{"id":37875,"date":"2024-04-05T12:16:48","date_gmt":"2024-04-05T16:16:48","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=37875"},"modified":"2024-04-30T10:35:14","modified_gmt":"2024-04-30T14:35:14","slug":"mapping-john-canadays-1976-new-york-times-guide-to-dining-out-in-new-york","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/visualization\/mapping-john-canadays-1976-new-york-times-guide-to-dining-out-in-new-york\/","title":{"rendered":"Mapping John Canaday’s 1976 New York Times Guide to Dining out in New York"},"content":{"rendered":"\n

Introduction<\/h2>\n\n\n
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Book cover for The New York Times Guide to Dining Out, 1976<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n

Over the past few months, I have been exploring ways of mapping New York City\u2019s evolving food landscape. I am particularly interested in creating resources that allow researchers and other food enthusiasts to explore restaurants, bars, and cafes that have come and gone over time. In a continuation of this research, I decided to map the restaurants featured in John Canaday\u2019s 1976 New York Times Guide to Dining Out in New York<\/em>. The book provides a rare chance to learn about restaurants that existed in a time before online directories. <\/p>\n\n\n\n

Today, we are highly accustomed to navigating the world around us through a digital map. My personal mental map of New York City, especially of my gastronomic memories, has been constructed through points on Google Maps. I often get frustrated, however, that businesses just silently fade away from the map when they close. When our knowledge of the city is informed by applications like Google Maps, how does this impact the way we remember the world around us? What can we learn about the city by mapping historic businesses in a familiar format? What stories are waiting to be told in these forgotten places? With these questions in mind, I set to work scanning and parsing John Canaday\u2019s restaurant data to build into an interactive map.<\/p>\n\n\n\n

Methodology<\/h2>\n\n\n\n

After scanning the 249-page book, I performed optical character recognition (OCR) to convert the images into recognizable text. Luckily, the guide is uniformly structured which allowed me to programmatically parse the data using Python. The first line begins with its rating and name, followed by its address and telephone number, and finally the review. <\/p>\n\n\n\n

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A scanned page of the restaurant guide, next to a screenshot of the text file. <\/figcaption><\/figure>\n\n\n\n

Right away, I separated the reviews from one another by replacing the first star in each entry\u2019s review with a regular expression string: ([BREAK]\\n\\*). Knowing that Python parses text files one line at a time, I wrote a script that counts what line number the script is at and resets when it encounters a [BREAK]. Based on the line count, the script then identifies where the name (line #1), address (line #2), and description (line #3 until the next break) is located in the text file and writes this to a CSV file. <\/p>\n\n\n\n

