Blogs.OpenStreetMap.org

July 30, 2015

"OpenStreetMap.org User's Diaries"

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Skoncentrowanie się na zakazach ruchu i drogach jednokierunkowych. Ewentualne poprawki statusu dróg. Trzebini, Chrzanów, Płaza, Babice, Zator, Wadowice ... kierunek Zakopane drogą 28, potem 47.

by JakubMap at July 30, 2015 10:34 AM

Mapping turn lanes in OpensStreetMap

Hi, folks!

I want to share with you my blog post about mapping turn lanes

If you have any suggestions, you can write it here in comments


Mapping turn lanes in OpensStreetMap

Complex intersections often involve lane-specific turn restrictions. See for instance these overhead signs on Utah State Route 92, crossing Interstate 15 just south of Salt Lake City.

turn lanes sign

Lane signage on Utah State Route 92. Photo: Garrett.

We need turn restrictions for every individual lane to provide precise directions for drivers. In OpenStreetMap we model turn lane information with two tagging schemes:

Here is a guide on how to map turn lanes with OpenStreetMap's JOSM editor. Note that type=turnlanes:turns is a proposed feature. You can join the proposal process by submitting your review on the relevant talk page.

Mapping Turn Lanes

First, install the turn lane plugin directly from JOSM's settings panel. It provides support for both Key:turn tags and type=turnlanes:turns relations.

As an example, let's look at how to map turn lanes in detail on this intersection alongside US 101 in South San Francisco.

image

Lane-specific turn restrictions along US 101 Exit 425B.

Enable the "Turn Lanes" panel in the "Windows" menu.

josm_open_turnlanes_dialog

Identify the number of lanes on all the roads leading to and from the junction.

image

Add the number of lanes to the appropriate ways as a regular tag - for example, lanes=2.

image

Split the ways that will be parts of relations. Then, for each turn lane restriction, select the nodes and ways involved to define a rule. You should see the junction modeled as shown once the lane count for all the roads have been set.

josm_open_turnlanes_construct_junction

Let's start with the motorway link from US 101 leading into the junction.

exit_to=Oyster Point Boulevard; highway=motorway_junction; ref=425B

Initially this way has one lane which splits into three before the junction. At 141 meters before the junction, add the left lane. At 92 meters, add the lane on the right side. These lanes can be added by clicking the white plus button and setting the start position by dragging the blue marker back from the junction.

josm_open_turnlanes_add_side_lanes

Add a lane to turn left for a small section of Dubuque Avenue. You can pan the model by dragging with the right mouse button.

josm_open_turnlanes_add_side_lane

Now create rules for the thoroughfare from motorway junction #425B with rules for each lane by dragging a route across the relevant junctions and lanes.

josm_open_turnlanes_add_turn_ruls

Add similar rules for all directions where applicable.

Now let's check our work by selecting nodes and reviewing traffic flow. To cycle through all directions press Ctrl+A for an overview of all restrictions on the junction.

josm_open_turnlanes_check_in_out

I hope you find these instructions useful to map detailed junctions in your area. If you have any questions or ideas on mapping turn lanes, drop me a line on Twitter or through my OpenStreetMap profile.

Header photo: Interstate 10 and Interstate 17 Interchange at Night by Alan Stark

by andygol at July 30, 2015 09:31 AM

July 29, 2015

"OpenStreetMap.org User's Diaries"

# Mapeando México con INEGI, contigo y...Cygnus

Publicado por mvexel el 29 julio 2015 Traducido al Español por @Mapanauta

México liberó una gran cantidad de datos abiertos hace poco tiempo. Huge: Mexico's statistical institute INEGI goes open data @INEGI_INFORMA (via @rodowi — Alex Barth (@lxbarth)

Mucha de esta información es geoespacial, por eso digo ¡yummie! Alex Barth escribió acerca de estos datos que vienen del Instituto Nacional de Geografía y Estadística (INEGI) en su diario poco tiempo después de la liberación con un excelente mapa para mostrar que tan rica es la información:

(Mis habilidades mediocres para animaciones GIF realmente no le hacen justicia – chequen la publicación del Blog de Alex para ver el mapa interactivo).

Ahora la pregunta es: ¿Cómo obtenemos una parte (o todo?) de estos datos en OSM? Esto no está claro –OSM ya cuenta con datos valiosos en muchos lugares en México que definitivamente queremos mantener. Aquí en el equipo OSM de Telenav obtuvimos la respuesta a esta pregunta. Le llamamos Cygnus - El portador del Equilibrio. Déjenme explicar con algunos visuales lo que hace Cygnus. Consideren esta área en la región de Aguascalientes. Existe actualmente información de OSM ahí:

Si vemos las imágenes aéreas de Bing, podemos ver que hay una población completa que ¡aún no ha sido mapeada!

Pero INEGI tiene la mayoría o si no todos las calles en esta población en su conjunto de datos ahora abierto llamado Conjunto de Datos Vectoriales de Carreteras y Vialidades Urbanas .

Después de convertir los atributos originales de los datos al etiquetado correcto de OSM, guardar los resultados a un archivo OSM y cargarlo en JOSM se ve de la siguiente forma:

OK, eso está bien pero aún tenemos dos diferentes capas que están desconectadas e incluso si se fusionan tenemos que resolver manualmente caminos duplicados y conecciones entre los caminos de OSM y aquellos provenientes de los datos de INEGI.

Aquí es donde Cygnus entra- desarrollamos en Telenav una nueva tecnología de fusión específicamente para afrontar esto.

Gygnus, como el Portador de Equilibrio, toma los que se ingresó como base en archivo OSM en formato PBF, así como lo que llamamos archivo 'mejorado', también en formato PBF. Después los compara y fusiona los dos ingresos (inputs) y da como resultado un archivo JOSM XML que puede ser fusionado con la base de datos OSM de manera inmediata. Estos detalles muestran las capas combinadas con todos los caminos 'importados' del INEGI antes de los caminos pre-existentes de OSM

Aunque Cygnus hace un trabajo impresionante de fusionar datos OSM con la capa ‘mejorada’, aún tienes que checar el resultado antes de hacer subir la información. Checa el ejemplo aquí:

La 'highway=secondary' ya estaba pre-existente a los datos OSM y la 'highway=residential' ; 'oneway=yes' vino de los datos de INEGI, está claro por las imágenes de Bing que hay dos caminos que deben estar conectados y todavía no lo están. Cygnus tiene un umbral de distancia (modificable) que utiliza cuando decide si dos caminos deben de estar conectados o no. En este caso, el camino de INEGI estaba muy lejos por ello permaneció desconectado.

Hay otras cosas por considerar cuando trabajas con archivos de cambio producidas por Cygnus:

  • Cygnus nunca va a degradar datos existentes de OSM
  • Cuando ambos OSM y la capa mejorada tienen el mismo camino, Cygnus va a mantener siempre la geometría original OSM pero opcionalmente va a importar 'name' y otras etiquetas útiles.

Estamos trabajando en un plan de importación para los datos del INEGI que harán uso intenso de esta tecnología ¡Más acerca de esto muy pronto! Mientras tanto checa nuestra charla en SOTM US con respecto a los datos de INEGI y OSM en México.

by Mapanauta at July 29, 2015 11:21 PM

CanVec Import for 085F05 (Fort Providence NWT)

I am working on improving the data availability for some of the major northern Canadian arctic communities, as improving this data in OpenStreetMap will also improve the Arctic Web Map project that is using OSM data.

I have downloaded the shapefiles from NRCan and I used ogr2ogr to convert them from EPSG:4140 to EPSG:4326. Then I am importing them a layer at a time using JOSM. I am using the CanVec Feature Catalogue and OSM CanVec Feature Guide for mapping values.

For the vegetation layer "085f05_11_0_VE_1240019_2" (Wooded area classified — polygons) I imported the shapefile into QGIS and used a dissolve operation to combine all the polygons. Then a Douglas generalize filter with a tolerance of 0.0001 was applied to remove redundant vertices. This reduced the vertex count from 363,000 to 40,000. Next I opened that shapefile in JOSM and changed the tags to natural=wood and manually merged the polygons with identical polygons in the neighbouring imported tiles.

