

  • In the case below, the image shows that there are restriction signages for each lane
  • Also the turn restriction added was a no_u_turn, which did not show up in the Mapillary.
  • In such a case, what is the approach community would suggest?

screen shot 2016-08-23 at 4 07 49 pm Link to the map.

Case 2

  • In this case the signboard is showing only_staright_on but it was added as two separate turn restrictions, no_left_run & no_right_turn.
  • As per the German wiki it should have been added as only_straight_on.
  • We would like to get feedback on adding such turn restrictions, what is the general convention the community follows?

screen shot 2016-08-23 at 5 38 25 pm Link to the map

Case 3

  • We have observed a no_straight_on restriction which seemed odd as it was on a dual carriage way with highway=teritiary tag.
  • We could not verify this with Mapillary images, as there was none on the road. It would be great this can be verified by the local mappers.

screen shot 2016-08-23 at 5 52 39 pm Link to the map

Based on the community feedback received, we are ready to add OSM notes for the missing turn restrictions if the community has no issue with this and also open to getting any thoughts you have on improving the OSM Navigation Map whether it's feature requests, or bug fixes here.

Thank you :)

Grüße,

From Mapbox Data Team

Experimenting with ClearTables, self-hosted vector tiles, and Tangram client-side rendering

Posted by pnorman on 24 August 2016 in English (English)

I've been experimenting with generating my own vector tiles and client-side rendering with Tangram in order to figure out how to best write a style in its language.

Tangram is a GL-based renderer written by Mapzen and normally used with their Tilezen vector tiles, but I'm interested in being able to make my own vector tiles with different cartographic choices.

Having a diverse selection of vector tile schemas is important, as is avoiding a situation where only large players in the market can get involved like right now.

For a toolchain I used osm2pgsql with ClearTables and Mapnik via Kosmtik to write vector tiles. With the demo I'm serving pre-rendered vector tiles from disk, but Kosmtik is useful in development with it's xray functionality. I input the style into Tangram Play, a web-based editor that automatically reloads the map when you change the style.

The cartography and vector tile definitions are loosely based on OSM Clear, a demo style I wrote. I didn't want to learn the language while designing new cartography at the same time. Being a learning exercise I don't consider the style complete or free of bugs.

The demo page is on my server at http://tangram-clear-demo.faramir.paulnorman.ca/ with the style and vector tile code at https://github.com/ClearTables/tangram-clear-demo.

I'm not sure what direction I'm going to take next as I don't have any particular style goals right now, or collaborators.

Cross-post from https://lists.openstreetmap.org/pipermail/dev/2016-August/029448.html

Data results for Parished/Unparished Areas

Posted by alexkemp on 24 August 2016 in English (English)

A recent Diary entry (A Suggestion to Fix Poor LSN in the UK) contained the phrase “Why those facilities fail for a substantial part (40%) of the UK”, and I promised to publish the raw data that led to the ‘40%’ claim. This is the fulfilment of that promise and be warned, it is long & intensely computer geeky.

In brief, that earlier Diary entry said:

  1. Location, Search & Naming facilities (LSN) require the presence of an “admin_level=10” (civil parish) area in the UK
  2. 40% of the UK does not have such an area, as it is unparished
  3. (thoughts on how to fix it)

The above both is, and is not, true (real life is usually a bit more complicated than that) but it was the best that I could manage & wanted to get the debate kicked off. Now for the methodology of acquiring, plus full results that led to, the 40% claim...

A site maintained by The Maarssen Mapper contains a page of all UK Civil Parishes in the form of GPX file downloads. The top of each file has an XML header that looks like this (this one is Birchgrove_Community):—

<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<gpx version="1.1" creator="Colin Smale (colin.smale@xs4all.nl)" xmlns="http://www.topografix.com/GPX/1/1">
    <metadata>
        <name>parish_region.shp</name>
        <desc>Derived from Ordnance Survey OpenData BoundaryLine data. Contains Ordnance Survey data © Crown copyright and database right 2016</desc>
        <copyright author="Ordnance Survey">
            <year>2016</year>
            <license>http://www.ordnancesurvey.co.uk/docs/licences/os-opendata-licence.pdf</license>
        </copyright>
        <time>21/03/2016</time>
    </metadata>
    <trk>
        <name>Birchgrove Community</name>
        <desc>Civil Parish or Community</desc>
        <number>8293</number>
        <extensions>
            <NAME>Birchgrove Community</NAME>
            <AREA_CODE>CPC</AREA_CODE>
            <DESCRIPTIO>Civil Parish or Community</DESCRIPTIO>
            <FILE_NAME>ABERTAWE_-_SWANSEA</FILE_NAME>
            <NUMBER>37</NUMBER>
            <NUMBER0>103</NUMBER0>
            <POLYGON_ID>53808</POLYGON_ID>
            <UNIT_ID>15978</UNIT_ID>
            <CODE>W04000561</CODE>
            <HECTARES>906.985</HECTARES>
            <AREA>0</AREA>
            <TYPE_CODE>AA</TYPE_CODE>
            <DESCRIPT0>CIVIL ADMINISTRATION AREA</DESCRIPT0>
            <TYPE_COD0 />
            <DESCRIPT1 />
        </extensions>

<HECTARES> is the important item in this header (the acreage of the area), although <FILE_NAME> is also used in the code below, as that helps group all the parishes by District, etc (“admin_level=8”).