import csv\n\ndescriptions_file = open('raw_guide_descriptions.csv', 'w')\ndesc_writer = csv.writer(descriptions_file)\n\nheaders = [\"name\", \"address_telephone\", \"description\"]\n\ndesc_writer.writerow(headers)\n\ndescriptions = {}\n\nwith open(\"nyt_diningout_1976.txt\") as txt_file:\n    for line in txt_file:\n        if line.strip() == '[BREAK]':\n            line_num = 0\n        if line_num == 1:\n            name = line.strip()\n            descriptions[name] = {}\n        if line_num == 2:\n            descriptions[name][\"address\"] = line.strip()\n            descriptions[name][\"description\"] = []\n        if line_num >= 3:\n            descriptions[name][\"description\"].append(line.strip())\n        line_num += 1\n\nkeys = list(descriptions.keys())\n\nfor k in keys:\n    write_list = [k, descriptions[k]['address'], \" \".join(descriptions[k]['description'])]\n    desc_writer.writerow(write_list)\n\ndescriptions_file.close()<\/code><\/pre>\n\n\n\n
Next, I brought the raw CSV file into Google Sheets where I performed some data cleaning. I separated the rating count from the name of the restaurant, removed the telephone numbers and corrected spellings in the address field, and corrected formatting and spelling in the description field. (I tried my best but due to OCR inaccuracies and time constraints, there may still be errors in the descriptions.) <\/p>\n\n\n\n
In order to map these restaurants, I needed to geocode their addresses to identify their coordinates. I did this using the Geopy Python package with the Nominatim geocoding service. <\/p>\n\n\n\n
from geopy.geocoders import Nominatim\nimport csv\nimport time\n\ngeolocator = Nominatim(user_agent=\"restaurants_study\")\n\ngeocoded_csv = open('raw_addresses_geocoded.csv', 'w')\ncsv_writer = csv.writer(geocoded_csv)\n\nheaders = ['name', 'address', 'found_address', 'latitude', 'longitude']\n\ncsv_writer.writerow(headers)\n\nwith open('raw_restaurants.csv') as csv_file:\n    csv_reader = csv.reader(csv_file)\n\n    for row in csv_reader:\n        print(row[1])\n        location = geolocator.geocode(f\"{row[1]}, New York, NY\")\n\n        if location is None:\n            lat = None\n        else:\n            lat = location.latitude\n\n        if location is None:\n            lon = None\n            \n        else:\n            lon = location.longitude\n\n        print(f\"{lat}, {lon}\")\n\n        if location is None:\n            found_add = None\n        else:\n            found_add = location.address\n\n        print(found_add)\n        print(\"-----------------------------------\")\n\n        write_list = [row[0], row[1], found_add, lat, lon]\n        csv_writer.writerow(write_list)\n        time.sleep(2)\n\ngeocoded_csv.close()<\/code><\/pre>\n\n\n\n
Next, I summarized Canaday\u2019s reviews using ChatGPT via the OpenAI API to create brief blurbs my users would see when hovering over a point on the map. <\/p>\n\n\n\n
import csv\nimport json\nimport openai\n\nopenai.api_key = \"INSERT KEY HERE\"\n\nsystem_setting = \"You are summarizing restaurant reviews.\"\n\nrest_csv = open('final_restaurant_data.csv')\nrest_reader = csv.reader(rest_csv)\nnext(rest_reader)\n\ngpt_summaries = {}\nerrors = []\n\nfor row in rest_reader:\n\n    prompt = f\"Tell me the dishes this restaurant serves, in 20 words or less:{row[20]}\"\n    res = openai.ChatCompletion.create(\n        model=\"gpt-3.5-turbo\",\n        messages=[\n            {\"role\":\"system\", \"content\": system_setting},\n            {\"role\":\"user\", \"content\": prompt}\n        ]\n        )\n    \n    gpt_summary = res.choices[0].message.content\n    gpt_summaries[row[0]] = gpt_summary\n    print(f\"SUMMARY: {gpt_summary}\")\n\nwith open(\"gpt_summaries.json\", 'w') as write_file: \n    json.dump(gpt_summaries, write_file, indent=2)<\/code><\/pre>\n\n\n\n
From there, I performed a ton of cleaning on my data using Google Sheets. In many cases, I needed to manually find the coordinates of restaurants whose addresses were incorrectly or unable to be geocoded. I also wanted to include additional information about whether or not the restaurants were still operating, which I did through old-fashioned, tedious Google searches. When all was said and done, I was left with the following dataset:<\/p>\n\n\n\n
Finally, I brought this into Tableau where I mapped my restaurants and designed a dashboard that enables users to geographically explore John Canaday\u2019s reviews. <\/p>\n\n\n\n
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Screenshot of the data dashboard<\/figcaption><\/figure>\n\n\n\n
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View the dashboard<\/a><\/div>\n<\/div>\n\n\n\n
