Tag Archives: Mapping

Voyant, CartoDB and Palladio: A Comparison

Using the same data for all three project was an enlightening experience. Intellectually I had accepted that different methodologies would yield varying results and elicit different questions. But actually doing to work has given me a deeper understanding of the magnitude of this concept.

As an archaeologist, I’m always very aware of place. I often ask “where” questions and think of  human activity taking place on the crust of the Earth at specific points. Therefore, CartoDB was fairly intuitive to me. Before beginning to play with the software I already had some idea of what producing a map could tell me. What did surprise me was the depth of information I was able to get about place with the other two tools.

Although I often work with texts to both point me in the direction of sites and to add nuances to the archaeological record, I’ve always struggled conceptualizing text within space. Voyant in conjunction with Carto helped me visualize this relationship. While Voyant gave me visualizations about words that occurred within the seventeen states, Carto helped me make spatial connections and situate these data in a place rather than just a word “Alabama.”

Likewise, Palladio helped me make further connections about the observations I had made in Voyant and Carto. Voyant acted more as a comparative tool. I could see how word frequencies changed across the corpus. Palladio was a comparative tool as well, but graphs visualized magnitude and categories, whereas Voyant was useful in discovering that these categories existed, but was less effective in presenting observations in relation to other data.

The observation I made after looking at the data in all three tools was that there was a significant movement of people after emancipation. Voyant provided the words used to describe this movement like place names and occupations following freedom. CartoDB conveyed how far afield people traveled after emancipation. Finally, Palladio showed me the movement of individuals. That dynamic action across time and space is not something that one application was able to fully convey.

That being said, the Voyant, CartoDB and Palladio each have their specific strengths. Palladio might have a mapping feature, but if your project has a heavy map component, use Carto. Voyant can be used to topic model, but use Palladio to visualize how the topics related to people. Carto can insinuate relationships, but rely upon Palladio to actually connect the dots.

After looking at the three tools side by side I can see real potential for projects that integrate more than one. That being said, I find that academics can get lost in the sea of knowledge. Some times we spend so much time trying to know everything we can about a topic we loose sight of our project. A successful project needs to be able recognize when a tool will be useful and when it will detract from the goal. These three programs are very powerful discovery and publication tools. I find it very challenging to balance discovery with putting knowledge out there. At some point I have to at least pause discovery, draw conclusions, and share what I’ve learned. And sometimes I find it incredibly fruitful to return to the discovery process. Palladio and CartoDB allow for that fluidity, whereas Voyant is much harder to return to.

Using CartoDB: A Reflection and Guide

CartoDB is a free online application that allows users to make GIS maps. The interface is user friendly and fairly straight forward for even the novice to navigate. I wouldn’t consider this a replacement for more powerful programs like ArcGIS, but this is certainly a better tool for projects looking to make clean, professional spatial visualizations. There are certainly tools that make deeper analysis possible, but not to the extent something like Arc would.

I tinkered with CartoDB using data derived from the WPA Slave Narratives, which I explained more fully in my last post about Voyant. Many interviews had GPS coordinates: where the interview occurred and where the interviewee had been enslaved. Those that did not have exact points were set in the middle of their city/region.  This exercise was intended to visualize the spatial elements of these interviews. As I discuss how to use some of the available features,  I’ll also reflect on the utility of the tools in this endeavor.

Getting Started

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The first thing you need to do is create an account: https://carto.com/signup. After that you need some data, which needs to be organized into a spread sheet (I used data that was prepared in Excel, but other options are available). Hopefully, you’ve prepared the data before even getting as far as signing up or logging in (there is functionality to draw your own polygons, lines, and points, but I did not explore those features).  To add new data select “datasets” from next to your username (where is says “map” in the above screen shot).
Add New photo Carto_Data_New_zpsh60sqoq2.jpg
Then, all you do is upload, or create, your data from the options shown above. I really like that users can create their data from the software they’re comfortable with, which makes sharing data easier. I did not create the data I used for my project, my professor had certain goals in mind about what he wanted our maps to show. The way tables are organized influences the kinds of information map visualizations will display, so this functionality, though seemingly simple, is actually pretty powerful.

