My final project is an extension of my second praxis assignment. In the previous praxis assignment, I focused only on the crime rate and its trend over time. However, in addition to this superficial phenomenon, the final project will include the study on racially discriminative policing in law enforcement and the relationship between the crime rate and racial disparity in policing. This project also aims to share the results with the public. This final project reflection will discuss what I felt while designing this project and the expected challenges.

The most impressive thing I learned in this introduction to digital humanities class is that the digital humanities think of sharing knowledge and ideas as an important task. In fact, through the preparation for this project proposal, I recognized that there were not many attempts to disseminate knowledge to the public in my major, criminal justice. Knowledge was mainly being shared among researchers, graduate students, and practitioners in academic journal articles. There are small numbers of attempts to disseminate knowledge to the public. Still, most of them took a simple form, such as presenting statistics without any interpretation. Furthermore, there was little room for the public to engage in spreading knowledge. As criminal justice is related directly to the feeling of safety and the protection of life and property of all citizens, I think it is necessary to establish an open platform in which two-way communications between experts and the public are pursued rather than one-sided transfer of knowledge.

My project involves collecting, analyzing, interpreting, visualizing data, and building an Internet archive. One of the biggest challenges will be how to select the official crime data and how to interpret the analysis results. Since federal or state agencies have collected public criminal data, it is easy to use them without questioning their validity or reliability. However, looking behind the scenes, there are issues as follows: 1) how many crimes are not reported to the police, 2) whether police officers objectively input information about the crime incidents, 3) the statistics themselves are the reflection of the interests of a particular class in society, and 4) whether the statistics exacerbate bias or prejudice against the disadvantaged or minorities. The key to reducing these weaknesses is to remain critical eyes to the data. Creating an alternative to such official crime data would be nearly impossible. However, I think that whether I maintain a critical attitude will significantly affect selecting, analyzing data, and interpreting the results.

I do not believe that communicating with the public with an open mind and maintaining a critical perspective is limited only to the digital humanities. Rather, I think it is a spirit that I should always keep and pursue as a doctoral student and as a researcher in criminal justice.

While preparing for the final project, one of the biggest concerns was how to incorporate a humanistic perspective into my project, as I have been familiar with the social science perspective. Especially, I have analyzed various crime data and demographic statistics since I started my doctoral program. However, I hardly ever doubted the validity and credibility of most data because the government and institutional agencies collected them.

I have learned that a continuous question about what the story underlying the data was and where the data came from was at the center of critical thinking. Especially, while I prepared for my final project, I re-read Drucker’s Humanities Approaches to Graphical Display (2011) and re-thought its meaning. The two sentences below in the conclusion of the piece inspired me:

The abandonment of interpretation in favor of a naïve approach to statistical certainly skews the game from the outset in favor of a belief that data is intrinsically quantitative – self-evident, value neutral, and observer-independent.

I am suggesting that we rethink the foundation of the way data are conceived as capta by shifting its terms from certainty to ambiguity and find graphical means of expressing interpretative complexity.

First, these two sentences directly criticized how I have been doing statistical analysis. Second, the argument above provided helpful guidelines on my attitude when dealing with data in my future project. Of course, as a doctoral student, it would be nearly impossible to create an alternative to the massive amount of crime data collected by government and research institutions. However, I think that maintaining critical eyes will significantly impact selecting and analyzing data and interpreting the results.

What I have done so far was reading numerical data and statistics and interpreting the result superficially, such as “the crime rate increases in certain communities,” “illicit drug use influences the crime,” and “the low level of self-control is the cause of the crime.” I do not think this way is sufficient for me as a doctoral student and a would-be researcher. In addition to the technical abilities to handle data and run software programs, I should have a keen eye to find and analyze serious problems underlying our society, such as discrimination, inequality, and alienation. For example, there are many issues that I can research from a different perspective what I have done so far: 1) why criminals choose to commit crimes, 2) why crimes are concentrated in a particular community or area, 3) what events in their life-courses make them criminals, and 4) some become criminals, and some do not even though they are under similar circumstances.

