Quality has been well recognized as an essential and critical consideration for geospatial data. Rigorous mapping specifications are developed to ensure the quality of geospatial datasets over time, especially government data. Many countries, like Indonesia, have encouraged sharing and using open government data. In 2018, the government of the Special Region of Capital Jakarta established the Jakarta Satu as an open data website for the general public. Several datasets, including road and building, are available starting from 2020. However, the temporality and fitness for the use of the datasets become an issue, as the Jakarta Satu is only updated every five years. Volunteered Geographic Information (VGI) is an alternative and complementary solution for updating government data, as it may practically update its data in minutes. OpenStreetMap (OSM) is one of the most used VGI platforms that have become popular in the past few years, with 8.8 million accounts registered in August 2022.

However, OSM’s data quality remains challenging because the establishment and maintenance procedure and the “universe of discourse” of OSM are pretty different from those of government data. We propose a data quality framework based on ISO 19157:2013 to evaluate the quality of VGI and compare the results to the government data to serve as a basis for choosing datasets for the intended applications. Additionally, OSM contributors’ behavior is assessed based on their mapping activities in Jakarta between 2006 and 2021.

The finding shows that OSM has better outcomes from the aspects of completeness of road and building datasets, the positional accuracy of road datasets, and the thematic accuracy of road, building, and landmark datasets as far as the currentness is concerned. It demonstrates the advantage of timely updates and the disadvantages of lack of quality for the VGI. Another finding indicates that 76.104% of the 6,792 OSM contributors in Jakarta are inactive, 22.805% are active, and 1.001% are removed. The OSM contributors’ ability is then categorized as experts and non-expert mappers according to the number of their contributions. The feature preferences of active and inactive contributors are different. The active volunteers are preferred to map highways (46.134%), buildings (25.644%), and addresses (18.686%), while inactive volunteers are more inclined to map buildings (25.493%), names (16.486%), and nodes (14.612%). We conclude that the quality of the OSM dataset is generally more reliable than the data of Jakarta Satu, but the quality issue still requires special attention. The applications of distributed volunteer data can be further expanded by including the dataset's usability considerations. As this research only focuses on the urban area, whether the findings can also be applied to the non-urban area would require further examination in the future.

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