Visual data quality analysis for taxi GPS data

We present a novel visual analysis method to systematically discover data quality problems in raw taxi GPS data. It combines semi-supervised active learning and interactive visual exploration. It helps analysts interactively discover unknown data quality problems, and automatically extract known problems. We report analysis results on Beijing taxi GPS data.