In this post we are excited to share some of our recent research into organized editing on OSM. This relatively new line of research has been motivated by two observations. The OSM community has seen a dramatic rise in organized editing over the last several years. This new presence has continued the historic debate on the role organized editing should play in OSM. We became interested to study how the editing habits of these new actors differed from the community as a whole, but were surprised by the lack of accessible data. We decided to use the public tools to create computational methods of understanding different editing behaviours to classify editors as being part of the organized group or volunteer.

Following is an attempt to share some of our initial results. We’ll begin by outlining a new method we created on classifying users based on their profiles. Then we’ll share some features we extracted that may affect whether a user is organized or volunteer. Finally, we’ll show initial prediction results.

Extracting a list of users

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The OSMF board has finally published the draft for the OSMF attribution guideline they have been working on - together with the firm intention to approve that in the next public board meeting without changes. This is unfortunately reminiscent of previous cases of the OSMF board developing policy and documents internally among themselves without public scrutiny and presenting them to the public as a done deal, ultimately with often very sub-optimal results.

So these comments are less in the hope that the board will revise their work style and discuss policy openly and publicly with the community from the start because they realize that this yields objectively better results and more to help the community understand the (in large parts somewhat confusing and difficult to understand) text and its provenance and implications.

The background of the attribution guidelines

I will not present a full history here - largely because it would make this text too long to read in a reasonable amount of time but also because much of that history has not been open to direct public observation and can only be reconstructed from minutes of LWG and board meetings which inevitably only show a selective record of history.

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What a pleasure to find out that Esri permits mappers to access their collection of past imageries known as World Imagery Wayback.

Map it like the early noughties!

Why older imageries can be useful, some might argue.

It was already stated in the diary post that “The older imagery may include the features you are editing with better clarity or relative accuracy.”

Indeed. So, this was proven during one of my edits made today.

Other mappers might consider me as a waterway mapper junkie. Yeah, I’m so very into it, when the right mood comes.

Straight into today’s anecdote: a KartaView picture suggests there is a nearby waterway.

The freshest Maxar imagery indicated so too. With a caveat.

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The Riverside Estate was missing all buildings and addresses. Roads haven’t been refined for over five years.

I’ve spent 3 days of my holiday to improve the estate’s mapping.

Done

• Update St James’ School buildings, roads, and tags
• Review Estate Roads
• Build Telephone Exchange area
• Build Cranmere Court care home building, car park, paths, green space, and fences
• Add house buildings, tagging apartments and bungalows when necessary
• Add garage areas and alleys
• Add Substations
• Review green spaces
• Add turn-around spaces at dead ends when necessary
• Add full addresses for all buildings
• Review surrounding land use tags
• Review and add footpaths

Todo

• Add house boundary fences
• Review surrounding common green spaces
• Review residential area boundary
• Review School land boundary

Discovering OpenStreetMap (OSM)

Several years ago, Esri made its World Imagery map accessible through OSM editors such as iD and JOSM. The goal of this was to give OSM mappers some additional options for high-res imagery when creating and editing features in OSM.

The Esri World Imagery map is compiled from multiple sources, including Maxar satellite imagery as well as aerial imagery from various GIS organizations (e.g. cities, counties, states/provinces, etc.). In general, the World Imagery map is curated to feature the most recent imagery we have available for a given area, though we do retain some older imagery for a while if it is better in other respects (e.g. currency, clarity).

The World Imagery map is updated every few weeks with the latest imagery that we’ve assembled and processed. During a release, we replace the current set of image tiles with a new set of image tile for several areas where we have updates. For example, in our most recent update, we updated the imagery in Western Europe with the latest Maxar imagery.

For most users and use cases, these updates are a good thing because they provide access to more current imagery. In some cases, however, it may be less desirable. The latest imagery may be more current, but it may also be more cloudy for a specific location, or the imagery might have shifted a few meters relative to existing features in the OSM data. In these cases, the previous or even older imagery may be preferred for editing. The older imagery may include the features you are editing with better clarity or relative accuracy. This is why many of us compare imagery background layers when editing OSM data for a given area.

