Grants:Programs/Wikimedia Alliances Fund/Historical Crowd-Source Spatial Data for Sustainable Development and Inclusive Mapping/Midpoint Report

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Midterm Learning Report

Report Status: Accepted

Due date: 2023-01-15T00:00:00Z

Funding program: Wikimedia Alliances Fund

Report type: Midterm

Application Final Learning Report

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General information[edit]

This form is for organizations receiving Wikimedia Community Funds (General Support) or Wikimedia Alliances Funds to report on their mid-term learning and results. See the Wikimedia Alliances Fund application if you want to review the initial proposal.

  • Name of Organization: Perkumpulan OpenStreetMap Indonesia
  • Title of Proposal: Historical Crowd-Source Spatial Data for Sustainable Development and Inclusive Mapping
  • Amount awarded: 50260 USD, 717999801.2 IDR
  • Amount spent: 258213800.9 IDR

Part 1 Understanding your work[edit]

1. Briefly describe how your strategies and activities proposed were implemented and if any changes to what was proposed are worth highlighting?

The key strategy to engage the youth community in our activities and involve them in the project is we conducted training for educating and strengthening capacity building for the youth and collect the Wiki Data and OSM data through edit-a-thon. We analyzed the priority area by identifying the GLAM data gaps in Wiki Data and OSM data and selecting areas near universities the participants go to, as they may have local knowledge about GLAM objects in the area.

We collaborated with ten universities in Indonesia to conduct the training entitled University Roadshow 2022. Before the training, we promoted our main activities by conducting two socialization sessions. In the socialization sessions, we opened the registration and selected the participants from their concern about GLAM in Indonesia. Then, we conducted three batches of training and edit-a-thon for the university students. In each batch, we had quite diverse participants from different universities in Indonesia. Furthermore, we had a mini competition for every batch to practice and add buildings and GLAM infrastructures into OSM and Wikidata. The mini-competition was organized for university students in the first two batches and opened to the public in the third batch for contributing knowledge by youth community members. Generally, we conducted the University Roadshow with training in one by one university, but in the program we implemented the new strategies. The training method was combined the universities in one session for diverse participants and interactive training session. The participants came with several background that bring the new insight and experience in the training session. POI and WMID divide the priority area into three region; region Sumatera, Jawa Bali, and Kalimantan Sulawesi that also indicated the participants in the training session.

2. Were there any strategies or approaches that you feel are being effective in achieving your goals?

Partnering with Wikimedia Indonesia in conducting training and edit-a-thon to enrich data in Wikidata and collaborating with ten universities in Indonesia to engage with youth effectively achieved our goals. The idea of combining Wikidata and OSM Data in training was relatively new in Indonesia, so the participants were \enthusiastic about learning and contributing to Wikidata and OSM. By collaborating with Wikimedia Indonesia, we could work hand in hand with the community with the same vision as we do, promoting the use of and contributing to open knowledge with open-source technology. The training was also straightforward to understand, hence a great way to enrich the data and information in Wikidata and OSM. Onwards, we plan to approach the youth communities interested in GLAM and history as we feel it will be far more effective.

In the training session, we created a video ( of integration between WikiData and OSM that support the training material and is easy to understand for participants. The interactive video was helpful and approached many participants to join the program. The total participants were 182 new contributors in WikiData and OSM. The results of training and edit-a-thon is 350,032 people put into the map, 1,416 GLAM objects mapped, and 2,316 edits of GLAM items on Wikidata.

3. What challenges or obstacles have you encountered so far?

Gathering participants was quite challenging as the date of socialization and the actual date of the main activities was not close enough. Every batch of training and edit-a-thon was attended by students from a few different universities. This also made it challenging to gather participants as they had different academic schedules. We had a mini-competition in each batch, but from the first and second batches, the participation level of the mini-competition was not as high as we expected. We then decided to open the third batch competition publicly. When we opened it to the public, the results were more effective than we initially expected for adding the GLAM data.

4. Please describe how different communities are participating and being informed about your work.

To approach the community with our project, first, we prepared the proposal activity to explain the project's impact and implementation. Then, we contacted lecturers from ten universities, explained our activity, and requested them to relay the information to their students. We sent them posters about our activity. We also promoted the public flash competition on social media using posters. As a result, we managed to engage students from 10 universities who participated in training and edit-a-thon.

The material was delivered by both POI and WMID trainers. Everyone had been informed about the training date through their lecturer. Their Lecturer also had been in the same chat room as POI trainers to keep them informed about training and edia-thon schedule. Besides, we always encourage them to spread the information regarding OSM-WikiData to their youth colleague in universities; hence everyone is exposed to this information.

