In this article, Lincoln Mullen discusses the practice of lynching as well as the reasoning behind lynching. He also shows and describes multiple visuals that were made to help readers visualize the prevalence of lynchings and to “awaken the public conscience by making the extent of lynching visible.” But, how affective are these visuals? The first visual is a chart made to show the number of lynchings per week by year from 1877 to 1950. But, the chart is unorganized and makes me feel as though I’m looking at a game of Mine Sweeper. The different shades of red are too similar to tell the number of lynchings that are being represented and all of this data is squeezed into too small of a space. The second visual is much more helpful, depicting the number of lynchings per year using a line graph. This graph clearly shows trends, is well organized, and is easy to understand. The third visual is an interactive map of the United States that shows number of lynchings per county. When the reader clicks on a county, they can see the number of lynchings, number of victims, and dates of the first and last lynchings in each specific county. I believe that interactive visuals are a great way to portray information to readers. The visual is another map, but it is less understandable because it is much older and not interactive, so this is understandable and possibly the best map the creators could make at the time. The final two visuals are both graphs of number of executions by race from 1800 to 2002. Although the graphs are similar, I believe that the second graph shows the information much more clearly than the first because it is not filled in and a reader can clearly track the different lines. SJ said “I genuinely believe the visualizations produced did not help with any new or essential trends,” but I disagree. The visuals make these trends accessible and understandable to the public.

The video by Loften and Vaughan-Lee follows the story of Jim Enote, a Zuni farmer who is creating unique maps that prioritizes story-telling and common ancestry. Enote is pushing our standard definition of what constitutes a map by incorporating personal identities into the map. Rather than viewing the area from an aerial 2D perspective, his maps focus on invoking the sense of place through depictions of landscape and areas noteworthy to the Zuni people. I agree with KS’s skepticism in their post about whether Enote’s work truly classified as a map. While Enote’s work depicts important landscape and features of the Zuni area, it has more value in its ability to envoke feelings and histories for the Zuni people rather than being an accurate representation of the area. While not practical, Enote’s maps allow him to tell a different story and make a statement that you can’t with classically structured maps.

The Native cartography video made me think a lot about how coming from different backgrounds we see things very differently. And often times these different backgrounds are not represented in our everyday world, in this case maps.

For this post I wanted to share a series of photographs by Edward Burtynsky which he has called “Anthropocene”. These photographs relate to Enote’s work in some ways as they too bring forward a new narrative, a narrative about the environmental poor. The aerial photographs he takes are mesmerising, because from a first look you’re not sure what you’re looking at. With a small description, you start to see the saws, the logs, and the phosphorous. These are places in our world that exist but not advertised because it hurts big corporation businesses. Sharing these images sparks conversation about pollution, similar to how Enote’s maps spark conversation about the Zuni people. These images recognises these places are vulnerable, and in danger.

I wonder if more images about the mills in Lewiston/Auburn are shared with the community will people spark more conversation . In some ways, the museum is already doing that by telling the history, giving the mills and the people who were there recognition.

 Morenci Copper Mine

Logbooms, Canada

Saw Mills

The brief article describes the introduction of data analysis software that help in distinguishing geographical locations mentioned in text based on their context and proximity to other adjectives that describe the unique locations. This was used to map out locations of interviews as well as the locations mentioned in the interviews. This is a useful way to understand and analyse migration patterns however the visualizations that explain this data are quite confusing. By placing only a section of what seems to be some landmass, I am unable to identify this location as a continent, country or state.  Also, the labels are to little to offer any form of guidance and the only thing I am able to deduce from this data presentation is that people moved around and lived in an area that seems to be the west coast of some bigger area.

For the reading “Locating Place Names At Scale: Using Natural Language Processing To Identify Geographical Information In Text”, I mostly was focused on the visualizations shown and also thought about the importance of knowing specification. Using text with a lack of specification has potential to cause real errors in data taken from that text, which is why NER (Named Entity Recognition) seems extremely important and useful to me in terms of collecting data from text. I liked both of the visualizations presented in this article because they were clear and easy to see, and centered on a specific location. However, I would be interested to see how both of these visualizations would look if the entire country was in the picture. The two questions posed in the post by TB intrigued me: “What if the location found is not what it seems to be? Does the algorithm discard the term if there is too much ambiguity?”. While it is useful and important, there are definitely possible problems that come to mind when reading about this process.

