Transparency in practice: using visualization to enhance the interpretability of open data

Urban data is gradually being opened to the public. Tools for exploitation, analysis and discovery of new knowledge in large data sets are the key to enable citizens to make sense of such large amount of data. The purpose of this work is to analyze how data analysis associated with visualization techniques in different levels can lead to the improvement of the interpretability of open data. With the support of machine learning techniques, these visualizations may improve pattern identification in urban data sets. To guide our discussion, a case study was conducted analyzing socioeconomic data released by the Chicago city government. We discussed the use of different visualizations in this scenario, tailored for univariate, bivariate and multivariate analysis. We also performed an evaluation of the different forms of visualization proposed in this work. We could observe that allowing the user to explore open urban data using some specific visualizations may lead to more effective data interpretation.