A User Study on Route Evaluation

Currently existing route direction representation like arrow on the route is vastly used and intuitive however it is not guaranteed to meet the requirement of application level use. With many routes overlapping with each other on a map, directions become vague and ambiguous for user to recognize. Route visualization is a vital part of map-related application. We developed several route direction representations to investigate which one performs best when user view the routes on actual map. The shapes we chose for these representations respectively are curve, arrow, triangle, right triangle, and taper. We established a complete mechanism to test route representations. First, it randomly generates 70 points with real GPS coordinates then randomly creates around 20 routes, each of which cover every point generated. Second, it queries the corresponding detailed geographical information of each route from Bing map. Third, we build up a website to displays images that contains different route direction representations based on the geographical information. Finally, subjects are asked to perform tasks on our website so that we can collect performance data such as accuracy, response time and so on. Our expected result in the pilot survey in lab, the result should show the combination of curve routes representation is better than all other representations. From the result demonstrated, the curve representation will be recommended in application-level use. We will continue investigating more combination of representations in further research and use crowdsourcing to let more subjects participate the survey on our website

MetricsVis: A Visual Analytics Tool for Evaluating Multidimensional Data

Visualization for multidimensional data is a popular topic and many methods have been created to visualize this type of data. We developed a visual analytics tool to visualize multidimensional data for two distinct fields: resource allocation in law enforcement departments and phenotype traits of sorghum crops. For law enforcement departments, we designed a visualization tool to measure and compare police officer’s experience in different types of crimes. Our tool supports the analysis of the amount of experience each officer has in each crime category. Meanwhile, the field crop modeling project requires the visualization of the measured value of multiple traits of each sorghum category. In general, our visualization tool is now able to represent these multidimensional data in multiple graphs and charts, with a rich interaction set of selecting, grouping, and filtering. MetricsVis has been expanded this summer with the addition of 6 new graphs, the ability to use the sorghum crops dataset, and more data manipulation features. By being able to explore the data through several graphs and charts at the same time, this allows the user to easily query the data or find peculiarities in the data that they would have otherwise missed. We describe several case studies to validate the importance of our tool in analyzing the data in both projects. In the future, we would like to expand our tool for other similar datasets

Visual Analytics Law Enforcement Toolkit

VALET, visual analytics law enforcement toolkit, is an interactive toolkit developed for law enforcement agencies to explore concerned crime information and make police resource allocation strategies. As a visual analytics toolkit, VALET is coupled with data collection, data analytics and data prediction. The objective of VALET is to assist law enforcement agencies to reduce crime rate by wisely allocating police resource based on the analytics of historical crime records. The program incorporates three steps to generate police patrol route and policeman allocation. The first step is to generate crime hotspots and crime contours of collected crime data. The next step is to analyze historical crime information and predict potential defects. Finally, the program is to compute police patrol routes and allocate police resource based on schedule and specialty. The results from the program allow us to generate risky area for different type of crimes, and evaluate policemen’s performance in dealing with different type of crimes. Thus, police department is able to assign police officers to designed patrol routes that suggested by prediction tool based on policemen’s specialty. This would take advantage of crime prediction and decrease the time of handling criminal activities. With VALET, law enforcement agencies are able to explore concerned crime information intelligently. At the same time, police department is prompted to allocate police resource wisely

MolSieve: A Progressive Visual Analytics System for Molecular Dynamics Simulations

