As mentioned in my previous blog post about detecting vandalism in OpenStreetMap (OSM) edits, it’s highly recommended that contributors use public changeset discussions when contacting other mappers regarding their edits. This feature was introduced at the end of 2014 and is used widely by contributors today. Each and every comment is listed publicly and every contributor can read the communication and, if necessary, add further comments or thoughts. In most cases where questions about a specific map edit come up, it is desirable that contributors take this route of communication instead of private messaging each other.

For my presentation at the German FOSSGIS & OpenStreetMap conference I created several statistics about the aforementioned changeset discussion feature. For this blog post I reran all analyses and created some new charts and statistics. Let’s start with the first image (above): It shows the number of commented or discussed changesets per month since its introduction. The peak in January, 2017 is based on a revert with several thousands of changesets.

In total, more than 92,000 changesets have been discussed in the past few years with around 151,000 comments. All comments were created by almost 14,000 different contributors. So far most changesets were commented in Germany, the United States, Russia and the UK, as you can see in the following images. This correlates to some extent, with the exception of Kazakstan, with the number of active contributors for each country (see e.g. OSMstats for active contributors). As shown on the right side, many changesets (71%) only received one comment or discussion. This means, in most cases the commented changeset did not receive a response by the owner/contributor of the changeset.

Which changesets are discussed and who creates comments? I think it’s not surprising to see that most changesets by new contributors receive a comment. However, as the following charts show, there are also changesets by long-time contributors that have some discussions. It’s also quite interesting to see that all kinds of contributors (new and long-time) created discussions. I would have expected a trend towards contributors with a higher number of mapping days.

What is the origin of the contributor who created the comment? Again, not surprisingly, this correlates with the number of active OSM contributors per country as mentioned above. The contributors’ origin is determined by his/her main activity areas which you can find/see on “How did you contribute to OpenStreetMap?“.

Some additional numbers about the text content of the changeset discussions: Roughly 22% of the changeset comments contain the word “revert”. On the other side, more than 17% include some sort of “Welcome”, “Willkommen”, “Hello”, “Ciao”, “Hola”, “Bonjour”, “nǐ hǎo!” or “привет!” text. The following image shows a word cloud of the most used words in the changeset discussions:

The last chart shows the accumulative changeset discussion contributors and comments. Almost 63% of all discussion comments have been created by around 2% of the contributors. However, I assume this looks very similar to other long tails of OpenStreetMap contribution charts. What do you think?

Want to see the latest OSM discussions in your area or country? Check this webpage.

Thanks to maɪˈæmɪ Dennis.

This blog post is a summary of my talk at the FOSSGIS & OpenStreetMap conference 2017 (german slides). I guess some of the content might be feasible for a research article, however, here we go:

Vandalism is (still) an omnipresent issue for any kind of open data project. Over the past few years the OpenStreetMap (OSM) project data has been implemented in a number of applications. In my opinion, this is one of the most important reasons why we have to bring our quality assurance to the next level. Do we really have a vandalism issue after all? Yes, we do. But first we should take a closer look at the different vandalism types.

It is important to distinguish between different vandalism types. Not each and every unusual map edit should be considered as vandalism. Based on the OSM wiki page, I created the following breakdown. Generally speaking, vandalism can occur intentionally and unintentionally. Therefore we should distinguish between vandalism and bad-map-editing-behavior. Oftentimes new contributors make mistakes which are not vandalism because they do not have the expert mapper knowledge. In my opinion, only intentional map edits such as mass-deletions or “graffiti” are real cases of vandalism.

To get an impression of the state of vandalism in the OSM project, I conducted a case study for a four week timeframe (between January 5th and February 12th, 2017). During my study I analyzed OSM edits, which mostly deleted objects from new contributors who created fictitious data or changesets for the Pokemon game. If you did not hear or read about OSM’s Pokemon phenomena, you can read more about it here. The OSM wiki page for quality assurance lists some tools that can be used for vandalism detection. However, for this study I applied my own developed OSM suspicious webpage and the quite useful augmented OSM change viewer (Achavi). Furthermore, a webpage that lists the newest OSM contributors may also be of interest to you.

So what can you do when you find a strange map edit that could be a vandalism case? The OSM help page contains an answer for that. First of all: Keep calm! Use changeset comments and try to ask in a friendly manner for the suspicious mapping reasons.

Results of the study: Overall I commented 283 Changesets in the aforementioned timeframe of four weeks. Unfortunately I did not count the number of analyzed changesets, but I assume that it should be around 1,200 (+- 200). The following chart shows the commented changesets per day. The weekends tend to have a larger number of commented/discussed changesets.