Although this is a page of Civil Parishes, being Britain, there are also a large number of Unnamed Shapes. These are the Black Holes of the Boundary world, the Unparished Areas. Those files' Headers are similar in almost all respects to the ordinary CPs. Importantly for ourselves, they contain both <HECTARES> & <FILE_NAME>.

I created a directory called ‘cps’ which contained all CP files, and another called ‘cps_un’. All Unnamed Shapes were extracted from ‘/cps’ and placed into ‘/cps_un’. The awk command (below) is using a space as a word-splitter, so it is important that <FILE_NAME> does NOT contain any spaces. Naturally, some files did in both dirs. The following command was used to discover them, then the header edited:

egrep -li "<FILE_NAME>.*[ ]+" JOSM/parishes/cps_un/*

The final item is that xml_grep is installed under Debian/Ubuntu as part of the xml-twig-tools package.

The following command-line script was built (testing all the way) to show unit & combined acreage for both sets of files:—

xml_grep
     --text_only
     --root HECTARES
     --root FILE_NAME
     --wrap JOSM/parishes/cps/* |    
paste -d " "  - - |    
awk '
    { sum[$1] += $2; TOT += $2; NUM += 1 }
    END
    {
        AVG=TOT/NUM;
        for (k in sum) { printf("%s: %d\n", k, sum[k]) }
        printf("ZZ %d Parishes; Total area=%d hectares; Average/Parish=%d \n", NUM, TOT, AVG)
    }
' |    
sort

(I've broken it down into sections, but it was used as a single line)
(being able to easily construct the above is one of the values of Linux)

The identical command was used on both directories, changing only the dir reference in the command. Here are the results:

