Micro-tasking as a method for human assessment and quality control in a geospatial data import

Abstract

Crowd-sourced geospatial data can often be enriched by importing open governmental datasets as long as they are up-to date and of good quality. Unfortunately, merging datasets is not straight forward. In the context of geospatial data, spatial overlaps pose a particular problem, as existing data may be overwritten when a naïve, automated import strategy is employed. For example: OpenStreetMap has imported over 100 open geospatial datasets, but the requirement for human assessment makes this a time-consuming process which requires experienced volunteers or training. In this paper, we propose a hybrid import workflow that combines algorithmic filtering with human assessment using the micro-tasking method. This enables human assessment without the need for complex tools or prior experience. Using an online experiment, we investigated how import speed and accuracy is affected by volunteer experience and partitioning of the micro-task. We conclude that micro-tasking is a viable method for massive quality assessment that does not require volunteers to have prior experience working with geospatial data.