Comparative Analysis between Split and HierarchyMap Treemap Algorithms for Visualizing Hierarchical Data

We carried out comparative analysis between Split treemap algorithm and a more recently introduced treemap algorithm called HierarchyMap. HierrachyMap and Split are Treemap Visualization methods for representing large volume of hierarchical information on a 2-dimensional space. Split layout algorithm has been developed much earlier as an ordered layout algorithm with capability to preserve order and reduce aspect ratio. HierarchyMap is a newer ordered treemap algorithm developed to overcome certain deficiencies of the Split layout algorithm. The two algorithms were analyzed to compare their rate of complexity. They were also implemented using object-oriented programming tool and compared using a number of standard metrics for measuring treemap algorithms. Their implementation shows that HierarchyMap and Split although maintain the same level of data ordering and usability but HierarchyMap algorithm has better aspect ratio, better readability, low run-time, and less number of thin rectangles compared to Split treemap algorithm. Since aspect ratio is an important metric for determining the efficiency of treemaps on 2-D and small screens, and the result of the analysis shows that HierarchyMap is better efficient than Split treemap alagorithm, we conlude that HierarchyMap is more efficient than Split treemap algorithm

Overlap-free Drawing of Generalized Pythagoras Trees for Hierarchy Visualization

Generalized Pythagoras trees were developed for visualizing hierarchical data, producing organic, fractal-like representations. However, the drawback of the original layout algorithm is visual overlap of tree branches. To avoid such overlap, we introduce an adapted drawing algorithm using ellipses instead of circles to recursively place tree nodes representing the subhierarchies. Our technique is demonstrated by resolving overlap in diverse real-world and generated datasets, while comparing the results to the original approach

Health Figures: An Open Source JavaScript Library for Health Data Visualization

The way we look at data has a great impact on how we can understand it, particularly when the data is related to health and wellness. Due to the increased use of self-tracking devices and the ongoing shift towards preventive medicine, better understanding of our health data is an important part of improving the general welfare of the citizens. Electronic Health Records, self-tracking devices and mobile applications provide a rich variety of data but it often becomes difficult to understand. We implemented the hFigures library inspired on the hGraph visualization with additional improvements. The purpose of the library is to provide a visual representation of the evolution of health measurements in a complete and useful manner. We researched the usefulness and usability of the library by building an application for health data visualization in a health coaching program. We performed a user evaluation with Heuristic Evaluation, Controlled User Testing and Usability Questionnaires. In the Heuristics Evaluation the average response was 6.3 out of 7 points and the Cognitive Walkthrough done by usability experts indicated no design or mismatch errors. In the CSUQ usability test the system obtained an average score of 6.13 out of 7, and in the ASQ usability test the overall satisfaction score was 6.64 out of 7. We developed hFigures, an open source library for visualizing a complete, accurate and normalized graphical representation of health data. The idea is based on the concept of the hGraph but it provides additional key features, including a comparison of multiple health measurements over time. We conducted a usability evaluation of the library as a key component of an application for health and wellness monitoring. The results indicate that the data visualization library was helpful in assisting users in understanding health data and its evolution over time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016

Cabinet Tree: an orthogonal enclosure approach to visualizing and exploring big data

Treemaps are well-known for visualizing hierarchical data. Most related approaches have been focused on layout algorithms and paid little attention to other display properties and interactions. Furthermore, the structural information in conventional Treemaps is too implicit for viewers to perceive. This paper presents Cabinet Tree, an approach that: i) draws branches explicitly to show relational structures, ii) adapts a space-optimized layout for leaves and maximizes the space utilization, iii) uses coloring and labeling strategies to clearly reveal patterns and contrast different attributes intuitively. We also apply the continuous node selection and detail window techniques to support user interaction with different levels of the hierarchies. Our quantitative evaluations demonstrate that Cabinet Tree achieves good scalability for increased resolutions and big datasets

InstaAnalytica

This project aims to analyze social media trends using Instagram as the primary source of data extraction and provides insights such as understanding engagement rates, top hashtags, optimal posting times, and post rankings based on engagement rates and sentiment scores thus the users can understand their audience and improve their posting strategies. Firstly, the profile trend chart and area chart based on the average engagement rate is generated to obtain day, week, or month engagement rates. Secondly, text preprocessing is done before generating a word cloud displaying the most frequently occurring words in the captions. The size of each word in the cloud is proportional to its frequency in the captions. By analyzing this word cloud, one can get an overall idea of the themes and topics that the user tends to discuss in their captions. Thirdly, the top hashtags are generated, and fourthly, using engagement rate as a metric the optimal time and day of the week are determined for the posts on a user account. Finally, posts are ranked based on their engagement rates and sentiment scores. By leveraging these analytics, users can better understand the type of content that resonates with their followers, which can inform their posting strategies to increase engagement and grow their following

Unboxing Cluster Heatmaps

Background: Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks. Results: We developed an approach to “unbox” the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results. Conclusions: The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps

SenseCluster for exploring large data repositories

Exploring and making sense of large data repositories has become a daunting task. This is especially the case for end users who often have limited access to the data due to the complexity of the retrieval process and limited availability of IT support for developing custom queries and reports based on the data. Consequently, traditional interfaces are no longer meeting these requirements. Instead, novel interfaces are required to fully support the sense making process. In this paper, we followed a design science approach and introduced a query clustering system (Sense Cluster) that could serve as a quick exploration tool for making better sense of large data repositories. We also present an evaluation of the effectiveness of our artifact using cognitive walkthroughs

An Evaluation-Guided Approach for Effective Data Visualization on Tablets

There is a rising trend of data analysis and visualization tasks being performed on a tablet device. Apps with interactive data visualization capabilities are available for a wide variety of domains. We investigate whether users grasp how to effectively interpret and interact with visualizations. We conducted a detailed user evaluation to study the abilities of individuals with respect to analyzing data on a tablet through an interactive visualization app. Based upon the results of the user evaluation, we find that most subjects performed well at understanding and interacting with simple visualizations, specifically tables and line charts. A majority of the subjects struggled with identifying interactive widgets, recognizing interactive widgets with overloaded functionality, and understanding visualizations which do not display data for sorted attributes. Based on our study, we identify guidelines for designers and developers of mobile data visualization apps that include recommendations for effective data representation and interaction