As mentioned in the introduction of the vandalism types, we should distinguish between different vandalism types. The following image shows the numbers for each category. In my prototype study, 45% of the commented changesets were vandalism related and 24% have already been reverted which was not documented in the discussion of the changeset. Sometimes I also found imported test- and fictitious data, which the initial contributor of the changeset didn’t revert. It should be clarified to everyone that the live-database should never be used for testing purposes. Interested developers can use the test API and a test database (see sandbox for editing).

Responses and spatial distribution: Overall I received 70 responses for the discussed changesets, sadly only 20 from the owner/contributor of the changeset. But, more or less every response was in a friendly manner. Most often the contributors wrote “thank you” or “I didn’t know that my changes are going to be saved in the live database”. Furthermore, if I received a response, it was within 24 hours.

The following map contains some clustered markers. Each one highlights areas where the discussed changesets are located. As you can see on the map, the commented changesets are spread almost all over the world. In some areas they tend to correlate with the number of active OSM’ers. However, here is some additional information about three selected areas: 1: USA – Several cases of Pokemon Go related and fictitious map edits. 2: Japan/China – Some mass deletions and 3: South Africa – Oftentimes new MissingMaps or HOT contributors tend to delete and redraw more or less the same objects such as buildings. I guess it was not explained well enough to these editors that this destroys the object history? However, the article about “Good practice” in the OSM wiki is quite useful in this case.

Conclusion: The study reveals that there is an ongoing issue with vandalism in OSM’s map data. I think we do need to simplify the tools for detecting vandalism. In particular we should omit work where several users review identical suspicious map edits. Maybe the best possible solution should be a tool which is integrated directly in the OSM.org infrastructure. However, my presentation also contained some statistics and charts about the OSM changeset discussions feature. This will be the content of a separate blog post in following weeks. Also, the prototype introduced at the end of my talk will (hopefully) be presented in the next few months.

Thanks to maɪˈæmɪ Dennis.

Over the past few years I have written several blog posts about the (non-) activity of newly registered OpenStreetMap (OSM) members (2015, 2014, 2013). Similarly to the previous posts, the following image shows the gap between the number of registered and the number of active OSM members. Although the project still shows millions of new registrations, “only” several hundred thousand of these registrants actually edited at least one object. Simon showed similar results in his yearly changeset studies.

The following image shows, that the project still has some loyal contributors. More specifically, it shows the increase in monthly active members over the past few years and their consistent data contributions based on the first and latest changeset:

However, this time I would like to combine the current study with some additional research. I tried to identify three different OSM contributor groups, based on the hashtag in a contributor’s comment or the utilized editor, for the following analysis:

1. Contributors of the MissingMaps-Project: A contributors of the project usually use #missingmaps in their changeset.
2. Contributors that utilized the Maps.Me app: The ‘created_by’-tag contains ‘MAPS.ME’.
3. All other ‘regular’ contributors of the OSM project, who don’t have any #missingmaps in their changesets and neither used the maps.me editor.

In the past 12 months, almost 1.53 million members registered to the OSM project. So far, only 12% (181k) ever created at least one map edit: Almost 12,000 members created at least one changeset with the #missingmaps hashtag. Over 70,000 used the maps.me editor and 99,000 mapped without #missingmaps and the maps.me editor. The following diagram shows the number of new OSM contributors per month for the three aforementioned groups.

The release of the maps.me app (more specifically the OSM editor functionality) clearly has an impact on the monthly number of new mappers. Time for a more detailed analysis about the contributions and mapping times: The majority of the members of the groups don’t show more than two mapping days (What is a mapping day, you ask? Well, my definition would be: A mapping day is day, where a contributor created at least one changeset). Only around 6% of the newly active members are contributing for more than 7 days.

Some members of the #missingmaps group also contributed some changesets without the hashtag. But many of those members (70%) only contributed #missingmaps changesets. Furthermore, 95% of this adjusted group doesn’t map for more than two days. Anyway, despite identifying three different contributor groups, the results are looking somewhat similar. Let’s have a look at the number of map changes. The relative comparison shows that the smaller #missingmaps group produces a large number of edits. The maps.me group only generates small numbers of map changes to the project’s database.

Lastly, I conducted an analysis for three selected tag-keys: building, highway and name. The comparison shows that the #missingmaps group generates a larger number of building and highway features. In contrast “regular” OSM’ers and maps.me users contributed more primary keys such as the name- or amenity-tag.