World Imagery Wayback

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Introduction

Data they say is the “new oil”. This phrase, although seemingly far fetched is nothing short of the truth in today’s ecosystem.

I’ve written an overview of the patterns of major roads across Australia, based on OpenStreetMap data.

https://littlemaps692810600.wordpress.com/2021/06/21/australian-roads-in-openstreetmap/

There’s lots of colorful maps, charts and tables. It’s a deep dive that breaks down the total length of motorways, trunk, primary, secondary and tertiary roads in all Australian states, and the proportion of each that is paved and unpaved. (With a couple of local exceptions, virtually all of these roads now have surface tags.) It focuses on the patterns of the roads themselves, not tagging patterns.

So, if you’ve ever wondered: (1) what proportion of all major roads is unsealed and unsealed; (2) how the Tasmanian road network varies from that in other states; (3) where to find the longest, unpaved, trunk road in Australia; and (4) many other nerdy road facts, please take a read.

My March 2021 in OSM

sorry for the delay

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I used a couple of services that sent me emails when a change was made in my area

They disappeared

Is there any alternative ?

Larger End Goal: To have a map embedded within an app or a website where people are able to easily view where the nearest postbox or post office is, and also be able to add new post offices and postboxes to the map if they aren’t yet recorded in the map.

Step 1: Display both post offices and post boxes on a uMap, using the Overpass API. These items should be on two separate layers, as the icons need to be styled differently.

Current issue: There are lots of post offices in the world, and even more postboxes. This causes the uMap to time out with a 429 error of “problem in the response.”

The current map: http://umap.openstreetmap.fr/en/map/postboxes_528331#13/53.5705/10.0067

Current suggestions:

• Use an alternative public instance: osm.wiki/Overpass_API#Public_Overpass_API_instances
• Run an Overpass API instance locally (really seems to be overkill for what’s needed for this project
• Download the information of all post_box’s and post_office’s points and store that locally. Not sure what would be the best way to do this though. It looks like you can download all the OS data following the instructions here, but 1.5TB is a bit much to handle.
• Maybe use the Overpass API itself to download the data (perhaps in chunks), although it seems we either already reached some limit or it is perhaps already overloaded and we don’t want to make it worse unless we’re sure it’s ok.

Current Questions:

• What is the best way to get all (amenity=post_box OR amenity=post_office) points from OSM without overloading anyone or downloading the whole OSM dataset?
• If we do go the route of downloading a copy of the data and querying this locally, what is the best way to get a snapshot of the relevant points from OSM without us causing unwelcome load to someone’s server?

What we hope for:

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Thanet is a very old place and sometimes it hard to make out individual buildings in built-up areas.

To help solve this, moving forward I will be tagging businesses/addresses on the main entrance node unless it’s easy to say for certain what physical space that address takes up in that structure (houses are a lot easier locally).

I’m starting with Holly Lane area of Northdown Road

When preparing geodata for smaller scales, you need generalisation algorithms. Douglas-Peucker and Visvalingam-Whyatt algorithms are usually used for lines (polygons). Unfortunately those are mathematical/geometrical algorithms which do not take into account cartographic requirements.

Wang–Müller algorithm tries to do exactly that - generalise lines (of natural objects) according to cartographic requirements, such as saving or even exaggerating characteristic features.

Original Wang–Müller article has a quite concise description of the algorithm and as there is no open source implementation of this algorithm (to my knowledge) it is impossible to use this algorithm in wider scale and there are a number of unanswered questions on how some particular aspects should work.

A student of Vilnius University, Motiejus Jakštys, has done a perfect job - he has not only implemented the main part of Wang–Müller algorithm using open technologies, but also described the algorithm in his paper, which you can find here:

https://github.com/motiejus/wm/blob/main/mj-msc-full.pdf

Besides describing the algorithm itself (in more detail that original paper), this paper also includes recommendations on values of parameters to use on different scales, a lot of pictures as well as propositions for future work on finishing and refining the open implementation.

