Author: dominiqueheijer

For my week 8 blog post I decided to look at the short story called Exotics by Callan Wink within the American Wild Granta magazine.

This fusion table shows the protagonist James Colson, and his degree of connection to each of the supporting characters. I created the nodes to represent each character and the edges connect a relationship between each of the characters for how they are associated with James Colson.

The story begins with James talking to Molly Hanchet, one of his 6^th grade students who wished him a good summer. Then James continues to his mistress Carina who is a teacher for troubled girls where he vents about his ex-girlfriend leaving him and taking her belongings. Carina had faced trouble at work where one of her students Ellen Realbird had committed suicide that day by cutting her wrists. James, upset with his current love life left where he arrives at his brother Casey’s and has multiple interactions with him and his wife Linda, whom he met through his brother. After his reflection period at his brother and sister-in-laws he takes a summer job as a rancher and is employed by a farmer named Karl.

The network graph does portray how he knows each of the other characters, and who he met them through or heard about second hand. The limitation with this visual is it does not show the depth of strength of the relationship or the label on each of the connections between the characters. The graph also does not explain the complexity behind the characters and the internal struggle of each relationship and how they met one another. The graph illuminates that the relationships are all equal, but some are simply acquaintances, some are family members and some are people he has just heard about through another connection; the visual does not explain how much he likes, dislikes, etc. each of the other characters.

For this weeks blog post I chose to look at the Caribbean Cholera timeline created in 2011 by the John Hope Franklin Humanities Institute. A team of 10 people from Duke University Haiti Laboratory tells the story that correlates Cholera outbreaks in the Caribbean region to natural disasters such as hurricanes and tropical storms.

This visualization shows Cholera outbreaks in 3 different segments of history of significant outbreaks among the Caribbean Islands, the first one from 1833-1834, the second from 1840-1856 and the third from 1867-1872. This map tells a narrative through the interactive timeline that scrolls through the dates to create a simultaneously changing Google Map of the Caribbean Islands where points of major natural disasters will show up, with pinned points where the Cholera outbreak consisted and convincing news article titles regarding the outbreak. The map visualization shows evidence of massive hurricanes/storms which later cholera outbreaks were found in surrounding areas.

David Turnbull gives the argument that maps only tell a story from the perspective of the map designer. The way in which you select and provide information will create a specific narrative. Turnbull provides a systematic review of maps that analyzes how maps represent knowledge but also signify how different cultures and societies view events and ideas.

Due to the information that creates the data visualization for the map, it suggests that natural disasters were the only cause for the outbreak of Cholera off the South East coast of the United States. I looked up a little more about Cholera and found that it is a bacterial disease typically spread through unsanitary water or food. It says that it takes between 12 hours and 5 days for someone to show symptoms of Cholera so it seems unlikely to me that all Cholera outbreaks were due to natural disasters as the massive hurricanes in 1830 did not have evidence of Cholera outbreaks or news stories until 1833. The map obscures a narrative that proposes only one source for this disease.

I think the current map is very assumptive of causes and does not consider alternatives to what could trigger this bacterial disease. I think the information does not have enough proof, as there were tropical storms in 1831 and 1832 that did not have outbreaks to follow. The creator of this map wants to attribute all blame of Cholera on natural cause. There needs to be more details provided on type of storm and length as the points on the map are not convincing enough.

If I were to imagine an alternative map I would suggest providing information on the economy or state of the cities at the time. Many links show a strong correlation to poor infrastructure and sanitation at the time of Cholera epidemics and so this information is bias towards avoiding any information through the fault of economy and potentially the leader during that time period, who controls how money in spent on health practices that could potentially affect the Cholera outbreaks.

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For my third blog post I decided to explore the Los Angeles City Payroll Calendar dataset. The data for each column entry includes year, month, month number, day, date and description. The description is what differentiates the data types between entries. The data types are broken down by payday types within a payroll calendar schedule that are important to the pay cycle so employers and employees know start, end, and holidays to account for.

A record is considered what day in the payroll cycle the entry is. The 5 types of days that matter in a payroll calendar are holidays, paydays, excess sick pay days, no deduction paydays or the day marking the end of the pay period. There are 255 records starting with January 2013 until December 2016.

Wallack and Srinivasan believe that the miss-interpretation of activities creates a divide between communities and the state because they emphasize different aspects of an issue. This inconsistency between the state and the community can represent an issue much differently than the people who are affected feel about the issue.   They argue that ontologies are a shared platform in which individuals become part of a greater group; simultaneously this means that ontologies will create exclusion between groups. This will create divides between the groups understanding or interpretation of an event. The government or employers will have a different understanding for this data than the employees or lower class.

The data is organized chronological by year then month then day within the month based on its payroll type. Using this definition, I would say that this data’s ontology is based on organizing financials for companies to keep their payroll expenses easy to reference and verify.

This ontology seems to make most sense to an accountant. The chronological order of dates with the significant days within the payroll are most relevant to the accountant who would need to know the ending and beginning of pay periods and they go through the dates in that sequence.

            This does not have much detail about which companies is following this payroll schedule, they also leave out any sort of numerical values if you wanted to compare pay periods to analyze trends within pay periods. I also would assume that different companies who have varying religious practices or are not a typical corporation would not all have this same payroll cycle. This dataset does tell me the generic payroll cycle for a company in LA and where the main control panel for organizing important dates is.

If you wanted to look at this from an economists perspective you could organize the payroll calendar by how much money was being paid within each of the pay periods, and could organize by company type and the company name with their total payroll expense for each payroll period to observe which months, weeks tend to have the most pay or when is the least. This information could be used to balance a company’s budget.

This week I observed the ‘Archive of popular American Music’, which is a collection of American music that dates back to the 1790’s with over 62,500 musical recordings and 450,000 sheets of music. UCLA’s particular musical archive is known for their large selection of theatrical music, motion pictures, radio and television.

You can search the archive through simplistic measures as just a keyword search or you can do a more advanced keyword search with multiple keywords and a range of dates. If you do not know exactly what you are looking for you can search from an extended list of artist names, titles, covert art, or date.

On the main homepage there is 9 albums covers from early 1920’s. The album covers all shared a similar theme of masquerade type font, jazz, love and dancing. This archive is called the archive of popular American Music yet the main page with the visual album covers only contains music from the early 1920’s and one album from 1919. I would take this to mean that the 1920’s are when America hit a stride in music. If I were to write a paper on this archive I would write a story on the 1920’s as being a turning point in America’s culture dependent on dancing, parties and music. The archive portrays the 1920’s filled with jazz, dolled up women dancing, extravagant hats and masquerade balls.

I cannot tell from this archive why the home page is only albums from 1920’s and no other time period is represented on the initial home page. I think that this would infer that this was America’s time period of their most significant music, during a time when America was in time of a booming economy and flourishing pleasure advancements with theater and music.

The archive might want to visualize on their home page a lively representation of America during the ‘roaring 20’s’ when the US was flourishing, instead of a time of turmoil. The first album on the home page is from 1919, which is the year after WWI ended. Then in 1929 the Great Depression hit America, which is why I would remedy that they chose the image of “America’s Popular Music” to be cover albums from the early 1920’s because it brings attention to a time when people were not stressed with external factors like war and unemployment. People could spend time and money on focusing on these luxurious activities like writing and producing music.