AIR 2015 Final Project

For my project, I have created a map of NYC that is based off of generated text. To start, I drew a map of the five boroughs of New York (admittedly, it is not the best map because I have no artistic training.) My intention was to make points on the map based on randomization of text from both TextMechanic and GenGen. I also wanted to create a procedure that could be applied to any map of any place that would yield similar results. Kind of like a color by numbers kind of activity, but hopefully cooler.

My first step in this project was to make a list of all the neighborhoods in each borough in NYC. I gathered them from the internet and put them in a plain text document.
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Once I had all of these sorted by borough, one by one I put the plain text lists into TextMechanic’s random line picker and came up with 15 places for Staten Island and 20 each for Manhattan, Brooklyn, the Bronx, and Queens. (Just because they’re bigger and have more communities. Poor Staten Island, always overlooked.)
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Here’s Staten Island’s neighborhoods in Text Mechanic.

So, once I had made my lists of what places I was going to include, I had to decide which points I was going to make, so using GenGen, I made a list of color coded options in which each color represents a different characteristic. The generator that tells me what color to ascribe to which section.
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Black: Means the neighborhood did not meet the characteristic specified for it.
Grey: The neighborhood has at least one high rise or skyscraper, or is otherwise identifiable as a business district.
Brown: The neighborhood has houses, or is otherwise identifiable as a residential community
Dark Blue: The neighborhood suffered one natural disaster (hurricane, flood, etc) or accident (fire, explosion, etc)
Light Blue: The neighborhood has at least one park, garden, pier/beach, or green space.
Dark Green: The neighborhood has at least one Starbucks coffee chain.
Light Green: The neighborhood has at least one locally owned coffee shop (believe it or not, Starbucks aren’t everywhere in NYC!)
Yellow: The neighborhood once housed an artistic or literary community, or is a neighborhood that has an artistic center.
Gold: The neighborhood has a university or a house of worship, like a mosque, a church, or a synagogue.
Bright Red (Orange): The neighborhood has at least on tourist attraction (like a museum or something Times Square-esque.)
Dark Red: The neighborhood is historical or is the location of a historical event (like a battle during the Revolutionary War.)



So, what inspired me to do this? Looking back to our friend Kenneth Goldsmith’s book in the chapter “Infallible Process,” I was inspired by the idea of instruction based art, with the example of Yoko Ono standing out especially. I really wanted to make a physical object from a text based procedure. What I like about this piece is that it’s unique, but it’s ubiquitous. This could easily be adapted by anyone with any map using the same rules and process and yet it would yield a completely different result. I realize that in doing this, the neighborhoods would meet more than one criteria, and just because it’s colored black doesn’t mean that it doesn’t meet any of them. I like to think that it’s more experimental, to see how it will come out versus how it should come out. Each neighborhood has a sense of pride attached to it that having a singular identifier tied to it like on this map wouldn’t do it justice. Looking at this map won’t tell you everything, but it will tell you something–Chelsea, for example, is marked by a yellow dot because the infamous Hotel Chelsea was a breeding ground for major artistic, musical, and literary talent in its heyday.

In making the legend for this map and deciding what each color would represent, I chose qualities that are obvious juxtapositions, like houses vs skyscrapers and Starbucks vs local coffee. Having the colors represent juxtapositions to me brings attention to the differences and unique identifiers present in each city. I think it points out that each place has a story and a specific sense of pride and self attached to it. I am very proud of this piece and am happy to stand by it.


For my final, I’ve decided I’m going to make a Twitter bot that will tweet the name of every single neighborhood within the five boroughs of New York City. I’ve transcribed them from the internet, and I plan to alphabetize them, then tweet them alphabetically by borough with the geographical location of each neighborhood in the tweet. So far, I have just arranged a list of the neighborhoods in the city in a plain text document that I have arranged in alphabetical order using Text Mechanic.

