TLViz - A Trend Visualization Tool for Readily Reusable (R-R) Data

TLViz - A Trend Visualization Tool for Readily Reusable (R-R) Data

The TimeLine Visualizer (TLViz) is an easy to use Windows PC tool. TLViz was originally conceived and developed for OpenVMS Engineering by Ian Megarity at Hewlett Packard in 2001. It was part of what became known as the T4 & Friends project (more about T4 in future posts).

Right from the start, TLViz was expressly designed to work directly with readily-reusable computer system trend data stored in CSV (comma separated values) files. The TLViz format for CSV files obeyed the few simple R-R rules outlined in our previous post on readily reusable data.

Since TLViz is a tool intended to dramatically enhance productivity for dealing with trend data through visualization, the best way to understand its capabilities is for you to try it out on some sample trend data yourself.

A working copy of the TLViz software can be downloaded without charge from the HP's T4 & Friends web site. It will take you just a few minutes to install it on your Windows PC.

At the HP T4 & Friends web site you will find additional background material about TLViz and about the T4 project. You might want to check out the T4 FAQ page and take a look at the Technical Journal Article on TimeLine Collaboration that's also available at that site.

In future posts, we will work through some examples showing the highlights of TLViz' capabilities. In the meantime, once you have it up and running on your PC, you can try out TLViz yourself with the following sample readily-reusable CSV trend data file. The source for this data is World Watch State of the World 2002. Many independent factors from many different areas of interest were combined together in a single file with each covering the same 1990 to 2000 time frame. The areas combined together include

• world population
• travel
• nuclear weapons
• population
• agriculture
• armed forces manpower
• fossil fuel use.

In the next post, we will show some possible impressions you might create/discover by playing with this composite readily reusable data file using TLViz by generating a few sample output graphs.

Update: January 2nd, 2006, 4:15 PM - If you would like a copy of the RR file, please send mail to steve.lieman@trendsthatmatter.com and I will forward it to you. We are working out different possible ways to make it easy for you to download such files in the future but they are not quite ready for prime time yet. My apologies for any inconvenience this may have caused you.

Timeline Collaboration Principles

The initial starting belief of this Change Over Time blog is that if you want to change the world, the best approach is to build better tools and then learn how to harness their power.

Peter Drucker tells us that FOCUS is the key to success and we follow his advice with a focus on timelines and trend data that tracks the areas of our lives that are most important to us. A key to unlocking the meaning of these data is a continuing search for the tools and methods and principles that best help us analyze, visualize, report and discuss our findings. We are on the lookout for tools that simplify, clarify, and especially those that save us time as we share our findings and collaborate with expert and non-expert alike. We wish to discover what the data means for us in our lives and what actional steps we might take for making the world a better place.

Why do we focus on trends and timelines?

First: Timeline data is often widely available for a substantical collection of key measures in every area of human interest. We are simply overflowing with such data. Where it is not available, it appears almost always possible to create a new data collector that will gather the missing metrics.

Second: Our observation is that most of the time, the available data is not put to its best use as key principles that would guarantee success are openly violated. Lots of opportunity appears within easy reach.

Third: In one domain after another, we have been establishing and documenting proof that substantial improvements in how we use trend data are already available or well within our reach by following a straightforward set of rules and principles and we can point to a growing number of examples on the web that show these approaches in action.

Fourth: In some cases, the existing work makes collaboration (especially between expert and non-expert) somewhat easier, but the collaboration aspect of making best use of trend data does not seem to have been actively explored. I believe that a handful of principles and standard practices can help us learn how to collaborate better by at least an order of magnitude. As we do so, we will advance towards having better and better control for shaping the future and achieving our fondest dreams.

Limited examples of how to use trend data more powerfully are popping up on the web. One of our goals on this blog is to find these examples of excellent practice. We want to use these best practices as models for what is possible if the underlying principles were applied to other domains and trend data collections. For example, we gave some examples drawn from the St Louis Federal Reserve Bank's interactive trending capabilitites named FRED. Other examples can be found at The Big Picture , the Bureau of Labor Statistics, Bureau of Justice Statistics , and Professor Pollkatz .

Here are the TimeLine Collaboration Principles that I believe are going to prove most important to the goal of this blog of making the best use of trend data. We have already discussed some of these in previous posts and will be preparing additional posts for these principles to explain the logic behind them in greater detail.

1. Share the data series with the chart. Make sure it is readily reausable
2. Multi-dimensionality is key
3. Data set includes entire time range even if chart doesn't
4. Explain how calculated quantities were obtained
5. Make sure the explanatory text is in close physical proximity to the trend chart
6. Data + Charts + Text creates a full package that encourages further conversation
7. Ask the expert in the subject matter domain: What are the most important factors?
8. Then, make sure you measure and record and create a timeline history of every one of these
9. If you have the most important factors, you'll find charts with but a single variable still tell a powerful story
10. Make sure the Axes and Titles and other text graphics are easily readable