In sum, it is crucial to take the data actively rather than take them as given. One of the important lessons learned in Drucker’s reading and this class is that it is essential to have a critical perspective, be intellectually curious, and ask “why” continuously.

When we decide whether a region is habitable, we consider many factors: infrastructure, environment, education, transportation, market, etc. Crime rate is one of these indices because it is closely related to our feeling of safety. As a criminal justice major, I feel interested in whether New York City is becoming a safer place to live. By visualizing the trends of seven major felonies from 2000 to 2020, I found that this city was getting less struggled with crimes. In addition, I also found that the future trends from 2021 to 2024 would be similar to those of the recent two decades.

I selected Tableau Public as my tool for data visualization because it could provide an easy way to visualize the data through a simple drag-and-drop, and it was free for public use. I collected the data from two sources: 1) the NYPD and 2) the US Census. First, I could access the historical NYC crime data on the NYPD web page, including the citywide seven major felony offenses from 2000 to 2020. The seven major felony offenses consist of 1) murder, 2) rape, 3) robbery, 4) burglary, 5) felony assault, 6) grand larceny, and 7) grand larceny of motor vehicles. Second, because the numbers of felony incidents alone cannot indicate the situation precisely, I included the information on the population of NYC and constructed the crime rate data. On the US Census webpage, I could acquire the NYC population in 2000, 2010, and 2020. Then, I divided the number of each felony crime by the population: the 2000 to 2009 crime data were divided by the population in 2000; the 2010 to 2019 crime data by the population in 2010; and the 2020 crime data by the population in 2020.

After constructing eight broken-line graphs, including the total and seven felony rates, I added the trend lines and forecasts in each graph. As shown in the graphs below, all felony rates, as well as the total rate, were declining during the two decades, although they were different in the extents. Based solely on these visualized data, NYC seems to become less affected by the major felony crimes. Specifically, the total rate per 10000 population decreased by more than 50 percent during the period (from 230.6 to 108.6). The rates of robbery (from 40.66 to 14.89), burglary (from 47.89 to 17.58), and grand larceny of motor vehicles (from 44.26 to 10.26) were also reduced by more than 50 percent. Even though not as dramatically as the previous three major felony crimes, the rates of murder (from 0.84 to 0.53), rape (from 2.58 to 1.62), felony assault (from 32.37 to 23.37), and grand larceny (from 61.97 to 40.33) decreased significantly.

The trend lines and forecasts also showed similar results with the past changes during the last two decades. The numbers in light red shadows presented the future trends from 2021 to 2024. Specifically, Tableau Public forecasted that the rates of total felony crimes (from 108.6 to 97.4), murder (from 0.53 to 0.42), robbery (from 14.89 to 10.74), burglary (from 17.58 to 13.23), and grand larceny of motor vehicles (from 10.26 to 6.60) would decrease. In contrast, it predicted that the rates of rape (from 1.62 to 1.84), felony assault (23.37 to 24.20), and grand larceny (from 40.33 to 46.71) would increase slightly. However, it is clear that the general trend is declining, as shown in the total felony crime rate. In other words, we can say that NYC will have fewer problems with serious crimes.

I feel that Tableau Public is an easy-to-use and robust instrument to visualize the data. For example, by doing drag-and-drops several times, I could create nice-looking graphs, including trend lines and predictions for the future. I believe that such tools can bridge the gaps between academia and the general public by making it easier to read data.

Link to the graphs:

https://public.tableau.com/app/profile/jinuk.jeong/viz/TheTrendsofSevenMajorFeloniesinNYCfrom2000to2024/Story#1

Data Sources

1) NYPD: https://www1.nyc.gov/site/nypd/stats/crime-statistics/historical.page

2) US Census: https://www.census.gov/quickfacts/newyorkcitynewyork

I mapped the life story of the most notorious serial killer in South Korea. I chose this topic because it is related to my major, criminal justice and criminology and because the serial killer in South Korea is a new world that is very unfamiliar to people in the US.