Bedford Park
Bronx River
Castile Hill
City Island
Clason Point
Co-op City
Concourse Village
Crotona Park
East Tremont
Fish Bay
Harding Park
High Bridge
Kings bridge Heights
Locust Point
Marble Hill
Morris Heights
Morris Park
Mott Haven
Mount Hope
Pelham Bay
Pelham Gardens
Pelham Parkway
Port Morris
Silver Beach
Spuyten Duyvil
Throgs Neck
University Heights
University Heights
Van Cortlandt Village
Van Nest
West Farms
Westchester Square

Bath Beach
Bay Ridge
Bergen Beach
Boerum Hill
Borough Park
Brighton Beach
Brooklyn Heights
Brooklyn Navy Yard
Carroll Gardens
City Line
Clinton Hill
Cobble Hill
Coney Island
Crown Heights
Cypress Hills
Ditmas Village
Dyker Heights
East Flatbush
East Gravesend
East New York
Fort Green
Fort Hamilton
Fulton Ferry
Fulton Mall
Gerritsen Beach
Greenwood Heights
Highland Park
Kings Bay
Kings Highway
Manhattan Beach
Marine Park
Mill Basin
Mill Island
New Lots
Ocean Hill
Ocean Hill
Ocean Parkway
Park Slope
Plum Beach
Prospect Heights
Prospect Lefferts Gardens
Red Hook
Remsen Village
Sea Gate
Sheepshead Bay
Strait City
Stuyvesant Heights
Sunset Park
Vinegar Hill
Windsor Terrace

Battery Park City
Financial District
Greenwich Village
Little Italy
Lower East Side
West Village
Alphabet City
Two Bridges
Gramercy Park
Kips Bay
Murray Hill
Peter Cooper Village
Stuyvesant Town
Sutton Place
Tudor City
Turtle Bay
Waterside Plaza
Lincoln Square
Manhattan Valley
Upper West Side
Lenox Hill
Roosevelt Island
Upper East Side
Hamilton Heights
Morning side Heights
Polo Grounds
East Harlem
Spanish Harlem
Wards Island
Washington Heights

Baisley Park
Bay Terrace
Belle Harbor
Breezy Point
Cambria Heights
College Point
Cunningham Heights
East Elmhurst
East Flushing
Far Rockaway
Floral Park
Flushing South
Forest Hills
Fresh Meadows
Fresh Pond
Garden Bay
Glen Paks
Hilltop Village
Hollis Hills
Howard Beach
Hunters Point
Jackson Heights
Jamaica Estates
Kew Gardens
Kew Gardens Hills
Liberty Park
Linden Hill
Little Neck
Long Island City
Middle Village
New Hyde Park
North Corona
Oakland Gardens
Old Astoria
Ozone Park
Pomona House
Queens Village
Queensboro Hill
Rego Park
Richmond Hill
Rochdale Village
Rockaway Park
Roosevelt Avenue
South Jamaica
South Ozone Park
Springfield Gardens
St. Albans
Tudor Village
Willets Point

Arden Heights
Bay Terrace
Castleton Corners
Elm Park
Emerson Hill
Fort Wadsworth
Grant City
Great Kills
Grimes Hill
Mariners Harbor
Meiers Corners
Midland Beach
New Brighton
New Dorp
New Springville
Ocean Breeze
Old Town
Pleasant Plans
Port Ivory
Port Richmond
Prince’s Bay
Randall Manor
Richmond Valley
Shore Acres
Silver Lake
South Beach
St. George
Todd Hill
Tongan Hills
West Brighton

Meditation #8

With graduation just around the corner, I decided to make my meditation a bit that does predictions for Fordham graduation for 2015. First, I made a Google spreadsheet for Bulk GenGen with 1,000 possibilities for what could happen, then I set the bot to tweet at first every minute, then every 30 minutes, then once an hour for the rest of the weekend. I tried to make them as outlandish as possible but also make them have relevance to Fordham. I also had some items on the spreadsheet that I was really hoping would match up, so maybe they will once my bot gets through the posts on the Google doc. Here’s the link to the account.

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I thought this was similar to my Guy Fieri Twitter because both of them are formatted a certain way and are based on generative text, so both of them could technically be read as automative bots whereas only one is. I’m thinking of turning Guy Fieri’s Fast Food into a Twitter bot as well.
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Some favorites:
Morgan Freeman will be the speaker then Kenneth Goldsmith will mispronounce names on Martyrs’ Lawn

Lena Dunham will be the speaker then Martha Stewart will get arrested on Eddie’s

Hologram Tupac will be the speaker then Dean Rodgers will get a Tinder match on the stage

Bono will be the speaker then Michael Bolton will shed a tear on Keating Steps

Meditation #7

For this meditation, I’ve decided to take one of the most interesting people in the world, Guy Fieri, and turn him into a parody Twitter account. I called the account Guy Fieri’s Fast Food, where Guy Fieri himself visits McDonald’s on an episode of Diners, Drive-Ins, and Dives and describes the food at McDonald’s like he does the dishes he tries on his show.

