Top 4 Techniques for Data Visualization That Will Improve Your Transit Planning


In your job as a planner, we know you're often fielding basic questions about public transit. We hope this Remix Primer blog series offers another resource as you educate your constituents.

When people think of data visualizations, they tend to think of pie charts, bar graphs, and line charts. Those are all valid visualization methods, but they won’t take your transit planning to the next level.

In order to create the most effective, accurate, and impactful planning process, it’s important to use cutting edge visualization tools. That means putting down the pie charts and turning to modern software that can integrate data sets and bring new ideas forward. Not sure what that looks like? Keep reading to discover the top four techniques for creating data visualization tools and how to implement them.

Big Data Is Here to Stay

Let’s face it: most enterprises today are drowning in data. Transit planners take in information on a constant basis about scheduling, passenger counts, train and bus frequency, and more.

But that’s not even the tip of the iceberg — living in the age of Big Data also means having access to a wealth of data points about other, related subjects. Data about weather, demographics, and health can be highly useful to a transit planner.

Will it rain much this month? How will that affect ridership? What’s the income level of people along this bus route, and what does that mean about their dependence on public transit? How fit and healthy are the people in this neighborhood? Will they be tempted by a new bike share program?

Success in the age of business intelligence means learning how to stop drowning in data and start interpreting it instead. Successful planning means turning information into an advantage and using it to create the most accurate, relevant plan possible for a given region.

Data Visualization Techniques

There is a large range of data visualization techniques available to transit planners. Here are four of the most effective approaches for gathering information, synthesizing data sets, and communicating information to the public.

Technique #1: Charts

‌Bar graphs, line charts, and pie charts all fall into this category. Charts are great for showing how one variable changes over time. They might be used to illustrate changes in one-dimensional metrics like monthly rainfall or ticket sales.

Pie charts are a little bit different. They’re used to show relationships between the components of a group. For example, pie charts can illustrate how a budget is allocated or what percentage of a neighborhood’s residents send their kids to private schools.

Charts are familiar and straightforward. Their great strength is in how accessible they are: they don’t require a lot of explanation, and just about anyone can read them. They are unbeatable when it comes to conveying fairly static information. However, their format doesn’t allow much room for variation or creativity.

Technique #2: Plots‌

Plots allow for more flexibility than charts. Plots make it possible to show relationships between several different data sets on the same graphic. Instead of just looking at how many tickets were sold over the course of a month, a plot can also layer in information about the ticket buyers’ income and whether they travelled alone or with friends or family.

Not only do plots highlight connections between data sets, they can also make it clear when there is no real connection at all. Picture a scatter plot, where individual data points are plotted on the same area. Where data points cluster, there is a clear connection between the data sets. When the points are all isolated, however, it’s evident that there is no real link between the variables being considered.

The most commonly used plots are the scatter plot and the bubble plot, although they are far from the only options. Data scientists also create box plots that have plenty of space to fit large data sets.

Technique #3: Maps

Maps are probably the most familiar form of visualization. However, this category is broader than just road maps, subway maps, and population maps. The category also includes blueprints, electrical maps, website layouts, and more.

Maps are particularly effective for transit planning. It’s useful to map data onto existing transit routes, or onto routes that are being considered for future development. Because maps are such a familiar tool, they are also great for conveying information to the public or powerful decision makers.

Technique #4: Diagrams and Matrices

‌The most familiar diagram is probably the tree diagram, which can be used to show a series of causal relationships between data points. Like tree diagrams, all diagrams can be used to illustrate complicated forms of interaction between different data sets. Some diagrams are hierarchical, showing how changes in different metrics affect each other. Others are multi-dimensional or even cyclical.

Matrices, like diagrams, illustrate the relationships between different data sets. Unlike diagrams, however, matrices aren’t static and can work with data sets that are continuously updating.

The Benefits of Good Data Visualization for Transit Planners

‌Data visualization fills a double need for transit planners.

For one, great visualization helps the planning process. Data representations give planners unique insights into the ways that their ideas will play out in the real world. Secondly, data visualization allows planners to communicate effectively with members of the public, and with key decision makers.

It can be a challenge to convey information using words and numbers alone. After all, most people are not trained to interpret numerical data. That’s why it’s important to be able to use visualizations and graphics.

How Transit Planners Can Use Data Visualization

Data visualization is a great way to combine the power of Big Data with the institutional data gathering that transit planners are already carrying out. Successful transit planners are already using visual representations to layer financial and population data into their plans for new transit routes.

Remix’s GIS Software for transportation planning enables users to put together demographic data with other relevant transportation data, empowering planners to achieve a fuller understanding of the needs of the populations they serve.

Getting Started With Remix

Interested in learning more about Remix’s unique and intuitive tools for creating data visualizations? Get in touch with us today to learn how Remix can help.