Shiyue Zhong

Human factor is one of the most crucial elements in crowdsourced mapping. This research explores how human bias affects the mapping process and whether such effects can be mitigated through targeted training with a behavioral experiment. The experiment uses a two-group randomized design. The treatment group receives more advanced training than the control group. There are two goals for the experiment. First, we aim to identify the common types of bias that amateurs from a specific demographic community have when using OSM. Second, we plan to explore whether training is helpful for reducing those biases and improve the quality of mapping.

OpenStreetMap (OSM) is one of the VGI platforms that has been curated primarily by volunteers, which indicates that the demographic differences of the backgrounds of volunteers might affect their understanding of mapping and their mapping behavior. For example, contributors with varying skills and experiences of mapping and GIS software might choose different objects to map and trace them in different levels of detail. There is a wide range of factors that could have an impact on how and what individual contributors choose to map: age, gender, expertise, education, income, etc. This study focuses on digging into the mapping behavior of a specific demographic community - residents from Singapore. We have observed changes in terms of tagging and editing behavior in OSM before and after different levels of training. This study has important implications for OSM mapping, especially platforms such as HOTOSM which largely rely on faraway amateur curators to provide up-to-date geographical information of a specific area in case of events such as wars, natural disasters, crimes or humanitarian emergencies.