Unparished Areas

ABERDEEN_CITY: 20561
ABERDEENSHIRE: 633826
ANGUS: 220323
ARGYLL_AND_BUTE: 716377
BARNSLEY_DISTRICT_(B): 14452
BATH_AND_NORTH_EAST_SOMERSET: 2867
BEDFORD_(B): 2221
BIRMINGHAM_DISTRICT_(B): 20871
BLACKBURN_WITH_DARWEN_(B): 3536
BLACKPOOL_(B): 4315
BOLTON_DISTRICT_(B): 9908
BOURNEMOUTH_(B): 4664
BRADFORD_DISTRICT_(B): 10864
BUCKINGHAMSHIRE_COUNTY: 2376
BURY_DISTRICT_(B): 9946
CALDERDALE_DISTRICT_(B): 14078
CAMBRIDGESHIRE_COUNTY: 4069
CHESHIRE_WEST_AND_CHESTER_(B): 9849
CITY_OF_BRISTOL_(B): 23533
CITY_OF_DERBY_(B): 7803
CITY_OF_EDINBURGH: 27300
CITY_OF_KINGSTON_UPON_HULL_(B): 8149
CITY_OF_LEICESTER_(B): 7334
CITY_OF_NOTTINGHAM_(B): 7461
CITY_OF_PETERBOROUGH_(B): 6058
CITY_OF_PLYMOUTH_(B): 8436
CITY_OF_PORTSMOUTH_(B): 6014
CITY_OF_SOUTHAMPTON_(B): 5638
CITY_OF_STOKE-ON-TRENT_(B): 9344
CITY_OF_WOLVERHAMPTON_DISTRICT_(B): 6943
CLACKMANNANSHIRE: 16392
COUNTY_DURHAM: 14844
COVENTRY_DISTRICT_(B): 8237
CUMBRIA_COUNTY: 11067
DERBYSHIRE_COUNTY: 11746
DEVON_COUNTY: 5233
DONCASTER_DISTRICT_(B): 8063
DORSET_COUNTY: 5088
DUDLEY_DISTRICT_(B): 9795
DUMFRIES_AND_GALLOWAY: 667605
DUNDEE_CITY: 6222
EAST_AYRSHIRE: 127033
EAST_DUNBARTONSHIRE: 17449
EAST_LOTHIAN: 70093
EAST_RENFREWSHIRE: 17424
EAST_SUSSEX_COUNTY: 11007
ESSEX_COUNTY: 28556
FALKIRK: 31493
FIFE: 137392
GATESHEAD_DISTRICT_(B): 12119
GLASGOW_CITY: 17644
GLOUCESTERSHIRE_COUNTY: 5805
GREATER_LONDON_AUTHORITY: 159411
HALTON_(B): 5420
HAMPSHIRE_COUNTY: 27784
HARTLEPOOL_(B): 4406
HERTFORDSHIRE_COUNTY: 26090
HIGHLAND: 2647274
INVERCLYDE: 17360
KENT_COUNTY: 25693
KIRKLEES_DISTRICT_(B): 20445
KNOWSLEY_DISTRICT_(B): 4330
LANCASHIRE_COUNTY: 39605
LEEDS_DISTRICT_(B): 25164
LEICESTERSHIRE_COUNTY: 11324
LINCOLNSHIRE_COUNTY: 9667
LIVERPOOL_DISTRICT_(B): 13353
LUTON_(B): 4335
MANCHESTER_DISTRICT_(B): 10910
MEDWAY_(B): 8167
MIDDLESBROUGH_(B): 4331
MIDLOTHIAN: 35527
MORAY: 225707
NA_H-EILEANAN_AN_IAR: 326856
NEWCASTLE_UPON_TYNE_DISTRICT_(B): 7355
NORFOLK_COUNTY: 9561
NORTHAMPTONSHIRE_COUNTY: 11347
NORTH_AYRSHIRE: 90390
NORTH_EAST_LINCOLNSHIRE_(B): 4238
NORTH_LANARKSHIRE: 47222
NORTH_LINCOLNSHIRE_(B): 3373
NORTH_TYNESIDE_DISTRICT_(B): 8481
NORTH_YORKSHIRE_COUNTY: 4577
NOTTINGHAMSHIRE_COUNTY: 26690
OLDHAM_DISTRICT_(B): 5406
ORKNEY_ISLANDS: 108621
OXFORDSHIRE_COUNTY: 3760
PERTH_AND_KINROSS: 541890
POOLE_(B): 7471
READING_(B): 4039
REDCAR_AND_CLEVELAND_(B): 6648
RENFREWSHIRE: 26923
ROCHDALE_DISTRICT_(B): 15812
ROTHERHAM_DISTRICT_(B): 6662
SALFORD_DISTRICT_(B): 9719
SANDWELL_DISTRICT_(B): 8555
SCOTTISH_BORDERS: 474265
SEFTON_DISTRICT_(B): 12073
SHEFFIELD_DISTRICT_(B): 18461
SHETLAND_ISLANDS: 165661
SLOUGH_(B): 2535
SOLIHULL_DISTRICT_(B): 5515
SOMERSET_COUNTY: 1354
SOUTH_AYRSHIRE: 123471
SOUTHEND-ON-SEA_(B): 5972
SOUTH_GLOUCESTERSHIRE: 737
SOUTH_LANARKSHIRE: 177404
SOUTH_TYNESIDE_DISTRICT_(B): 6710
STAFFORDSHIRE_COUNTY: 10200
ST_HELENS_DISTRICT_(B): 6382
STIRLING: 225481
STOCKPORT_DISTRICT_(B): 12604
STOCKTON-ON-TEES_(B): 3132
SUFFOLK_COUNTY: 6305
SUNDERLAND_DISTRICT_(B): 11379
SURREY_COUNTY: 55290
SWINDON_(B): 3649
TAMESIDE_DISTRICT_(B): 9449
THE_CITY_OF_BRIGHTON_AND_HOVE_(B): 8107
THURROCK_(B): 18431
TORBAY_(B): 11313
TRAFFORD_DISTRICT_(B): 7652
WAKEFIELD_DISTRICT_(B): 11778
WALSALL_DISTRICT_(B): 10397
WARRINGTON_(B): 1898
WARWICKSHIRE_COUNTY: 10827
WEST_DUNBARTONSHIRE: 18277
WEST_LOTHIAN: 43158
WEST_SUSSEX_COUNTY: 10895
WIGAN_DISTRICT_(B): 17008
WINDSOR_AND_MAIDENHEAD_(B): 3697
WIRRAL_DISTRICT_(B): 25639
WORCESTERSHIRE_COUNTY: 8570
YORK_(B): 2836
ZZ 3069 Parishes; Total area=9229902 hectares; Average/Parish=3007

Civil Parishes:

