Tag Archives: visualizing data

Selecting Mapping Software

We are currently in the process of selecting a mapping service for our internship project. The goals is to map the various museums by country and possibly by region. Part of the final selection will be determined by what we want to accomplish through data visualization.

One of the George Mason interns Andris Straumanis started a Google spreadsheet to help determine what services are available and the pros and cons of each. The list included the following options:

  • Carto Engine
  • GeoDjango
  • ArcGIS
  • Google Maps
  • Google Fusion Tables
  • Tableau
  • Simply Analytics

The table lists cost (if any), data point possibilities, features, mobile capabilities, and restrictions. I also added columns for examples as a way to help visualize the capabilities of the different options. I used Google Fusion Tables for a digital project during my previous position so I created a sample or test with my data for museums in Denmark. I shared this example in the spreadsheet with a link and a screen capture. This Google spreadsheet helped us to communicate and collaborate virtually, a much-needed option with a virtual internship.

Google Fusion Example:

Testing the Denmark museum data in a Google Fusion Table
Testing the Denmark museum data in a Google Fusion Table


During our most recent conference call, we discussed the pros and cons of the suggested services. While there is a strong preference for Google Fusion Tables, we are waiting to get feedback from supervisor Brian Daniels. We want to confirm that this platform meets our data needs and that the logistics are in place for the Smithsonian to host this data output.

Benefits of Google Fusion Tables:

  • Created to simplify merging/fusing multiple tables;
  • Offer developers an API
  • Options to share and embed (Optional template to embed as an iframe on your own site)
  • Customize the Information Window
  • Search location with filters
  • Real-time collaboration (essential for our intern group)
  • Google Group for help
  • All steps can be completed inside own browser
  • Ability to include images
  • Ability to include information beyond museum name and location (e.g. museum description notes)

I have highlighted some of the benefits that stand out to me in terms of our project needs.


This is the second time I’ve worked with Voyant. Both times I was enamored of all the tools available in this tool, but I was also overwhelmed by the choices. The most useful aspect for me is to be able to take a large data and suddenly see all the connections. The first time I used the tools, I uploaded a CVS file (created from an Excel spreadsheet) detailing the artists’ books in the library collection. It was a fast and informative way of seeing where our collection strengths and even weaknesses were. For example, we could now very easily see that books with handmade paper was a strength of the collection. Plus, we had an easy way to share that information with colleagues.

Guide forĀ Voyant

Voyant, Carto, and Palladio

Each tool has advantages and disadvantages.

I appreciate the wide variety of visualizations offered through Voyant (word clouds, bubbles and bubblelines, corpus grid and summary). You are spoiled for choice in how to best visually represent your data. Even through you can share these visualization, such as embedding them on a website, there a a clear downside that you cannot create an account. For example, I can easily go back into Carto and retrieve the visualizations I already created based on the data I uploaded previously. I can then continue to work with this data set, tweaking where needed, and play around with the options. With Voyant, I often find myself having to start all over each time I use it.

Looking more closely at Carto, being able to create an account is a definitely advantage to being able to set up an account. As mentioned, you can create your maps and then go back to them later without starting from scratch. It is also helpful that you can create an account using your existing Google or Github accounts. As a start-up company, Carto made a smart decision in making account option easy to use. However, users may find that they prefer using Google maps or Google fusion for represent information in a similar way. This is where Carto’s fee accounts can offer greater flexibility and storage as an advantage over Google.

Palladio presents an similar issue to Voyant in that there is not account creation. However, options for how to represent your data are numerous. The exercises we did for Palladio show the enormous options for representing the same data set in slightly different ways. It allows scholars to ask and answer a variety of research questions and share those answers with their peers. However, some of the networking visualizations were overwhelming and difficult to read because of the number of nodes. This is where I appreciate the option in Google fusion to de-select certain nodes within a group. However, it seems like Palladio is still more robust in terms of how much it can handle since Google fusion usually cuts me off at a much lower number than the nodes that are present.

Depending on the research questions being asked, all of these tools offer information ways to visualize and share data. I can see how some of these questions would require a combination of these tools.