I will continue importing one layer at a time until this NTS tile is complete.

by JamesBadger_CanVecImport at July 29, 2015 11:06 PM

Mapeando México con INEGI, contigo y...Cygnus

Publicado por mvexel el 29 julio 2015 Traducido al Español por Mapanauta

México liberó una gran cantidad de datos abiertos hace poco tiempo. Huge: Mexico's statistical institute INEGI goes open data @INEGI_INFORMA (via @rodowi — Alex Barth (@lxbarth)

Mucha de esta información es geoespacial, por eso digo ¡yummie! Alex Barth escribió acerca de estos datos que vienen del Instituto Nacional de Geografía y Estadística (INEGI) en su diario poco tiempo después de la liberación con un excelente mapa para mostrar que tan rica es la información:

(Mis habilidades mediocres para animaciones GIF realmente no le hacen justicia – chequen la publicación del Blog de Alex para ver el mapa interactivo).

Ahora la pregunta es: ¿Cómo obtenemos una parte (o todo?) de estos datos en OSM? Esto no está claro –OSM ya cuenta con datos valiosos en muchos lugares en México que definitivamente queremos mantener. Aquí en el equipo OSM de Telenav obtuvimos la respuesta a esta pregunta. Le llamamos Cygnus - El portador del Equilibrio. Déjenme explicar con algunos visuales lo que hace Cygnus. Consideren esta área en la región de Aguascalientes. Existe actualmente información de OSM ahí:

Si vemos las imágenes aéreas de Bing, podemos ver que hay una población completa que ¡aún no ha sido mapeada!

Pero INEGI tiene la mayoría o si no todos las calles en esta población en su conjunto de datos ahora abierto llamado Conjunto de Datos Vectoriales de Carreteras y Vialidades Urbanas .

Después de convertir los atributos originales de los datos al etiquetado correcto de OSM, guardar los resultados a un archivo OSM y cargarlo en JOSM se ve de la siguiente forma:

OK, eso está bien pero aún tenemos dos diferentes capas que están desconectadas e incluso si se fusionan tenemos que resolver manualmente caminos duplicados y conecciones entre los caminos de OSM y aquellos provenientes de los datos de INEGI.

Aquí es donde Cygnus entra- desarrollamos en Telenav una nueva tecnología de fusión específicamente para afrontar esto.

Gygnus, como el Portador de Equilibrio, toma los que se ingresó como base en archivo OSM en formato PBF, así como lo que llamamos archivo 'mejorado', también en formato PBF. Después los compara y fusiona los dos ingresos (inputs) y da como resultado un archivo JOSM XML que puede ser fusionado con la base de datos OSM de manera inmediata. Estos detalles muestran las capas combinadas con todos los caminos 'importados' del INEGI antes de los caminos pre-existentes de OSM

Aunque Cygnus hace un trabajo impresionante de fusionar datos OSM con la capa ‘mejorada’, aún tienes que checar el resultado antes de hacer subir la información. Checa el ejemplo aquí:

La 'highway=secondary' ya estaba pre-existente a los datos OSM y la 'highway=residential' ; 'oneway=yes' vino de los datos de INEGI, está claro por las imágenes de Bing que hay dos caminos que deben estar conectados y todavía no lo están. Cygnus tiene un umbral de distancia (modificable) que utiliza cuando decide si dos caminos deben de estar conectados o no. En este caso, el camino de INEGI estaba muy lejos por ello permaneció desconectado.

Hay otras cosas por considerar cuando trabajas con archivos de cambio producidas por Cygnus:

  • Cygnus nunca va a degradar datos existentes de OSM
  • Cuando ambos OSM y la capa mejorada tienen el mismo camino, Cygnus va a mantener siempre la geometría original OSM pero opcionalmente va a importar 'name' y otras etiquetas útiles.

Estamos trabajando en un plan de importación para los datos del INEGI que harán uso intenso de esta tecnología ¡Más acerca de esto muy pronto! Mientras tanto checa nuestra charla en SOTM US con respecto a los datos de INEGI y OSM en México.

by Mapanauta at July 29, 2015 10:55 PM

Stammtische und Berlin im Detail

Da mir am Wochenende danach nach war, tauchte ich etwas tiefer in die Geschichte des Berlin-Brandenburger Stammtisches ein. Anfangs fand er jeden 2. Donnerstag, heute abwechselnd jeden 2. Donnerstag bzw. 2. Freitag statt. Seit diesem Juni findet der Stammtisch zusammen mit dem FOSSGIS-Stammtisch statt, der zuvor in Potsdam seinen Sitz hatte. Da ich seit dem 2.Treffen regelmäßig dabei bin, der Meinung war, dass Berlin einer der ältesten sein müsste und wir bereits sehr viele Treffen hatten machte ich mich auf die Suche.Zum Glück waren bis auf das erste Treffen alle Treffen im Wiki geplant. Also öffnete ich mein LibreOffice und legte los, sind ja nur knapp rund 600 Versionen von der Wikiseite vorhanden. Der erste Stammtisch in Berlin fand am 10.07.2008 in der Pizzeria SI statt. Zunächst habe ich nur die verschiedenen Lokalitäten zusammen getragen, die ich dann auf der Wikiseite zum Stammtisch in den Abschnitt historisches eingetragen habe.

Lokalitäten

Folgende Restaurants und Lokalitäten dienten in den Jahren als Unterschlupf für den Stammtisch:

  • Restaurant/Pizzeria SI (Juli 2008)
  • Café auszeit (August 2008 - Februar 2009)
  • Tante Elli (März 2009 - Juli 2010)
  • c-base (August 2010 - Juli 2013)
  • Resonanz (August 2013 - heute)

Neben diesen Lokalitäten fanden 2 Treffen in den Jahren 2010 und 2011 im Romiosini am ICC statt, weil in der Woche vom Stammtisch auch der LinuxTag stattfand. Hier haben wir uns dann am Freitag zum Abendessen getroffen. Wie ich feststellen musste, fiel auch 2014 LinuxTag und Stammtisch in die gleiche Woche, auch hier war ein Abendessen geplant, aber es fand sich niemand für die Organisation. So war es seit Beginn des Stammtischs der Mai 2014 der einzige Monat an dem es kein Treffen gab. Auch in den anderen Jahren gab Freitagabends die Zusammenkünfte vom gemeinsamen FOSSGIS- und OSM-Stand. Diese flossen, da der Stammtisch auch regulär statt fand, nicht in die Statistik mit ein. Des Weiteren gab es nur im Mai 2010 eine Verlegung des Stammtisches um eine Woche. Der ursprüngliche Termin war ein Herrentag. Auch im Jahr 2015 fiel der Stammtisch auf einen Herrentag, dieser wurde aber nicht verschoben, es waren insgesamt 5 Teilnehmer vor Ort, bei 3 Anmeldungen im Wiki, somit genauso viel wie im Vormonat.

Zahlenspielerei

Anzahl der Treffen

Hier die Lokalitäten mit den Anzahl der Treffen sowie durchschnittliche Anmeldungen im Wiki.

  • Cafe auszeit - 7 Treffen - 6,29 Besucher
  • Tante Elli - 16 Treffen - 6,19 Besucher
  • c-base - 35 Treffen - 3,97 Besucher
  • Resonanz - 23 Treffen - 4,00 Besucher

Die maximale Zahl von Anmeldungen war am 13.08.2009 mit 11 Teilnehmer, danach haben sich nie wieder mehr als 9 Teilnehmer angemeldet.

Hierbei muss man sagen, dass die Wiki-Anmeldungen nicht die wirklichen Besucherzahlen widerspiegeln."Es gab einmal einen Abend mit 18 Stammtischbesuchern, an dem bis auf eine Person alle erstmals den Stammtisch besucht haben." - siehe Der Stammtisch im Wandel der Zeit

Anzahl der Besuche

Hier die Top 5 der Besucher (84 Stammtische):

  1. Peter (B10xyz) 43x
  2. Thorsten (Bluescreen) 41x
  3. Christopher 40x
  4. Marcus (Roald-linus) 31x
  5. Sasha (Toaster) 28x

Abgänge / Zugänge

Zudem wurde die durchschnittliche Zahl der neuen OSM-Nutzer (Zugang) sowie die Nutzer die letztmalig auf dem Stammtisch waren (Abgang) nach Standort zusammengefasst:

  • Cafe auszeit - 3,00 Zugang - 1,86 Abgang
  • Tante Elli - 1,38 Zugang - 1,13 Abgang
  • c-base - 0,57 Zugang - 0,57 Abgang
  • Resonanz - 0,48 Zugang - 0,74 Abgang

Da für den ersten Stammtisch keine Daten vorliegen, so ein paar Details zum 2. Stammtisch. Es waren 8 Teilnehmer angemeldet, 3 hiervon kamen nicht wieder bzw. haben sich nicht angemeldet.