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I wanted the descriptions to be super random and ridiculous like most of Guy’s commentary is, so my first step was to put together a spreadsheet of McDonald’s items, descriptive words for food, and a super ridiculous one-liner that Guy used. To prepare for this, I watched a few episodes of Diners, Drive-Ins, and Dives and saved a few key-one liners and perused the online menu on McDonald’s website. In order to pick the ones I would tweet, I had to edit some of them for grammar and I even tried combining two of them into one tweet, which usually didn’t work because they’d end up being too long. Most of them are just what they came out as when I clicked generate.
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Here’s a link to the GenGen generator.

So, now for the tweeting. I picked twenty of the best descriptions from the generator and tweeted them periodically throughout the weekend, mostly on Saturday and Sunday.

The reason why I chose to do my meditation this way was because I wanted to do something funny, and I figured that Twitter is a good place to do something like this because it’s short and funny and you don’t have to pay too much time reading into it to get the idea. I expected that people would favorite or retweet some of fake Guy Fieri’s tweets to show to their friends if they thought any of them were funny, but disappointingly enough, there was no response to the account I made, just a few people who followed and unfollowed.

Meditation #6

For my site-specific writing meditation, I decided to do a tribute to my dog Rosie who died at the beginning of my spring break. I chose spaces both in and outside of my house where she liked to go when she was alive, specifically the corner where her bed used to be in the kitchen and the tree she always liked to stop and sniff for several minutes while my family or I were taking her for a walk outside. I took a newspaper (typical dog symbol, right?) and I painted the words “Rosie Was Here” on it, then I put it on these places she used to like.IMG_1956
Here’s Rosie in her usual spot in the kitchen corner. I like to think that this photograph is meme worthy.

photo 1
photo 2

Imagining these spaces without my dog being attached to them is really weird and difficult to adjust to, but I chose to intervene in them because they were her spots, with a sense of belonging to and reminder of Rosie’s presence. They are a way to preserve her memory, to mark her place on the world that she once belonged to. Although the second picture is in my house, the first is a tree outside on my street, accessible to my neighbors and the public. I guess the goal here would be making the statement that Rosie is gone but not forgotten.

Meditation #5

For my first piece, I’ve used the eDiastic generator, and for the text, I chose a specific chapter from the novel Jane Eyre by Charlotte Bronte (I couldn’t copy and paste the text into this post because I used the entire chapter and it’s about 16 pages long. If you’d like to read it, I linked the Project Gutenberg file of the novel, where you can do a command/control F search, it’s Chapter XXIII.) The reason why I chose this chapter is because it contains one of my favorite quotes from the novel. For the seed text, I chose part of this quote, “I am no bird.” My procedure in using the generator was as follows:
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I did this five times so that I got five randomized lines so that the text would have a stanzaic, poetic appearance and sound:

I. “I am no bird”

attached impetuous
not love
both nightingale far wisdom

arms amazed
now comes
breast with her shadow

also emotion
now fortnight
but sit her loud

and am
no voice
blue hit for words

a am
not holy
by with her hold

and emerged
notice No
back his for aside

I was pretty surprised and a little disappointed by how it came out. I was hoping that the text selection was long enough for some variety and coherent formulation.

For the second part, I used GenGen to make a combinatory text generator from a Google Spreadsheet called Wicked Weather. I used 6 conditions using a few typical (and a few atypical) phrases to ultimately deliver a funny fake weather forecast. Here’s a screen shot of the words/phrases I chose:
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And here’s a link to the generator 

A few highlights:
“Current conditions: black clouds with a chance of raining kittens”
“Today’s forecast: grey clouds with a chance of hailing farts”
“Current conditions: clear snowfall with a chance of raining lava”
“Today’s forecast: grey flurries with a chance of flying locusts”
“Current conditions: grey clouds with a chance of falling meatballs”
“Today’s forecast: evil skies with a chance of exploding meatballs”

Meditation #4

For the first part of my meditation, I took a recording on my computer of a phone call I placed with Keurig customer service regarding an issue with my Keurig coffee brewer. I’ve transcribed it word for word without touching up what I heard myself and the representative say:
CS: Keurig Customer Service
KF: Yours Truly

