These are a few of my recent visualization projects, including a few each from my personal, academic, and professional lives.

Beer Across America

I made Beer Across America to visualize the The Beer Mapping Project, a database of beer locations throughout America. There are five types of locations, and this visualization shows you how each state in America compares to the other states for each type of location.

Check out Beer Across America!

Beer Across America

A Scalable Visualization for Comparing Multiple Large Sets of Attributes for Malware Analysis

Recently, the number of observed malware samples has rapidly increased, expanding the workload for malware analysts. Most of these samples are not truly unique, but are related through shared attributes. Identifying these attributes can enable analysts to reuse analysis and reduce their workload. Visualizing malware attributes as sets could enable analysts to better understand the similarities and differences between malware. However, existing set visualizations have difficulty displaying hundreds of sets with thousands of elements, and are not designed to compare different types of elements between sets, such as the imported DLLs and callback domains across malware samples. Such analysis might help analysts, for example, to understand if a group of malware samples are behaviorally different or merely changing where they send data.

To support comparisons between malware samples’ attributes we developed the Similarity Evidence Explorer for Malware (SEEM), a scalable visualization tool for simultaneously comparing a large corpus of malware across multiple sets of attributes (such as the sets of printable strings and function calls). SEEM’s novel design breaks down malware attributes into sets of meaningful categories to compare across malware samples, and further incorporates set comparison overviews and dynamic filtering to allow SEEM to scale to hundreds of malware samples while still allowing analysts to compare thousands of attributes between samples.


Gove R, Saxe J, Gold S, Long A, and Bergamo G (2014), "A Scalable Visualization for Comparing Multiple Large Sets of Attributes for Malware Analysis", VizSec 2014. Paper PDF

A Scalable Visualization for Comparing Multiple Large Sets of Attributes for Malware Analysis

Visualizing Uncertainty in Project Management

Organizations face complex decisions about cost, schedule, and risk management activities. Project managers often employ probabilistic methods to capture inherent uncertainty in project execution. Currently project managers use scatter plots, Gantt charts, and bar charts of sensitivity metrics to analyze this uncertainty. These tools do not adequately enable project managers to conduct in-depth analysis of the many probabilistic critical paths (the most time consuming paths through an uncertain schedule) or uncertain resource usage phased over time.

We propose two visualization techniques to help analysts and project managers develop better project plans:

  1. Visualize critical path trees with LifeFlow diagrams, and use milestone folding to reduce the complexity of the critical path trees.
  2. Visualize uncertainty in resource utilization estimates over time using a variation on 4-Dimensional heat maps.

These two visualizations support analyses of all probabilistic critical paths and all resource utilization estimates by showing overviews of the data, tasks that are not well supported in existing tools. We demonstrate the usefulness of these methods by applying them to two government projects, and we find that these visualizations can highlight insights about uncertain schedules that can be difficult to ascertain using standard cost and schedule risk analysis tools.


Gove R and Herzog B (2013), "Visualizing Uncertain Critical Paths in Schedule Management", IEEE VIS 2013. Paper PDF

Gove R and Herzog B (2013), "4D Heat Maps: Visualizing Uncertain Resource Utilization Over Time", IEEE VIS 2013. Paper PDF

Polaris critical path tree Polaris resource utilization heat map

Non-contiguous Cartogram of US Electoral Map

This visualization shows the USA electoral map, with states sized by the number of electoral votes for each state.

I created this visualization because I thought most electoral map visualizations don't tell the whole story. Most maps try to size states according to their geographic area, but visually this makes Wyoming look like it has much more weight in the election than D.C., even though they both have the same number of electoral votes. The New York Times created a great interactive electoral map that solves that problem, but it also makes all the states square shaped. I thought it would be fun to take an alternate approach and retain the states' shapes in order to support recognition over recall.

Note that this visualization makes no attempt to show how Maine or Nebraska's votes might be separated. The NY Times visualization does show this, however.

View the interactive version of this map.

Non-contiguous cartogram of the US electoral map

NetVisia: Heat Map Visualization of Network Evolution

NetVisia is a social network visualization system designed to support users in exploring temporal evolution in networks by using heat maps to display node attribute changes over time. NetVisia’s novel contributions to network visualizations are to (1) cluster nodes in the heat map by similar metric values instead of by topological similarity, and (2) align nodes in the heat map by events.


Gove R, Gramsky N, Kirby R, Sefer E, Sopan A, Dunne C, Shneiderman B, and Taieb-Maimon M, "NetVisia: Heat Map & Matrix Visualization of Dynamic Social Network Statistics & Content", in SocialCom '11: Proc. 2011 IEEE 3rd International Conference on Social Computing. pp. 19-26. (Acceptance Rate: 9.8%, 193/1969). Paper PDF

NetVisia overview NetVisia network evolution heat map