Jubiläum

Da der Stammtisch im Mai 2014 nicht statt gefunden hat, wir der Stammtisch im November 2016 zum 100. mal statt finden. Ich denke das sollten wir irgendwie feiern.

Vergleich mit anderen Städten

Um Herauszufinden wie der Berlin-Brandenburger Stammtisch im Vergleich zu den anderen Stammtischen da steht habe ich mich auf allen Stammtischseiten im Wiki umgeschaut um herauszufinden ob diese noch aktiv sind und wie lange diese bereits existieren. Einige Städte haben seit dem ersten Stammtisch eine "Chronik" angelegt, dort war es ganz einfach den ersten Stammtisch herauszufinden. Bei anderen Stammtischen war das nicht so einfach und man musste auf der Stadtseite suchen in welcher Version der erste Eintrag zum Stammtisch vorgenommen wurde. Insgesamt wurden dabei 58 Stammtische in Deutschland betrachtet.

Hier die Liste der deutschen Stammtische, die sich noch regelmäßig treffen, das Datum zeigt das erste Treffen an:

  1. München 12.10.06
  2. Hamburg 09.10.07
  3. Dresden 03.01.08
  4. Hannover 18.05.08
  5. Karlsruhe 24.06.08
  6. Berlin 10.07.08
  7. Stuttgart 19.07.08
  8. Essen 11.08.08
  9. Dortmund 21.09.08
  10. Göttingen 30.09.08
  11. Lüneburg 21.10.08
  12. Braunschweig 02.12.08
  13. Rostock 19.01.09
  14. Lübeck 02.04.09
  15. Bonn 15.06.09
  16. Zittau 05.08.09
  17. Landshut 05.10.09
  18. Viersen 22.02.10
  19. Düsseldorf 31.03.10
  20. Augsburg 08.04.10
  21. Darmstadt 04.10.10
  22. Passau 03.02.11
  23. Ulm / Neu-Ulm 09.02.12
  24. Lüchow-Dannenberg 18.10.12
  25. Bremen 29.04.13
  26. Freiberg (Mittelsachsen) 17.10.13

Sollte sich ein aktiver Stammtisch hier nicht wieder finden bzw. das Jahr falsch zu sein, so werde ich das entsprechend korrigieren.

Ich hoffe wir sehen uns zur Nummer 85 am 14.08.2015 19:00 im Resonanz Berlin Stadtteil Schöneberg.

Christopher

by Christopher at July 29, 2015 06:52 PM

Mapping Mexico with INEGI, you and...Cygnus

Mexico released a huge amount of open data not too long ago.

Huge: Mexico's statistical institute INEGI goes open data @INEGI_INFORMA (via @rodowi — Alex Barth (@lxbarth)

A lot of this data is geospatial, so I say yummie! Alex Barth wrote about this data, that comes from the Mexican national statistical agency INEGI, on his diary before, with a nifty map to show how rich this data is:

inegi-mapbox

(My mediocre animated GIF skills really don't do it justice - check out Alex's blog post to see an interactive map.)

So now the question becomes: how do we get some (or all?) of this data into OSM? This is not straightforward - OSM already has rich data in many places in Mexico we would definitely want to keep.

Here at the Telenav OSM team, we have come up with an answer to this question. We call it Cygnus - The Bringer of Balance. Let me explain in a few visuals what Cygnus does.

Consider this area in the Aguascalientes region. There is some OSM data there:

base osm

If we look at the Bing aerial images, we can clearly see that there's an entire village there that is not mapped though!

base osm with bing

But INEGI has most if not all the roads in this village in their now open dataset Conjunto de Datos Vectoriales de Carreteras y Vialidades Urbanas.

After converting the original data attributes mapped to OSM-appropriate tagging, saving the result as an OSM file, and loading it into JOSM, it looks like this:

cygnus-osm-cvu

OK, that is nice, but we still have two separate layers that are unconnected, and even if we merge them, we will still have to manually resolve duplicate ways and connections between the ways from OSM and those from the INEGI data.

This is where Cygnus comes in - a new conflation technology we developed at Telenav specifically to tackle this.

Cygnus, as the Bringer of Balance, takes as its input a base OSM file in PBF format, as well as what we call an 'enhancing' file, also in PBF format. It will then compare and conflate the two inputs and output one JOSM XML file that can be merged with OSM base data straight away. This detail shows the merged layers with all the INEGI 'imported' ways connected to the pre-existing OSM way:

cygnus merged

Even though Cygnus does a pretty amazing job merging OSM data with an 'enhancing' layer, you will still need to check the result before you upload. Take this example here:

cygnus miss

The highway=secondary was the pre-existing OSM data, and the highway=residential; oneway=yes came from INEGI data. It is clear from Bing imagery that the two ways should be connected, yet they are not. Cygnus has a (tweakable) distance threshold it uses when it decides if two ways should be connected or not. In this case, the INEGI way was too far away, so it remained disconnected.

There are a few other things to consider when you work with a Cygnus-produced change file:

  • Cygnus will never degrade existing OSM data
  • When both OSM and the enhancing layer have the same way, Cygnus will always keep the original OSM geometry, but it will optionally import name and other useful tags.

We are currently working on an import plan for INEGI data that will make heavy use of this new technology. More about this very soon! In the mean time, watch our SOTM US talk on the INEGI data and OSM in Mexico.

by mvexel at July 29, 2015 06:05 PM

Restoring measurements from crashed database of Tower Collector

I'm an OpenCellID contributor. I use Tower Collector application on Android phones for recording cell data. I have recorded data for more than 500,000 locations.

One day one of my phones has shut down on a trip after 80 kilometers which I will never do again. I have checked Locus where the tracklog stopped: a few kilometers behind. Started Tower Collector, it showed zero locations! Oh my god. I had another trip previous day which was not uploaded yet.

This device was rooted, started root browser, checked the application's data directory:

    /data/data/info.zamojski.soft.towercollector

In directory "databases" I have seen an empty measurements.db (a few kB), but there was another file: measurements.db.back with 600 kB! Made a backup copy of the whole directory and resumed my route.

At home I have checked the db. It is an sqlite database, but has been corrupted.

    sqlite> pragma integrity_check;
    Error: database disk image is malformed

I have made a dump of the database:

    echo .dump | sqlite3 measurements.db.back > measurements.sql

There were 2059 measurements for 76 cells, valuable data. How to restore them? Load back to Tower Collector. I did not have time for that for weeks, but continued collecting new data. I was frustrated because this phone did not remember the cells collected before the crash, displayed a most of cells as new. I have also missed the data from that two trips. Some weeks later I have tried to load data.

At first I have created a new database using the recovered data but I have failed many times. I replaced measurements.db with the new one, app crashed (SQLiteCantOpenDatabaseException). Checked directory and file permissions via Root Browser, it showed root as owner of every file, but later I have checked the same thing via adb and it showed totally different owners: every app has a userid and owner should be set correctly.

After setting owner for the database, I had another kind of crash: Tower Collector said "upgrading database" and crashed again. Stack trace had an SQL statement: "CREATE TABLE cells" which failed because cells table already existed. Why does it upgrade? I did not see any difference in schema of old and current database.

Then I started a different way. Prepared only data from the dump: kept only INSERT statements for the following tables:

    INSERT INTO "measurements" ...
    INSERT INTO "cells_archive" ...
    INSERT INTO "cells" ...

Uploaded this file via adb:

    adb push inserts.sql /data/data/info.zamojski.soft.towercollector/databases/inserts.sql

Then restored the last working measurements.db and logged in via adb:

    $ adb shell
    ~ # cd /data/data/info.zamojski.soft.towercollector/databases
    # sqlite3 measurements.db

Here I had an almost empty database, only cells_archive table had 1227 rows. I have truncated this to make history clean:

    # DELETE FROM cells_archive;

Then I imported the SQL file just uploaded:

    # .read inserts.sql

Logged out and tested: app displayed 2059 measurements for 76 cells. Quickly uploaded to OpenCellID and checked newly uploaded measurements there.

