Uncertainty of Visualizations for SenseMaking in Criminal Intelligence Analysis

Abstract

Uncertainty in visualization is an inevitable issue for sensemaking in criminal intelligence. Accuracy and precision of adopted visualization techniques have got greater role in trustworthiness with the system while finding out insights from crime related dataset. In this paper, we have presented a case study to introduce concepts of uncertainty and provenance and their relevance to crime analysis. Our findings show how uncertainties of visualization pipeline influence cognitive biases, human awareness and trust-building during crime analysis and how provenance can enhance analysis processes that include uncertainties.