I think the diagrams in this blog post are quite interesting because they show that the #missingmaps mapathons can activate members that contribute many map objects. But they also indicate that the majority of these elements are traced from satellite imagery without primary attributes. In contrast the maps.me editor functionality proofed to be successful with its in-app integration and its easy usability, which resulted in a huge number of new contributors. In summary, I think it would be good to motivate contributors not only to participate in humanitarian mapathons but also to map their neighborhood in an attempt to stick to the project. Also, I guess it would be great if the maps.me editor would work on the next steps in providing easy mapping functionality for its users (of course with some sort of validation to reduce questionable edits).

Thanks to maɪˈæmɪ Dennis.

Back in 2010 & 2011 I conducted several studies to detect underrepresented regions a.k.a. “unmapped” places in OpenStreetMap (OSM). More than five years later, some people asked if I could rerun the analysis. Based on the latest OSM planet dump file and Taginfo, almost 1 million places have been tagged as villages. Furthermore, around 59 million streets have a residential, unclassified or service highway value. My algorithm to find unmapped places, works as follows:

1. Use every place node of the OSM dataset which has a village-tag (place=village).
2. Search in a radius of ca. 700 m for a street with one of the following highway-values: residential, unclassified or service.
3. If no street can be found, mark the place as “unmapped”!

My results for the entire OSM planet can be found under the following webpage.

Overall we have more than 440,000 unmapped places in OSM. As you can see in the picture above, most of the places are around Central Africa, Saudi Arabia or China. However, I hope that this analysis helps to complete some of the missing areas or to revise some incorrect map data. Some remarks about “false=positives” or why your village is marked as unmapped? Some possible reasons: Is the used tag for your place correct? Compare the wiki page for further information. Sometimes “hamlet” could be the correct tag value. Are the nearby highways tagged correctly? (OSM wiki)

Amount of unmapped places for each continent:

• Africa 119,084
• Asia 241,833
• Australia 212
• Europe 44,819
• North America 16,464
• Oceania 837
• South America 15,576

Technical Stuff: The OSM data for the analysis is prepared by a custom OSM PBF reader. The webpage, which shows the results, is based on Leaflet 1.0.0-rc1 and the really fast PruneCluster plugin.

*Update*: You’ll find the date of the latest data update in the header -> “(Date: Apr. 9th, 2018)”

Thanks to maɪˈæmɪ Dennis.

The reputation of a contributor in OpenStreetMap (OSM) plays a significant role, especially when considering the quality assessment of the collected data. Sometimes it’s difficult to make a meaningful statement about a contributor by simply looking at the raw mapping work represented by the number of created objects or used tags. Therefore, it would be really helpful if we would have some additional information about the person who contributes to the project. For example: Does she/he help other contributors? Is her/his work somehow documented or based on one of the “discussed” proposals? Or does she/he work as a lone warrior in the OSM world?

In 2010 I created “How did you contribute to OpenStreetMap?” (HDYC) as a kind of fun side project. Nowadays many people use it to get some detailed information about OSM contributors. Some of you are probably familiar with the “verified” icon used on some celebrity Twitter accounts. I created a similar new feature for the aforementioned HDYC page. If you connect your related OSM accounts, your profile will be marked as “verified”.

What do you have to do to get a verified contributor profile, you ask? First of all, you have to create at least 100 OSM changesets. Secondly, you need a login (username) for the OSM Help Forum, for the common OSM Forum and for the OSM Wiki. Last but not least, you have to list your OSM related accounts on your OSM profile page. After that, you should be able to see your accounts in your HDYC profile and your account will be automatically marked as verified.

Malenki already mentioned his usernames as an example in his profile. He also described it in a tiny OSM diary. Overall this feature is optional. So if you don’t want to “connect” or show your accounts for privacy protection, please don’t mention them on your OSM profile. My script checks the OSM profiles of the latest active OSM contributors every 24h. That’s it.

The HDYC profile now also shows the number of your changeset discussions and, if mentioned in your OSM profile, the page shows your Mapillary account as well.

Notice: If someone is trying to cheat with other people’s accounts, I will blacklist her/his username.

Thanks to maɪˈæmɪ Dennis.

Almost one year has passed again. This means it’s time for the fourth OpenStreetMap (OSM) member activity analysis. The previous editions are online here: 2014, 2013 and 2012. Simon Poole already posted some interesting stats about the past few years. You can find all his results on the OSM wiki page. However, similar to last year, I try to dig a little deeper in some aspects.

Overall the OSM project has officially more than 2.2 million registered members (Aug, 9th 2015). For several of my OSM related webpages I create a personal OSM contributor database, based on the official OSM API v0.6. Anyway, when using this API, the final table will show a list with more than 3 million individual OSM accounts (Aug, 9th 2015). I’m not sure what the cause for this gap of almost 1 million members between the official number and the member number extracted with the API could be. Maybe some of you have a possible explanation? However, I think many accounts are created by spammers or bots.

