What kind of foods do millennials want to eat, and where?
This project was inspired by my love of food and how important food diversity is to my peers and I when determining where we want to live. Millennials are expected to become the largest demographic in the U.S. in 2019 (US Census Bureau). The restaurant and food industry is being heavily influenced by their preferences. Because there are many food-related factors that millennials consider when evaluating a city, corporations such as Amazon should take this into account when deciding where to open large offices.
Methodology
Tools Used: Webscraper Chrome Extension, Excel, OpenRefine, Tableau
I focused my analysis on major millennial dining trends, looking at how well these trends are represented in Philadelphia. Furthermore, I investigated how millennials' favorite cuisines are represented in different Philadelphia neighborhoods, as food variety, pricing, and quality varies based on location within the city.
My data draws upon the following neighborhoods (grouped by proximity):
Do note that:
I started with the best neighborhoods to live in within central Philadelphia (listed above). I also analyzed current millennial dining and food trends. This was determined through qualitative research on the Philadelphia housing market and crime rates along with consumer trends articles.
I then separated restaurants in Philadelphia on Yelp by the neighborhood category. Using the Web Scraper Chrome Extension, I created a sitemap that parsed through restaurants on Yelp, segmented by neighborhood. I scraped restaurant name, price, cuisine, and star rating. This took many hours due to the separation of data into 8 different datasets. Overall, 1,500-2,000 restaurants are represented in the dataset.
Next, I used Excel and OpenRefine to clean the data. Utilizing the 'Text to Columns' feature in Excel, I used the Fixed Width and Delimiter functions to separate out the HTML code of Yelp star ratings, only keeping the number. of stars and deleting the rest of the irrelevant data. I also used IF statements and the LEN function to convert Yelp $ signs into integers.
To merge data and create visualizations, I used Tableau to append restaurants from each neighborhood.
Tools Used: Webscraper Chrome Extension, Excel, OpenRefine, Tableau
I focused my analysis on major millennial dining trends, looking at how well these trends are represented in Philadelphia. Furthermore, I investigated how millennials' favorite cuisines are represented in different Philadelphia neighborhoods, as food variety, pricing, and quality varies based on location within the city.
My data draws upon the following neighborhoods (grouped by proximity):
- Chinatown/Market East/Midtown Village
- Fishtown
- Graduate Hospital
- Logan Square/Spring Garden/Fairmount
- Old City/Society Hill/Queen Village
- Rittenhouse Square
- University City/Spruce Hill/Cedar Park
- Washington Square West
Do note that:
- Small differences in star ratings are significant indicators of restaurant quality - 1 star is a huge difference on Yelp
- All prices are the numerical conversion of Yelp prices (eg. $$ = 2, $$$$ = 4)
- Not every neighborhood is represented
I started with the best neighborhoods to live in within central Philadelphia (listed above). I also analyzed current millennial dining and food trends. This was determined through qualitative research on the Philadelphia housing market and crime rates along with consumer trends articles.
I then separated restaurants in Philadelphia on Yelp by the neighborhood category. Using the Web Scraper Chrome Extension, I created a sitemap that parsed through restaurants on Yelp, segmented by neighborhood. I scraped restaurant name, price, cuisine, and star rating. This took many hours due to the separation of data into 8 different datasets. Overall, 1,500-2,000 restaurants are represented in the dataset.
Next, I used Excel and OpenRefine to clean the data. Utilizing the 'Text to Columns' feature in Excel, I used the Fixed Width and Delimiter functions to separate out the HTML code of Yelp star ratings, only keeping the number. of stars and deleting the rest of the irrelevant data. I also used IF statements and the LEN function to convert Yelp $ signs into integers.
To merge data and create visualizations, I used Tableau to append restaurants from each neighborhood.
Trend 1: Convenience and Casual Dining
This is by far the biggest trend in the dining industry when it comes to young people. In a world filled with distractions, an increasingly faster pace of life, and the prevalence of food delivery, millennials prefer easy and convenient meals over fine dining and dining out. Most casual and convenient dining is similarly priced but varies greatly in quality, so my analysis weighs ratings over price.
The Yelp categories I focus on are:
This is by far the biggest trend in the dining industry when it comes to young people. In a world filled with distractions, an increasingly faster pace of life, and the prevalence of food delivery, millennials prefer easy and convenient meals over fine dining and dining out. Most casual and convenient dining is similarly priced but varies greatly in quality, so my analysis weighs ratings over price.
