Zainab Ramadhanis

Urban areas, particularly slum areas, experience several problems that impact residents' lives, such as a lack of resources, unstable housing conditions, and restricted access to essential services. Although many people are unaware of the importance of geospatial data, vulnerable areas like slums require it to understand the community's issues. Therefore, the availability of open geospatial data, such as OpenStreetMap (OSM), is very beneficial in resolving the problems in slum regions. However, because each contributor has their own "universe of discourse" to map on OSM, it is still difficult to improve the data quality of OSM. Another issue is that slum areas have higher population densities than non-slum areas, which causes satellite images of buildings to show indistinct rooftops. We propose a framework data quality based on ISO 19157:2013 to evaluate the quality of OSM building data in both slum and non-slum areas. The behavior of OSM contributors is also assessed in their mapping activities between 2006 and 2023. The results show that completeness, logical consistency, positional accuracy, and thematic accuracy are all problems with slum area building data. The completeness and logical consistency show a significant amount of errors because of unclear satellite imagery in slum regions, despite a slight gap in positional difference in positional accuracy between slum and non-slum areas. Another research shows 33.974% active contributors and 8.974% non-experts in slum regions. The two groups' overall active contributor rates are comparable, with non-slum areas having a rate of 32.800%. The results of this study have important implications for sustainable urban development. Decision-makers can efficiently prioritize urban interventions, such as electrification, public facilities, waste management, and sanitation, in line with Sustainable Development Goal 11 by enhancing building data quality in slum regions. Moreover, we suggest doing field surveys to improve the quality, especially the completeness, even though OSM data quality in slum regions is trusted enough in positional accuracy. Another advantage of this study is that the active contributor percentages in slum regions point to a promising future for data improvement. This study presents an advanced analysis of Ramadhanis's (2022) work titled "Can We Depend on Volunteered Geographic Information? A Case Study of OpenStreetMap," presented at the State of the Map Asia conference in 2022. The primary objective of this research is to underscore dissimilarities in the quality of building data between slum and non-slum areas, aiming to demonstrate the reliability of building data in slum regions despite the complex nature of building footprints, including substantial overlap features and completeness challenges. Remarkably, despite its comparative quality issues compared to non-slum zones, the rate of active contributors in slum areas exceeds that in non-slum regions. Furthermore, these findings hold the potential to support Sustainable Development Goal 11, which emphasizes creating sustainable cities and communities. The outcomes of this investigation can be effectively leveraged by urban decision-makers to prioritize interventions like electrification, public infrastructure, waste management, and sanitation in Jakarta's slum areas, thus contributing to sustainable urban development initiatives.