For me, which tool I would use to map this topic was another matter. Actually, since I had little background knowledge about mapping, I had a fear of mapping and thought a lot about which tool I should choose. Among the several tools on the syllabus, I found that ArcGIS StoryMaps would be the most attractive and appropriate. In particular, looking at the voyage of Captain James Cook on the ArcGIS StoryMaps website, I thought it would be best for mapping a person’s life or longitudinal life course.

What I learned from various materials such as web documents, media reports, and academic articles for this mapping assignment was that his life was a series of misfortune except for murders and other crimes. He was abused in childhood, suffered from poverty, failed to adjust to school, and was divorced. Like the findings of the life-course criminological theory, negative experiences in life were associated with antisocial behavior or crime. Surely, his criminal behavior, especially his serial killings, will never be justified. I think that he was just a monstrous criminal in which the combination of his negative life experiences and his violent personalities were expressed as the extreme form of serial murders.

The mapping itself was an interesting experience. I think the mapping tool has strength in storytelling. Usually, when we talk about serial killers, like my topic, it is more likely to focus only on criminal behavior and its cruelty. In other words, it is more likely to ignore why one became a cruel criminal. However, mapping and other visual materials allowed us to see one’s life or context from a broader perspective. I personally believe that the mapping tools can more effectively reveal the immediate cause and underlying cause of a phenomenon like a historical context.

The link to my mapping assignment : https://storymaps.arcgis.com/stories/cf84fa2b5fe3425cbdb5a557a20392dd

During this semester, I attended two workshops held by the CUNY Digital Initiatives: 1) Tools for the Digital Humanities (Sep 17th) and 2) Introduction to R and R Studio (Oct 1st). I would like to share my experience of participating in the Introduction to R and R Studio workshop between the two workshops.

Actually, criminal justice research takes a much more quantitative approach, and statistical analysis of empirical data is the key process in practical research. To meet this tendency, I learned to use some statistical software programs, including Stata, SPSS, and R. But, looking back on my experience, I have rarely used R because R is a coding-based program while Stata and SPSS adopt more graphics-based interfaces. In other words, I need to input complicated commands that look like machine language when using R. In contrast, it is sufficient to click on buttons several times when utilizing Stata and SPSS.

But, things have changed. This semester, professors in other classes use R as the main statistics software, and I also became interested in data visualization. For now, I cannot turn away R anymore. I need to get familiar with R and understand this software more deeply. The Introduction to R and R Studio workshop provided me with a good opportunity.

The workshop began with a quick introduction to how to install R and R studio. Then, the instructor explained how to handle the data and showed some examples of data visualization. It seemed that R had its clear strength in data visualization compared to other statistical software programs. For example, the shapes and colors of treemaps and mosaic plots were more discernible and attractive. We could also easily customize these graphics based on our preferences. And some kinds of data visualization were that I had never seen in other software programs. I think such power of R comes from passionate user groups and various packages with nearly infinite expandability.

As a criminal justice major, my interest in data visualization still remains abstract and unclear. In other words, for now, I do not have a specific idea about how I can make a good connection between crime and data visualization using R. Bridging crime and data visualization would be a time-consuming and laboring task. It is because I need to search for more pieces of the literature and get familiar with using R. But, just knowing that R can provide compelling data visualization and that I need to have an in-depth understanding of R has made this workshop worthwhile.

Maps are an excellent tool for providing a visual and intuitive way to know the current phenomena and distributions. Monmonier (1996) argued that scale, projection, and symbolization were used to make maps more accurate and informative. But, he also asserted that these techniques could be potential sources of distortion due to the improper use of such techniques and their intrinsic limitations. For example, on a map using the Mercator projection, Greenland appears to be as large as South America, but in reality, it is only one-eighth the size of South America. Thus, a good map for Monmonier would be a precise map that reflects the reality as much as possible so that a map will not lead the reader to erroneous conclusions.