ABERTAWE_-SWANSEA: 42089
BARNSLEY_DISTRICT
(B): 18454
BATH_AND_NORTH_EAST_SOMERSET: 32244
BEDFORD_(B): 45418
BIRMINGHAM_DISTRICT_(B): 5907
BLACKBURN_WITH_DARWEN_(B): 10165
BLAENAU_GWENT_-BLAENAU_GWENT: 10872
BOLTON_DISTRICT
(B): 4071
BOURNEMOUTH_(B): 52
BRACKNELL_FOREST_(B): 10938
BRADFORD_DISTRICT_(B): 25777
BRO_MORGANNWG_-THE_VALE_OF_GLAMORGAN: 33967
BUCKINGHAMSHIRE_COUNTY: 154118
CAERDYDD
-CARDIFF: 14951
CAERFFILI
-CAERPHILLY: 27738
CALDERDALE_DISTRICT
(B): 22317
CAMBRIDGESHIRE_COUNTY: 301330
CASNEWYDD_-NEWPORT: 21776
CASTELL-NEDD_PORT_TALBOT
-NEATH_PORT_TALBOT: 45186
CENTRAL_BEDFORDSHIRE: 71566
CHESHIRE_EAST
(B): 116635
CHESHIRE_WEST_AND_CHESTER_(B): 86880
CITY_OF_PETERBOROUGH_(B): 28285
CONWY_-CONWY: 115348
CORNWALL: 361335
COUNTY_DURHAM: 208425
COUNTY_OF_HEREFORDSHIRE: 217416
COVENTRY_DISTRICT
(B): 1626
CUMBRIA_COUNTY: 707179
DARLINGTON_(B): 16790
DERBYSHIRE_COUNTY: 243329
DEVON_COUNTY: 658375
DONCASTER_DISTRICT_(B): 48791
DORSET_COUNTY: 252208
EAST_RIDING_OF_YORKSHIRE: 249479
EAST_SUSSEX_COUNTY: 161509
ESSEX_COUNTY: 340974
GATESHEAD_DISTRICT_(B): 2288
GLOUCESTERSHIRE_COUNTY: 264647
GREATER_LONDON_AUTHORITY: 58
GWYNEDD_-GWYNEDD: 262237
HALTON
(B): 3611
HAMPSHIRE_COUNTY: 346006
HARTLEPOOL_(B): 5437
HERTFORDSHIRE_COUNTY: 138216
ISLE_OF_WIGHT: 39492
ISLES_OF_SCILLY: 2284
KENT_COUNTY: 334066
KIRKLEES_DISTRICT_(B): 20409
KNOWSLEY_DISTRICT_(B): 4319
LANCASHIRE_COUNTY: 268695
LEEDS_DISTRICT_(B): 30006
LEICESTERSHIRE_COUNTY: 195307
LINCOLNSHIRE_COUNTY: 598647
MANCHESTER_DISTRICT_(B): 654
MEDWAY_(B): 18739
MERTHYR_TUDFUL_-MERTHYR_TYDFIL: 11195
MIDDLESBROUGH
(B): 1123
MILTON_KEYNES_(B): 30483
NEWCASTLE_UPON_TYNE_DISTRICT_(B): 4156
NORFOLK_COUNTY: 540552
NORTHAMPTONSHIRE_COUNTY: 225481
NORTH_EAST_LINCOLNSHIRE_(B): 16116
NORTH_LINCOLNSHIRE_(B): 84195
NORTH_SOMERSET: 39080
NORTHUMBERLAND: 507818
NORTH_YORKSHIRE_COUNTY: 800716
NOTTINGHAMSHIRE_COUNTY: 181998
OLDHAM_DISTRICT_(B): 8827
OXFORDSHIRE_COUNTY: 256833
PEN-Y-BONT_AR_OGWR_-BRIDGEND: 25531
POWYS
-POWYS: 519545
REDCAR_AND_CLEVELAND
(B): 18740
RHONDDA_CYNON_TAF_-RHONDDA_CYNON_TAF: 42415
ROTHERHAM_DISTRICT
(B): 21991
RUTLAND: 39374
SEFTON_DISTRICT_(B): 8404
SHEFFIELD_DISTRICT_(B): 18331
SHROPSHIRE: 319727
SIR_BENFRO_-PEMBROKESHIRE: 165027
SIR_CEREDIGION
-CEREDIGION: 180586
SIR_DDINBYCH
-DENBIGHSHIRE: 84638
SIR_FYNWY
-MONMOUTHSHIRE: 88605
SIR_GAERFYRDDIN
-CARMARTHENSHIRE: 243894
SIR_Y_FFLINT
-FLINTSHIRE: 48943
SIR_YNYS_MON
-ISLE_OF_ANGLESEY: 74902
SLOUGH
(B): 718
SOLIHULL_DISTRICT_(B): 12313
SOMERSET_COUNTY: 349264
SOUTHEND-ON-SEA_(B): 817
SOUTH_GLOUCESTERSHIRE: 52927
STAFFORDSHIRE_COUNTY: 252130
ST_HELENS_DISTRICT_(B): 7253
STOCKTON-ON-TEES_(B): 17840
SUFFOLK_COUNTY: 379052
SUNDERLAND_DISTRICT_(B): 2583
SURREY_COUNTY: 111716
SWINDON_(B): 19359
TAMESIDE_DISTRICT_(B): 866
TELFORD_AND_WREKIN_(B): 29031
THE_CITY_OF_BRIGHTON_AND_HOVE_(B): 430
TORBAY_(B): 633
TOR-FAEN_-TORFAEN: 12624
TRAFFORD_DISTRICT
(B): 2952
WAKEFIELD_DISTRICT_(B): 22083
WARRINGTON_(B): 16339
WARWICKSHIRE_COUNTY: 186925
WEST_BERKSHIRE: 70416
WEST_SUSSEX_COUNTY: 191451
WIGAN_DISTRICT_(B): 1808
WILTSHIRE: 325533
WINDSOR_AND_MAIDENHEAD_(B): 16145
WOKINGHAM_(B): 17896
WORCESTERSHIRE_COUNTY: 165481
WRECSAM_-WREXHAM: 50377
YORK
(B): 24364
ZZ 11329 Parishes; Total area=14199250 hectares; Average/Parish=1253

(added later):

Hectare Totals for each Region:

