Author: jaydhindsa

I decided to read and analyze the short story, “Self-Made Man” by Mark Gevisser. It essentially speaks about the life of an 18 year old named Liam Kai who was born a female in China and adopted at six months. He was originally named Lucy by his American mothers. Although he was living as a tomboy all of his life, at age thirteen he began to live as a boy. The article goes on to talk about the different approaches and views his lesbian parents had about his gender identification. One parent (Beth) was accepting while the other insisted he wear dresses because it would make him healthier and happier to accept himself as a female. At age eighteen, when he can finally have surgery legally, he decided to transition his body from a female to a male with surgeries and hormones.

I created a list of characters from the story and put them in categories from 1-5. The higher the number, the more influence the character had on Liam’s life. This graph basically shows the similarities between the characters with equal amount of influence on Liam.

It shows that the most influence in Liam’s life was that of his mother Beth along with Andrea and Carol.

I decided to choose “Digital Harlem” as a DH mapping project to look at. Digital Harlem Map depicts the everyday life in Harlem between 1915-1930. It uses legal records, newspapers, and other published and archival sources obtained from district attorney’s files, probation department files, and newspapers. The map depicts the borders of black settlements in 1920, 1925, and 1930.

Website’s visitors are able to navigate through the site by using the search option panel available. You are able to narrow the results by selecting the type of event, date, charge/conviction, birthplace of participant, occupation of participant, race, gender, or by street name and location type. Although this attempts to show the everyday life of black settlements, it seems that the data is one sided and only shows the ontology of police officers and district attorneys or the state in general instead of showing the views and perspectives of the community. As Turnbull asserts that all mapping of data is always subjective and perspectival and speaks to the narrative of its author, it is also true in this case. As it has come to light recently with improved phone cameras and the ability for anyone to record at any moment, police officers are over patrolling African-American communities and treating that community more harshly than others. It stands to reason that almost a hundred years ago, relationship between the police and black community was more at odds than now. With the limited to no rights given to the black community made them an easy target for police officers.

This data doesn’t show the other side to the story. For instance, it could be improved if they also took in account of the defenses offered by the public defender’s office (if those convicted were given a public defender). A new map could also show how many of those convictions were based on a trial where the jurors found them guilty and those that simply took the plea deal. Therefore, I would like to see sources that don’t always side with the district attorney’s office. For instance, the police officers and district attorneys work together closely and would never turn on one another. This is the main reason why police officers who do atrocious acts do not see criminal charges brought against them.

For this week, I decided to work with this dataset regarding poverty statistics from 97 countries. Data types associated with this dataset include birth rates, death rates, infant mortality rates, life expectancies, and per capita GDP.

 

First glance at the data, once I sorted it by GDP, it becomes obvious that countries that have high GDP per capita do well in most datatypes such as dead rates, infant mortality rates, and life expectancies. However, I wanted to see the data visualization in order to confirm what I hypothesized while looking at raw data.

This chart, which is sorted by GDP, shows that the life expectancy for both males and females increases as the GDP increases. This clearly illustrates that those living in countries where the average income per person is higher tents to live longer. You can tell by the cluster of condensed lines, that it is more concentrated and severe for those living in countries with the lowest GDP where the lowest life expectancy is 38.1 years. Meanwhile, the highest life expectancy reaches 80 years. That is a huge discrepancy and horrifying to visually be able to see that some people are living less than half as long as others due to their income. Consequently, the death rate is also very high (25%) in countries with the lower GDP per capita compared to wealthier countries (9.5%). The yellow line, however, suggests that these low income countries are having more children as their birth rate is extremely high. To understand this, I created a variable chart comparing the infant mortality rate with the birth rate.

 This shows that the countries with the highest birth rates are also the countries with the highest infant mortality rate. It becomes obvious that families in poor countries are having more children because they’re experiencing far more child deaths than wealthy countries.

After looking at this dataset, it becomes obvious that having more money literally means you get to live more than twice as long as those with little or no money. It also means that you are far less likely to experience the death of your newborn.

This chart visualizes the obvious; if a country has a higher death rate, it also has a lower life expectancy. As the the death rate decreases, the life expectancy increases. These visualizations are able to clearly show the power of money and its affect on life. It is harder to see this from the data itself, but these visualizations make it incredibly clear.

