In the article “Locating Place Names At Scale: Using Natural Language Processing To Identify Geographical Information In Text”, the authors discuss how similarly named locations are identified using broad context without needing a human to decipher the text. Researchers use Named Entity Recognition (NER), which is a method that computers can use to process language, to locate names of places within text. They then start to find references to cities, states, or countries using Google API to narrow down the possibilities until there is only one left. If there is more than one possibility, whichever place is closest to the reference points is deemed correct. As DA put it, this article “explore[s] the fault in some programming” and data which is  a result of these faults. The article also demonstrates a way that people were able to circumvent these faults and create a solution. 

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Jim Enote’s Counter Mapping was an incredibly thought provoking video that explained the different ways to think about maps. Enote strongly believes that maps took more land from native people than physical altercations. Often times, we think about maps like Google Maps that show the world from an aerial view. In his video, Enote challenges that perception through maps that “evoke a sense of place” and maps that prioritize “storytelling and shared knowledge over plots and boundaries.” By creating maps that include cultural and ancestral tokens, Enote aims to create a sense of community for the people who read them.  In doing so, he hopes to reclaim his people’s region and spark a renewed sense of culture and community.

In the article, Locating Place Names At Scale: Using Natural Language Processing To Identify Geographical Information In Text, the author demonstrates how researches are able to identify the place of an area located in the text. Computer scientists use programs such as, Named Entity Recognition (NER), and Natural Language Processing (NLP) in order to identity the names of places with text documents. Researchers can then use Google API in order to create reference points in which help highlight the geographical location of the named places. This article was extremely interesting because it made me think about the levels of depth when it comes to computer programming. DA sums this up well when the student says, “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.”

In the text “Locating Place Names At Scale: Using Natural Language Processing To Identify Geographical Information In Text” the writers discussed the problem of “How might we be able to more accurately predict the exact location using the broader context?”. After they gave the solution to that problem by using computational methods that can predict the exact geolocation of the place mentioned in the text analyzed by its context. Those methods included NER, Google API and FWP. At last, they showed how can that make a difference in the two figures attached to the article.

I think this article was an eye-opener since I have never considered how we should take everything in mind when interpreting our data. 

This article was very short but provided a lot of information about an issue that arises when trying to extract spatial information. The ambiguity that comes when trying to locate places with similar names is an example of how things can go wrong. In order to help fix this issue, computer scientists utilize the context of documents to associate places by references points to narrow down this such as country, state and city. Through these reference points and programs such as NER and NLP, users can identify places talked about in interviews and then map locations for visualization. This visualization created helped show the moving process of people in Virginia around this time. 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.

The article does a great job of describing the visualization surrounding the lynching victims in the south through different methods such as maps and graphs. They begin to describe their goals to find and expose patterns in the lynching victims and cases across the south through proper visualization. I found that the map visual was the most helpful in explaining a pattern of location based “hot-zones” where the larger circles represented greater amounts of cases in a certain region or county. This visualization method presented the data extremely well in order to expose the patterns of increased or decreased cases in different locations. They mention that “Such visualizations are possible because of the datasets that have been gathered by scholars over many decades.” This explains the usefulness of data in cases such as this, more data is always better for creating and building answers to questions we have, the data never goes away.

In NB’s post, they note that “It would be interesting to see if policy during this time had an effect on this instrumental change in executions.” I agree with this because the data was taken over a period of great change in the political scene of the country and I would be intrigued to see if the various civil rights movements over this time had an effect of the amount and severity of lynchings across the region.

In “The Southern Rite of Human Sacrifice,” Donald Mathews argues that lynching can be understood as a religious ritual, a symbolic act as pregnant with religious meaning as with political meaning. In this article, he states they he believes lynching was symbolic of power, however, I believe that this strive for power is inhumane. This practice of religion based power was something that was very prevalent back in the day and this was thought to be a proper punishment handed down by the law. It was not until anti-lynching movements came along that this practice was ultimately deemed unlawful and cruel. This practice did not however fully leave its ideologies behind this paved the way for many different ways of punishment that people thought was going to go unnoticed. A prime example of such things are murders committed by men and women of law or also known as police officers. This led a statistical analysis and study from the author of this article. Mathews explains that in this literature, especially Stewart E. Tolnay and E. M. Beck’s 1995 book A Festival of Violence, “the demographics, economics, seasons, and politics of lynching were patterned and correlated statistically to establish trends.” This study made us find out that lynching in the south was so prevalent that it seemed every southern city had a lynching tree and this practice was brought on by White Supremacy which paved the way for this unethical punishment. 

For today’s class, I read “Lynching, Visualization, and Visibility”. Regardless of any reasoning behind the people who executed lynchings, it is obviously an inexcusable, terrible part of history. Nonetheless, data can be effective in analyzing it for a few reasons. Finding details and patterns behind how it all happened and being able to look at it in different ways with different visualizations, such as the ones shown in this article can give people better perspective about the seriousness of the topic and provide more knowledge about ways things such as this can be prevented. The visualizations presented in this article definitely show aspects of lynching that many are not aware of. “KL” also posted about this article and she used the phrase “thoroughly illustrate the past” about the visualizations shown about lynching. I thought this was a great way of putting it and expressing how the data visualizations about history can be effective.

In the article “Lynching, Visualization, and Visibility,” many visual displays are shown to articulate findings about lynchings in the United States throughout the 20th century.  The first chart attempts to make the pervasiveness of lynchings visible with a grid for each week in each year. This chart was a little difficult to decipher findings. Time is expressed on both axes and the color scale didn’t have too much contrast. The subsequent line graph that illustrates lynchings per year as a function of time is a much easier plot to interpret and see trends over time. This graph is very similar to the number of executions by race later in the article. This visualization is very sophisticated in its ability to separate executions by race over a period of time, but the results are surprising. We see a drastic drop off in executions in the decades following 1950, but the number of executions increase again somewhere near 1980. It would be interesting to see if policy during this time had an effect on this instrumental change in executions.

In SJ’s blog post, they question some of the visualizations displayed. The author notes that , “Although the data collected was important, I genuinely believe the visualizations produced did not help with any new or essential trends that were deemed not discovered prior to the author’s research.” I somewhat disagree with this statement. Some of the visualizations didn’t do the best job of highlighting these results, but I believe they all are important for showing trends in lynchings. The comparison between lynchings and executions is a necessary comparison to make in the context of the article.