CS: May I assist you?
KF: Yes, hi, I’ve having problems with my brewer. Um, I’ve noticed that it’s not, like, I have the single cup version, right? So I’ve been noticing that lately it’ll…like I can put the pod in okay, I can put the water in okay, but when I press brew, no water is coming out of it. Um, is there anyway I can like troubleshoot it on my own?
CS: Yes, we have videos online where you do the videos and um, it’ll have you…clean out the needles, you can clean out the needles and it’ll show you how to do that ’cause it sounds like it just has a clog.
KF: Okay.
CS: Kay, is there anything else I can do to assist you?
KF: No, um, where can I find this information?
CS: Just go to under the support tab, bring up brewer support, hit k-cup brewer system, and the videos and frequent asked questions will be listed for you.
KF: Okay, thank you so much.
CS: You’re welcome, thank you for calling Keurig.
KF: Okay, bye.
CS: Bye bye.

For the second part of my meditation, I took a photo of the New York City Subway map on the D train and transcribed all the names of the stops (yes, the entire Subway.) Once I finished this, I took the plain text and alphabetized it using Text Mechanic. Repetitions could be street stops on different line (for example, 14 St on 6th or 8th Av). Of course, I have transcribed with my own eyes, so this could account for anything that is repeated too many times or unintentionally left out.