Then I merged the new cells captured since the failure. Dumped cells_archive from the previously backed up version of measurements.db, replaced table name "cells_archive" to "cells", loaded into the database using the same way and then deleted them. This database has a trigger which archives rows deleted from table "cells" to "cells_archive" ignoring duplicates.

I had seen 6176 cells before the crash. Since then there were 1227 new, merging resulted 6793 cells, 617 new. The rest were duplicates.

The last step was setting total number of locations to the sum measurements before and after crash:

UPDATE stats SET total_locations = 156440+29641 WHERE row_id=1;

Now I have a database which knows all cells and number of locations since I have started collecting data. I am happy.

by Kolesár at July 29, 2015 04:32 PM

Mappa Mercia (UK Midlands)

August 2015 monthly meeting

This will take place in Rugby, with the usual format of early evening mapping followed by food and beer and chat in a local pub. We usually gather in the pub for about 8pm but depending on weather and how hungry we are, some people get there early and some later.

Date: Wednesday August 5th

Venue:  Bacco Lounge

There’s no shortage of stuff to map in Rugby. Let us know where you intend to map either by comment here or on the talk-gbwestmidlands mailing list

by Brian Prangle at July 29, 2015 04:00 PM

Geofabrik

Everything you wanted to know about OSM waterways

A new, world-wide water and waterway debug layer is live at the OSM Inspector web site. We’ve done away with the old VMAP0 river reference, and extended coverage from Europe-only to the whole planet.

02overview_zoom11

There’s tons of new features – for example, OSMI will detect when a river changes its name, or starts out of nowhere (or ends in something that is not another body of water).

03coastline_zoom12

Rivers without names are highlighted, as are directional problems where two parts of a river flow towards, or away from, the same point.

01water_direction_error

The software backing these new layers has been written in C++ (using Jochen Topf’s excellent Osmium library) by Geofabrik intern Nathanael Lang. It is Free Software, and can be run in a standalone fashion to convert an OSM .pbf file into a SQLite database if you’d like to run your own analyses. Fork it, or report issues, on GitHub!

by fred at July 29, 2015 02:15 PM

"OpenStreetMap.org User's Diaries"

July 28, 2015

"OpenStreetMap.org User's Diaries"

Extracting regions from planet.osm ?

I've been using Merkaartor on LInux for some years. Usually I import a track, zoom in, then use "download more" to download OSM for that region. But there's a 50,000 item limit, so if I'm not zoomed in far enough, it fails.

Years ago I had read that I could get planet.osm as a default start set, but I never did. Now, when I look, it's huge. Merkaartor can't handle that, at least not with the amount of RAM I have. It can't even handle all of Canada without swapping.

I see that the US is split into states, but all of Canada is one big file. Is there any easy way, preferably command-line on Linux, that would let me split out a region from the Canada OSM - one province, or a smaller area ?

by adaviel at July 28, 2015 10:17 PM

Improving the OSM map - why don't we? [7]

How do we deal with multiple values for a key?

We all know this situation: you need to add a telephone number to a node and add the line:
phone=00311198765432
Then you find out that there is a second phone number for that node, but you can't add a second phone= tag because OSM doesn't allow that.
The general question is: how do I tag multiple values for one key?
Let's investigate how mappers have solved that problem sofar. The screenshots all are made with OpenPoiMap.


[1.]
This example is the Eiffel tower in Paris for which four architects worked together, but only one gave his name to the final product!
In the source the names are separated with semicolons:
Stephen Sauvestre;Gustave Eiffel;Maurice Koechlin;Émile Nouguier


[2.] One piece of art created by 5 artists (somewhere in Seattle).
Create a new key with a sequential number attached to it for every member of this group. In this specific case I would have started numbering with artist_number_2 because the first one is already in artist_name. Even better, I would have started with artist_name_1 and used it for Andrew Keating and would have omitted artist_name altogether.


[3.] This example is to show how to map multiple sets of related tags to one node. In this case we have man_made=mast which has attached to it 4 (mobile phone) antennas at different heigth and each working with a different technology.
An underscore could have been used as in the previous example, but there seems to be a tendency to tag situations like this with a key:N notation, where N is running over the natural numbers.


[4.] Here we see a combination of both methods. Adding a number (with underscore) at the end of the key to count them or adding the number after the colon. Again, in the case of the fuel I would have started with fuel:diesel:1 for the Biosolar.

Any different opinions on this subject?

by marczoutendijk at July 28, 2015 07:28 PM

All GPS-tracks in an area?

Hi there,

is there somehow a possibility to visualize all GPS-tracks that were gethered for the same area? For example I use JOSM, I define my area of interest and for that specific AoI I want to fetch all available GPS-tracks. Hope that was clear!

Thanks!

by geoeki at July 28, 2015 02:59 PM

Vendiendo casas de lujo por todo el mundo

Me dedico a la venta de casas de lujo y a partir de ahora donde haga una venta lo pondré en el mapa.

by Rodrigao at July 28, 2015 10:38 AM

Removing phone booths in Belgium

All Belgacom/Proximus telephone booths in Belgium are removed from the streets. With changeset #32926155 those that had operator=Belgacom or Proximus have also been removed from OSM.

The remaining phone booths before their removal. Most of them are near Brussels.

I used the following Overpass query:

/* Find all Belgacom/Proximus phone booths in bbox */
(
  node[amenity=telephone][operator~"[Bb]elgacom|[Pp]roximus"]({{bbox}});
  <;
  >;
);

out meta qt;

This fetched all phone booths and also any ways they are connected to. No relations were matched, which was good. Then, in JOSM, I did the following searches to obtain phone booths that are member of a way:

  • type:way (no phone booths were tagged as ways)
  • amenity=telephone child selected

I removed all tags on those 4 nodes.

Then I searched all remaining amenity=telephones and removed the nodes.

Some stats:

  • There were 320 Belgacom/Proximus phone booths left in OSM in Belgium.
  • 316 had operator=Belgacom, 2 belgacom and 2 Belgacom NV
  • 3 phone booths had covered=yes, 1 had covered=no.

Note that at the time of writing there are still 320 other amenity=telephones left in Belgium. (Exactely the same number as I removed!) They haven't been tagged with operator=Belgacom and should be manually surveyed and removed if necessary.

You are invited to add a note like verified that this phone still exists 2015-07-28 to any phones that you have surveyed, to prevent mappers from armchair-mapping them away. (They shouldn't, but just to be sure.) An operator tag is appreciated as well.

Search for amenity=telephones in Belgium

/* Find all phone booths in Belgium */
area["name:en"="Belgium"]->.belgium;
(
  node(area.belgium)[amenity=telephone];
);

out qt;

by M!dgard at July 28, 2015 09:53 AM

Jazda z satelity (geoportal, wspomaganie bing).

Wrzucam z satelity co się da, weryfikując status dróg pod renderowanie dla Yanosika. Teren Trzebinia i Młoszowa. W razie wątpliwości weryfikacja w terenie. Główne cele: 1) Tagi ograniczenia ruchu. 2) Jakość drogi - które drogi nie nadają się do jazdy autem o słabszym zawieszeniu.

by JakubMap at July 28, 2015 07:51 AM

Peter Batty

My mapwheel story

Yesterday I backed a Kickstarter project called Mapwheel, I think it’s a really cool idea. They let you design a custom “toposcope” or map wheel showing the direction and distance of places of interest from the location where you live (or any other location you choose). You can choose various materials (wood or metal) and customize the design in various ways. Working on the design has been a

by noreply@blogger.com (Peter Batty) at July 28, 2015 03:28 AM

July 27, 2015

"OpenStreetMap.org User's Diaries"

Treinando aos 60

Pois é; Eu que passei uma vida, com excepção dos tempos de escuteiros, dos 9 aos 19 anos de idade, a não praticar nenhum desporto, vejo-me agora ao completar os 60 anos de idade, entusiasmado com a prática desportiva. - Não é para bater recordes, ou atingir metas, apenas para manutenção e manter o corpo em forma, porque os músculos já começavam a ficar flácidos. Desportos: Ciclismo, corrida e caminhadas.

by Rogério Paulo at July 27, 2015 09:02 PM

Недельное задание 20: статистика

Темой задания прошлой недели был районный центр Монастырище в Черкасской области. Рисовали по спутниковым снимкам, проект в Tasking Mananger находится здесь.

Как круто изменился населённый пункт за эту неделю, видно на анимации. Из-за большого размера не встраиваю её, а только даю ссылку.