The video produced by AEON depicts the power of visualization on the basis of remembrance and understanding. Visualization of mapping areas through traditional illustration provides a better connection to what the land truly consists of. The narrator exclaims the power of remembrance, focusing on the thoughts from elders and how important it is to remember their history, similar to the power of the maps. This visualization of the areas of his property may not be understood from anyone from any background, but can be historically understood by those who are Zuni and forever looked at as historical data specimens. The author of “Mapping” makes an interesting point about elaboration within the video. Although it was clear that the point was about mapping and the best way in which to do so, some aspects of how the mapping works and its importance could have been more deeply described.

I thought the video “Native cartography: a bold mapmaking project that challenges Western notions of place” was very thought-provoking. There were a few parts I wished he elaborated on more, such as “more lands have been lost to Native peoples through mapping than physical conflict”. I thought his designs were cool, although I don’t know if I would place them in the same category as the maps I’m used to (but maybe that’s because I haven’t learned otherwise). It felt more like a piece of art that told a story. I’m not sure that is the same purpose of satellite view on Google Maps. I think at its core this video showed that there’s a story behind every piece of earth, and that is something that is extremely important to never forget.  When we work with data that deals with history, we have to remember that each entry is a part of an individual’s experience, and the human experience as a whole.

There are two aspects of this piece that are particularly intriguing. The first is that there are STILL more aspects of data that can be missed or misinterpreted in analysis. Each week, the readings reveal another aspect that I never would have thought could a) be misleading or b) be missed entirely, yet this week showed that place-based data also falls into this category (Given that main has a Norway, Paris, Denmark, Naples, Sweden, Poland, Mexico, Peru, and China, perhaps I should have considered this earlier). The second intriguing aspect is the technology’s ability to “locate place names using the document’s context.” Given that humans have trouble discerning important information using context clues, the idea that technology has this ability is amazing (this and sentiment analysis seem all-too human). In my peer’s blog, they wrote of the arc diagram Lauren Klein developed “to visualize the people with whom Jefferson corresponded” about Hemings. The current article reminded me of Klein’s efforts, as she was able to extract information using the context that  provides additional information pertaining both to the original piece and perhaps to tangential investigations as well.

In the article “Locating Place Names At Scale: Using Natural Language Processing To Identify Geographical Information In Text” by Lauren Tilton, the author speaks about the notion of understanding historical data differently by using programming to look at the data in a broader context. The author gives the example of names of geographic places, how at first glance a historical piece of data reading Paris could mean Paris, France or Paris, Texas and with the help of broader context we are able to deduce which is which. Computer programming has worked to streamline this process by using Named Entity Recognition (NER), a natural language processing technique, and applying it to interviews taken of American experiences during the New Deal to help to give a broader understanding of movement and place in America. I echo DA’s sentiments which they say, “I think this article was a great way to explore the fault in some programming and show how searching for specific things does not always get you the results you need. Sometimes we need to look outside what we are actually looking for.”

A lot goes on behind geo-tagging historical texts. A piece by Tilton, Arnold, Rivard titled “Locating Place Names at Scale: Using Natural Language Processing to Identify Geographical Information in Text” explains the process fairly thoroughly and in simple terms. From identifying the location fragment in the source, to trying to find other references, to finding general geographic closeness, the steps the algorithms takes is laid out in an easy to comprehend way. Though very concise, the writing gives us a good understanding of how the goal is accomplished.

There are some lingering questions, however. What if the location found is not what it seems to be? Does the algorithm discard the term if there is too much ambiguity? Depending on the use of the results, this could have some implications, where incorrect locations are tagged and conclusions may be skewed.

However, this article for the most part is very informative, and as SA says “an eye-opener” to consider what goes on behind the scenes of this sort of interpretation of data.