Tag Archives: praxis assignment

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Embroidery showing stick-figure Buffy with crossbow and arrow that reads "Buffy will patrol tonight"

In Every Generation There Is a Chosen Text Mining Tool

From the moment the text analysis assignment was mentioned, I knew I wanted to do something with transcripts from the TV series Buffy the Vampire Slayer (seven seasons airing from March 10, 1997, to May 20, 2003). I had no idea what I wanted to examine or any question I might want to answer, but it’s a show I love and have seen many times, and I figured it would just be fun.

Text Gathering

I decided to do a “bag of words” comparison in Voyant using the Cirrus word cloud tool, and I originally expected to use each of the seven season finale episodes as my texts, assuming that a season finale encapsulates the overall themes from the entire season and I might be able to identify some kind of series-wide arch. But some finales were two-parters, so the amount of text being compared across seasons wouldn’t be the same, which I thought could be a problem. Inspired by our class conversation last week as we tried to answer the question “What is text?,” I realized there are several episodes of BTVS that play with some of the concepts we were discussing. So I decided to play around with the transcripts from select episodes instead to see what I might learn. I had wanted to take all of my transcripts from the same source, but this proved problematic as not every archive had complete transcripts for every episode. For the first two episodes I chose, I got the transcribed text from angelfire.com. For the latter two episodes I chose, I got the transcribed text from transcripts.foreverdreaming.org. None of these are official transcripts.

I initially copied the text for each episode into a word document, as I wanted to make sure there wasn’t any hidden metadata creeping into the text that might distort my visualizations/analyses. Some of the transcripts including stuff like “ACT I” or “Commercial Break,” both of which I removed. I was initially worried about the scene/stage directions being highly subjective as they were written by different fans (and not from the original scripts used in production) and also about the “Name: dialogue” format for the lines. But I figured most of this type of text also exists in other narratives like books when the author is setting scenes and defining who is speaking. However, when I put in the text for each episode, all of my word clouds were primarily just the names of the main characters and the most prominent secondary characters in each episode, which didn’t really seem very interesting in terms of analysis potential. So I then went back into each of the transcripts and removed the names of the four main characters (Buff, Willow, Xander, and Giles) as well as any of the other characters who are prominent in the series or were just prominent in that episode (e.g., Ms. Calendar, Angel, Spike, Anya, Oz, Tara, Dawn, Riley, Wesley, Jonathan, etc.). I also expanded the word clouds to include the top 155 words from each episode.

I Robot, You Jane

The first episode I chose is “I Robot, You Jane” (season 1, episode 8) in which a demon, Moloch the Corrupter, that had been imprisoned inside a book is released into the internet when the book is scanned as part of a digital archiving initiative at the school. Rupert Giles, the school librarian, gets into an argument with the computer science teacher Ms. Calendar, who is leading the initiative, at the beginning of the project:

Ms. Calendar: Oh, I know, our ways are strange to you, but soon you will join us in the 20th century. With three whole years to spare! (grins)

Giles: (smugly) Ms. Calendar, I’m sure your computer science class is fascinating, but I happen to believe that one can survive in modern society without being a slave to the, um, idiot box.

Ms. Calendar: (annoyed) That’s TV. The idiot box is TV. This (indicates a computer) is the good box!

Giles: I still prefer a good book.

Fritz: (self-righteously) The printed page is obsolete. (stands up) Information isn’t bound up anymore. It’s an entity. The only reality is virtual. If you’re not jacked in, you’re not alive. (grabs his books and leaves)

Ms. Calendar: Thank you, Fritz, for making us all sound like crazy people. (to Giles) Fritz, Fritz comes on a little strong, but he does have a point. You know, for the last two years more e-mail was sent than regular mail.

Giles: Oh…

Ms. Calendar: More digitized information went across phone lines than conversation.

Giles: That is a fact that I regard with genuine horror.

http://www.angelfire.com/ny4/amai/Buffy/s1ep8.html

Several scenes later, their argument continues:

Ms. Calendar: (exasperated) You’re a snob!

Giles: (incredulous) I am no such thing.