For this blog, I decided to choose and explore the police expenditures dataset which details the financial data of police expenses from June 2011 to January 31, 2014. The data types are as follows: ID number, Fiscal year, Department name, Vendor name, Transaction date, Dollar amount, Authority, Business tax registration certificate, Government activity, Fund group name, Fund type, Fund name, Account name, Transaction ID, Expenditure type, Settlement/judgement, Fiscal month number, Fiscal year-month, Fiscal year-quarter, Calendar month number, Calendar month/year, Calendar month, Data source, Authority name, Authority link. Along with these columns, there are 226,210 rows of detailed expense transactions. The record in this data set is the total sum of all police expenditures which amounts to roughly 4.86 billion dollars during the aforementioned time period. Additionally, this dataset keeps record of each expense transaction made by the police department which makes it easier to determine the proper allocation of the money into/from appropriate funds.

As Wallack’s and Srinivasan’s definition of ontology suggests that it “merely implies a distinction between groups’ mental maps of their surroundings”, a dataset’s ontology is the transparency of links and boundaries which allows us to further understand a given dataset (Page 2). In other words, a dataset’s ontology is essentially ways in which a dataset’s connections can be recognized and traced. Similarly this dataset’s ontology allows for transparency and understanding of the funds being used by the police department. For instance, it can be reviewed to make sure that no illegal expense transaction occurred or for simple accounting purposes. This data can be helpful to those government agencies that have to estimate the amount of money that should be set aside for the police department from the budget. It creates accountability by both government and public. It also increases transparency for the tax payers who can track their tax dollars at work. It organizes which fund the transaction is to pull money from. It also keeps track of which officer is submitting the expense.

This dataset can be organized in many different ways to provide more information. It can give you a lot of information about where or which vendors the department spends most or least of its money to help understand where the resources are being pulled. The expenses range from cellphone bills to water bottles. You are able to prioritize whichever column you want allowing you freedom to organize the data in any order.   Dataset can tell us exactly where the department is using its money and can be helpful in times of budgeting.

I think what got left out was more details. The dataset uses a lot of broad categories and at times uses the same type for many different transactions. For instance, the expenditure type “supplies and other services” is repeated for more than half of the items. It would help to create additional subcategories to keep the data clean and legible.

If I were starting over with data-collection and describe a completely different ontology, I would create more data types in order to organize the data even more. For instance, I would break up “supplies and other services” to additional data types which would further organize data and specify which commodities were purchased and see if I can’t put those into a separate category or data type. I would also add data types showing different communities or regions of LA and the money being spent there. Therefore, if we are policing in one area more than another resulting in overspending resources in one area, we ought to address the underlining issues of such a region and explore the true cause of turmoil there instead of simply over-policing and overspending.

I decided to look at the data available for Finding Aid for the Walt Disney productions publicity Ephemera 1938-198x for this assignment. This collection begins by describing the contents of the overall collection. For instance, it introduces the establishment of Walk Disney Company in 1923 and how it has morphed into a giant film production company. This gives an overall list and brief  descriptions of different items in the archival collection including photographs, press kits, etc. It shows the progression and success of Walt Disney with each successful film or project from 1938-1980s. It is organized alphabetically by names of movies.

Some of the historical narratives that we might be able to explore based on the materials in this collection is that we could discuss the growth and progression of a successful Hollywood giant that is Walt Disney. We could discuss how the movie industry changed from having silent movies or short animated movies to lengthy, more sophisticated films like “Snow White”. The collection can also be organized in many different ways depending on what we want to show. We can organize the movies in chronological order and show the different types of changes that have accumulated over the lengthy success of Walt Disney or to show the topics being discussed in movies based on the mental state of environment or society.

 

Mapping indigenous LA

This project was created to help show reminisce and history of indigenous people like the Tongva and Tataviam in the LA area by the use of images, videos, interactive maps, etc. It uses digital story telling to show the displacement of such indigenous people by government policies.

Sources: This project used a lot of archival material and teaching material from historical documents such as images, videos, maps, etc. It uses these documents to show the waterways, educational timeline, and to create a story map that tells you a brief history of the indigenous people who were displaced in LA area.

Process: The story maps are created by ESRI, which helped to create interactive story maps to illustrate the data in a more visually stimulating and easy to follow manner.

Presentation: The presentation is very fluid and easily navigational. The website provides story maps which provides images and timelines of indigenous people like the Gabrielino/Tongva, pacific islanders, Latin American indigenous diaspora. Each story map includes links, videos, pictures, maps, etc.