Наполовину выполненное задание сейчас выглядит так: task

Статистика по изменённым объектам - ниже:

statistics Да-да, раньше там не было ни единого здания, а теперь их почти 5 тысяч!

statistics statistics statistics

Традиционно активность участников, то есть количество всех точек, линий и отношений-мультиполигонов, чья последняя версия принадлежит тому или иному пользователю. statistics

Кроме того, было добавлено:

  • 6,6 км2 сельскохозяйственных полей
  • 0,1 км2 кладбищ
  • 45 500 м2 гаражей
  • 5 000 м2 травы
  • 39 км линий электропередач

и много другого.

Приняли участие 6 человек, которые внесли 101 пакет правок с 33 тысячами изменений.

P. S. Кажется, это первый отчёт о недельном задании, вышедший в понедельник :)

На этой неделе: почтовые отделения.

by edward17 at July 27, 2015 07:04 PM

CycleStreets

Beautiful new galleries page unveiled

We are pleased to unveil the new Galleries front page, which brings your beautiful photos and content to the front and centre. Galleries is a really neat feature to group cycling-related media for presentation or campaigning.

There is also a lot more flexibility available while adding a new gallery – you can now navigate away from the Create Gallery form to find more photos to add, and when you return all the fields will be exactly as you left them. You can even close your browser window and come back later, and the gallery creation form will still show your data as you left it.

As well as the graphical front end, our intern Patrick has been busy developing a new Galleries API for developers, which enables API calls to list and show the content of Galleries, and create and update Galleries.

We hope you enjoy browsing and adding to the Galleries.

Screen Shot 2015-07-27 at 17.13.12

by Patrick Johansson at July 27, 2015 05:00 PM

Peter Reed

OSM Retail Survey: Part-11

The occupants of a retail premise will change over time, and as a result we should expect retail data in OSM to continually evolve as well.

Most of the retail data within OSM is less than 5 years old, so the chances are that the bulk of this is still more-or-less current. Around 5% of the data is 5-years old, and 2% is 6 years old. A growing proportion of this older data could now be inaccurate, but across the country it is likely that the proportion of data that is out-of-date will only represent a few percent of the total.

In some places (e.g. Islington, Leeds, and Sheffield), more than 10% of shops were added to the database over five years ago. In parts of Kent more than 30% of the shops were added more than five years ago. So there may be a case for some local reviews of older data, to update anything that has changed since it was last recorded.

In most locations, though, the current priority will still be to add missing data, then later work towards greater accuracy.

In the longer term, that picture is likely to change. In the last 12 months 28,000 retail properties in England have been edited. That's 5% of retail properties that have either been added to OSM, or updated. Some of the changes in OSM data over the last year will have been to correct a spelling, or to adapt tagging, and will not have involved a re-survey. But we can't easily measure how much has been fully updated. So for now, let's be optimistic, and assume that every edit brings that particular shop up to date.

If nothing changed on the ground, then at this rate it will take more than a decade to approach complete coverage of retail. But the situation on the ground does change. In 2014 the average length of a retail lease was less than nine years, and almost half of retail leases were for less than four years. Retail leases used to be for a longer period, and because of peaks in construction activity in 1990 and 2000 an unusually high number of 25-year, and 15-year leases are currently due for renewal.

 Not all retail property is leased, leases will sometimes be renewed without change of occupant, and some might carry forward for generations. So I don't know what proportion of OSM high street data we should expect to change over a year. If only 5% changes then current levels of editing activity are sufficient to maintain existing data, and gradually close the gap of missing data. But if we assume 10% of existing retail premises change over a year then the current rate at which OSM retail data is being edited will not be enough to deliver and maintain complete and accurate data on all retail properties in England.

Nationally, perhaps something like 7,000-14,000 entries on the database should be updated each year. Around where I live, the rate of change looks closer to 10% per year, rather than 5%, so I'm guessing a decent estimate of the national picture will be closer to the higher figure.

As database volumes rise there will be more to maintain. If contributors concentrate on adding missing retail properties, then by the time coverage reaches about 50%, the existing data will be going out of date as fast as new data is being added. If contributors concentrate on maintaining what has already been added, then they will have no time to add the missing 50% of retail properties. Either way, for the foreseeable future, there is going to be a lot of retail data that is either missing from OSM, or incorrect on OSM.

 If we can wait long enough, other factors might help. A decline in the number of retail premises would also accelerate progress towards 100% coverage, and the chart shows the effect of a 2% reduction in the number of retail properties each year. Even if this is factored in, reaching a worthwhile level of retail cover still looks like a slow process. Too slow.


It is not only individual shops that change. Retail business models also evolve, and over the long term we should expect this to affect the choice of tags. Some formats which once were common on the high street no longer exist (ironmongers into hardware, then homeware). A traditional grocery, or a video rental shop is now unusual.

On the other hand, perhaps candle shops are returning to the high street (“shop=candle”), and e-cigarettes are a recent arrival. The data on chains of mobile phone shops may be an example of how this process continues. Currently these chains are tagged with a mix of “mobile_phone” (95%), and “electronics” (5%). Perhaps contributors are adapting their tagging, in recognition that an established speciality has now matured, and the offer is starting to evolve as retailers extend into adjacent markets.

by noreply@blogger.com (gom1) at July 27, 2015 10:00 AM

"OpenStreetMap.org User's Diaries"

aydında ilaçlama

Özellikle kırsal kesimlerde böcek sorunları ile daha fazla karşılaşılabiliyor. Böyle olunca da böcek ilaçlama firmalarına daha fazla ihtiyaç duyuluyor. Siz de eğer Aydın merkezide ya da ilçelerinde oturuyorsanız ve böcek sorununuz varsa bununla savaşması için Aydında ilaçlama bağlantılarını gönül rahatlığı ile kullanabilirsiniz. Aydın ilinde yaptıracağınız ilaçlamalar için Defne ilaçlama hizmetlerini kullanabilir ve diğer ilaçlama şirketlerine oranla daha kaliteli hizmeti alabilirsiniz. Eğer siz de defne ilaçlama hizmetlerini kullanmak istiyorsanız internet adresi üzerinden buna ulaşmanız mümkündür.

Aydın’da bulunan ilaçlama firmaları içerisinde ise Defne ilaçlama hizmetleri kurumsal çalışması, 7/24 hizmet verebilmesi ve acil durumlarda hemen imdadınıza yetişebilmesi ile oldukça önemli konumdadır. Sizin de ilaçlama işleriniz varsa bunu Defne ilaçlama şirketi ile daha rahat ve güvenilir bir şekilde halledebileceğinizi göreceksiniz. Defne ilaçlama şirketleri hakkında detaylı bilgiyi internet adresini kullanarak da edinebilir ve böylece fikir sahibi olabilirsiniz.

by defneilaclama at July 27, 2015 09:29 AM

Криво сведенные карты,

Ситуация такая, прямо посреди города свели криво 2 разных снимка, причем сделаны они в разное время, в итоге объекты не сходятся с картой. Из за этого невозможная дальнейшая работа с городом, так как не провести дороги и не обозначить новые дома Просьба администрации разобраться с этим Альтернативный текст Альтернативный текст Альтернативный текст г.Рыбница

by AdventurerRussia at July 27, 2015 12:01 AM

July 26, 2015

Peter Reed

OSM Retail Survey: Part: 10

Specialised types of shop offer a narrow range of categories, but provide wide choice within their specialist area. Generalist retailers offer a broad range of product categories, with less choice within each category. Large generalists (e.g. supermarkets) are able to offer both numerous categories and broad choice.

We use different terms for a large “supermarket” (with breadth and depth), a small “convenience” store (with some breadth and less depth), and a "butcher" or “newsagent” which we expect to be more more specialised. We expect a newsagent to offer a wider choice of newspapers and magazines than a convenience store, but we would still expect a convenience store to offer newspapers. We expect a convenience store to offer much more than newspapers, and we would be surprised if a newsagent offered nothing but newspapers. We expect a butcher to offer a wider choice of meat than a convenience store. Ours does excellent sandwiches, ready meals, pies, vegetables, and various other items as well. Although the principles are fairly clear, the precise boundaries between retail categories are always going to be difficult to pin down.

As a result, it doesn't matter how clear the definitions are for different terms covering different levels of specialisation. We should still expect some inconsistency in the way that different tags are used. Some retailers have a business model that is closer to the boundary than others, so it is inevitable that there will be a grey area where it is difficult to maintain a consistent boundary. The proper question isn't whether tagging ought to be consistent. It's whether there should be more consistency than we find.

