Maps play a critical role in disaster response, enabling humanitarian organisations to better identify people in need, coordinate response and provide assistance where it’s needed most. However, many of the communities where disasters occur are literally missing from any map. First responders working with these communities often have to cover large areas to find out where the population is affected, but lack the data necessary for an efficient, effective response.

Many tools for mapping in OSM can be daunting for people without a technical or mapping background. This constitutes a high barrier to join open mapping initiatives for beginners and people that are not willing to spend that much time on training or that don’t have access to a laptop.

MapSwipe is an open-source mobile app that makes it even easier for anyone to contribute to humanitarian mapping, with tasks that can be done in just minutes and in-app tutorials to get started. In this talk we will provide an overview on it from three angles:

1. community
2. app (react-native)
3. backend (python and postgis).

Since its start in 2015, MapSwipe has scaled to more than 30,000 users mapping 1,300,000 km2. MapSwipe is built and maintained by volunteers, with the support of the British Red Cross, HeiGIT and the GIScience Research Group, Humanitarian OpenStreetMap Team and Medecins Sans Frontieres.

Alongside the OpenStreetMap community, the users of OSM data are manifold: from academics to businesses and humanitarian actors. The OSM community has over 1 million active contributors, around 50,000 of which are active each month. Four out of the "big five" mega-corporations are already using OSM data in their products and are actively contributing to the OSM data set.

OpenStreetMap data is used more and more widely, which means that data quality and fitness-for-purpose analyses are becoming more and more relevant. Many scientific papers have been written about OSM data quality, but most only apply methods to few small regions, and results are available for the single point in time when the papers are published. Results are often not easy to replicate, e.g. to check the transferability of a method to other regions. Ideally, one would like to calculate data quality measures on a global context in a fine spatial (and temporal) resolution.

This talk will present how HeiGIT and MapAction are addressing OSM data quality questions with the open source ohsome platform to perform in depth data analysis of spatio-temporal statistics of the OSM geo data set.