5. Please share reflections on how your efforts are helping to engage participants and/or build content, particularly for underrepresented groups.

In addition to adding GLAM objects and their information, we also encourage participants to add information about disability accessibility to GLAM objects in OpenStreetMap. Specific information regarding this disability can be found in OSM in the form of “Key” and “Value” to indicate whether a GLAM object accommodates the disabled community in their building or area. Participants were then asked to identify these through their local knowledge or other open-source data. This special tag is added through training and edit-athons, mini-competitions, and flash competitions. Participants will also be given additional points if they add information about disability access for GLAM objects. This will encourage them to add more data about disability accommodations in the GLAM Object.

6. In your application, you outlined your learning priorities. What have you learned so far about these areas during this period?

It was hard to keep the participants’ consistency in attending the training (it was a three-day training). There were quite a lot of participants who only attended one day of training. This might be because they had different interests or clashing schedules. In the future, we want to try engaging communities with the same interests, especially in history, arts, and literature. We would also like to try a different method where we only have one university in one batch, so it’s easier to synchronize schedules.

Competition is still considered the best way to raise contributions. It is possible to maintain data quality with some set of rules and conditions. One of the lessons learned from the competition that we held is that to ensure that we have enough time to assess participants’ works, if needed, add several days as spare time to assess.

7. What are the next steps and opportunities you’ll be focusing on for the second half of your work?

We want to engage more people from different backgrounds in our activity, perhaps from different sectors such as communities and government. We have planned our next strategy to achieve our goals in this project. Based on the statistical data we have collected from the previous progression, we will focus on utilizing OSM and WikiData GLAM objects. Moreover, we will implement the competition to use the GLAM data and involve the community members participating in the competition.

Part 2: Metrics[edit]

8a. Open and additional metrics data.

Open Metrics
Open Metrics Description Target Results Comments Methodology
N/A N/A N/A 82000 81403 buildings and 1416 GLAM objects mapped in OSM. We have conducted 2 internal competition that involving only university students who participated in the training. And we also successfully hosted a public competition for youth. In total there were 3 competitions, 2 internals, 1 public. Trained university students to contribute using OSM. Held a mini competition during the training. We use the tools for calculating the data,, and generate the data in QGIS.
N/A N/A N/A 1000 More than 800 GLAM objects were added to OSM

97 GLAM items were added, and 141 GLAM items were edited on Wikidata in 2 internal competitions 276 GLAM items were added, and 420 GLAM items were edited on Wikidata in the public competition

Trained university students to contribute using OSM. Held a mini competition during the training. We use the tools for calculating the data,, and generate the data in QGIS.
N/A N/A N/A 2000 More than 2000 edits have been recorded during the competition for the WikiData platform. 2 Internal competitions, and one public competition Trained university students were adding data to WikiData during training and competition
N/A N/A N/A 60 A total of 60 Participants register and involve in the mini-competition. The result of the data is added more than 80.000 features, including GLAM and OSM Buildings The registration form that we distributed in the training event and edi-a-thon event.
N/A N/A N/A 10 We collaborated with 10 Universities The registration form that we distributed in the training event and edi-a-thon event.
Additional Metrics
Additional Metrics Description Target Results Comments Methodology
Number of editors that continue to participate/retained after activities N/A N/A N/A
Number of organizers that continue to participate/retained after activities N/A N/A N/A N/A N/A
Number of strategic partnerships that contribute to longer term growth, diversity and sustainability N/A N/A 2 We have two universities that are interested in creating MoU through the program. The results of training, calculate the data through the registration form
Feedback from participants on effective strategies for attracting and retaining contributors N/A N/A N/A N/A N/A
Diversity of participants brought in by grantees N/A N/A 10 Collaborating with ten universities by provinces in Indonesia, any background academic with different department and concern Registration form
Number of people reached through social media publications N/A N/A 6401 Target numbers were gathered from Instagram Insights 2-weeks before the campaign started (5 - 18 December 2022)

Result numbers were gathered from Instagram Insights 2-weeks within the period of the campaign (19 Dec 2022 - 1 Jan 2023)

Create an interactive, clean and simple design to attract people to join the competition

Doing ‘Collaboration Post’ with Wikimedia already has 13K followers on Instagram

Number of activities developed N/A N/A N/A N/A N/A
Number of volunteer hours N/A N/A N/A N/A N/A

8b. Additional core metrics data.

