Using gamification to increase map data production during humanitarian volunteered geographic information (VGI) campaigns

ABSTRACT

Volunteered geographic information (VGI) offers a solution to inequalities in authoritative map data that can limit our response to humanitarian crises. However, sustaining voluntary contributions of map data can be difficult and hybrid machine learning-VGI (ML-VGI) workflows developed to encourage sustained volunteer contributions have been demonstrated to be insufficient. Gamification can be used to encourage volunteers to map for longer, however evaluations of gamification to increase humanitarian mapping contributions are rare. Here we develop a gamified humanitarian ML-VGI mapping platform (“Map Safari”) and evaluate the use of game elements to encourage sustained volunteer contributions without reducing contribution quality. Our results suggest that gamification makes mapping more fun, particularly for first time mappers, without degrading map data quality. Competition is demonstrated to be important for encouraging enjoyment of game elements and increasing map data contributions. Future gamified mapping platforms should emphasize competition and ensure there are enough game elements to make platform use feel game-like. This research demonstrates that gamification can be used to encourage continued voluntary contributions of map data thereby increasing the amount of map data available to humanitarian organizations.

KEYWORDS:

• Volunteered geographic information
• humanitarian mapping
• volunteering
• motivation
• user evaluation

Acknowledgments

Ethical approval was obtained from the Environment, Education and Development School Panel PGR at the University of Manchester (Reference: 2021-12205-20267). We thank our participants who took time during a global pandemic to take part in the research. We also thank the reviewers for their feedback, which greatly improved the article. This research benefited from the following open source libraries: Leaflet.js (https://leafletjs.com/), Leaflet Path Transform (https://github.com/w8r/Leaflet.Path.Transform), Turf.js (https://turfjs.org/), OSM-Request (https://github.com/osmlab/osm-request), OSM-auth (https://github.com/osmlab/osm-auth), Leaflet-plugins (https://github.com/shramov/leaflet-plugins/blob/master/layer/tile/Bing.js); the following data sources: Bing Imagery (https://docs.microsoft.com/en-gb/bingmaps/rest-services/imagery/), Microsoft Uganda-Tanzania Building Geometries (https://github.com/microsoft/Uganda-Tanzania-Building-Footprints); and the following services: OSM APIv3 (https://wiki.openstreetmap.org/wiki/API_v0.6) and Overpass API (https://wiki.openstreetmap.org/wiki/Overpass_API). Computational facilities were provided by Research IT at the University of Manchester.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The code that supports the findings of this study are openly available at https://gitlab.com/kirstywatkinson/map-safari.

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2022.2156389