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Last week, the U.S. federal government finally agreed to retire a number of racially insensitive names of landforms and bodies of water in Texas, 30 years after the state legislature petitioned the federal government to rename them after accomplished Black Texans. (Most of them had originally been named with an even worse racial slur.) OpenStreetMap has been updated to reflect the new names that are in GNIS, the federal government’s official gazetteer. The corresponding Wikidata items have also been added or updated, to prevent data consumers from overriding OSM names with the offensive old names, but also to link to more information in Wikidata and Wikipedia about the person whom the feature now commemorates.

Most of the features had to be mapped for the first time, because GNIS doesn’t have enough detail about linear features like valleys and creeks to import them, and NHD had only been imported in a few parts of the state. Except for Emancipation Pond, which is in a residential development under construction, a traveler is unlikely to ever stumble upon these remote features in the vast Texas landscape. Even so, much more accessible locations get renamed practically every day. Hopefully these OSM and Wikidata entries will serve as a handy model for mappers to follow as we find more efficient ways to track and respond to name changes.

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I use to explore and map the area on the border between North and South Tyrol, and I have noticed that part of the border between Austria and Italy is wrong in openstreetmap. I noticed the error, because some abandoned Itallian military infrastracture had moved into Austrian territory.

Here is an example (before fixing): I know that the house with the geodetical tower is in Italy. The boundary stones are located some meters northern. The border between North and Tyrol runs on the watershed, so the border is exactly on the Sandjöchl Pass.

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There is going to be an OSM-Carto map reading workshop run by me at this year’s virtual State of the Map conference on July 11 at 12:15 UTC. This is going to be a new, experimental format i would like to try where i will explain the cartography and the reasoning behind styling decisions of the standard style shown on openstreetmap.org based on case examples you can submit beforehand.

For that purpose i have created a wiki page as well as an Etherpad where you can submit such examples. In the simplest form such an example is just a link with the URL pointing to a certain place on the map you would like to see discussed and explained. Ideally you should add a few words on what specifically you would like to see discussed because often there will be other things visible in the map as well.

I plan to make this an interactive workshop allowing those who submit an example to participate in the discussion if they would like to (though this is strictly optional of course). But to allow me to prepare for the case examples it would still be desirable to submit those in advance.

You can submit your case examples on any topic and any place on the map but of course i reserve the right to pick those examples from the submissions i consider most interesting for a wider audience (and you can also indicate if you are interested in examples submitted by others on the wiki/pad). If there are more examples submitted than can be covered in the workshop i will try to discuss them on the wiki after the conference.

Feel also free to add any comments and thoughts on the workshop idea and format here or on the wiki talk page.

I have a few passions in life. Two of them overlap in an interesting fashion…

A number of years ago, I decided to look at how engineering (and specifically full-lifecycle analysis) could support improvements in road safety. While many road safety issues are primarily behavioural (speeding, awareness, road rage, etc), I strongly believe that infrastructure should guide road users to making better decisions over time through feedback, reinforcement of good behaviours, etc. Anyway… I decided to buy a dashcam and figure out if there was a way to collect data in a way that would provide strong arguments for change at both a local and national / international level. Due to other commitments and a lack of interest from the general public I haven’t made much progress. :(

My other rationale for getting a dashcam was to allow imagery capture to support mine and others’ OpenStreetMap activities. I drive quite a bit on my way to climb mountains and explore the south coast of Ireland (sometimes to play MMORPG Ingress). Capturing imagery and contributing it to the global community seemed like a good way to get additional “mileage” out of my travels!

But dashcams aren’t setup for sending data to Mapillary I here you say… True… but I never give up!

The OpenStreetMap Ireland community recently decided to capture imagery and authoritatively map the #WildAtlanticWay, a 2500+ km route along the west coast of Ireland. While there is some existing imagery of the route, there are some notable gaps and we wanted to fix that.

