For today’s blog post, I chose to make a character chart for the short story, “Exotics“. The narrative follows the main character, James, who works as a teacher in a single-room school house, and spends his summer working on a ranch in Fort Worth, Texas. The story takes place just after one of James’ students commits suicide in the school’s bathroom.
The character chart that I created links James to every character he interacts with within the story. Each link signifies a direct conversation that occurred between each character. Absence of links signifies that the characters are not related within the story. As seen through the graph, while many characters share connections, the common thread between all characters in the story is, unsurprisingly, James. Unfortunately, much is left out from this graph, such as the nature of the connections, the themes and symbols that appeared throughout the story, or even who the characters are. In fact, it’s a bit frustrating how little the chart elucidates, and is arguably more distracting than it is telling. For this reason, it might not be appropriate to graph connections between characters, but perhaps maybe appropriate to connect symbols with the themes that they represent, as the character connections play a small role in the larger scheme of the story.
This week I chose to analyze the Digital Harlem map, which congregates legal records from newspapers, police records, and other sources to demonstrate the events that occurred from 1915 to 1930.
The “About” screen states that the project was created to not only provide an exposé of African American artists during the time period, but also to give a representation of everyday life of African American individuals in New York City. The website displays a map, of which the user can toggle between the years 1920, 1925, and 1930 to see Harlem’s growing size over the span of ten years. Additionally, users are able to toggle the layers that they wish to see on the map. Some examples of the layers include law violations such as arrests, illegal gambling, assault, pickpocketing, etc… as well as provides layers for everyday life events such as the locations for pick-up basketball games, parties, weddings, and charitable events. When creating a layer, the user may input a name for the layer, then the map displays location-based points for where the event occurred, and the date it occurred. There are also layers that have already been created by the allows the user to select through a set of four different predetermined layers: January 1925, Number Arrests, Fuller Long, and Churches. I chose to explore the Number Arrests layer. When the user chooses the Number Arrests, the map immediately fills with blue dots to indicate various arrests throughout the specified years. If one chooses to click on an point on the map, the map will display the individual’s name that was arrested and what he or she was arrested for. If one chooses to click the “More Detail” link, the dialog box will display a news clipping of the news story from when the arrest was initially reported, the date of the arrest, the parties involved, the addresses involved, and the legal consequences.
While the map does provide the user with a great wealth of information, I would argue that the map does not necessarily display the everyday life individuals living in Harlem from 1915-1930. In fact, the map paints a more grueling image for those living there, and focuses more heavily on crime more than anything else. Doing so conjures a more grim picture, and might not necessarily be realistic of the greater majority of the individuals living in Harlem during the time. Additionally, while the map tells the user textual details about the events that occurred, it does not include images. Had images been provided, the user would be able to construct a greater archetypal image for “Daily Life” in Harlem from 1915 to 1930.
Scatter Plot comparing body fat as a function of age
For this week’s blog post, I decided to create a data visualization for body fat. The data contains several statistics related to the measurements from 252 men including their total body fat, age, weight, height, neck size, wrist size, thigh width, and other statistics. I was particularly interested in seeing whether or not certain factors contributed or were an indication of body fat. I utilized a scatter plot in order to visualize the most common trends and determine whether or not there would be a correlation between values on the x and y axes. The first visualization I created compared age and body fat. I believed that as age increased, so would body fat. To my surprise, there was no direct correlation between body fat and age, as evidenced by the visualization.
Scatter plot comparing body fat as a function of thigh width (inches)Scatter blot comparing body fat as a function of wrist size
Next, I tested thigh width, which appeared to be positively correlated with increasing body fat. Through this test I became curious as to whether or not other areas of the body gave such a strong indication of body fat. Interestingly, when comparing body fat and wrist size, the scatter plot demonstrated that generally, as wrist size grew, so did body fat. Overall, Google Fusion Tables was a very powerful tool when studying general trends that utilized quantitative measurements. It was really easy to switch between different groups of data by simply clicking on different columns in my data sheet. Unfortunately, while the scatter plots are great at demonstrating group trends, individual outliers are not effectively represented and therefore, when addressing causality, one cannot say that any of these factors is a true indication of body fat (or any other correlated data set).
This week I decided to exam the LA Controller’s Office‘s dataset of the compilation of funds that the city of Los Angeles disperses to projects related to Health, Environment, and Sanitation. Despite LA’s infamy for its cloud of pollutants that engulf the city, I wanted to see how much the city council allocates to environmental projects.
The dataset includes 37 different “funds” in which the total monies budgeted for environmental protection adds to roughly $390,000,000. While that number may seem large, the city of Los Angeles has nearly 4 million civilians the overall revenue that the city collects through taxes is well over 390 million. The dataset further includes the type of fund, denoted by a three digit fund number, the fund name, the department requiring the fund, and the fund’s purpose.
The LA Controller’s Office’s dataset exemplified the issues discussed in the Wallack and Srinivasan article (Local-Global: Reconciling Mismatched Ontologies in Development Information Systems). The article examined how there exists a disconnect between the purpose for using the monies and the program’s lack of total, intended execution. For instance, nearly $19 million went fund the amenities for Sunshine Canyon; the intended purpose of the funds is to “fund the amenities for the Sunshine Canyon landfill facilities”, and gives little information on how the amenities are used, why the amenities consume a large portion of the budget, and if the funds are actually implementing environmentally safe landfill management. Additionally, there is a $5 million fund allocated to Mobile Source Air Pollution Reduction, with a purpose of “for air pollution reduction projects” but little is told about what those projects are, how effective they will be, or how they will be implemented.
