blogging, infofails

infofails: mapping Taiwanese food

At some point in 2022, I was collecting information about Taiwan and its national identity. Among many, I read some articles like this one and documented records about their increasing drive to identify themselves as Taiwanese and progressively distance themselves from the Chinese. The subject is very deep and tangled, I’m not going to pretend to understand it, but in short, Taiwan’s heritage is deeply tied to the mainland (PRC), and blends with many other influences from its diverse past. In such a way, its particular conditions have created some interesting things that have taken root in the simplest things of everyday life.

One of those things that any one can see on the streets is food. Not only its dishes that have evolved and conquered the world like the yummy Bubble Tea, but the simplest things like how the business are tagging their restaurants in food delivery platforms.

Uber Eats and Foodpanda use labels to make it easier to find what you’re looking for, just like any other platform you might be familiar with. In those “categories” you can find Japanese, American, Chinese, Thai, Taiwanese… I’m sure you know how it works, but just in case here’s a screenshot of what I mean:

I scrapped that data just to see how popular the Taiwanese tagging versus Chinese tagging. The gray squares on the map below are restaurants listed on Foodpanda and Uber Eats in Taipei:

A map of Taipei showing restaurants listed in the area

It was really interesting to see how numerous the places with Taiwanese tag were. Look at the same map, but with yellow circles for Taiwanese restaurants.

A massive difference with those showing Chinese tags on its categorization. Same map but red circles for Chinese tags.

In fact, American tagging for restaurants is way more popular than the Chinese label in Taiwan. Green circles show restaurants with American tags:

I ran the same script for all of the listed cities in Taiwan for those food delivery services, and the story was similar no matter where you looked along the island. FoodPanda displayed about 4,000 restaurants across Taiwan, 36% of those were tagged as Taiwanese and less than 3% Chinese. Uber Eats followed the same trend, I pulled data for +600 restaurants and 6 of every 10 were Taiwanese, while only 1 or none was listed as Chinese.

I understand some restaurants use more than one tag, but looking at how many of them prefer to be labeled Taiwanese rather than Chinese says something about customer preferences.

They ideas never flourished, I was completely dedicated to Ukraine stories and the data just got older and older. Basically it lost momentum to gain a spot on the news, this happens very often actually, it seems that time is never enough to do all the stories you want to do.

Anyway it was a fun exercise pulling this data and see the trends.

About the data

I used a python script to pull data from Uber Eats and Foodpanda, I’m sure there’s a smarter way of collecting this data… I’m not a developer. But if you want to try your self like I did, you will need to collect all the urls from these companies, often offered by city, then add them into something like this:

from email.headerregistry import Address
from selenium import webdriver
from import By
from import Options

import pandas as pd
import csv

restaurantList = []
driver = webdriver.Chrome('/usr/local/bin/chromedriver')

name = [ e.text for e in driver.find_elements(By.XPATH, "//*[@id='main-content']/div[6]/div/div/div/a/h3")]
category = [ e.text for e in driver.find_elements(By.XPATH, "//*[@id='main-content']/div[6]/div/div/div/div/div/div[2]/div[2]")]
location = [ e.text for e in driver.find_elements(By.XPATH, "//*[@id='main-content']/div[6]/div/div/div/div/div/div[2]/div[4]")]

dtable = {'Name_ZH': name,'Category': category, 'Address': location}
df = pd.DataFrame(dtable)



Note that you may need to install a few dependencies to run this code, but eventually it will spit a lovely .csv file with a column for the restaurant name, a col for address and one more for category listed in Uber Eats. Food Panda uses a different structure, but the code is pretty much the same except by the urls and the targeting of fields.

If you are working on something similar, I’ll love to see the outcome, reach me out on Twitter.

About infofails post series:
I believe that failure is more important than success. One doesn’t try to fail as a goal, but by embracing failure I have learned a lot in my quest to do something different. My infofails are a compendium of graphics that are never formally published by any media. These are perhaps many versions of a single graphic or some floating ideas that never landed.

In short, infofails are the result of my creative process and extensive failures at work.

Are you liking infofails?, have a look to previous ones:

01: Wildfires
02: Plastic bottles
03: Hong Kong protest
04: The Everest
05: Amazon gold
06: The world on fire
07: A busy 2021 kick off
08: Olympics
09: Floods
10: Doodles for news
11: Random Failed Maps

12: The Mismatch


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