Region              Total  Parished    %    Unparished  %
_______________ _________  _________  ____  _________  _____
East Scotland   1,829,536          0   0%   1,829,536  100%
Highland,Island 4,190,496          0   0%   4,190,496  100%
NE Scotland       654,387          0   0%     654,387  100%
SW Scotland     1,348,202          0   0%   1,348,202  100%
East England    1,957,808  1,846,210   94%    111,598    6%
East Midlands   1,577,508  1,484,136   94%     93,372    6%
London            159,469         58   0%     159,411  100%
NE England        864,605    785,200   91%     79,405    9%
NW England      1,494,539  1,248,658   84%    245,881   16%
SE England      1,935,958  1,760,956   91%    175,002    9%
SW England      2,438,091  2,357,941   97%     80,150    3%
West Midlands   1,299,810  1,190,556   92%    109,254    8%
Yorks., Humber  1,556,169  1,403,029   90%    153,140   10%
East Wales        778,164    778,164  100%          0    0%
West Wales      1,344,282  1,344,282  100%          0    0%
_______________ _________  _________  ____  _________  _____
               23,429,024 14,199,190 60.61% 9,229,834  39.39%

(added even later):

Hectare Totals for Population Cores:

Parishes Grouped by   Area                  Region              Total  Parished    %    Unparished  %
____________________  _____________________ _______________ _________  _________  ____  _________  ____
ABERDEEN_CITY         Unitary Authority     NE Scotland        20,561          0    0%     20,561  100%
ABERTAWE_-_SWANSEA    Unitary Authority     West Wales         42,089     42,089  100%          0    0%
BARNSLEY_DISTRICT_(B) Metropolitan District Yorks., Humber     32,906     18,454   56%     14,452   44%
BATH_AND_NE_SOMERSET  Unitary Authority     SW England         35,111     32,244   92%      2,867    8%
BEDFORD_(B)           Unitary Authority     East England       47,639     45,418   95%      2,221    5%
BIRMINGHAM_DISTRICT_( Metropolitan District West Midlands      26,778      5,907   22%     20,871   78%
BLACKBURN_WITH_DARWEN Unitary Authority     NW England         13,701     10,165   74%      3,536   26%
BLACKPOOL_(B)         Unitary Authority     NW England          4,315          0    0%      4,315  100%
BOLTON_DISTRICT_(B)   Metropolitan District NW England         13,979      4,071   29%      9,908   71%
BOURNEMOUTH_(B)       Unitary Authority     SW England          4,716         52    1%      4,664   99%
BRADFORD_DISTRICT_(B) Metropolitan District Yorks., Humber     36,641     25,777   70%     10,864   30%
BURY_DISTRICT_(B)     Metropolitan District NW England          9,946          0    0%      9,946  100%
CAERDYDD_-_CARDIFF    Unitary Authority     East Wales         14,951     14,951  100%          0    0%
CASNEWYDD_-_NEWPORT   Unitary Authority     East Wales         21,776     21,776  100%          0    0%
CASTELL-..PORT_TALBOT Unitary Authority     West Wales         45,186     45,186  100%          0    0%
CHESHIRE_CHESTER_(B)  Unitary Authority     NW England         96,729     86,880   90%      9,849   10%
CITY_OF_BRISTOL_(B)   Unitary Authority     SW England         23,533          0    0%     23,533  100%
CITY_OF_DERBY_(B)     Unitary Authority     East Midlands       7,803          0    0%      7,803  100%
CITY_OF_EDINBURGH     Unitary Authority     East Scotland      27,300          0    0%     27,300  100%
CITY_OF_K..HULL_(B)   Unitary Authority     Yorks., Humber      8,149          0    0%      8,149  100%
CITY_OF_LEICESTER_(B) Unitary Authority     East Midlands       7,334          0    0%      7,334  100%
CITY_OF_NOTTINGHAM_(  Unitary Authority     East Midlands       7,461          0    0%      7,461  100%
CITY_OF_PETERBOROUGH  Unitary Authority     East England       34,343     28,285   82%      6,058   18%
CITY_OF_PLYMOUTH_(B)  Unitary Authority     SW England          8,436          0    0%      8,436  100%
CITY_OF_PORTSMOUTH_(  Unitary Authority     SE England          6,014          0    0%      6,014  100%
CITY_OF_SOUTHAMPTON_( Unitary Authority     SE England          5,638          0    0%      5,638  100%
CITY_OF_STOKE..TRENT  Unitary Authority     West Midlands       9,344          0    0%      9,344  100%
CITY_OF_WOLVERHAMPTON Metropolitan District West Midlands       6,943          0    0%      6,943  100%
COVENTRY_DISTRICT_(B) Metropolitan District West Midlands       9,863      1,626   16%      8,237   84%
DARLINGTON_(B)        Unitary Authority     NE England         16,790     16,790  100%          0    0%
DONCASTER_DISTRICT_(  Metropolitan District Yorks., Humber     56,854     48,791   86%      8,063   14%
DUNDEE_CITY           Unitary Authority     East Scotland       6,222          0    0%      6,222  100%
FALKIRK               Unitary Authority     East Scotland      31,493          0    0%     31,493  100%
FIFE                  Unitary Authority     East Scotland     137,392          0    0%    137,392  100%
GATESHEAD_DISTRICT_(  Metropolitan District NE England         14,407      2,288   16%     12,119   84%
GLASGOW_CITY          Unitary Authority     SW Scotland        17,644          0    0%     17,644  100%