To my mind there are several areas where the data does not look consistent enough. This is particularly true in the case of large stores which sell a broad range of goods (the big generalists).

For example, a data user who searches for “supermarket” and relies on the wiki for the definition, will expect to find “a large store for groceries and other goods” “a full service grocery store that often sells a variety of non-food products as well”. They will assume (perhaps because the wiki tells them) that “stores that do not provide full service grocery departments are generally not considered supermarkets”.

In practice they will find results that include a high proportion of outlets that fit this description, including most branches of the major chains that they will expect to find: ALDI, ASDA, Booths, Co-op, Iceland, Lidl, Morrison's, Sainbury's, Tesco, Waitrose, etc. However, they will also pick up a lot of convenience stores, and some stores tagged “supermarket” where few shoppers would expect to find groceries: Argos, Homebase, Matalan, Mothercare, Pets at Home, etc.

I estimate that around 10% of the data that they retrieve will not be what they expect.

Commercial search engines face a similar problem, because  smaller convenience stores often call themselves a supermarket, and this is inevitably picked up in their keyword searches. But OSM has a more structured data model. We should expect to perform better.

The situation with department stores is even more difficult for data users. The major chains are well covered, but they only represent about half of all retail outlets tagged as a department store. Data users who rely on the Wiki definition will be expecting “a large store with multiple clothing and other general merchandise departments”. They probably won't expect to pick up Poundstretcher, Argos, Matalan, Pets at Home, Staples, Superdrug, TK Maxx, etc. - but they will.

Wilkinson's (Wilko) is a difficult boundary case - with a particularly wide range of different key values for different branches.  My own view is that something like “homeware” would be the best description of their format, but only about 2% of contributors agree with me. And in practice, what should matter to data users is not what I think (even when I am right). What has to matter to data users is the consensus that develops across the majority of contributors. And in this particular case there is little consensus. It is difficult for anyone to know whether to consider Wilkinson's a department store or not. What is even more unsatisfactory for data users is that 25% of Wilkinson's stores are considered to be a department store, and even though that's the most popular option, 75% are tagged differently.

Neither of these examples is the result of a problem with the definition of the tags for a supermarket or a department store. The problem is that the same tags are being quite widely used for branches of chains where most contributors prefer an alternative. Good data on department stores and supermarkets is polluted by inconsistent data on other retail formats.

Looking further, the confusion lies partly in representing scale consistently, and partly in representing the degree of specialisation consistently.

Most specialists offer some categories of product that fall outside their main area of activity. Some position themselves as specialists in more than one area. As a result contributors can find it difficult to draw a consistent distinction between a specialist and a generalist outlet. If they are uncertain about the right specialist term to use, they tend to look for something more generic, and fall back on terms intended for generalists. This isn't entirely unreasonable behaviour. For a long time, the guidance, when in doubt, is to pick a popular tag that best fits the situation (rather than inventing a new one). Contributors don't necessarily have an understanding of all the tags  in use, and the result is that popular tags that were originally intended to apply to large outlets which offer a broad range are quite commonly used for smaller outlets offering a broad range, and for unusual specialists that are difficult for contributors to classify.

Looking at this another way, we have a choice of terms for shops which offer a broad range. Contributors who find it difficult to pick an appropriate tag veer towards picking one from a higher row in this table - they are the ones that are most widely used.


Primarily food Primarily non-food Hardware / building materials
Large generalists
“supermarket”
“department_store”
“doityourself” (or sometimes “trade”)
Other generalists
“convenience”
“general” (rare) or “variety” (for pound shops)
“hardware”
Specialists
“bakery”, “butcher”, “cheese”, etc.
“clothes”, “beauty”, “houseware”, etc.
“garden_centre”, “paint”, etc.


One result of tending towards tags for larger generalists is that supermarkets are over-represented in OSM. Industry figures show 6,410 stores in this category in the UK, whereas I found 7,045 (110%) in OSM. Convenience stores, on the other hand are under-recorded. I found 9,717 out of 48,303 identified by the industry (i.e. just 20%).

It is obvious from the data that contributors find it difficult to to make a distinction between a supermarket and convenience store. In England and Wales the law on opening hours varies for different sizes of store, with restricted hours on Sunday for those of more than 208 sq. metres (3,000 sq. ft.) So a supermarket of less than 280 square metres (3,000 sq. ft.) would be normally be considered a convenience store, and a convenience store of more than 280 square metres would be considered a supermarket. However, in OSM, at least 9% of outlets marked as a supermarket in OSM (and recorded as an area rather than a node) have a floorspace of less than 280 sq metres. Around one in three of the stores operated by one of the major convenience store chains is tagged as a supermarket. Convenience stores don't have to offer extended opening hours, we can't really expect contributors to measure the footprint, and the situation is further confused because some convenience stores describe themselves as a supermarket. The upshot is that almost a thousand convenience stores in OSM are marked as a supermarket. And meanwhile, because convenience stores are generally under-recorded, around 30% of the general grocery sector has yet to be added to OSM.

Changing tack, department stores sell a range of general merchandise, typically including clothing, household appliances, toys and games, personal-care products and garden equipment. Some also sell food, but non-specialised food stores are properly classified as supermarkets. With very few exceptions the major UK department store chains, such as John Lewis, Debenhams, and House of Fraser are tagged correctly as a department store. However, not all retail premises tagged “department_store” comfortably fit the description.

Examples include branches of Argos (normally tagged “catalogue”), TK Maxx and Matalan (normally tagged “clothes”), Poundland (normally tagged “variety_store”, sometimes “convenience” or “supermarket”), Mothercare (normally “baby_goods”, sometimes “clothes”), Wilkinson's (“department_store” for 25% of branches, plus a wide range of different alternatives).

The Wiki describes Do-It-Yourself-stores as being similar to hardware stores, except generally larger, stocking a wider range of products, and targeting customers who are non-professionals working on home improvements, redecorating, gardening, etc. Pure DIY stores are well covered in the database, and consistently tagged. In the case of Homebase, B&Q and Wickes, for example, more than two-thirds of branches are in the database,  and well over 90% are tagged as “doityourself”.

The same is not true of builders' merchants (which according to the documentation are properly tagged as “trade”). Fewer than 10% of Jewsons, and Travis Perkins branches are in the database, and they are tagged with a mix of “doityourself”, “hardware”, and “trade”, with “doityourself” as the most common.

There seem to be two issues here. One is that many trade outlets also serve non-professionals, so their business model overlaps with the scope of “doityourself” (this is accepted in the documentation on “shop=trade”, but contributors are either uncomfortable with it, or simply don't recognise these as trade outlets). The other issue is that there are different degrees of specialisation in the trade side of the market. Specialists in supplying the trade with building materials, timber, plumbing, bathroom furniture, electrical goods, tools, etc. all seem to be under-recorded, and inconsistently tagged. Again, where there is no clear consensus, contributors have fallen back on common tags such as “doityourself” and “hardware”, that were originally intended for generalists supplying the non-professional, and so are more widely used.

Branches of Wilkinson's and Robert Dyas don't fit comfortably into any of the most common categories, so they tend to suffer from highly inconsistent tagging (department_store, doityourself or hardware). We could blame contributors, but surely some of the tagging inconsistency shows that there may be a need for:

  • more specific options to cover particular retail format that do not comfortably fit the current categories
  • more generic options, so that contributors have an alternative to popular tags intended for large generalists 

by noreply@blogger.com (gom1) at July 26, 2015 06:56 PM

"OpenStreetMap.org User's Diaries"

Starting up again

Two things got me back into mapping. One I am thinking of retiring and this will give me a lot more time to do it. And two we recently acquired a puppy dog (A Coton de Tulear) that needs taking for walks so I have been exploring local pathways to use for said walks. In any event I have also been a fairly regular user of an iphone/ipad app called Galileo for quite some time now and I love maps.

I realised that it must be well over a year since I looked at doing any mapping. JOSM is not on my current computer (replaced 18 months ago). I do however have loads of photos, routes and speech commentaries that I have meant to use in doing map updates. Always happens when I am on holiday and I never got time to apply them on my return. Places like Ireland, San Francisco, Athens, Sidney, Queensland and Singapore are obviously all the poorer for not having my map updates applied onto OSM!

