This workshop will introduce two methods on how to identify unmapped highways in OSM. First by making a comparison between OSM and government (open) data, second by calculating the distance between population and OSM highway data. This method will assist OSM contributors to understand where are the unmapped highway without having to manually check the imagery and what is on OSM. The workshop will be delivered using open-source GIS software and publicly accessible data.
OSM evaluation data has been done by many parties on various aspects, starting from tagging correctness until the geometry accuracy. Another aspect that can be evaluated as well as the data coverage. This analysis aims to evaluate how's the good coverage of OSM data, whether OSM has been covering all the area, specifically on the road network data coverage. A good map data ideally can cover the whole area. Thus, the results from this analysis, hopefully, could help the mapper to get an insight into the quality of OSM mapped road network data and identify where are the unmapped areas. There are two methods offered in this analysis. First, OSM data will be compared with the official road network data from a trusted source, such as a dataset released by the government. Second, OSM road network data will be compared with the population distribution to know whether all the populated areas have access to the nearest road network. In the ideal world, all populated areas should have close proximity to the road network, ensuring them having access to public services such as transportation and public facilities. The closer population to the road network, the easier it is. The analysis will be using open-source GIS software, which is QGIS, and a publicly accessible dataset.
Speakers: Emiliani Dewi Yantisa Akhadi