However, Bonilla & Hantel (2016) and Sen (2017) told different stories. According to them, a map that accurately shows the current state is the product of Western-centrism or colonialism. First, Bonilla & Hantel emphasized the historical context. To them, territories on a map separated by the standard of sovereign states merely reflected the concept of sovereignty formed in Westphalia. What is important to them is the historical process of forming the Caribbean countries as they are today. Next, Sen argued that platforms like Google Maps revealed a pervasive digital divide in the world. For example, most American villages are represented to the extent that we can see minor trails in the countrysides, while some towns in India appear blurry no matter how they are zoomed in. According to Sen, this is the symbol of inequality and colonialism. Therefore, if a map accurately reflects only the current state and does not show the historical process and values that locate under the present phenomenon, people can take a specific phenomenon for granted. This tendency will exacerbate distortions and inequality in the world.

Which approach is more appropriate? I think the answer also depends on the context and academic tendency. For example, if I am currently mapping violent crime rates by region, I will have to combine different mapping techniques and create an accurate map not to mislead readers. However, if someone else is studying Korean-American immigration and community formation, simply mapping the current distribution of Korean Americans on a map would never be sufficient. Like Bonilla & Hantel’s approach, rather than pursuing map accuracy, a mapping that can represent both historical origins and temporal/spatial aspects of migration would be more appropriate. In other words, thoughts about whether mapping can contribute to the goals pursued by different disciplines may provide insight into what is a more appropriate mapping.

References

Monmonier, Mark. (1996). How to Lie with Maps. 2nd ed. The University of Chicago Press.

Bonilla, Yarimar, and Max Hantel. (2016). “Visualizing Sovereignty” Sx Archipelagos, no. 1 (May).

Sen, Mayukh. (2017). “Dividing Lines. Mapping platforms like Google Earth have the legacies of colonialism programmed into them“Platforms like Google Earth Have the Legacies of Colonialism Programmed into Them.” Real Life, March 27, 2017.

It is hard to say that the digital humanities has a well-established single definition because it is still an expanding field. However, roughly speaking, the digital humanities refers to various research activities in which information technology and humanities are assimilated. The early digital humanities was criticized for focusing more on peer reviews and being disinterested in political issues. However, the current digital humanities is expanding into political topics such as feminism and racism.

After reading and exploring the five materials and the four websites that cover the recent decade of the digital humanities, I thought that the digital humanities had an outstanding strength in presenting historical contexts. Of course, the digital humanities is excellent at presenting a social phenomenon itself through mapping, data visualization, and word clouds, but it also has the advantages of revealing the historical context behind a phenomenon by using digital archives.

To be specific, the Early Caribbean Digital Archive (ECDA) and the Colored Convention Projects (CCP) present records, documents, photos, and illustrations of the past to show, directly or indirectly, how differences resulted in discrimination, how discrimination aggravated, and how minorities fought for their rights. Of course, the ECDA and the CCP do not provide clear answers about why the discrimination issue has not been fully solved to this day, and how we should address the problem. However, these two websites offer an implication, whether implicit or explicit, that it is necessary to consider historical contexts in order to understand the problem accurately. In other words, we can understand the immediate and remote causes of a social phenomenon only by considering historical contexts.

In In Search of Respect: Selling Crack in El Barrio, an ethnography about Puerto Rican drug dealers in New York’s East Harlem area, Bourgois (2003) emphasized the historical context, especially the oppressive colonial history. After the American colonization of Puerto Rico, the lands were expropriated and handed over to large corporations. The Puerto Ricans, once landowners, turned into wage laborers and resisted the colonization. According to Bourgois, even after their mass migration to the United States, this culture of resistance remained and formed a street culture (or a criminal subculture) as resistance to exploitation and marginalization in American society.

As a criminal justice major, I am studying crime, criminal, and punishment. If crime is not caused solely by individual factors (psychological and biological deficits), then it is more likely to be a social phenomenon. In addition, if a large amount of crime is committed by members of a particular social class or group, it would be important to understand the historical paths they have passed to know why some become criminals and others not. I believe that the digital humanities may give clues by providing historical contexts in a digital archive format.

Reference

Bourgois, P. (2003) [1996]. In Search of Respect: Selling Crack in El Barrio (2nd edition). New York, NY: Cambridge University Press.