Analysis and Visualization Features in PEVNET

Abstract

The information visualization of networks has been a tricky task during the last decade. Visualization of features by way of merging, linking, and grouping of entity attributes is provided to criminal network investigators. It is difficult to understand such large amounts of statistical data. A number of solutions have been proposed to tackle this bulk of information. We have found that the prevailing challenges to information visualization can be eliminated to a large extent by detecting evolving network patterns which are extracted by way of visual analysis of criminal activity based on temporal data, by examining some dynamics of criminal networks, and by making use of some novel interactive features. The current study will help to understand interesting patterns in criminal data by way of visualization. Besides our previously proposed network visualization features, we have appended five new features. These features include ‘Pie-chart feature’ which has been proposed for better ‘details on demand’ facility to the analysts. A ‘Trend analysis feature’ is proposed for visualizing the variation in different crimes over some span of time. The ‘Graphical Trend Analysis feature’ provides a graphical interface to the analysts. There is a unique ‘Encircle feature’, with the aid of which the desired clusters can be dragged away from the dense network for easy manipulation. With ‘Similar node feature’, the analysts may get summarized information regarding the activity of different nodes which are at distant apart. We have made an evaluation of our proposed visualization features by conducting an experiment. Thirty-two participants evaluated the system. The experiment was performed in two phases. In the first phase, a usability evaluation and qualitative feedback was carried out to check whether the features provided adequate results to the users. In the second phase, the comparison of the features had been performed against some other state-of-the-art tool. These tasks were to be performed in the groups of participants. The public data set of Chicago Narcotics was used. We found that the participants, of the PEVNET group, performed the tasks faster as compared to the other techniques used in the experiment. We have demonstrated the usability of the new features with examples by employing the datasets. We have proposed a unique way of visualizing the clustering of data, with which the analyst gets a sound visualization of the data. The usability, of the proposed features, indicates that the crime analysts will get a valuable insight into the criminal networks.