Being one of the primary contributors in Cork (Ireland’s largest county by land area), I was motivated to try and contribute more imagery in Co. Cork and Co. Kerry. So I started digging into the tools available for processing and uploading data again (to see what improvements had been made since I last tried).

Processing dashcam videos so they are usable my mapillary

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Due to ill-considered from the Wikidata community using old and outdate 2017 dataset from Directorate General of Budget, Accounting and Statistics (DGBAS), Executive Yuan, Taiwan, the newly found villages in 2018, like the villages mass modification in Taiwan in 2018, or split villages due to increase population, these new villages was not included and imported into Wikidata. So there are some OpenStreetMap village relations could not link to Wikidata.

For the OpenStreetMap part, typebook using relative new and up-to-date shape files from National Land Survaying and Mapping Center, Ministry of Interior Affairs, to add the Code of Household Registration and Conscription Information System (HRCIS Code) and the followup Wikidata item. There are only mssing Zhengxin Village, Dauliu City, Yulin County on OpenStreetMap. OpenStreetMap Taiwan community could just add relation to fix the problem on OpenStreetMap.

For the Wikidata part, there are several users start to import villages data to Wikidata, which started from 2019. If you look closer to some of the Wikidata items, the reference they used link to files from the DGBAS, which is out-of-data and the last update time is 2017-01/31. DGBAS decide to switch to use the HRCIS Code. It indicated that Wikidata users using old dataset to import villages, so the 75 new villages was not included, is missing on Wikidata database.

When Wikidata Taiwan community start to deal with the villages data in 2020, there are many villages already combined in disappear. But due to the fact they used DGBAS out-of-date dataset, they could not found out their are missing mass amount of villages. The DGBAS also missing the villages English name, so we see the imported item missing English label.

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In the year of twenty-twenty one, I still map using an external Bluetooth GPS receiver unit (yes, and yes, GPS explicitly refers to the American constellation only).

The device in question is Qstarz BT-Q818XT. It was updated with a firmware that kinda fixes the Y2K problem of GNSS.

Of course things can work with Android devices

Purchased in 2014, I used to hook it up with the BlueGps app with a tablet running Android Jelly Bean.

Latest Android versions shipped with devices come with time. The BlueGps app stopped working in Android KitKat. Luckily, there’s another app in the Google Play Store that still could do exactly that: it’s simply called as Bluetooth GPS. The APK copy of it is still able to function independently in devices without Google Play Services.

With latest dessert release, things start breaking up

Fast forward to the dessert release of Pie. Significant changes in more recent releases of Android means apps that no longer patched, with several legacy API calls begin to cease functioning properly. At least the app would just still work on Android 8.1.0 Oreo.

What other alternative is available?

Browsing through the Google Play Store appeared to be hopeless at first. Similar apps like Bluetooth GPS are common - they stopped received any updates for quite a long time. I don’t want to take the risk to give those shady apps a go, until I stumbled upon Bluetooth GNSS - GPS, Galileo, GLONASS and BeiDou. What a long, descriptive name for an app.

No adverts? Cool? More like cooler! I took a dive.

Yes, it works on Android 9.0 Pie. As the time of writing (the app received an update on 26 May 2021), the app simply works on Nokia 2.2 running Android 11 (warning: a 6MB GIF!). I suspect it would run OK on devices with near vanilla Android (Pixel, Android One, etc).

Some disclaimer: I don’t receive anything to share this.

Custom ROMs and the Bluetooth GPS receiver unit thingy

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Some words that I left at the MapComplete Telegram Group:

Several years ago, there are some mappers start a drinking water map in Taiwan. These mappers are planing to change the map subject, using the code of the drinking water map to deploy more theme map to promote OpenStreetMap. But unfortunately we couldn’t get the resource to make it possible to get plenty of theme maps to get Taiwanese people interested.

Glad to see you guys got supported from OpenStreetMap Foundation, Open Knowledge Belgium, Trage Wegen, OSGeo Belgium, Humanitarian OpenStreetMap Team, Missing Maps.

Some background information about the Drinking Water Map: 1, 2.