GREATER_LONDON...     County                London             15,9469        58    0%    159,411  100%
HARTLEPOOL_(B)        Unitary Authority     NE England          9,843      5,437   55%      4,406   45%
INVERCLYDE            Unitary Authority     SW Scotland        17,360          0    0%     17,360  100%
KIRKLEES_DISTRICT_(B) Metropolitan District Yorks., Humber     40,854     20,409   50%     20,445   50%
KNOWSLEY_DISTRICT_(B) Metropolitan District NW England          8,649      4,319   50%      4,330   50%
LEEDS_DISTRICT_(B)    Metropolitan District Yorks., Humber     55,170     30,006   54%     25,164   46%
LIVERPOOL_DISTRICT_(  Metropolitan District NW England         13,353          0    0%     13,353  100%
LUTON_(B)             Unitary Authority     East England        4,335          0    0%      4,335  100%
MANCHESTER_DISTRICT_( Metropolitan District NW England         11,564        654    6%     10,910   94%
MEDWAY_(B)            Unitary Authority     SE England         26,906     18,739   70%      8,167   30%
MIDDLESBROUGH_(B)     Unitary Authority     NE England          5,454      1,123   21%      4,331   79%
MILTON_KEYNES_(B)     Unitary Authority     SE England         30,483     30,483  100%          0    0%
NEWCASTLE_UPON_TYNE.. Metropolitan District NE England         11,511      4,156   36%      7,355   64%
OLDHAM_DISTRICT_(B)   Metropolitan District NW England         14,233      8,827   62%      5,406   38%
PEN-Y-BONT..BRIDGEND  Unitary Authority     West Wales         25,531     25,531  100%          0    0%
POOLE_(B)             Unitary Authority     SW England          7,471          0    0%      7,471  100%
READING_(B)           Unitary Authority     SE England          4,039          0    0%      4,039  100%
REDCAR_AND_CLEVELAND  Unitary Authority     NE England         25,388     18,740   74%      6,648   26%
ROCHDALE_DISTRICT_(B) Metropolitan District NW England         15,812          0    0%     15,812  100%
ROTHERHAM_DISTRICT_(  Metropolitan District Yorks., Humber     28,653     21,991   77%      6,662   23%
SALFORD_DISTRICT_(B)  Metropolitan District NW England          9,719          0    0%      9,719  100%
SHEFFIELD_DISTRICT_(  Metropolitan District Yorks., Humber     36,792     18,331   50%     18,461   50%
SLOUGH_(B)            Unitary Authority     SE England          3,253        718   22%      2,535   78%
SOLIHULL_DISTRICT_(B) Metropolitan District West Midlands      17,828     12,313   69%      5,515   31%
SOUTHEND-ON-SEA_(B)   Unitary Authority     East England        6,789        817   12%      5,972   88%
SOUTH_TYNESIDE...     Metropolitan District NE England          6,710          0    0%      6,710  100%
ST_HELENS_DISTRICT_(  Metropolitan District NW England         13,635      7,253   53%      6,382   47%
STIRLING              Unitary Authority     East Scotland     225,481          0    0%    225,481  100%
STOCKPORT_DISTRICT_(  Metropolitan District NW England         12,604          0    0%     12,604  100%
STOCKTON-ON-TEES_(B)  Unitary Authority     NE England         20,972     17,840   85%      3,132   15%
SUNDERLAND_DISTRICT_( Metropolitan District NE England         13,962      2,583   19%     11,379   81%
SWINDON_(B)           Unitary Authority     SW England         23,008     19,359   84%      3,649   16%
TAMESIDE_DISTRICT_(B) Metropolitan District NW England         10,315        866    8%      9,449   92%
TELFORD_AND_WREKIN_(  Unitary Authority     West Midlands      29,031     29,031  100%          0    0%
T..BRIGHTON_AND_HOVE  Unitary Authority     SE England          8,537        430    5%      8,107   95%
THURROCK_(B)          Unitary Authority     East England       18,431          0    0%     18,431  100%
TORBAY_(B)            Unitary Authority     SW England         11,946        633    5%     11,313   95%
TRAFFORD_DISTRICT_(B) Metropolitan District NW England         10,604      2,952   28%      7,652   72%
WAKEFIELD_DISTRICT_(  Metropolitan District Yorks., Humber     33,861     22,083   65%     11,778   35%
WALSALL_DISTRICT_(B)  Metropolitan District West Midlands      10,397          0    0%     10,397  100%
WARRINGTON_(B)        Unitary Authority     NW England         18,237     16,339   90%      1,898   10%
WIGAN_DISTRICT_(B)    Metropolitan District NW England         18,816      1,808   10%     17,008   90%
WINDSOR..MAIDENHEAD   Unitary Authority     SE England         19,842     16,145   81%      3,697   19%
WIRRAL_DISTRICT_(B)   Metropolitan District NW England         25,639          0    0%     25,639  100%
WOKINGHAM_(B)         Unitary Authority     SE England         17,896     17,896  100%          0    0%
WRECSAM_-_WREXHAM     Unitary Authority     East Wales         50,377     50,377  100%          0    0%
YORK_(B)              Unitary Authority     Yorks., Humber     27,200     24,364   90%      2,836   10%
____________________  _____________________ _______________ _________  _________  ____  _________  ____
                  83                                Totals: 2,127,947    903,361 42.45% 1,224,586 57.55%

