Graphs with Palladio

This past week, we were revealed to the simplicity and complexity behind networks and the visualization of networks. Weingart’s article, “Demystifying Networks,” begins with a real simplification of a network: “a net-like arrangement of threads, wires, etc.” which later incorporated “stuff and relationships.” Moreover, network visualizations explore these relationships in many dimensions: degree, attributes, directionality, and more (Powerpoint about Social Networks).

As a class, we used Palladio to gain first-hand experience with creating network visualization. Using this sample data pertaining to Ralph Neumann, we utilized the Graph function of Palladio.

First, I set the Source to “Giver” and the Target to “Recipient.” Highlighting the “Giver,” however, was not useful, because it didn’t distinguish any of the dots, signifying that everyone was a Giver.

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Instead, I highlighted the Recipients. As a result, a few Recipient dots were highlighted, pointing out who received help.

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Then, I changed the Target to “Time Step Start” and checked “size nodes,” and the following was revealed.

who gave help and when

Here, we can easily see the amount of people that were helped according to who is connected to the larger center dot with a number. However, here we have a questionable large dot on the bottom that has no links.

Still playing with Sources and Targets, I changed the Source to “Recipient.” This graph visualizes who was present to receive help at the same moment of time. But again, we have a dot at the top without any information. This may indicate that there is missing data in our dataset.

visualizes who was present to receive help at the same moment in time

Lastly, I experimented with the Timespan filter. This timespan allows us to limit the graph to only visualize those who received help within this period “0012-01-1 and 0012-12-29.”

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Experimenting with Palladio raised a lot of concerns for me; one being, I should definitely know my data before plugging it into a tool. I am not familiar with how the data was collected or what the numbers represent, which limit my ability to make any conclusions. Secondly, I would prefer if there were more visual options to customize, such as color of nodes or adding arrows to signify direction. Lastly, all the images pasted here are screenshots throughout the process. The download option saves as a .json file that can be reopened in Palladio but not reposted onto a blog like this. A screenshot does not do Palladio justice, because resizing impedes the lengths of lines and size of the font. All in all, it is a groundbreaking tool in network visualization, definitely considering that the data uploaded will not be datamined for other purposes.

Digital Mapping

Advances in technology have made digitizations of maps possible; despite it having pros and cons, this progress has also segued into the concept of spatial history.

Programs such as Google Fusion Tables and Palladio have mapping functions. We were fortunate enough to experiment with a cleaned up comma delimitated (.csv) file, the Cushman Collection.

Using Google Fusion Tables, Google automatically detects the geocoordinates column and places dots for every photo taken at that location. The perks, in my opinion, of Google Fusion Tables is you receive an expanded amount of information when you hover over a dot. In addition, you can utilize street view, which could be helpful in comparing the location up to the date Google maps took the picture with the photo taken in the past.

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On the other hand, Google Fusion Tables’ map function only has two overlays: “map” and “satellite.” So the two images here display the only two options available to view. (Above is Satellite view and below is Map view)

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Google has become so prominent in our society today that it doesn’t get questioned often. However, Patricia Seed, in “A Map is Not a Picture…,” calls us to reexamine our standards and how credible maps are. The borderlines above seem irregularly straight, so I searched for another U.S. map displaying the state borderlines.

2000px-Map_of_USA_with_state_names.svg

This picture is provided by Wikipedia (click the picture for direct link). If we closely examine Wyoming, for example, the borderline on the West is slanted, but on Google Fusion Tables, it is completely straight.

Palladio can look almost exactly identical with Google Fusion Tables’ map, by using the Streets and Terrains tiles. Palladio, for one, has more options for view-ability of the map, but it lacks the street view and amount of information shown when hovering over a dot, like Google Fusion Tables. Palladio only features a number description over a dot.

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Additionally, Palladio offers more functions with the map function. The Timeline below displays a bar graph of how many photos is taken in that time and the colors represent the different types of pictures.

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Above, I highlighted the timeline between ~1947-1949 to only display the photos taken at the time as the dots on the map. This function is especially useful when examining the correlation of time, location, and types of photos taken.

Playing with these two mapping tools, Google Fusion Tables and Palladio, have caused me to be more aware of mapping in general. Though maps can be manipulated to be aesthetically pleasing, examining them closer is the only way to really decipher any problems or information.

Day 1: Palladio (Oct. 29)

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Using the Cushman Collection, we uploaded it to Palladio and explored the Map feature. Above, I created two map layers: Street and Satellite view. Then, I limited the points to display the photos taken between 1940-01-01 to 1942-01-05 by using the Timeline feature. This function is useful, because when you hover over the points, more details show.

Still, lots more to explore.

Is a map a picture?

Technically, yes; a map is a visual image, but subsequently, it is not and should not be treated as a picture. Patricia Seed is very vocal in the argument of “a map is not a picture.” Print has been the “gold standard” of maps, thought to be the most trusted and reliable map, because it has to be accurate to be printed, right? Not right. The digitization of maps that makes reproduction possible has its kinks: subtle changes in axes and in color; however, Seed points out that alterations are sometimes intentional. Maps have been treated as pictures – sellers can only sell what is aesthetically pleasing, thus encouraging them to straighten lines, heighten color contrast, brighten, or darken. On the other hand, digitization has changed the map game for the better: introducing more detailed study of maps, better and easier magnification, and, of course, portability. It is just ironic that digitization is the cause of the loss of fidelity for maps. “Maps cannot be treated as illustrations” – they must maintain their intellectual integrity, or the consequences may be serious (the example used is poor planning of irrigation systems).

"Mappemonde." from the online David Rumsey Map Collection
“Mappemonde.” from the online David Rumsey Map Collection

Take this map, for example; this print of a map is for purchase from the David Rumsey Map Collection. The note entails that the map was “hand colored;” chances are, the colors have been heightened or contrasted in this digital photo and will be in the reproduction. We would not know, though, because it does not state anything about the possibility of being photoshopped. Seed suggests that sellers clearly state “after the original” to explicitly show that it has been altered – changing intellectual property for aesthetics can be harmful.

 

Google Fusion Tables: Cushman Collection

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Using the Cushman Collection and Google Fusion Tables (for the first time), I made a pie chart with the category “Genre 1.” With this graphic, I limited the maximum amount of slices to 5; Google chose the five most reoccurring genre titles to display.

Screen Shot 2015-10-22 at 2.33.11 PMThen, I changed the maximum amount of slices to 15, but it seems as though the maximum it could produce was 13.

These piecharts are helpful to visualize how many pictures are being taken in that specific genre. For example, most of the photos are “identification photographs.”

Comparing these two pie charts also reveals how data publishers could conceal information by limiting what they reveal (the maximum amount of slices in this case).