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Discoveries<\/strong><\/h2>\n\n\n\n
John Canaday\u2019s 1976 New York Times Guide to Dining out in New York<\/em> is a product of the earliest years of professional restaurant criticism. His predecessor at The Times, Craig Claiborne, is often regarded as the father of the modern restaurant review. As the newspaper\u2019s food editor, Claiborne began writing weekly reviews in 1962. At first, these reviews were brief, directory listings with just under 100 words. Eventually, he started including more in depth commentary. In 1970, he published his first \u201cDirectory to Dining,\u201d  which subsequently sparked the country\u2019s fascination with restaurant criticism. (Pete Wells for the NY Times<\/a>) <\/p>\n\n\n\n
After Claiborne, Raymond Sokolov and John Hess had brief stints contributing restaurant reviews between 1971 and 1974. John Canaday, who had been working as the Times\u2019 art critic, contributed reviews in his spare time between 1974 and 1976. He promoted Mimi Sheraton as the first full-time restaurant critic in August 1976. (Eater.com<\/a>)<\/p>\n\n\n
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So what does this guide tell us about the state of dining in 1976 New York City? Who was its audience? As Robert Sietsema, a restaurant critic at The Village Voice,<\/em> wrote for the Columbia Journalism Review in 2010, many of these 1970s restaurants were considered \u201cthe exclusive province of businessmen with expense accounts and the idle rich.\u201d (Robert Sietsema for Columbia Journalism Review<\/a>) Canaday\u2019s reviews certainly seem to cater to an upper middle class who spends their free time attending performances in the Theater District and Lincoln Center. The geography of the restaurants, predominantly centered in Midtown, support Sietsema\u2019s statement that businessmen were likely some of the primary patrons of these establishments. <\/p>\n\n\n\n
These restaurants also illuminate broader trends that characterized American dining throughout the mid to late 20th century. In 1976, French cuisine clearly dominated the fine dining scene. Just under a third of all restaurants included in Canaday\u2019s guide are classified as French. Colloquially, this is known as the era of the Le\u2019s<\/em> and La\u2019<\/em>s in fine dining\u2013 La Grenouille<\/em><\/strong>, <\/em>Le Cygne<\/em><\/strong>, <\/em>La Caravelle<\/em><\/strong>, <\/em>et cetera. Italian food was also just beginning to enter the upper echelons of fine dining. Canaday made headlines when he gave Italian restaurant Parioli Romanissimo four stars in 1975 (Eater.com<\/a>). <\/p>\n\n\n\n
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Note: \u201cContinental\u201d cuisine is an umbrella term that refers to a general Western European-style cuisine, typically French, Spanish, and Italian-influenced. <\/em><\/figcaption><\/figure>\n\n\n\n
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Notable food and cultural history is threaded throughout the stories of these establishments. The \u201c21\u201d <\/strong>club was a former prohibition-era speakeasy<\/a> that closed in 2020. Spanish restaurant Meson Botin<\/strong> was an offshoot from Spain\u2019s pavilion at the 1964 World\u2019s Fair<\/a>. Yunnan Yuan<\/strong> is where owner Peng Chang-kuei invented General Tso\u2019s chicken<\/a>. <\/p>\n\n\n\n
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(Top to bottom) <\/em>Menus for Manny Wolf’s Chophouse<\/strong>, Les Mareyeurs<\/strong>, Gian Marino<\/strong>, Le Chateau Richelieu<\/strong>, and The Box Tree<\/strong>. From the New York Public Library. <\/em>NYPL’s “What’s on the Menu?<\/a>” project features menus for many of the restaurants featured in Canaday’s guidebook. <\/figcaption><\/figure>\n\n\n\n
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At all of these restaurants, it is clear that food and socialization were tightly linked. La Cote Basque<\/strong> was a 1960s hot spot for socialites and celebrities<\/a> the likes of Jackie Kennedy Onassis and Frank Sinatra. Les Pleiades<\/strong> was an art world hangout<\/a>, attracting directors of the city\u2019s prestigious museums and prominent gallerists. <\/p>\n\n\n\n
Other restaurants catered to specific communities. Bernstein-On-Essex <\/strong>and Moshe Peking<\/strong>, were some of the first Kosher Chinese restaurants in the city. Their operation contributed to a distinctly New York Jewish identity<\/a>. Uptown, Czechoslovakia Praha<\/strong>, Ruc<\/strong>, and Vasata<\/strong>, were a part of what was once referred to as Little Czechoslovakia<\/a>, an Upper East Side enclave of Czech and Slovak immigrants.<\/p>\n\n\n\n
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