Ms. Calendar: Oh, you are a big snob. You, you think that knowledge should be kept in these carefully guarded repositories where only a handful of white guys can get at it.

Giles: Nonsense! I simply don’t adhere to a, a knee-jerk assumption that because something is new, it’s better.

Ms. Calendar: This isn’t a fad, Rupert! We are creating a new society here.

Giles: A society in which human interaction is all but obsolete? In which people can be completely manipulated by technology, well, well… Thank you, I’ll pass.

http://www.angelfire.com/ny4/amai/Buffy/s1ep8.html

The episode aired in 1997, and Giles’s character is generally portrayed as a technophobe throughout the series. His latter argument against technological innovation as good only because of its newness and against technology being necessarily the direction of “progress” reminds me of Johanna Drucker’s “Pixel Dust: Illusions of Innovation in Scholarly Publishing.” And Ms. Calendar is clearly trying to promote technology and digital archives as means to open access and inclusion. Does any of this come through in the word cloud? I think a little bit, with computer and book coming through as two of the highest used words (at 39 and 37, respectively). Cut is the most used word at 93, but I’m fairly sure that is because of stage directions.

Voyant Cirrus word cloud for BTVS season 1, episode 8.

Earshot

The second episode I chose is “Earshot” (season 3, episode 18). In this episode Buffy is fighting some demons, per usual, but something goes wrong when some bodily fluid from a demon is absorbed through her hand. She is infected with “an aspect of the demon,” which turns out to be an ability to hear other people’s thoughts. At first this is exciting as Buffy hears what other people are thinking of her and finds out interesting secrets people are keeping; however, the power grows and grows to the point at which it is driving her mad because she is hearing everything from everyone and cannot distinguish any of it. I thought of this episode when we were debating in class whether everything could be text and an example given was everything that people say, whether or not it is recorded or preserved. As we touched on text being understood through symbols, I thought words that we see only in our minds but do not share, either written or aloud, could also be text. This episode also felt like an apt analogy to what it might feel like for a human mind to analyze text on the same scale as a computer. What does this word cloud show? Looks, look, and looking are all very prominent, as are thoughts and demon. Cut again is very high, but this again I think is attributable to the stage directions.

Voyant Cirrus word cloud for BTVS season 3, episode 18.

Hush

The third episode I chose is “Hush” (season 4, episode 10). In this episode, the town of Sunnydale is the setting for a gristly fairy tale, where the Gentlemen come in and silence everyone so that nobody can scream as they harvest the requisite number of hearts to terrorize humanity another day. So here we have an episode (and text) that is essentially quiet, with communication limited to succinct phrases and crude pictures easily written/drawn on portable white boards and the nonverbal—body language and pantomime. Are these forms of communication text? I’m inclined to say yes as ultimately an entire storyline is conveyed to the audience just as it is for any other episode of the show. Given that this episode has the least amount of dialogue and relies the most on stage directions, which were written by a fan and are not from the original script, I’m guessing this word cloud says more about the word preferences of the specific person who wrote this transcript more so than any of the other word clouds (though likely the original script would do the same of those writers, and now I’m wondering whether my assumption of a single author is even remotely accurate as each episode in the show and across the series would have had multiple and at times different authors). Gentlemen/Gentleman are both relatively dominant in this word cloud, as well as lackey/lackeys (who assist the Gentlemen). Looks is also high up there again, and picture and talk seem to be about the same size, though I think talk is present here because of its absence, especially as can’t is also very prominent (“Can’t even talk/can’t even cry” is part of the Gentlemen’s grim fairy tale rhyme).

Voyant Cirrus word cloud for BTVS season 4, episode 10.

Once More, with Feeling

The last episode I chose is “Once More, with Feeling” (season 6, episode 7). The musical episode! This time all of Sunnydale is under the spell of a demon who forces everyone to communicate through song and dance. It’s a feast for the eyes and the ears. Someone in class had discussed sheet music as text, and certainly song lyrics are text. (Would choreography also count as text?) Going off of that, then, this episode would contain two texts (three?) actually, the music as well as the lyrics. How do these texts “speak” with and inform one another? How is the message conveyed differently? I’m not sure this word cloud can really measure that as it only represents the lyrical text and not the musical text. Sweet is by far the most used word (65); looks and looking feature prominently again (are these stage directions or spoken?). Song, singing, and dancing are also high up there, and I can see other music-related terms like musical, refrain, sing, sings, dances, music, rock. I’m happy to see that bunnies also makes the cut for this cloud (because “It must be BUNNIES!”).

