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.
The basic layout of upper management in a crime network.
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.
Phylogenetic data of coronavirus genomes and mutations from mid-March 2020 shown in symmetric layout using Tom Sawyer 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.
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:
Top five teams for home runs over the past five years
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.
Simple trade history of Washington Nationals star pitcher “Mad Max” Scherzer
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
At AWS re:Invent, we revealed our partnership with Amazon Web Services (AWS) that supports the integration of Tom Sawyer Graph Database Browser and Tom Sawyer Perspectives with Amazon Neptune. Customers can take advantage of the integration and build applications with Perspectives for Amazon’s newest cloud-based graph database service, Neptune.
Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Our flagship product, Tom Sawyer Perspectives, is designed specifically for building and deploying web applications that visualize and analyze highly related datasets. The two products are tightly integrated.
Tom Sawyer Graph Database Browser makes it easy for anyone to load data into Amazon Neptune and start analyzing the graphs. The application is the first and only to visualize and analyze data in Amazon Neptune as well as automatically execute native Neptune load commands to import data directly from Amazon Simple Storage Service (S3) into the database.
Together with AWS Amazon Neptune, our deep integration creates a super convenient and easy-to-use cloud-based, fast, and scalable graph database and visualization solution.
Want to try visualizing AWS Amazon Neptune Data?
It’s easy to test drive the Tom Sawyer Graph Database Browser using a free trial on AWS Marketplace. It works with other graph databases too.
The Graph Database Browser includes a number of unique capabilities that go beyond a typical database browser to enhance the AWS Amazon Neptune user experience. Below are just a few.