So first step was to downloaded JOSM which I did yesterday 25/07/15. Some quick reading on how to use - somehow seems easier this time round and then off on a long walk to gather some data. This is an enjoyable and healthy way to do mapping. I like walking and exploring with a purpose and actually find it very relaxing. However the battery on my iphone ran out half way through so I will need to revisit some of the walk. Not too unhappy about that either! I specifically wanted to correct one of the local footpaths which is incorrectly shown on OSM and unfortunately my battery had run out before I got to the relevant bit.

As a result of the above walk however I did work out how to upload traces in .GPX format to OSM. Been meaning to do that for ages and I have a lot of traces to upload. Additionally when using the photos I took on this walk for a location fix I discovered that it did not match with what JOSM was saying. On reading up it seems that I need a plug in to correct this . It appears to work out the correct offset for you when you position objects on your map to line up with the imagery. I haven't tried it yet.

I then started doing some updates - simple things like house numbering - automatic allocation can't handle infills such as the three houses behind ours which all have the same number but designated A B and C to give them unique addresses. Occurred to me that just getting my local town (pop. 6200) up to scratch is a considerable amount of work. I also did some research on data sources for this exercise such as .gml files from the Land registry (another plug in I believe) and a uk post code map. I failed completely after some hours researching in getting what is said to be freely available post code amps as a layer to use on my maps.

So as a result of all this my list of things to follow up got bigger and bigger - am I looking at taking on an unpaid full time job. :) List is :

• Get offset on Bing maps sorted out.
• Get plug in to handle .gml files available from land registry (INSPIRE index polygon data.)
• Work out how to get more layers such as electoral boundaries , district council boundaries etc.
• Work out how use tags better (a lot of reading) - such as how to represent a house barge (I live on a river)
• Look at a tags for entering statistical information freely available from the Office of national statistics such census date.
• Start looking at writing code to help with some of the above - I have an IT background but promotion has meant not doing any coding for quite some time and I miss it!
• Find out about adding photos to the map
• Find someone (preferably local) who I can chat / work with on this.

No doubt this list will keep getting bigger!

by Mapping49er at July 26, 2015 04:32 PM

Post-Boxes

Added a few post-boxes recently. Roberts postbox site is excellent for finding postboxes that are not yet mapped. Its unusual to know what you don't know. Learning a bit more about the appropriate tags too.

Then hopefully I'll get back to walking around and adding a few more footpaths. I wonder what other people think the OSM lacks that makes a OS map more useful - and if its something we could add easily. I think the OS will win for walking in the wilds guided only be a map and compass; but OSM should win for towns; and should be as good if GPS guided. Thoughts?

by Stuart H 42 at July 26, 2015 02:04 PM

My new endeavour

I would be mapping my surrounding area at Ebute Meta LSDPC.

Starting for the religious building such as mosque, churches and mechanic workshops.

by akinEmma at July 26, 2015 12:09 PM

Me interesa

Me interesa es una revista digital en la que en cada número iré publicando los sitios que voy visitando sus gentes, sus costumbres...

by Te interesa at July 26, 2015 09:23 AM

July 25, 2015

"OpenStreetMap.org User's Diaries"

Mapping solidarity groups in Thermaikon

Hi,

I started making a mapped directory od self-organized solidarity initiatives in Thermaikon area. Any help will be appreciated.

by Petros Polonos at July 25, 2015 06:08 PM

Habrahabr OpenStreetMap (RU)

[Перевод] В погоне за самим собой, или отличный способ начать свой день



Перевод поста Mariusz Jankowski "A Rat Race, or a Great Way to Start the Day".
Код, приведенный в статье, можно скачать здесь.
Выражаю огромную благодарность Кириллу Гузенко KirillGuzenko за помощь в переводе.

Не так давно, когда бушевала зима, расчищая подъезд к дому от завалов снега, я решил вспомнить о хорошей погоде, рассмотрев с использованием Wolfram Language свой велосипедный маршрут на работу.

В прошлом году я решил заняться такой весьма распространённой деятельностью, как запись данных своей активности. Я отметил, что за последние несколько лет мои поездки становились все быстрее и давались мне проще по мере того, как сезон приближался к концу, так что мне стало интересно удостовериться в наличии подобных улучшений своей физической формы. Используя лишь смартфон и соответствующее приложение, я записал 27 поездок между домом и работой, а затем использовал Wolfram Language для чтения, анализа и визуализации результатов.

Вот изображение с Google Earth, показывающее мой утренний велосипедный маршрут, имеющий расстояние чуть меньше 18 км, пролегающий с востока на запад.


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by OsipovRoman at July 25, 2015 01:10 PM

Peter Reed

OSM Retail Survey: Part-9

False synonyms

True synonyms add to the confusion, provoke debate, and may discourage some data users, but in practice I suspect “false synonyms” are a bigger problem. By this, I mean tags that are used interchangeably by contributors, even when they are not true synonyms according to the guidelines. Again, we can use major chains to do some cross-checking of whether tags with similar meanings are applied consistently.

  • Almost every major chain of pharmacies has a mix of outlets tagged as “shop=pharmacy” and “shop=chemist”. 
  • Similarly “alcohol”, “wine”, “beverages” seem to be used interchangeably for chains of off-licences and wine merchants, with “alcohol” as the most common of these. The less common “off-licence” is not widely used on retail outlets
  • For chains such as Ladbrookes and William Hill, “bookmaker'” is the most common, but “betting” and “gambling” are also quite common 
  • There is a lot of overlap between outlets that are described by the relatively common “doityourself”, and the less common “hardware”, “building_supplies”, “trade”
  • For “mobile_phone” the less common alternatives are “phone” and “electronics”. Tagging "electronics" could be a symptom of an evolving retail format. Phone looks like a false synonym.

The documentation in the wiki makes it reasonably clear that the above are not true synonyms, but contributors have treated them as synonyms in the sense that similar branches of the same chain use a mix of different values. As a result, data users are unable to tell where there is a true difference, and where there is imprecise tagging. In effect data users are being pushed to treat these as synonyms, even though they are documented as having different meanings.

These are examples of retail formats that contributors have difficulty with. Data users, those who maintain the documentation, and those who advocate changes to tagging need to be sensitive to where these occur. We'll look in more detail at some common examples shortly.

Multi-specialists

The above are all examples of specialist retailers. Multiple specialities are another area that give contributors a problem. Halfords is one of the most easily identified examples. How best to tag a store that offers both bicycles and car parts? The solutions that contributors have come up with include around 30 different variants:

  • Choosing just one of the options: “bicycle”, “automotive”, “car_accessories”, “auto_accessories”, “car_parts” and ignoring any other area of specialisation
  • Contributing a list of options separated by semi-colons: “bicycle;car_parts”, “car;bicycle” “bicycle; car_accessories”, “motor;bicycle”
  • Using a more generic category: “doityourself”, “hardware”

The usual way to assign multiple values to a key is a list separated by semi-colons. In practice this is not widely used for shops (less than one in a thousand examples), but there are examples which give an idea of other multiple specialities that are giving contributors problems:

  • “hairdresser;beauty”
  • “kitchen;bathroom” 
  • “greengrocer;florist”
  • “dry_cleaning;laundry”
  • “art;frame”
  • “car;bicycle”
  • “shoe_repair;key_cutting”
  • “bicycle;car_parts”
  • “tattoo;piercing”

Noticeably, these are all pairs. Happily, there don't seem to be any long lists of shop types. Contributors recognise that the intention is to record mixed types of speciality shop, not to list all the categories of good for sale.

The limited number of examples mean that these won't give data users a great problem. If they chose to ignore them they won't lose much data. If they prefer to break out the list then it won't give the much difficulty. More importantly, to my mind, contributors are sending signals here about retail formats that they find it difficult to categorise. This could be valuable information for those who maintain the documentation, and those who advocate changes to tagging.

by noreply@blogger.com (gom1) at July 25, 2015 09:06 AM

"OpenStreetMap.org User's Diaries"

July 24, 2015

"OpenStreetMap.org User's Diaries"

Wieprz z nazwami ulic

Wprowadziłem nazwy ulic wg danych przygotowanych przez UG Wieprz.

Brakuje jedynie ulicy Leśnej, gdyż jej przebieg budzi spore wątpliwości - czekam na uściślenie wariantu.

by wAndrychowiepl at July 24, 2015 09:43 PM

Improving the OSM map - why don&#39;t we? [6]

Redundant or weird tagging?