Sacupira do Riachão

Posted by GugaMap1248 on 23 August 2016 in Brazilian Portuguese (Português do Brasil)

Nenhuma das ruas desta cidade estava mapeada, fiz tudo, passei uma hora fazendo tudo, porém, ainda falta o nome das ruas.

Experience with Mapping

Posted by Amisha Singla on 23 August 2016 in English (English)

I decided to spend some time mapping on OpenStreetMap after starting at Mapbox to help build better mapping tools later. After having show and tells with Mapbox buddies, I started off with mapping my hometown. Though I had not very perfect memory of the places in my hometown as I am away from it from many years, but there were few places which I was sure about. As it is advised to add data with 100% accuracy only, therefore I tagged only the sure places in OSM. When I downloaded the data for my city Gandhidham in JOSM, to my surprise, the city was well traced by a remote mapper. Therefore I worked on adding known POIs. It was fun to look for my home from the satellite imagery and tag its address.

Next I traced roads in city called Raipur. It was like taking part in an enjoyable drawing task while keeping a few rules in mind. Adding intersections, junctions, classification of roads has a huge impact on the routing. So I did that quite carefully.

Later I moved on to tracing buildings in Baga Beach, Goa. While working on this task I found many buildings that were quite interesting. Also, there were many row houses present in that area. Therefore to do it efficiently, I tried exploring the shortcuts of JOSM i.e. Making a big building and splitting it into pieces.

The most fun and interesting part of mapping was Field Mapping. To execute this we went in a group and split into sub-groups for covering a bigger region to map. I teamed up with Srividya. We planned to collect details like building addresses, levels, amenities , trees, streetlights in the neighborhood area of Mapbox office.We used OSM Tracker, mobile application for field mapping. For mapping all the buildings and amenities, we assigned each building a text note with all its details and took few photographs of the same which later helped us to upload the information in JOSM. For mapping trees and streetlamps , the presets came in handy, as you just have to tap on the mobile screen, whenever you encounter any tree or streetlamp. But the problem being we could not find any preset for tree and streetlamp. Therefore Srividya and me found out this trick to encode things up. We used 'Shelter' preset for tree and 'Surveillance' preset for streetlamp. Field mapping helped to explore and understand the neighborhood better.

Overall, mapping the places was a wonderful experience. Looking forward to keep making more edits in OSM.

Learning Mapping

Posted by Amisha Singla on 23 August 2016 in English (English)

What is Mapping?

It is an operation which associates elements of one set with the one/more elements of another set. When we talk about mapping in OpenStreetMap, the similar concept is followed. In that, we associate the real life objects (Home, Parks, Schools, Roads, Water bodies) to 2D elements like node, ways and polygons. This means that we can traverse any place virtually by looking at the map and can a get a sense of directions, locality, etc.

What are the objects which can be mapped?

It can be mapping different amenities, POIs, roads, buildings, water bodies, turn restrictions, different transport networks, Trees, Street lamps, so on. This list is never ending as each of them has a special purpose of being added in the map. The more detailed it is, the more it helps in understanding the place geographically. Details

How do we Map?

Basically there are two steps involved to map a place:

  • Tracing - With the help of satellite imagery and tools like In-browser editor/JOSM, one is able to trace various buildings, roads, water bodies remotely. For learning JOSM, one can follow this blog. Tracing

  • Tagging - Once we are done with tracing, we can start adding particular details like name, type, etc depending what kind of entity ( node / way / polygon) it is. If one is well familiar with the details of the place, then it can be done remotely. The best example for it will be mapping your hometown. For the mapping the unfamiliar places, Field mapping comes in picture. It is a technique in which a person goes to the actual area and maps it. There are mobile applications like OSMTracker, Vespucci which are helpful for field Mapping. One can also use the field papers. To learn more about field mapping, one can follow this blog. Labelling

Location: Indiranagar 2nd Stage, Indiranagar, Bengaluru, Bangalore Urban, Karnataka, 560001, India

MAPS.ME

Posted by Nesim on 23 August 2016 in Turkish (Türkçe)

Maps.Me Navigasyon Uygulaması

Sade ve Kullanışlı

Android ve İos işletim sistemlerinde bulunan ve Openstreetmap haritasının verilerini kullanan en güzel ve sade navigasyon uygulamalarından biridir. Haritanın akışkanlığı, tasarımı ve sadece temel navigasyon ayarları ile geniş bir kullanıcı kitlesine hitap ediyor.

