Author: Jillian Coleen

Hello all,
This is the link to our finished final project. Our Omeka site encompasses popular Disneyland Park rides from “It’s a Small World” to “Autopia” and explores the rich histories of the rides as well as how they have changed over the years. Some rides such as “It’s a Small World” have additional pages that explore what happens to the ride during the holiday season or any special events that happen on that ride. “Matterhorn Bobsleds” has a page that is dedicated to the climbers that climb the mountain and also to Tinkerbell who flies off of the mountain.

Click on ‘browse exhibits’ to explore!
Link is below, hope you enjoy!
-Jillian

Disneyland Rides Exhibit

Previously, I made a blog post about Palladio and creating “special” maps. This week in class, we went a little further and used Palladio to make a map of networks. In the article “Demystifying Networks”, Scott Weingart explains network in the simple terms of “stuff” and their relations.

We were given a data set to use by Marten Durer h people in the time of World War II that showed such things as t heir gender, who gave/received help, and who may have given help or received it. It was a lot of information to take in since it was all on a spreadsheet, but once I put it into Palladio and generated a network map, there were different options that I could choose so I could see the information in different ways.

The first attempt of the networking maps that I made is of the one above. I screenshotted this from the Palladio website and whenever I do that it does come out blurry so the names are probably hard to read on this. For this map, I selected it to show the network between the gives and receivers.  Essentially, this map shows who gave help to who and who received it and the string of networks that these people were in. It’s hard to see, but there are darker circles in this map that represent people who both gave help and received it as well.

The second map I attempted to do was “race”. This map essentially made separate networks of people based on their race and it was actually kind of interesting. Part of the map cut off in the screenshot, but there were more networks down below that were very minimal. The biggest network in the middle was all German names and had multiple repeats. The people with the same last name were networked together. Below the large network were two smaller network of people also grouped together by their race. What I found interesting was that “AushweisNazi” was on the edge of the large network alone connected down to a smaller network. I am not quite sure what that means and couldn’t really figure it out.

I think I had a harder time deciphering what these maps. With the Spacial Maps, everything is clearly labeled and obvious to explain. With networks, there are so many names that are connected that for me, it is hard to tell what they represent. Overall, it is interesting to see how many data points connect though.

In class, we have been talking about the difference between maps and “Spacial Maps”. Essentially, Spacial maps are not your ordinary 2D map but instead have many different layers to it. Spacial Maps can be interactive and show such things as, how many people migrated to an area at a certain time, the distance that a herd of animals traveled, or even where a photograph was taken. In an article by  Jenna Hammerich that we read in class, she talks about how this technology can answer historical questions  such as  “Why did African American families settle almost exclusively on the near north side of St. Louis in the 1940s?”. Below is a link to an example of a Spacial Map made by the Humanities Team at Standford:

https://www.hastac.org/news/check-out-latest-version-palladio-dataviz-platform-humanities-design-stanford

I used the website palladio.designhumanities.org to create my own spacial maps using the Cushman Collection data set.

This is one map that I created. The “dots” are all based off of a data set and represent where each photograph was taken.  To make this map, I simply uploaded the data set and added the layers of geocoordinates and land.

In the map above (I know it is a little blurry that’s how my screenshots kept coming out whenever I saved them) I used the “size point” future on the dots instead. With the size point feature, the dots appear larger where more photographs are documented. I also changed the view of the map to ‘streets’ so that I could see actual state / city names instead of just land. Large dots appear frequently in California so I can learn that a majority of the photos in the Cushman Collection were taken on the West coast of the United States.

Taking it a step further, I focused in on the map in California so I could see which cities actually had the most documented photos. From this, I was able to see that there were a lot of photos scattered along the Bay such as San Francisco and Oakland. However, Salinas, Fresno, and Los Angeles had the largest dot sizes telling me that those cities individually had a lot of photographs taken there.

This map, that I found on http://ed101.bu.edu/StudentDoc/Archives/ED101fa10/mogavero/maps.html is a economic activity map. This map is very similar to the Spacial maps that we have gone over. It has a legend in accordance with colors that shows where in the south there were farmers, forests, manufacturing and trade, livestock, fishing, mining, and places of no activity.

This past week in class, we dedicated our time making exhibits and learning about the ways of Omeka. For me, this was a completely new experience and I had never heard of Omeka before let alone know such exhibits existed online. This is one of my favorite activities that we have done in class and I learned a lot from it mostly because it was very hands on.

First, I learned that a lot goes into attributing a source. Dr. S gave us resources such as wikimedia.org where we could find images on our own and use it to build exhibits on Omeka. While in the past I would have thought that it’s as easy and copy and pasting, the Omeka site taught me that there are endless possibilities to attribute the source and even explain what the picture represents. For example, I used a photo that was a statue of two gladiators. While I definitely needed to attribute who sculpted the statue, where it was from, and what year it was made in, the interesting thing I learned was that the source I was using was actually just a PICTURE of the sculptures and not the physical sculptures themselves. Therefore, I then had to attribute the photographer who took the picture, when it was taken, and where the original picture came from. It was interesting to see how many layers of copyright there actually are.

Next, I really enjoyed creating an exhibit with my group on a subtopic. Like I said, this site was completely new to me and I didn’t even know that it was possible to do this. I thought it was really cool how each of member of my group created their own page yet all of our  pages came together in one exhibit. It was easy to navigate through and a great way to store a lot of knowledge. It was also really interesting to hear all of the other groups present their exhibits and see what their thought process was.

Overall, I thought the Omeka website was a very interesting learning tool and it is something that I would have never thought of. Omeka can be very useful if you are researching something and want to keep track of your attributes and citings as well as also present your information to the world.

-Jillian

https://www.google.com/fusiontables/DataSource?docid=14vX2CCeH1BOV3SxoURmYte2Ua2dBYMNZAiZHMruJ

The link is made public !

-Jillian S.

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