Project Overview

Our Team created an exciting project called the European Taste Test, where we utilized

a dataset containing over 100,000 restaurants across Europe. As international students, we want to

encourage trip planning and help others enjoy their trip with minimal stress. Our goal was to create an

interactive map plotting all these restaurants to provide users with a comprehensive

view of the culinary landscape across the continent. We wanted to improve the efficiency

of trip planning and provide a tool to effectively spread culture among tourists [1].

To achieve this, we leveraged the power of programming languages such as Python, HTML, Javascript

and CSS, along with the OpenView API and Leaflet, to create an engaging and user-friendly platform.

European Taste Test offers users an opportunity to explore the diverse food culture of Europe, with

easy navigation features and detailed information on each restaurant. It is a one-of-a-kind

experience that combines the power of data analysis and programming with the love of food

and travel. We were inspired by research done in similar genre of cuisine analysis and interaction[2].

You can use our demo video to help you get started on finding the best food in Europe!

To check out the dataset itself, you can check it out on Kaggle Restaurants Info for 31 Euro-Cities .

Our source code is available on github. Go ahead and try it out!

Tech Stack

1. Python | JavaScript | HTML | CSS
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Interactive Visualizations

1. OpenStreetAPI Map

Open source map API[3] used in conjuction with Leaflet [4] javascript library to display a map of Europe.

User can zoom, pan and interact with markers displayed on the map to recieve detailed information regarding each

restaurant.

2. Interactive Filters

There are multiple filters [5] city, cuisine, dietary restrictions, ratings and price range for the user to change the displayed

markers depending on their needs. This allows the user to interact with the data while visualizing

the adaptations to the dataset in a meaningful way.

3. HTML Table

The display on the marker gives the user an option to add the respective restaurant into a table for further comparisons.

The name, city, cuisine,rating and price range of the selected restaurant will be shown, allowing the user to

easily visualize the data in a tabular format. The user has the option to delete and favorite the selected

restaurants for more efficient planning, inspired by the storytelling concept with visualization [6].

4. Sunburst Chart

Using D3.js, the sunburst chart is connected to the filters, and provides another interactive

visualization along with the map and table for the user to view the data in a different way.

The hierarchal data displays the data through categories and subcategories so that the user can filter layer by layer.

Learning Objectives

1. Implementing OpenStreet and Leaflet API

We learned how to integrate multiple open source libraries to illustrate our visualizations.

2. Large Scale Data cleaning using Python

We were exposed to the details of data collection and how that impacted our visualizations.The learning was to be

meticulous about identifying relevant information within a large dataset and how to make it accessible to our project.

3. UI/UX Development with HTML, CSS and JavaScript

Learning these new languages helped us analyze user friendly designs and produce the front-end of our project.

4. Data Visualizations using D3.js

All of us experimented with the different D3 graphs while analyzing the pros and cons of each one,

and decided on the sunburst chart. Through this, we learned the fundamentals of visualizing information in an impactful way.

5. Project Management with Github

We learned how to collaborate effectively to create this project with interactive data.