I believe this would make the most sense to the individuals writing the budget reports and the politicians disbursing the city’s money. They are the one’s creating these budgets and the lack of detail allows them to easily justify receiving large portions of funds.
This dataset demonstrates that the money collected by government may not always be used as efficiently as possible, and raises some questions as to why the descriptions are so strangely vague. It also brings into question the efficacy of these projects. Are they (the projects) really solving the problems that they are intended, if not, to what extend are they remedying the environmental issues?
If I were not picking this dataset apart and perhaps an individual from Bakersfield, I would be astonished by the sheer amount of money that the city of Los Angeles sets aside for environmental projects. However, of course, Bakersfield is a lot smaller than Los Angeles and one would need to take into account the relative amount of money in addition the extremity of the environmental issues in order to assess whether or not the city is realistically making an effort to curb its pollution situations.
I examined the George Meyer Simpson Script files from 1990-2004. Within the finding aid, one may find a seventy-eight box collection of Meyer’s works in which he produced for the comedy show, The Simpsons. The boxes each contain several scripts, drafts, and annotations.
The finders aid begins with a short narrative biography on Meyer, shortly detailing his college education and his climb to fame. The short narrative describes his undergraduate career at Harvard University, and his short-lived ambitions to enroll in medical school; hindered only by his own procrastination to actually — enroll. The biography then goes to describe several of his own “pet projects” such as the magazine that he published, which only contained three printed issues. Later, it delves into Meyer’s careers writing for other shows such as David Letterman, and how he created some signature skits for the show and thus propelled his own success.
Viewing the finding aid, it appears that the organization of materials is done somewhat… counter-intuitively. It took me a few moments to finally realize that the articles were organized alphabetically rather than chronologically, and I was confused as to why the box numbers were out of order. The finding aid would probably prove more useful if it were organized chronologically, therefore one researching Meyer and his work on The Simpsons could appreciate and examine his growth as a comedy writer and perhaps changes in style/behavior. Organizing chronologically would also allow for one to analyze how Meyer evolved the characters throughout the series.
Trying to understand as to why one might organize the archive alphabetically, I reason that it might be ordered this way in order to assist researchers in finding specific scripts, but in actuality this is not completely necessary. One could simply search the title of the episode in Google and find the date that the episode was aired or recorded.
The narrative that one might create through the articles within this finding aid would specifically include Meyer’s time working for The Simpsons. It would leave out his legacy post The Simpsons and his history prior to The Simpsons as well. In addition, while it does include notes, many of his own inspirations for certain lines in the scripts may not appear because the finding aid and archive do not contain personal interviews. Overall, while it does give a complete list of artifacts, the scope is limited to simply the scripts and archives that it contains. Interestingly, however, the archive does contain a list of censor notes which could be useful to explore if one would like to research Meyer’s most crude examples of humor.
Screenshot of the Photogrammar counties map feature.
Photogrammar compiles a collection of nearly 90,000 photographs from the Farm Security Administration – Office of War Information (FSA – OWI). These photographs were taken throughout the years 1935-1946 and include photos of farming communities throughout various regions of the United States.
Photogrammar presents the photos amongst several mediums; a map, of which a user can explore various locations of a photo, a treemap, where the user can browse the collection by keywords that group similar photos together, and a metadata dashboard, which displays the photos on a map and groups them by location, date and relationship between subjects within the photos.
When President Roosevelt’s Resettlement Administration received great criticism, the FSA-OWI set out to document the success and relief that the Act brought to farmers. The photos that are compiled date back The Great Depression and extend to the end of World War II. The entire project was the largest government funded project that the FSA-OWI had ever undertaken.
When utilizing the map to discover photos, the user can choose between “dots” or counties. Dots displays locations on the map where specific photographers’ photos were taken. I believe counties offers a more expansive and insightful view of the project, however. Counties displays a map of the United States, the user can then click on a county and view all photos in the database from the selected location. When viewing the photos, one can gain an intuitive feeling for the hardships that the individuals underwent. Some of the most telling photos included crowds enjoying their favorite past times. It is not difficult to recognize the great disparity between pastimes in 2016 and pastimes in the late 1930s. For example, here is a photo of a crowd lined up in front of a ticket office for a rodeo show, whereas today one might see intensely larger crowds lined up at a venue such as the Staples Center to see a basketball game.
by Russell Lee. Found via Photogrammar.
Another thing to note is the progression of technology as you browse photos taken at the beginning of the project and photos taken towards the end. At the beginning, photos were largely black and white whereas photos taken in later years begin to show greater color and detail.
Search feature on the Photogrammar website.
In addition to a browse by location feature, Photogrammar also includes a search engine where a user may manually enter a keyword for a photo and the website will return all photos that contain subjects or objects attributed to that keyword. For instance, if I search the word “horse” the search engine will display all photos that are either of a horse, or contain horses within the picture. The user-interface for the search engine is slightly overwhelming at first-glance, however. The search feature is not actually hard to use, therefore I would recommend they hide the advanced search features until the user actually requests them in order to maintain a clean interface.
All in all, I do believe that the project does a good job of directing researchers to a large database of photographs they may be looking for. The advanced photo subject/geolocation/photographer tagging that the team implemented makes finding specific photos very easy to find and gives users a real feel for what life may have been like throughout the implementation of the Resettlement Act.
For today’s blog post, I chose to make a character chart for the short story, “Exotics“. The narrative follows the main character, James, who works as a teacher in a single-room school house, and spends his summer working on a ranch in Fort Worth, Texas. The story takes place just after one of James’ students commits suicide in the school’s bathroom.