Your Data

Data Table photo Carto_DataView_zpsct483rsn.jpg
Now that you’ve got your data uploaded, you’re ready to start mapping. But, first, you might want to go through and make sure Carto understands your data. The above screenshot is what I saw upon uploading my data. I had to make sure all the numbers were recognized as such, but especially that the date was an actual date. I find this to be a helpful exercise in ensuring you understand your data, especially if you didn’t create it. Before I even saw the map, I began to wonder about how these categories related to each other on the map. Chances are, that if you’ve made your own data, you already have an idea.

Making Your Map(s)

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Now, all you have to do is click the “Map View” tab.  Carto automatically plots your points and zooms to the extent of those points. Like other GIS software you can work in layers if you so choose.
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When you click on the side bar where you see the “+” and “1”, the tools open. I started by clicking the speech bubble (hover and see it called “infowindow“) to select what columns of my data I wanted to be displayed when I clicked on points that would help me identify one point from another, like the name of the interviewee. That was helpful for me, not just because my instructor told us to do that first, but because I was able to start visualizing what I might want my map to look like and the kind of information I wanted to show.

The wizard tab is the tool you’ll spend the most time with. This is where you select what kind of map you want. The default is “simple” which just plots your points. Here you’ve really got to think about what kind of information you want to convey. I was working with two sets of data: where the interviews occurred and where the interviewees were enslaved. Although I could have mapped both of these using two layers, it wouldn’t be very helpful as just points on a map, so I looked (and was instructed) to view the data in two separate maps.
Category photo Carto_Category_zpsicbzernq.jpg
As I played with the wizard I found that some elicited interesting questions while some just muddied the data. The map of where interviews occurred tells me more about the interviewers than interviewees. The “category” wizard gave a unique color to each interviewer and patterns emerged regarded how much or little they traveled. The “torque” map can be equated with the timeline feature in ArcGIS. I had dates for when the interviews occurred and was able to play an animation of when the interviews occurred. This allows for temporal questions to be asked: When was the zenith of the project? How were the interviews carried out- in a logical progression across the state or seemingly randomly? The cluster map was also useful for analyzing the data for this map. Where were the most interviews? Kernal density maps were not particularly helpful in illuminating this data, however. The interviews were already pretty tightly clustered in Alabama.

The other set of data, where the interviewees had been enslaved, did lend toward kernel density maps since they were spread out over a larger area.The simple map was still helpful getting an idea of where these people had been enslaved, but given the limitation that the exact X,Y coordinates were more often than not uncertain, the information presented as particular points might be misleading. Something like a heat or density map gives a more honest visualization of the available data.
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In the end, I did combine the two maps to see how the locations of the interviews related on the face of the globe to the places of enslavement. I used a heat map for the places of enslavement and simple points for the interview. The resulting two layer map revealed that most of interviewees were enslaved in the metropolitan areas they were interviewed in. The major pitfall is that the map is rather hard to read, since Carto considers heat maps to be animations and therefore must be the top layer. I would have preferred for the points to be the top layer so they could be more visible. I played with the transparency of the heat map until I felt I had struck a balance between the visibility of the simple map and the color saturation of the heat map. You can add text to your map, but I found great difficulty producing a legend, the key component of conveying information.

A Brief Reflection

Mapping allowed me to ask “where” questions, which comes as no surprise. This exercise also elicited questions that require returning to the text for answers. Why did the people who were enslaved elsewhere move to Alabama? Are those ex-slaves who were enslaved in Alabama the same who were interviewed? Why did certain interviewers conduct their work where they did? Plotting points on a map is helpful because it reminds the researcher that these interviews were conducted in a place and place influences thought. Just how place and thought interact is the job of the researcher to investigate, but these questions are best broached with a map visualization.