Lab Assignment Week 6

Big Horse Red Saddle Map

By Ryan / October 24, 2022
Creation, Sale, Importation, and Destination of the Big Horse Red Saddle

This map (screenshot)shows the locations that our museum object has been at: It was created in China(purple dot), sold to Hong(green dot), imported by California(red dot) and finally reaching Carleton College (blue dot). You can check the webpage here.

I think that web mapping is a great way to visualize geospatial data especially if the amount of data that we have is huge. For instance, regarding the exercise that we did in class on squirrel data, it would be hard to visualize how the distribution of the squirrels would be across space if we did not map the data. Furthermore, ArcGIS allowed us to choose certain attributes about the squirrels to show such as fur color.

I think that ArcGIS is an extremely helpful web mapping tool in that it allows us to visualize data effectively, and as a result, we can easily find out relationships between attributes.

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3 thoughts on “Big Horse Red Saddle Map”

  1. I totally agree about mapping and big data. Looking through some of the maps that ArcGis already had and with working with the squirrel data set, it was interesting to see how helpful maps can be in displaying large amounts of information. I am excited to hopefully become more familiar with working with these large scale maps in the future.

  2. I think ArcGIS is an excellent web mapping tool as well. Aside from its function in visualizing geospatial data, I also noticed that its group project feature that allows people to contribute to a single project is like GitHub for geospatial data. This increases accessibility in the DH community, bringing more perspective into projects, and leading to more advanced visualizations.

  3. Thanks for including the significance of each point in your description! I agree that mapping is one of the best ways to visualize large data sets (like the squirrels) and am excited to learn more about how it is used to manage humanities data in the 21st century. One thing I am curious about is how to effectively map small data sets that are very broadly distributed, since so much of the final product is occupied by empty space.

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