The following chart shows a trend similar to the one of previous years: The project attracts a large number of newly registered members, but the sum of contributors that actively work on the project is fairly small. As mentioned in earlier posts, this phenomenon is nothing special for an online community project and has been analyzed for previous years already.

Described in numbers (July 31st, 2015):

• Registered OSM Members (OSM API): 3,032,954
• Registered OSM Members (Official): 2,201,519
• Members who created 1 Changeset: 562,670
• Members who performed >= 10 Edits: 343,523
• Members who created >=10 Changesets: 137,591

Personally, I really like the following diagram: It shows the increase in monthly contributor numbers over the past few years and their consistencies in collecting OSM data based on the first and latest contributed changeset of an OSM member. It’s great to see that at least some experienced mappers are still contributing to the project after more than five years.

Some background information on how I created the stats: To retrieve the registration date of the members, I used the aforementioned OSM API. The other numbers are based on the OSM changeset dump, which is available for download here.

Next to the presented results above, you can find some daily updated statistics about the OSM project on OSMstats.

Thanks to maɪˈæmɪ Dennis.

OSMstats contains several statistics about the OpenStreetMap (OSM) project, such as daily-created objects, the amount of active contributors or detailed numbers for individual countries. One way to determine the sum of created or modified Node objects, is to use the minutely, hourly or daily OSM replication change files and counting the values for each country of the world. Sadly, this approach has some drawbacks. Firstly, the official files do not contain, for example, all Nodes of a modified way, which is required, when trying to find the country where the change took place. Furthermore, the determination of the country for a specific OSM object really depends on the border’s level of detail: More detailed country borders make the processing quite time-consuming. Some of you probably experienced this problem before when using Osmosis or a different OSM processing tool. Anyway, for calculating additional country statistics I tried a new approach:

1. Determine the country of a changset based on its center position
2. Use the changeset country information for all objects within this changeset.

Of course, the determined country of the changeset can “only” be generalized for the entire changeset content, but how does it compare with the current method utilized in OSMstats? I compared last week’s numbers of OSMstats for each country of the world with the newly introduced approach. In total, the number of active members per country differs for each weekday by around 3% (min. 1% and max. 5%). The average difference of created, modified and deleted Nodes per country is quite similar with 4% (min. 2% and max. 9%). The presented approach could produce partially incorrect results whenever a changeset contains border changes of two or more countries or if the center of the changeset is in the wrong country. But IMHO the assumption to use the changeset centers is sufficient to calculate results and determine changes per country. As you can see in the figure above, most OSM changesets happen in a manageable area within one country. Yes I know, exceptions prove the rule.

So, why am I doing this? The main idea behind this approach is to change the entire processing task for OSMstats within the coming weeks. The changes per country will then be based on the introduced approach. Another advantage will be, that this newly created information, gathered from the changesets, can be utilized to create additional contributor statistics.

Thanks to maɪˈæmɪ Dennis.

The first book about the OpenStreetMap (OSM) project was written by Frederik Ramm and Jochen Topf, two well-known OSM enthusiasts, in 2008. The first version was in German which was later translated into an improved English version. It contains similar information as can be found in the book by Jonathan Bennett, which was published in 2010, detailing how the projects’ geodata is collected, which editors can be used, some explanations about tags, key and values and how the rendering stack works. Both books are great resources to learn about the OSM basics and to get an overview about useful software.

However, besides these more technical books, the research community has been very active in recent years and has published several articles about OSM data quality, conflation attempts with other datasets or about the contributors of the project. Each of us (Dennis Zielstra and I) wrote a dissertation with different aspects about crowd-sourced geodata and the OSM project: Dennis’ work is about OSM data quality in comparison to proprietary and governmental data with emphasis on pedestrian shortest path routing and data imports. Pascal’s work tackled the issue of how user-generated geodata can be utilized for disabled people friendly route planning. Both dissertations contain more than 13 publications in total.

Now the important part for you: Both dissertations are now freely available. You can download Dennis’ work here and Pascal’s thesis here: Combined more than “480” pages about the OpenStreetMap project!