Voyant Cirrus word cloud for BTVS season 6, episode 7.

Future Text Mining

What did I learn here? I’m not entirely sure. Also, I had all of my Voyant tabs open for a while, and I noticed that the layout of the word clouds kept shifting (like the stairs at Hogwarts). In checking my word cloud links, the displays have even changed from the versions I took sceenshots of (WHY?). This makes me even more confused about how Voyant determines the layout of this particular visualization tool and how useful it is to compare different words clouds against each other. Going forward I’d be curious to know how these clouds might look differently if I were working from the finalized scripts used for producing each episode. I’m also curious to dig a little deeper into the difference between stage directions and spoken dialogue. Do we need to distinguish them? It seems like they both ultimately work together to tell the story of each episode. Are there visual cues in the final episodes that are not represented in the stage directions? What does that say about translating text into images and vice versa?

Word Soup! – Voyant’s Text Analysis Tool

I wanted to test out Voyant’s proficiency when it comes to using a text with multiple languages. To do this, I inserted various texts into the software: English, Spanish, and two texts with a mixture of both. Was Voyant able to 1. distinguish between the two languages and 2. make connections between words and phrases in both English and Spanish?

I first used Red Hot Salsa, a bilingual poem edited by Lori Marie Carlson. The text is composed of English and Spanish words adding authenticity to the United States’ Latin American experience. Voyant could not recognize, distinguish, nor take note of the differences in word structure or phrases. The tool objectively calculated the amount words used, the frequency by which they were used, and wherein the text, these words appeared. Another test consisted of a popular bilingual reggaeton song entitled Taki Taki performed by DJ Snake, Ozuna, Cardi B, and Selena Gomez. The system was able to again capture the amount of words and their frequent appearance. Yet, the way it measured the connection was through word proximity and in a song which repeats the same words and phrases, this measurement is not clear.

Finally, I decided on an old English text, one of my favorite poems: Sweetest Love, I do not Go by John Donne. Here I looked at the links tool and noticed the connection between the words die, sun, alive, and parted. The tool gave me a visual representation of metaphors inside the poem ( just because we are apart, we won’t die, like the sun, I will come again, alive ). I found the links section the most useful part of Voyant.

While exploring this tool, I recalled Cameron Blevin’s experience with text mining and topic modeling (Digital History’s Perpetual Future Tense). Like most of these digital apparatuses, one must go in with a clear intention prior to the text’s analysis and background. Without this, the quantitative measures will be there, but they will not have much meaning. They will become just Word Soup!

Visualizing Algal Blooms in New York State

I was recently catching up with a friend that lives out in Jamaica and she mentioned that the neighborhood was named after beavers since New York was once home to a diverse ecosystem of wildlife before the settlers landed. She mentioned that Queens was once all swamp and marshes and that the word “Jamaica” derived from the word “Jameco” which is the word for beaver among the Lenape people. Hearing this brought back memories of the early settlers that I would learn about in my elementary class when I was about eight and remembered that New York was once home to wild animals that did not include rats, pigeons, squirrels, and the occasional raccoons. With this in mind, I wanted to visualize the decline of New York City’s natural wildlife. The goal I set myself is to show that pollution is one of the driving forces for the removal of these wild animals and that we should be placing the necessary resources to conserve sanctuary spaces and parks.

Not too long ago more and more residents have reported coyotes roaming around central park and even around the Bronx. These sightings were becoming so frequent that New York City’s official Parks website posted a “Living With Coyotes in New York City” blog post on their webpage. As I took off to find the appropriate data, I realized that I was dealing with too ambitious set of data points that included too many variables with missing dates which play an important role in my visualization. I then went on the search again for what else I can possibly visualize. The colder nights and the fact that the days are getting shorter made me miss summer and the lakes and beaches. This helped me settle on reports of harmful algal blooms that affect most large bodies of water, especially lakes where the water can remain stagnant for weeks on end. The dataset I downloaded contained the reports on the condition of the body of water. “S” being a suspicious bloom, “HT” meaning it contains high toxins, and “C” reporting a confirmed bloom. Adding the definitions to the abbreviations in the visualization proved to be difficult without first changing it on the source. I therefore went ahead and left it as is on the dashboard of Tableau and pressed forward.