Çevrimdışı ve Ücretsiz

Uygulamayı herhangi bir ücret ödemeden istediğiniz ülkenin haritasını cihazınıza yükleyerek internetsiz bir şekilde özgürce kullanabilirsiniz.

Harita Düzenleme

Openstreetmap kullanıcıları için en önemli özelliği budur. Bildiğiniz gibi openstreetmap'e katkıda bulunmak için her zaman bilgisayar başında bulunamıyoruz. İşte bu noktada dışarıdayken gördüğümüz haritaya eklenmemiş eczane, market, kafe vb. bir çok noktayı anında uygulama üzerinde "Haritaya Bir Ekleyin" seçeneğiyle ayrıntılı tanımlamalarla birlikte ekleyebiliyorsunuz. Ayrıca daha önce haritaya eklenmiş binaların kapı numarası, sokağı gibi ayrıntılar eklene biliniyor.

Sesli Yönlendirme

Oluşturduğunuz rotada Türkçe sesli yönlendirme seçeneği de bulunuyor.

Güncelleme Aralıkları

Bir çok uygulama harita verilerini aylarca güncellemiyor. Ancak Maps.Me uygulaması genelde 1 ay olmadan openstreetmap'teki verileri çekerek haritalarını güncel tutuyorlar.

Popülerdir

Android'te 10 milyon fazla indirme rakamına ulaşmış.

Progress of Navigation Mapping in Canada!

Posted by poornibadrinath on 23 August 2016 in English (English)

With an aim of making OpenStreetMap more navigable and accurate in routing, we started mapping turn restrictions and exit-destinations in Canada in its five important cities: Toronto, Ottawa, Montreal, Vancouver and Calgary. The tasks which spread over a month have been completed; we have finished adding and validating both turn restrictions and exit-destinations in the selected cities of Canada with the support from the OpenStreetMap community.

Summary of improvements

Mapping turn restrictions was flagged off on 21 of July with data team and the community working on adding missing turn restrictions and validating the ones that are present.

As the mapping progressed, workflow was getting updated every time the team had some doubts regarding how best to map a particularly different turn restriction that was detected. The questions we had were posted on the mapping ticket we used and the community got back to us almost immediately with clarifications to our questions. We completed both adding and validating turn restrictions in 14 days.

Exit-destination mapping started on August 11. For exit and destinations, a slightly different approach was followed, unlike how we mapped previously using only checkautopista2. Each highway was considered a separate task, which was integrated into tasking manager, with a specific link to checkautopista2 that loaded that particular highway that was selected using tasking manager.

Below is the full breakdown of how many turn restrictions and exit-destinations were mapped:

image

Status of existing data and Mapillary coverage

We could map extensively in Toronto because of great Mapillary coverage. Ottawa had us verifying the existing exit-destination tags rather than adding new ones because most of them were already mapped. 🎉 The community raced us in adding exit-destination tags in Vancouver! 🚀 Due to less Mapillary coverage, we couldn't map much in Montréal and Calgary. We wrapped up adding and validating exit-destinations in 9 days :)

Community support

Both the projects were met with an amazing response from the community. It was great to have you all working alongside us, helping us in adding missing data, calling out and correcting our errors, keep tabs on our edits, and clarifying doubts and questions, letting us make edits to previously added data. We thank everyone, especially Andrewpmk, Rps333, James2432, Bootprint, Puec, Fmarier, Scruss, for your support and guidance and hope the collaboration and involvement continues in all our future projects. We will continue navigation mapping in Canada, specifically in Montreal and Calgary once there is enough Mapillary or OpenStreetView coverage for us to add data and verify them.

Until then,

Cheers!

Mapbox Data Team :)

Spotting Cemeteries in Texas

Posted by mvexel on 23 August 2016 in English (English)

I am collaborating with agencies in Texas to update both OSM and Texas data. The pilot project deals with cemeteries. I received a file with almost 7000 cemetery locations. (Even if the idea that there are more people living today than have died thus far in human history turns out to be a myth, I think that is quite a lot!).

The first phase of this collaboration is to see which cemeteries in the Texas data actually exist. We will use MapRoulette for that. Simply go to the Cemetery challenge at maproulette.org and start looking at tasks.

If you see a cemetery in the aerial image, click 'skip' to go to the next one. If you don't see a cemetery, click 'False Positive'. If you are in doubt, click 'skip'.

How can you tell if there is a cemetery? Sometimes it is hard. Look for fine patterns defining the plots, and usually there will be a service road connecting the cemetery to the road network. Sometimes, in larger cemeteries, you may also see paths inside the cemetery. Finally, the marker may not be right on the cemetery, so look around a bit as well. Below are some examples of cemeteries and non-cemeteries.

Once we complete stage 1, we will turn to mapping all the cemeteries that are not yet in OSM yet!

cem

There is a cemetery here: fine regular pattern indicating plots, some paths.

cem

There is a cemetery here also.

cem

No cemetery here, just some grass.

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