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:
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.
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.
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!
Put Me In, Coach
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:
Blue Edge: Appeared in playoffs
Green Edge: Won the World Series
Orange Edge: Neither won nor appeared in the playoffs
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.
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):
Who’s on the playoff team rosters? Do most players start on other teams? Which players have been traded and from which teams?
What’s with all the homers? 6,770 home runs were hit during the regular season this year–a new record! Sure, they’re fun to watch, but do they lead teams to the playoffs or the World Series?
Let’s pull some data, make some graphs, and see what we can figure out. Today we’ll:
walk through the over-arching process, using MLB datasets as our subject
drop some knowledge about how the graphs were created
show how we used the customization tools resident in Tom Sawyer Perspectives to easily tailor the graphs to show important statistics we can use in our analysis