I set off with the goal to visualize which county in New York had the greatest amount of reports of harmful algal blooms, my guess being counties in upstate since that is where all the lakes and rivers are. But as I placed my necessary pills into the correct columns and row sections, something very surprising came up. It is actually Suffolk county that came in with the most reports and Westchester coming in second. After seeing the bar graphs, it did make sense that Long Island would have the most reports seeing as they are surrounded by water all around where some leaks my seep through to the nearby lakes and reservoirs. What I wish I could do more research on however, is finding a way to standardize the data by population since I am fairly certain that some parts of Upstate are more densely populated than others. Westchester is also easily accessible by New York City residents so that may also play a role on the county placing second.

Best Viewed if you click here

Hopefully this entices people to be more careful with what they leave behind by the body of water since most residents look forward to spending their time in lakes during the hot summer days. These algal blooms, if exposed to high enough concentrations, could be detrimental to someone’s health, especially those that enjoy eating shellfish where the toxins can easily transfer between the animal and person. And with the right resources we can have the right department take the necessary steps to make sure that these algal blooms are within a reasonable count where the rest of the ecosystem faces little to no harm and pose no threat to people.

Model of the U.S. Supreme Court

Visualizing Supreme Court Justice Term Lengths

Given the recent death of Ruth Bader Ginsburg and the current Senate hearings for her nominated replacement, Amy Coney Barrett, the Supreme Court is all over the news. Supreme Court justices are not elected, and once approved by the Senate, they receive lifetime appointments. I wanted to know what those lifetime appointments translate to in actual term lengths.

Collecting and Sorting the Data

The majority of the data I wanted were conveniently available on the Supreme Court website, where they list every justice since the court began in 1789 to the present, including their title (chief justice or associate justice), the date they took their oath, and the date their service ended, as well as what state they were appointed from and which president appointed them. They list some caveats on their site regarding the dates, but for my purposes I chose to ignore them and just accept the dates given.

I copied their data into an Excel file, and integrated the chief and associate justices together, sorting in chronological order by oath date. In doing so, I realized there are three justices who held both titles, John Rutledge was the first, with a break in between titles (total of both terms is approximately 1.40 years). Harlan Fiske Stone and William H. Rehnquist also held both titles, being promoted from associate directly to chief justice (Stone’s total term length is 21.14 years, and Rehnquist’s is 33.66 years). In my tables, each of these three people have two listings by their names because of this, so it does slightly skew my average term length of 16.16 years (more than twice as long as the current term limits on presidents).

The Supreme Court was created in 1789, initially with one chief justice and five associate justices. It was expanded in 1869 to consist of one chief justice and eight associate justices (the number we have today). I wanted to calculate the length of each justice’s term in both days (because it would be whole numbers) and years (as these would be more recognizable and understandable), which was tricky in Excel as it does not recognize dates before January 1, 1900. For all of the justices who had both oath and termination dates after 1900, I was able to use existing formulas to calculate these values. For the dates preceding this, I copied them into a new sheet, and I added 1000 to each year to get Excel to recognize the values as dates, and then I was able to use the same formulas as before, though I had to copy the unformatted numbers into Word before pasting back into my main Excel table to make sure it kept the values rather than the formulas. For the current justices, I put in “termination” dates of today, October 13, just to get some sense of their term lengths thus far, though again I realize this is skewing my average and trends.

In addition to the term length, I also wanted to note the gender of each of the justices (which admittedly is problematic as I am assuming a gender binary and also ascribing gender to these people based on their name and pictures). Of the 119 appointed justices, only 4 have been women.

