It’s a tale as old as time: you organize a massive database of facts and figures only to discover that the opening visualization is…a giant hairball! Sure, you can zoom in to 800% and slowly start to see connections, but the time that would take is too exhausting to consider.
Fear not! Our new Bundle Layout, now available in Tom Sawyer Perspectives 9.1.0, makes it possible to untangle, demystify, and succinctly visualize complex datasets.
Read More “Untangle the Hairball with Bundle Layout”
As the amount of data available to law enforcement agencies increases, so does the pressure for them to use that data in efficient and predictive ways to thwart crime. Centrality analysis helps agencies ask specific questions of their big data and get answers quickly.
We’ve applied our graph analytics and visualization expertise to tough problems in law enforcement, fraud detection, cybersecurity, telecommunications, and more. Of the 30+ data analysis algorithms available in Tom Sawyer Perspectives, there are four centrality algorithms that answer questions like:
Read More “Using Centrality Analysis to Fight Crime”
In the past month, the internet has exploded with information about coronavirus and the disease it causes, COVID-19. The vast amount of data feels like too much to keep up with. But that’s where graph and data visualization thrives! So, we used the power of Tom Sawyer Perspectives to visualize the continually updated genomic epidemiology data provided by Nextstrain to reveal previously unseen coronavirus mutations.
Read More “Coronavirus Mutations Revealed with Perspectives”
You gather your data, choose the best layout, and find an analysis method that gets your users the insight they need. But before you can declare victory, you need to assign meaning to the objects in your graph. Adding labels to nodes and edges might seem easy enough, but sometimes—especially with graph edge labels—their placement can cause readability issues. Since a “messy” presentation can compromise the message and meaning of your graph visualization, you’ll want to do all you can to prevent these.
Read More “3 Quick Ways To Perfect Graph Edge Labels”
We can’t quit you, baseball! The season might be over, but we want more. So, we’re dipping into the baseball data to see what else we can learn. Read on for one more run around the bases!
This season, all anyone talked about was home runs. There were 6,770 homers hit during the regular season this year. That’s 665 MORE than the previous record! And exactly half of the teams in the league set franchise home run records. Holy homer!
But, do all these home runs lead teams to the playoffs? We looked at the past five years (2014-2019), pulling the top five teams for home runs in each year. Edge colors indicate:
Here’s the result:
How do other stats affect a team’s success? To find out, we layered in Earned Run Average (ERA), Strikeouts (SO), and Runs Batted In (RBI) one by one.
Read More “Rounding the Bases”
2019 marks the 115th year for World Series baseball in the United States. In a year when home run records were smashed with authority, we thought we’d take a look at America’s pastime through the lens of graph visualization.
Over the next several weeks, we’ll ask two fundamental questions about Major League Baseball (MLB):
Let’s pull some data, make some graphs, and see what we can figure out. Today we’ll:
Come on…let’s play ball!