Core Metrics Summary
Core metrics Description Target Results Comments Methodology
Number of participants Improving historical infrastructure database: at least 50 participants

Innovative ideas and publication of historical data competition: at least 30 participants Crowd-sourced mapping information platform: at least 20 people from the local community and government will be visited and used the platform

100 345 The number of participants from the socialization event were 345 people and joined in the training and edit-a-thon session were 182 participants Online training on adding GLAM objects and mapping buildings into OSM and Wikidata
Number of editors Improving historical infrastructure database: at least 20 new editors, if involving local Wiki volunteers: at least 10 new editors and 10 returning editors

Improving historical infrastructure OSM database: at least 20 new mappers Innovative ideas and publication of historical data competition: at least 20 editors

60 665 Conducted three batches of online training with the total new contributors in WikiData 322 people and OSM 343 people The registration form that interest with the training
Number of organizers Improving historical infrastructure database: POI: 8 staff, Wikimedia Indonesia: 3 staff, 1 speaker from the local community, and 1 speaker local universities

Innovative ideas and publication of historical data competition: POI: 6 staff and Wikimedia Indonesia 2 staff Crowd-sourced mapping information platform: POI: 4 staff and Wikimedia Indonesia 2 staff

15 15 Collaboration between POI and WMID staff
Number of new content contributions per Wikimedia project
Wikimedia Project Description Target Results Comments Methodology
Wikidata Improving historical infrastructure database: add Wikidata properties for at least 60% of historical infrastructures in the targeted area.

Innovative ideas and publication of historical data competition: add Wikidata properties to at least 5 historical infrastructures and use the OSM and Wikidata for innovative ideas

50 322 322 new contributors to the WikiData platform The registration form that we distributed in the training event and edi-a-thon event.

9. Are you having any difficulties collecting data to measure your results?

No, we have a method and tools to calculate the data.

10. Are you collaborating and sharing learning with Wikimedia affiliates or community members?


10a. Please describe how you have already shared them and if you would like to do more sharing, and if so how?

Yes, we are collaborating with Wikimedia Indonesia and sharing learning with communities. We ask for feedback and share experience and knowledge with Wikimedia Indonesia in all aspects of our activity, including preparation, conceptualization, and socialization. For example, when developing the details for training and edit-a-thon activity, we discussed with Wikimedia Indonesia to decide which universities we would approach, how and when to schedule the event, and how the rundown would be. Throughout the timeframe of training and edit-a-thon activity, we also had a regular monthly meeting with Wikimedia Indonesia to update the progress of the activities and to talk about lessons learned, if there are any. We are still having this regular meeting as we are now conceptualizing our next activity.

11. Documentation of your work process, story, and impact.

  • Below there is a section to upload files, videos, sound files, images (photos and infographics, e.g. communications materials, blog posts, compelling quotes, social media posts, etc.). This can be anything that would be useful to understand and show your learning and results to date (e.g., training material, dashboards, presentations, communications material, training material, etc).
  • Below is an additional field to type in link URLs.

Part 3: Financial reporting and compliance[edit]

12. Please state the total amount spent in your local currency.


13. Local currency type


14. Please report the funds received and spending in the currency of your fund.

  • Upload Documents, Templates, and Files.
  • Provide links to your financial reporting documents.

15. Based on your implementation and learning to date, do you have any plans to make changes to the budget spending?


15a. Please provide an explanation on how you hope to adjust this.

We arrived at the end of the year and implemented the project in 6 months with Wikimedia Indonesia and communities. We have accomplished the activities based on the work plan and had great experiences in collaboration.

During the project, we discussed with WMID to distribute the salary of the WMID staff by WMF budget allocation. They confirmed that they could not receive the salary from this WMF budget. The total personnel-related expenses WMID in budget allocation is 86,337,027.00 IDR we proposed in the proposal. Therefore, we plan to add a new activity for using the budget allocation. The overview of a new activity is conducting the OSM and Wiki Data Edit-a-thon in person with the youth community in Bandung City. Through the activity, we will approach the local community to add data and information about Historical Infrastructures and increase the awareness of open data technology knowledge. They also can contribute to the Wiki Data and OSM ecosystem.

16. We’d love to hear any thoughts you have on how the experience of being a grantee has been so far.

We are proud to implement the project with Wikimedia Indonesia and develop the local community in Indonesia. Collaborating gives us insights into new training methods and techniques and added OSM data. While implementing the project, we also got an award from Information Geospatial Agency (BIG) for the Bhumandala award for collecting toponym objects in Indonesia. The Wikimedia Alliance Fund program is one of the contributions to our achievement and thriving this year with adding the toponym GLAM objects and collaborating with the local communities.