Given that justices have lifetime tenure, I also wanted to see how the terms were ending. Going into this, I had assumed that most justices’ terms ended with their deaths; however, it turns out there is a pretty even mix between resignation and death. This information wasn’t included on the Supreme Court website, so I searched each justice’s Wikipedia page to compare the date their term ended with their death date. Some entries made a distinction between resignation and retirement, but for my purposes I selected “resignation” for any justice whose term ended before they died. Back to Rutledge, he resigned twice; Stone and Rehnquist were both promoted directly from associate to chief. There are eight justices whose terms are ongoing—who I noted as “current.”

Visualizing the Data

I decided to use Tableau again to get more familiar with its features and tools. I played around with the data a bit to see if I could spot any trends in the term lengths—e.g., did term lengths get longer as life expectancy increased? To my untrained eye, the term lengths don’t seem to follow much of a trend. When looking at resignations versus deaths, there do appear to be some groupings—though I’m not sure what, if anything, could be inferred by this.

In playing around, I was able to visualize which presidents had the most sway in the Supreme Court, in terms of the total term lengths for all of the justices they appointed. I assumed Washington would be the clear leader, having the benefit of being the president to appoint the most justices, but several others beat him out, and Franklin D. Roosevelt has almost double the total terms (85.8 vs. 148.6 years). There are only four presidents who did not appoint any Supreme Court justices (William Henry Harrison, Zachary Taylor, Andrew Johnson, and Jimmy Carter).

Lastly, because why not, I wanted to see the term lengths as they related to the states where the justices were appointed from. There are many states that have never had any Supreme Court justices, especially as you move further west. Again, I’m not sure what arguments about representation could be made here; I would think this speaks more to “manifest destiny” and the way in which states were created and admitted to the union than anything else.

Ideas for Future Expansion

Initially I had been curious to compare all of the justices by the age at which they began their terms, but that would have been a bit too time-consuming. I’m still curious to see if there are any trends here. All Article III federal judges are appointed for lifetime tenure (technically they can be removed, but in practice it seems like appointments are more or less until death or resignation), so it would be great to get data for all of them as well and see what kind of trends pop up.

Map of restaurants visited pre-lockdown and post-lockdown

As a first semester data analysis and visualization student I was eager to get started on this assignment as it would be a great way to see if everything I have read and learned from my visualization class has stuck with me. My software of choice is Tableau and below is a collection of maps of restaurants I have visited prior to lockdown and post lockdown, spanning from January 2019 to March 2020 and June 2020 to September 2020, respectively. Thanks to Yelp, Google Maps, bank statements, Venmo, and Cash App, and just the old handy-dandy method of writing down recommendations from friends, family, and colleagues, I was able to just about map all the restaurants I have visited within the last year. Writing the Excel spreadsheets with all the restaurant names, addresses, geo-coordinates, and the type of food they served was already a tedious task and took up a good bulk of time to complete.

Once the spreadsheets were completed it was off to Tableau to get my maps. Tableau is a great mapping tool since it has a somewhat easy learning curve and the drag and drop nature of the program lets you choose your variables with a click of a button. Then it is all up to the user to choose which visualization they want to proceed with. One problem I did run into however was when it was time to create the dashboard. I am not sure if it is either user error or if it is part of Tableau, but you cannot create two separate dashboards from two separate sources under the same workbook. After playing around with the settings to see if I can find a way to work around this hurdle, I decided two create two separate dashboards and call it a day. I then realized I should do the same for Excel which I did. The initial setup in my head did not go quiet as planned but I was still able to come out with four visualizations that I am proud of.

While meticulously copying and pasting the geo-coordinates of the restaurants onto the spreadsheet, I found that a few of them have either permanently shut down or relocated outside of the five boroughs. Reading all of that placed a damper on my mood. These were places I went out with friends, co-workers, family, dates, and have made solid memories sharing experiences with them at these restaurants. Immediately after, I turned to my phone and scrolled through a few photos I have taken at these places. Most of them are of the food or drinks but the few shots of me and the person I was catching up with brought back fond memories.