Sometimes we have to tag a shop without knowing what kind of shop it is. Then we use: shop=yes.
If you know the shop is a clothes shop, then shop=clothes would suffice. Using amenity=shop as in the example below is not encouraged: My advice is to clean-up such tags whenever you encounter them.


The next examples left me puzzled:
Is there really a jewelry in that bar?
What does amenity=printer mean? Can you buy a taxi in that shop? Does it come with the driver?

I used openpoimap for all the examples with this code:
amenity][shop
which translates in: "find all nodes\ways\relations\ that have both an amenity key and a shop key, irrespective of value of that key".
Try it out in your own area!

by marczoutendijk at July 24, 2015 07:15 PM

Падарыў Мінску новы дом

Сёння я падарыў Мінску дом па праспекце Незалежнасці (адразу 1963 года пабудовы). Я проста заўважыў, што ў базе няма дома 57 па праспекце, на яго месцы Дарашэвіча 2. Паехаў на месца і удакладніў дадзеныя дома. Гэта адназначна менавіта дом 57 па Незалежнасці, а не 2 па Дарашэвіча.

Так што сустракайце, мой падарунак Мінску: спасылка!

by Andrej Zakharevich at July 24, 2015 06:54 PM

Historyczne grobowce

Historyczne grobowce słynnych i sławnych osób a także grobowce związane z II wojną światową i nie tylko

by Adam_Piszczek at July 24, 2015 06:25 PM

Korean nodes

As I removed in changeset 32842753, there are way too much Korean(English) which can be changed to use i18n correcrly.

I'm gonna do where I can but wtf it's so YOOOOOO.

by revi^ at July 24, 2015 03:39 PM

Turismo rural ecológico en castellon

Estaba pensando en como definiría el proyecto de la aldea ecorural que empezó, hace ya casi 4 años y se me han ocurrido estas palabras…

A ver que os parecen?

Turismo rural ecológico en Castellon con casas románticas y rurales para las parejas y mascotas, disfrutando del Hidromasaje en el dormitorio para la máxima relajación.

Bueno pues ya esta dicho lo que pensaba sobre la Definición de la Aldea Ecorural en Castellon

Saludos Aldea Ecorural

by ecoturismo at July 24, 2015 10:04 AM

Peter Reed

OSM Retail Survey: Part-8

Consistency

One way to assess tagging consistency is to examine differences in tagging across similar outlets of the larger retail chains. Contributors don't always agree on how to tag similar shops, they don't always follow the guidelines, the guidelines aren't static, and they aren't always consistent.

Regardless of what the documentation might say, and the merits of any minority view, in practice data users will have to follow the consensus that has been adopted by the majority of contributors.

In principle crowd-sourcing will end up tagging most of a retail chain with the “correct” tag. By examining variations in tagging across a retail chain we can get an idea of the proportion of outlets that have been tagged according to the consensus, and how many fall outside the consensus. Data users will be able to accommodate variations, to some extent, but they won't be able to accommodate all of them.

  • In the case of banks, for example, there is very little variation in tagging: 100% of Barclays,  HSBC, Natwest, and Lloyds / TSB branches are tagged “amenity=bank”. 
  • In other sectors, Subway doesn't fall far behind the consistency of banks at 95% tagged “amenity=fast_food”.
  • At the other extreme there are more challenging examples. Wilkinson's seems to be one of the more difficult chains for contributors to classify: “department_store” is the most common choice, but only accounts for 25% of examples. “hardware”, “variety_store”, “doityourself”, “supermarket”, “general”, “convenience”, “household” and “houseware” are also popular. Robert Dyas, with a similar retail format, faces similar difficulties. 
  • In the case of Halfords 42% of branches are tagged “shop=bicycle” (which doesn't really capture their business format) and the rest use a wide variety of tags. 
  • Argos has 40% tagged “shop=catalogue” and the rest a variety. 

In general the most specialised chains tend to be tagged more consistently, and the most consistent tagging of all is found within chains of smaller outlets, with a well-established, widely understood,  unambiguous specialisation (“estate_agent”, “funeral_directors”, “hairdresser”, “toys”, “optician”, “laundry”, and “travel_agency”).

Less consistent tagging is found in chains where the specialisation is more ambiguous (“gift”, “catalogue”, “accessories”).

There are many shops that are not part of a chain, and we can't easily assess how consistently they are tagged. But if we assume that the pattern of tagging inconsistencies across retail chains is repeated across the whole of the retail market, then we can get some idea of how consistent tagging might be overall. In practice there tends to be more consistency across larger chains, and less across smaller chains, so results vary according to how widely we cast the net. As a broad indication we should probably anticipate that something in the region of 20% of retail outlets have been tagged with a value that differs from the one that the majority of contributors would choose (and hence the value that data users would have to expect).

Some variation is inevitable: retail business models evolve over time, and vary from place to place; different contributors place different emphasis on different characteristics; tagging guidelines change as they are refined. However, if 20% of existing data is tagged with a value different to the one that most contributors would chose, then across England there are almost 30,000 retail premises in the database that data users will find it hard to recognise, and which should perhaps be brought more into line. After the 385,000 missing retail premises, it seems to me that this must rank as the second largest data quality issue.

Synonyms

Many community discussions of tagging inconsistencies revolve around synonyms. The controversy often lies in deciding when different contributors are using different terms to describe exactly the same thing, and when they are using different terms to describe subtle differences.

Any list of synonyms invites debate, but examples that are unlikely to be controversial, and where the difference is more than a spelling mistake would probably include travel_agent / travel_agency, newspaper / newsagent, jewellery / jewelry, and deli / delicatessen. I suspect that most would also count baby / baby_goods, seafood / fish / fishmonger, bathroom_furnishing / bathroom, beauty_salon / beauty, etc.  as true synonyms.

If this is anywhere near a complete list, then true synonyms do not look like a significant problem across all retail data. Including spelling mistakes they account for fewer than 1% of all shops in the database. However, they represent a higher proportion of data within some categories of shop, and they can account for a significant proportion of the more unusual categories.

The retail categories where synonyms are likely to present the greatest problem are where they account for a significant proportion of an important category. Everyone will have different ideas of what makes a proportion significant, and a category important, so it is worth considering a couple of real examples.

I reckon there are about 2,500 delicatessens in the UK, and I can find just under 500 in the database. Of those, 456 are tagged “shop=deli”, and 39 are tagged “shop=delicatessen”. Any data user who searches for “shop=deli” will miss 39 delicatessens in the database with the “wrong” tag value, and will miss about 2,000 delicatessens that aren't in the database at all (or at least not with a recognisable tag). Of the two, the bigger problem is surely the 2,000 missing delicatessens.

On the other hand, some synonyms have a more balanced mix of values. Out of 950 independent fishmongers in the UK, 80% haven't been recorded at all.  Of the 20% of fishmongers that are in the database, 47% are tagged “shop=seafood”, 44% are tagged “shop=fishmonger”, and 9% are tagged “shop=fish”. This is more problematic, because anyone who looks for just one of the values is going to miss about half of the available data. Nevertheless, I suspect that anyone who is thinking of trawling the data for a fishmonger is still going to be scuppered by the 80% that are missing from the database altogether, not the inconvenience of testing for two or three different tag values.

I reckon that even within the more problematic categories the issues with synonyms aren't difficult to manage. Where data volumes are small, it is not difficult to fix the data. Where data volumes are large, and one value is dominant, then data users who don't look for a synonym will only lose a small proportion of the data. Where volumes are large and synonyms equally matched then keen data users will go to the trouble of testing for several different values.

Problems with spelling and synonyms are not difficult to fix, but they are relatively small in number, so not the highest priority. The bigger challenge is to achieve greater consistency in the choice of tag for similar shops. The data can provide some pointers on how to do that, but they will wait for the next post.

by noreply@blogger.com (gom1) at July 24, 2015 09:19 AM

July 23, 2015

"OpenStreetMap.org User's Diaries"

WordPress plugin &quot;Tabulate&quot; now has OSM export

I don't suppose it's of use to many people, but in the interests of adding to the list (somewhere) of software-that-supports-OSM, here's one more: http://samwilson.id.au/2015/07/23/tabulate/

Or on the WordPress plugin directory: https://wordpress.org/plugins/tabulate/

by Sam Wilson at July 23, 2015 11:45 PM