OpenStreetMap BlogsPascal Neis
Who and Where? Analyzing the New Company and Location Fields in OpenStreetMap Profiles
In May 2025, the OpenStreetMap (OSM) website introduced two optional fields in public user profiles: company and location (see Github). Both fields accept unstructured free text and are not validated in any way. Since these fields are publicly visible, I wondered whether they could be useful for my “How Did You Contribute to OpenStreetMap?” (HDYC) […]
In May 2025, the OpenStreetMap (OSM) website introduced two optional fields in public user profiles: company and location (see Github). Both fields accept unstructured free text and are not validated in any way. Since these fields are publicly visible, I wondered whether they could be useful for my “How Did You Contribute to OpenStreetMap?” (HDYC) page, particularly for identifying organized (paid) mappers. Some more background information about organized editing can be found on the OSM Wiki. According to the wiki, contributors involved in organized editing projects should be documented there and should ideally include a short description in their OSM user profile. This information is currently one of the signals used by HDYC to mark or flag potential paid editing accounts. Until now, I have relied on a semi-automated script that detects paid contributors based on text patterns found in OSM user profile descriptions. This works reasonably well for larger technology companies such as Apple, Amazon, or Meta, where contributors often mention their employer directly in their profile text. The newly introduced company and location fields therefore looked like interesting candidates for a small data quality and content analysis. The goal was to evaluate whether these fields could serve as an alternative or complementary signal to my existing semi-automated detection approach.
Data Collection and Analysis
The internally maintained OSM user profile dataset used by my services is updated daily for all contributors who have been active within the last 24 hours. For this analysis, I evaluated approximately 66,000 OSM user profiles. These profiles belong to users who:
- have been active since May 2025
- have mapped on at least three distinct days
- have created at least three changesets
Usage of the Company and Location Fields
A simple aggregation grouped by company and location provides an initial overview of how these fields are used. By the end of January 2026, only a relatively small number of users had filled the company field. In total, around 1,000 distinct company names were identified, although some entries likely represent spam or low-quality data. The “small numbers” here refer not to the number of companies themselves, but to the number of users associated with each company entry. Interestingly, large technology companies still appear to rely primarily on the free-text profile description rather than the dedicated company field. The location field provides slightly more interesting insights. Most users appear to enter their country name, although other formats also appear. By the end of January 2026, around 2,200 unique location values could be identified.
Conclusion
At the moment, my conclusion is that I will probably not use these fields for HDYC. The HDYC profiles currently contain, in my opinion, more reliable signals derived directly from collected and analyzed contribution data rather than from self-declared free-text profile fields. While working on HDYC improvements, I also implemented another feature that has been on my wish list for quite some time: username history. Inspired by the “Who’s That?” page, I finally implemented my own username history feature. The information is derived from the complete minutely changeset replication history. However, I am not fully satisfied with the current presentation. The feature may not work equally well for all users, especially for accounts with a long change history. I would therefore appreciate some feedback. Which option would you prefer?
- Move the username section further down the page
- Add a time-based filter (for example showing only the last 3–5 years or 10 years)
- Remove it – I don’t find it useful
If you prefer, you can also leave feedback on my OSM diary here.
In May 2025, the OpenStreetMap (OSM) website introduced two optional fields in public user profiles: company and location (see Github). Both fields accept unstructured free text and are not validated in any way. Since these fields are publicly visible, I wondered whether they could be useful for my “How Did You Contribute to OpenStreetMap?” (HDYC) page, particularly for identifying organized (paid) mappers. Some more background information about organized editing can be found on the OSM Wiki. According to the wiki, contributors involved in organized editing projects should be documented there and should ideally include a short description in their OSM user profile. This information is currently one of the signals used by HDYC to mark or flag potential paid editing accounts. Until now, I have relied on a semi-automated script that detects paid contributors based on text patterns found in OSM user profile descriptions. This works reasonably well for larger technology companies such as Apple, Amazon, or Meta, where contributors often mention their employer directly in their profile text. The newly introduced company and location fields therefore looked like interesting candidates for a small data quality and content analysis. The goal was to evaluate whether these fields could serve as an alternative or complementary signal to my existing semi-automated detection approach.
Data Collection and Analysis
The internally maintained OSM user profile dataset used by my services is updated daily for all contributors who have been active within the last 24 hours. For this analysis, I evaluated approximately 66,000 OSM user profiles. These profiles belong to users who:
- have been active since May 2025
- have mapped on at least three distinct days
- have created at least three changesets
Usage of the Company and Location Fields
A simple aggregation grouped by company and location provides an initial overview of how these fields are used. By the end of January 2026, only a relatively small number of users had filled the company field. In total, around 1,000 distinct company names were identified, although some entries likely represent spam or low-quality data. The “small numbers” here refer not to the number of companies themselves, but to the number of users associated with each company entry. Interestingly, large technology companies still appear to rely primarily on the free-text profile description rather than the dedicated company field. The location field provides slightly more interesting insights. Most users appear to enter their country name, although other formats also appear. By the end of January 2026, around 2,200 unique location values could be identified.
Conclusion
At the moment, my conclusion is that I will probably not use these fields for HDYC. The HDYC profiles currently contain, in my opinion, more reliable signals derived directly from collected and analyzed contribution data rather than from self-declared free-text profile fields. While working on HDYC improvements, I also implemented another feature that has been on my wish list for quite some time: username history. Inspired by the “Who’s That?” page, I finally implemented my own username history feature. The information is derived from the complete minutely changeset replication history. However, I am not fully satisfied with the current presentation. The feature may not work equally well for all users, especially for accounts with a long change history. I would therefore appreciate some feedback. Which option would you prefer?
- Move the username section further down the page
- Add a time-based filter (for example showing only the last 3–5 years or 10 years)
- Remove it – I don’t find it useful
If you prefer, you can also leave feedback on my OSM diary here.
Identificação em campo de Handroanthus impetiginosus (Ipê-roxo-de-bola)
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DOI: 
Exemplo de etiquetas usadas no mapeamento de um indivíduo da espécie Tabebuia rosea. 
After consultations and careful observation, we selected Duaripara Slum as our core study area. Over the following days, we collected data from more than 400 households, representing over 8,200 people. But beyond the numbers were conversations, stories of survival, adaptation, and hope. Community leaders shared their concerns about flooding, health risks, and extreme heat. Residents spoke about their struggles but also about their determination.
Mapping was no longer just about coordinates. It became about understanding lived realities.
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