What can you expect from our dissertations? Our work had to be more science oriented (after all they had to fulfill the strict guidelines our universities gave us to get the PhD). This means it contains a bunch of information that can be useful to other researchers; for example, methods to analyze geodata quality or an introduction on parameters that are important for disabled people in a road network. However, we always tried to make the results and findings always as understandable to the general public as possible. We always felt that VGI research about an open source project such as OSM should not only generate results that are so convoluted that only a hand-full of researchers worldwide would understand the concepts in the end. Any OSM contributor should have a benefit from the findings that are published in those dissertations and we hope we accomplished this goal. We also wished we could have published each publication in open source journals to make the results freely available to everyone but this is a whole new topic for a different blog post. Anyway, by providing the dissertations for free we basically accomplished this task now too.

And what can you not you expect from our work? We do not describe how your object of interest should be tagged or how you should run a mapping event. We feel there are already enough sources out there that tackle these issues.

Anyway, we bet that you will find some information in the dissertations about the OSM project which you have not heard about yet, such as the evolution of the German or the United States OSM street network, analyses about data imports or several research projects about contributor behavior, vandalism detection and a quite comprehensive overview about recent developments and future trends in VGI research in general.

Let us know what you think and enjoy the information overload 🙂

Dennis & Pascal (@pascal_n)

The Missing Maps project is a collaboration between the Humanitarian OpenStreetMap Team (HOT) and various partner agencies, such as the American or the British Red Cross. One of their main objectives is “to map the most vulnerable places in the developing world, in order that international and local NGOs and individuals can use the maps and data to better respond to crises affecting the areas.” You can find additional information about the Missing Maps Project on the OpenStreetMap (OSM) wiki and their project page.

A year ago, I created a webpage where you can filter OSM changesets by a specific comment. Sadly the webpage provides only a search for the latest seven days. However, the Missing Maps project asked me, if it’s possible to “look over a longer time scale”? Here we go, based on a similar concept that I used for a webpage that I created for the HOT Ebola Response, I made a Map that displays all OSM changesets which have the hashtag #MissingMaps in the comment attribute and have been created since August 1st, 2014. It’s online here and being updated on an hourly basis: http://resultmaps.neis-one.org/osm-missingmaps

The webpage also contains overall information about the number of OSM Contributors and map changes. So far more than 800 contributors created more than 1.5 Mio map changes in almost 22,000 changesets. The volunteers contributed in more than 30 countries such as Congo-Kinshasa, Sudan, Central African Republic, Indonesia, Ethiopia, Chad, Zimbabwe or Rwanda. Additionally the number of created changesets and contributors of the last seven days are displayed in two charts at the left-hand side. The time slider at the bottom of the map can be used to show the changesets between two specific dates. Finally an overview page lists the names of all OSM contributors who used the hashtag #MissingMaps in their changesets comment. Same as last time: Thank you & keep up the good work!

Thanks to maɪˈæmɪ Dennis.

For almost eight months the OpenStreetMap (OSM) community has been collecting geo information for the West Africa Ebola outbreak response now. The collective work of the crowd is somewhat managed by the Humanitarian OpenStreetMap Team (HOT). For example, the Task Manager provided by HOT gives interested contributors information in which areas map features are needed. However, you can find additional information in an article by Pierre Beland, which he wrote during a conference where he presented the efforts of the OSM community. The OSM wiki contains some useful information about the West Africa Ebola Response too. Matt Irwin also wrote a summary about the OSM mapper contributions and created an interesting visualization of all the mapping work.

I created a response map for the OSM mapping activities in West Africa, a similar approach as I previously used for the “Typhoon Haiyan” deployment. It displays all OSM changesets created since March 1st, 2014. The analysis extent is displayed by a black rectangle. In total, more than 2,000 contributors made more than 10 million changes to the map. At the bottom you will find a time range slider which can control the displayed changesets. Sorry for the sometimes slow performance, but the community (maybe you too) collected a huge amount of data! The map is online here: http://resultmaps.neis-one.org/osm-ebola

A complete list of all OSM contributor names with their number of map changes can be found here. It’s really fantastic, “Thank you & keep up the good work!”. This time I have also spent some time to analyze what type of contributors helped in this scenario. The following diagram shows the number of contributors per month in the above mentioned analysis extend. Additionally, it contains information on how long the individual contributors have been collecting map data. In contrast to the first months, many new members contributed data in the past three months.

Based on the information from “How did you contribute to OSM“, which shows in what country a member performed the most map changes, I created the following chart. It displays the distribution of the contributor’s countries of origin. I assume that the high number of new registered members, who created many changesets during their Ebola response, moved their origin towards West Africa. However, overall more than 2,000 contributors from almost 110 countries helped so far, AWESOME! What do you think?

Lastly, I would like to point to the following blog post which shows you how to search for changesets with a specific comment for any region of the world: “Filtering OpenStreetMap Changesets by a Specific Comment“.

Thanks to maɪˈæmɪ Dennis.