Although outdoor dining is a great way to bring in revenue for these businesses it can only do so much before the cold weather starts to set in. I know I have read news articles about indoor dining returning to New York sometime in October but with only a 25% seating capacity. There is going to be some tough times ahead these businesses if the bulk of the hospitality industry is still left behind. I sympathize for the workers because I am one of them and was out of work for a good chunk of spring and into the summer. If anything I suggest you go out and visit the more local stores, the ones that are family based with that mom and pop shop feel to them; I believe they are the ones that will need the most help. Only time will tell what will happen to these places but all we can do now is mitigate the spread by following the guidelines imposed by the health care professionals. Hopefully us as students can return back to in-person sessions of classes and connect with one another in spaces once again such as restaurants, parks, and large events by next year.

Mapping Cemeteries in Queens and Brooklyn: Praxis Mapping Assignment

I was incredibly excited about this project when it was introduced in our first class, but as the due date kept getting closer, I was at a total loss as to what I could possibly want to map, having both too many ideas and not enough ideas at the same time. I took a deep breath, and I thought, it’s almost Halloween (my favorite time of year), let’s draw some inspiration there. And that’s how I settled on cemeteries. Call it a morbid curiosity, but they’re some of my favorite places to visit. There’s a tension between remembering and forgetting, especially in older cemeteries. If there is no one living with memories of a person, is their headstone doing the remembering for us? Is that really the same thing as remembering, and is it enough? The headstones themselves are already reducing a whole life into a few data points: name, birth and death dates, and maybe a title/relationship, quote, or a decorative symbol. And what happens when even those data points are eroded away and are no longer readable by visitors? Is it enough to be in a dedicated place of the dead and know that its inhabitants once lived? What do I even mean by “enough”? What is the responsibility of the living to the dead?

Initial Premise and Data Search

I live in Queens very close to Calvary Cemetery, which claims the largest number of interments (about 3 million) in the United States, and it’s an incredibly massive feature in my daily landscape. So first and foremost, I was curious to know how much physical space cemeteries are taking up in New York City. Many of them must be full, or close to it; indeed, many of the cemeteries in Queens and Brooklyn were established when Manhattan burial grounds were facing a severe shortage of space, exacerbated by a cholera outbreak in 1847. What happens when the cemeteries in the outer boroughs fill up? Is the current land usage sustainable? In addition to urban planning concerns, there are also many environmental concerns about some of the more popular death rituals (burial and cremation), but I wasn’t sure how to include that here. I mostly was hoping to see the relationship between the space allotted for the dead and the rest of the city—the space allotted for the living (though admittedly cemeteries are perhaps more for the living than they are for the dead).

Based on the suggestion in class, I initially tried to find data on cemeteries from NYC Open Data; there were no search results. So I googled “cemeteries in NYC.” Most of the results feature a selection of the oldest, or forgotten/hidden, or most unique, or most notable dead. There are also websites like Find a Grave, where you can search for specific headstones in their database. But I wasn’t seeing any datasets showing all of the cemeteries in the city. So I decided I should start to make my own dataset from a Wikipedia listing and searching in Google maps (admittedly a problematic start). This quickly proved time-consuming and frustrating as many of the cemeteries listed don’t have entries in Wikipedia, and even cemeteries that have their own entries don’t always include information about area, or they contain measurements that are vague (e.g., qualified by “nearly,” “about,” “more than”). Not to mention I’m not sure who accumulated this list and how complete it is. From a cursory search, I know that there are cemeteries in Manhattan that have been built atop of (again see the 6sqft article “What lies below: NYC’s forgotten and hidden graveyards”)—sometimes with bodies relocated, and sometimes not. Should these count as cemeteries on my map? (I’m inclined to think yes.) I was also curious to see when the cemeteries were established, but even that proved to be a tricky data point. Does that mean when the land was purchased for the purpose? Or when it was incorporated? Or when the first bodies were interred?

From the outset, I’m already seeing that there is no neutrality in the data I’m collecting—a la Johanna Drucker’s “Humanities Approaches to Graphical Display”—and it’s time-consuming even to just find a list of cemeteries. So I immediately scaled back to just focus on Queens, and then I added in Brooklyn when I realized there are several cemeteries that span both boroughs.