Map of the NYC Subway

1 Av
103 St
103 St
103 St
103 St-Corona Plaza
104 St
104 St
110 St
111 St
111 St
116 St
116 St
116 St
116 St Columbia University
117 St
121 St
125 St
125 St
125 St
135 St
135 St
137 St City College
138 St-Grand Concourse
14 St
14 St
14 St-Union Sq
145 St
145 St
145 St
149 St-Grand Concourse
15 St Prospect Park
155 St
155 St-8 Av
16 Av
161 St Yankee Stadium
163 St-Amsterdam Av
167 St
167 St
168 St
169 St
170 St
170 St
174 St
174-175 Sts
176 St
18 Av
18 Av
18 St
181 St
182-183 Sts
183 St
191 St
2 Av
20 Av
20 Av
207 St
21 St
21 St Queensbridge
215 St
219 St
225 St
23 St
23 St
23 St
23 St
231 St
233 St
238 St
25 Av
25 St
28 St
28 St
3 Av
3 Av-138 St
3 Av-149 St
30 Av
33 St
33 St-Rawson St
34 St Penn Station
34 St-Herald Sq
36 Av
36 St
36 St
39 Av
4 Av-9 St
40 St-Lowery St
42 St Bryant Park
42 St Port Authority Bus Terminal
45 St
46 St
46 St-Bliss St
47-50 Sts Rock Ctr
5 Av
5 Av/53 St
50 St
50 St
51 St Grand Central-42 St
52 St
53 St
55 St
56 St
57 St
57 St-7 Av
59 St
59 St
59 St Columbus Circle
6 Av
61 St-Woodside
62 St
63 Dr-Rego Park
65 St
66 St Lincoln Center
67 Av
68 St Hunter College
69 St
7 Av
7 Av
7 Av
71 St
72 St
72 St
74 St-Broadway
75 Av
75 St-Elderts Ln
77 St
77 St
79 St
79 St
8 Av
8 Av
8 St-NYU
80 St
81 St-Museum of Natural History
82 St-Jackson Heights
85 St-Forest Pkwy
86 St
86 St
86 St
86 St
88 St
90 St-Elmhurst Av
96 St
96 St
96 St
Alabama Av
Allerton Av
Aqueduct N Conduit Av
Aqueduct Racetrack
Aqueduct Racetrack
Astor Pl
Astoria Blvd
Astoria Ditmars Blvd
Atlantic Av
Atlantic Av Barclays Ctr
Atlantic Av-Barclays Ctr
Avenue H
Avenue I
Avenue J
Avenue M
Avenue N
Avenue P
Avenue U
Avenue U
Avenue U
Avenue X
Bay 50 St
Bay Pkwy
Bay Pkwy
Bay Pkwy
Bay Ridge 95 St
Bay Ridge Av
Baychester Av
Beach 105 St
Beach 25 St
Beach 36 St
Beach 44 St
Beach 60 St
Beach 67 St
Beach 90 St
Beach 96 St
Bedford Av
Bedford Nostrand Avs
Bedford Pk Blvd
Bedford Pk Blvd Lehman College
Bergen St
Bergen St
Beverly Rd
Beverly Rd
Bleecker St
Borough Hall
Botanic Garen
Bowling Green
Briarwood Van Wyck Blvd
Brighton Beach
Broad Channel
Broad St
Broadway Junction
Bronx Park East
Brook Av
Brooklyn Bridge City Hall
Buhre Av
Burke Av
Burnside Av
Bushwick Av Aberdeen St
B’way-Lafayette St
Canal St
Canal St
Canarsie Rockaway Pkwy
Carroll St
Castle Hill Av
Cathedral Parkway 110 St
Cathedral Pkwy (110 St)
Central Av
Central Park North (110 St)
Chambers St
Chambers St
Chambers St
Chauncey St
Christopher St Sheridan Sq
Church Av
Church Av
Church Av
Clark St
Classon Av
Cleveland St
Clinton Washington Avs
Conelyou Rd
Coney Island Stillwell Av
Cortlandt St
Court Sq
Court Sq-23 St
Court St
Crescent St
Crown Hts Utica Av
Cypress Av
Cypress Hills
DeKalb Av
DeKalb Av
Delancey St-Essex St
Ditmas Av
Dyckman St
E 143 St St Mary’s St
E 149 St
E 189 St
East 105 St
East Broadway
Eastchester Dyre Av
Eastern Pkwy Brooklyn Museum
Elder Av
Elmhurst Av
Euclid Av
Far Rockaway Mutt Av
Flatbush Av Brooklyn College
Flushing Av
Flushing Av
Flushing Main St
Fordham Rd
Fordham Rd
Forest Av
Forest Hills 71 Av
Fort Hamilton Pkwy
Fort Hamilton Pkwy
Franklin Av
Franklin Av
Franklin St
Freeman St
Fresh Pond Rd
Fulton St
Fulton St
Fulton St
Gates Av
Graham Av
Grand Army Plaza
Grand Av Newtown
Grand St
Grant Av
Greenpoint Av
Gun Hill Rd
Gun Hill Rd
Halsey St
Halsey St
Harlem 148 St
Hewes St
High St
Houston St
Howard Beach JFK Airport
Hoyt Schermerhorn
Hunters Point Av
Hunts Point Av
Intervale Av
Jackson Av
Jackson Hts Roosevelt Av
Jamaica 179 St
Jamaica Center Parsons/Archer
Jamaica Van Wyck
Jay St Metro Tech
Jefferson St
Junction Blvd
Kew Gardens Union Tpke
Kings Hwy
Kings Hwy
Kings Hwy
Kingsbridge Rd
Kingsbridge Rd
Kingston Av
Kingston Throop Avs
Knickerbocker Av
Kosciuszko St
Lafayette Av
Lexington Av/53 St
Lexington Av/59 St
Liberty Av
Livonia St
Longwood Av
Lorimer St
Lorimer St
Marble Hill 225 St
Mercy Av
Metropolitan Av
Mets-Willets Point
Middle Village Metropolitan Av
Middletown Rd
Montrose Av
Morgan Av
Morris Park
Morrison Av-Soundview
Mosholu Pkwy
Mt Eden Av
Myrtle Av
Myrtle Willoughby Avs
Myrtle Wyckoff Av
Nassau Av
Neck Road
Neptune Av
Nereid Av
Nevins St
New Lots Av
New Utrecht Av
Newkirk Av
Newkirk Plaza
Northern Blvd
Norwood 205 St
Norwood Av
Nostrand Av
Nostrand Av
Ocean Pkwy
Ozone Park Lefferts Blvd
Park Pl
Park Place
Parsons Blvd
Pelham Bay Park
Pelham Pkwy
Pelham Pkwy
President St
Prospect Av
Prospect Av
Queens Plaza
Queensboro Plaza
Ralph Av
Rector St
Rector Street
Rockaway Blvd
Rockaway Park Beach 116 St
Roosevelt Island
Seneca Av
Sheepshead Bay
Shepherd Av
Simpson St
Smith 9 Sts
South Ferry
Spring St
Spring St
St Lawrence Av
Steinway St
Sterling St
Sutphin Blvd
Sutphin Blvd Archer Av JFK Airport
Sutter Av
Tremont Av
Union St
Utica Av
Van Cortlandt 242 St
Van Siclen Av
Van Siclen Av
Vernon Blvd Jackson Ave
W 4 St Washington Sq
Wakefield 241 St
Wall St
West 5 St NY Aquarium
West Farms Sq E Tremont Av
Westchester Sq East Tremont Av
Whitehall St
Whitlock Av
Wilson Av
Winthrop St
Woodhaven Blvd
Woodhaven Blvd
World Trade Center
York St
Zerega Av