Molecular Dynamics (MD) simulations are ubiquitous in cutting-edge physio-chemical research. They provide critical insights into how a physical system evolves over time given a model of interatomic interactions. Understanding a system's evolution is key to selecting the best candidates for new drugs, materials for manufacturing, and countless other practical applications. With today's technology, these simulations can encompass millions of unit transitions between discrete molecular structures, spanning up to several milliseconds of real time. Attempting to perform a brute-force analysis with data-sets of this size is not only computationally impractical, but would not shed light on the physically-relevant features of the data. Moreover, there is a need to analyze simulation ensembles in order to compare similar processes in differing environments. These problems call for an approach that is analytically transparent, computationally efficient, and flexible enough to handle the variety found in materials based research. In order to address these problems, we introduce MolSieve, a progressive visual analytics system that enables the comparison of multiple long-duration simulations. Using MolSieve, analysts are able to quickly identify and compare regions of interest within immense simulations through its combination of control charts, data-reduction techniques, and highly informative visual components. A simple programming interface is provided which allows experts to fit MolSieve to their needs. To demonstrate the efficacy of our approach, we present two case studies of MolSieve and report on findings from domain collaborators.Comment: Updated references to GPCC

VAST2015 Challenge Two: Event Analysis from Communication Data

Social Media is a very good example of a large communication network. Typically, most data generated by social media are embedded with spatiotemporal stamps which hold crucial information than can help law enforcement agencies analyze the intensity of a calamity or chaos. Currently, not much research is done in designing a visual analytics system that incorporates clustering methods to analyze communication patterns. This research seeks to develop an analysis tool that represents such diverse data sets in user-friendly visual forms, to provide insights into the data that will improve the efficiency of event analysis. To analyze this data we have employed a community detection algorithm that will help us group people together who exhibit similar behavior. To visualize these clusters and the relationships between each cluster we have used a force-directed graph which will help law enforcement officials interpret communication patterns and discover suspicious ones. Each cluster in the graph is colored distinctly and a list is also provided to display the people arranged in descending order of their communication frequencies with other people in the same cluster. This visualization allows users to find the most influential people in a group/cluster. The tool designed has been used to analyze the VAST 2015 Mini-Challenge 2 Data Set in order to detect some suspicious groups of individuals. Although this tool has been currently designed to analyze the VAST 2015 datasets, it can easily be modified to visualize other data sets such as twitter or any other similar social media source

VAST 2014, Challenge One: Event Analysis Within Big Data

News articles and email conversation data could be very useful in the analysis of developing and ongoing events, such as preventing a potential threat or possibly even locating a missing person. There is currently no “one-size-fits-all” solution to visualizing diverse forms of datasets and their sheer sizes are far too great to efficiently analyze by brute force methods. However, using principles of Visual Analytics, it is possible to take this information overload and transform it into a useful tool to help increase the efficiency of event analysis. A visualization system was developed for email conversation networks using web technologies. An interactive force diagram was constructed, allowing for an easy analysis of communication links between people. This force diagram was able to be filtered down to specific people or emails and with color coded nodes based on positions held in a company. A dynamic list of email headers was created that allowed for filtering based on specifically chosen people or by user defined importance. Lastly, a slide-out menu was implemented to allow for a side by side comparison between two selected people by displaying their employee records. The system created was used on a data set from the VAST 2014 mini challenge 1 and it allowed for the successful analysis of a fictional companies email network. Although this specific system was designed around the VAST 2014 data set, it could easily be modified to work with diverse email conversation network data to aid in various forms of analysis

FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images

Feature selection is used in machine learning to improve predictions, decrease computation time, reduce noise, and tune models based on limited sample data. In this article, we present FeatureExplorer, a visual analytics system that supports the dynamic evaluation of regression models and importance of feature subsets through the interactive selection of features in high-dimensional feature spaces typical of hyperspectral images. The interactive system allows users to iteratively refine and diagnose the model by selecting features based on their domain knowledge, interchangeable (correlated) features, feature importance, and the resulting model performance.Comment: To appear in IEEE VIS 2019 Short Paper

Route Packing: Geospatially-Accurate Visualization of Route Networks

We present route packing}, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes. The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical colors, and decorates them with glyphs to show their directions. Furthermore, nodes representing sources and sinks use glyphs to indicate whether routes stop at the node or merely pass through it. We conducted a crowd-sourced user study investigating route tracing performance with road networks visualized using our route packing technique. Our findings highlight the visual parameters under which the technique yields optimal performance