Choosing a Mapping Tool and Creating My Map

I assumed that a static map rather than an interactive map would be easier to start with, having no experience in using mapping tools. I wanted to try to use an open access tool, so I immediately nixed ArcGIS and started with QGIS, but I realized that neither of the all-in-one release versions are compatible with my Mac setup. From the interactive map tools, I didn’t want to wait for approval access with Carto, so I opted to sign up for a free license of Tableau Desktop.

Very quickly, I was uploading my dataset, consisting of five columns—name, borough, geo coordinates, area in acres, and year established—and tried to make a map. I was dragging and dropping each of the columns into different fields in the Tableau workspace, but I was only able to get it to create graphs. I soon learned that the mapping would work better if I separate my geo coordinates into two separate categories for latitude and longitude (using the decimal values). After some trial and error, I figured out that you need to put longitudinal values into the columns category (y axis) and latitudinal values into the rows category (x axis), and finally I was seeing my cemetery dots. My original dataset had about 10 cemeteries in it, and my map was frankly looking really sad, so I decided to dig a little deeper and generate some more names and see if I could find info for the cemeteries without entries in Wikipedia. Thankfully I found the New York City Cemetery Project by Mary French. Through her research, I was able to fill my dataset out to 33 cemeteries in Queens and Brooklyn—there are very likely more as her project also includes historical information about potter’s fields and family burial grounds.

I used the area in acres category to be the scale, so that the dots appeared on my map on a size scale in relation to each other. Ideally, I would love this scale to relate to the scale of the map of the city in the background, but I could not figure out how to do this. Adjusting the scale is a matter of sliding a button up and down on a linear scale without any numbers, so I just picked a size scale that I found aesthetically pleasing. I’m also not 100% confident in my values for latitude and longitude as most of them were derived from my searching for the cemetery name in Google maps, and then right clicking “What’s Here?” for the values—and in doing so sometimes my mouse was clicking somewhere slightly different than where Google had placed it’s pin, and also Google sometimes seemed to have multiple pins for the same cemeteries, so I had to choose which to use, and sometimes it was placing pins near entrances and sometimes in the middle of the spaces. I also noticed there are many cemeteries that appear grouped together on the map, and there are instances where Google seems to be placing two pins in the same spot for two adjoining but different cemeteries.

Going back to NYC Open Data, I was able to find a dataset with information on all of the city parks, including area in acres. However, when I was trying to import this data to my map, I couldn’t figure out if the parks were in the correct location as that dataset was using different coordinates than I had used in my own dataset. Also, the acres column was coming through as a string rather than numbers—I cannot for the life of me figure out why—and so I have no confidence that what I was mapping for the parks was comparable in scale to what I had generated for the cemeteries, so I ultimately decided to scrap their data and just present my data on cemeteries in Queens and Brooklyn.

Map of cemeteries in the boroughs of Queens and Brooklyn shown in relative acreage to each other (not to scale with the map of New York City in the background).

Ideas for Future Expansion

The first area of expansion would be to expand my map to all of the boroughs. Given the importance of having access to outdoor spaces—especially during the current pandemic—and knowing that picnicking in cemeteries was at one time a common practice, I would like to further dig into the visiting practices at each of these cemeteries (e.g., are visitors allowed, are visitations limited, have visitor policies changed during COVID-19?). And also find out how to map the cemetery spaces in comparison with other green spaces in the city. I’d also be curious to see the density in each of the cemeteries (number of interments compared with acreage) and average cost of burial.

In addition to urban planning and environmental concerns, I think cemeteries are a great starting point for discussions about access, community building, and even broader ideas of what it means to be human (and which people are “worthy” of remembering). Burials are expensive, and those without means have generally been buried differently—both in ceremony and location. And access to different cemeteries has been restricted based on other factors like race, ethnicity, and religion. A prime example is the African Burial Ground National Monument, whose original grounds included the remains an estimated 15,000 Black people—both enslaved and free. The original cemetery was closed and slated for redevelopment in 1794, later to be “rediscovered” and “re-remembered” when the land was being excavated for the proposed construction of a federal building in 1991. What does this purposeful forgetting of a cemetery mean for that group, and how do cemeteries contribute to our understanding/claims of belonging to certain communities and specific locations?