The Yelp categories I focus on are:
- Cafes
- Chicken Shops
- Chicken Wings
- Fast Food
- Food Court
- Food Stands
- Food Trucks
- Pizza
The best types of convenient food in Philadelphia, according to Yelp reviewer, are Food Trucks and Food Stands. This is a huge draw for millennials (and is a popular choice of food seen around Penn as well), and Yelp reviewers have raved about not just the food quality, but the low prices as well. Given that there are over 60 food trucks and stands listed on Yelp, the casual and convenient food scene is strong in Philadelphia.
Trend 2: Healthy and Green
There are many factors millennials take into account when it comes to healthy eating, including environmental impact and personal dietary restrictions. For example, many young people are vegan for moral and environmental reasons. Healthy food is often priced higher than other types of food, so my analysis weighs price more heavily than ratings when comparing different healthy and dietary categories.
The Yelp categories I focus on are:
There are many factors millennials take into account when it comes to healthy eating, including environmental impact and personal dietary restrictions. For example, many young people are vegan for moral and environmental reasons. Healthy food is often priced higher than other types of food, so my analysis weighs price more heavily than ratings when comparing different healthy and dietary categories.
The Yelp categories I focus on are:
- Vegan
- Vegetarian
- Kosher
- Halal
- Gluten-Free
- Salad
- Juice Bars and Smoothies
Philadelphia has cheap gluten-free, halal, and juice/smoothie offerings, making it attractive for millennials looking for low-priced food options that appeal to their dietary restrictions. Although vegan food is slightly more expensive, it has been rated extremely high by Yelp users, positioning Philadelphia as a great location for vegan and health-conscious people.
The next part of my project focuses on where to eat different cuisines in Philadelphia. When my peers and I first visited Philly (and even now), we wanted to know the best spots to satisfy our cravings and find our favorite foods.
I focus on the two of the most popular cuisines with millennials:
I focus on the two of the most popular cuisines with millennials:
- East Asian
- Latin American
Cuisine 1: East Asian
A striking observation about Asian food in Philadelphia is that it is relatively cheap, with average prices staying below $$. The Old City, Society Hill, and Queen Village neighborhoods have the best Asian food.
Millennials also look for variety when it comes to food - eating the same cuisine over and over again gets boring when there are so many options to choose from. Each neighborhood has its own set of highly rated East Asian restaurants (with the exception of Chinatown, which understandably has an equal amount of variety across cuisines). For example, Graduate Hospital has a set of great Japanese curry restaurants while University City, Spruce Hill, and Cedar Park have good Cambodian, Hot Pot, and Japanese. Someone moving to Philadelphia can clearly see the variety of East Asian food prevalent across the city and can use this to tailor where they live or spend their free time.
Cuisine 2: Latin American
Latin-American food has a greater spread in price than Asian food. The Chinatown, Market East, and Midtown Village neighborhoods have much higher prices for Latin American food (they cater to a largely Asian customer demographic), but areas such as Fishtown and Graduate Hospital are great for cheap, highly rated Latin American food. Millennials are increasingly hopping onto Latin American food trends such as Taco Tuesday.
While areas such as RIttenhouse Square have similarly rated types of Latin American food, Fishtown has really good tacos (a millennial favorite) and Old City, Society Hill, and Queen Village have amazing tapas.
Takeaways and Next Steps
Philadelphia has a thriving food scene, and this analysis only scratches the surface of the food diversity in the city. Millennials would have an abundance of options to choose from, from a quick and/or healthy meal or their favorite cuisine. A large corporation such as Amazon could take advantage of this by partnering with restaurants, offering deals to their young employees, and more.
Moving forward, I would like to take a deeper dive into the representation of every type of cuisine in the city and utilize location data to map out these restaurants and make conclusions about variables such as areas of Philadelphia with the lowest priced food, most Italian restaurants, etc.
Philadelphia has a thriving food scene, and this analysis only scratches the surface of the food diversity in the city. Millennials would have an abundance of options to choose from, from a quick and/or healthy meal or their favorite cuisine. A large corporation such as Amazon could take advantage of this by partnering with restaurants, offering deals to their young employees, and more.
Moving forward, I would like to take a deeper dive into the representation of every type of cuisine in the city and utilize location data to map out these restaurants and make conclusions about variables such as areas of Philadelphia with the lowest priced food, most Italian restaurants, etc.
