Helping Restaurant Owners Make Informed Decisions
Yelp is an information hub to get reviews about local businesses and make reservations for consumers, but it can also serve as a great resource for business owners to conduct competitive analysis. However, the user interface of Yelp is primarily designed for consumers, rather than business owners to make business decisions. In this data visualization, we followed the user-centered design process to create an interactive visualization using Yelp’s database with a more comprehensive and user-friendly interface to help current and potential restaurant owners compare the attributes of local restaurants and make more informed business decisions.
In this visualization, we incorporate multiple variables that are vital for business decision making, including restaurant location, numbers of review, cuisine types, and neighborhood information for users to get all these data at one glance. Here are the key design elements.
We use color to represent different types of cuisines, which makes it easier for users to distinguish the location of restaurants on the map. For example, it's easier to find out which part of the city that has higher density of Chinese restaurants. Using size of dots to represent number of reviews of restaurants also helps find out the regions with higher restaurant popularity.
In addition to the map view, we also incorporate a scatterplot to show the distribution of restaurants and star rating, which gives users a more comprehensive understanding of what the local restaurant scene looks like. It also works as a graphic filter with brushing and linking capability, so users can better and easier navigate the map and see the distribution of star ratings at the same time.
Details on Demand
This visualization met all the requirements of visual information-seeking mantra. Users have a brief overview of the distribution of key variables first, with the ability to zoom in/out and filter to narrow down their scope on a specific market. They can also mouse over on the map to check details on demand.
In order to provide more insights into choosing restaurant attributes/services, we experiment with a line chart that shows the correlations of price and star rating in different noise levels. Interestingly, in average noise level, more expensive restaurants seem to get lower ratings.
We started our visualization with the Tableau map. However, we found that Tableau’s default map was extremely slow to load and did not provide enough details, such as neighborhood information or places of interests, which was found critical for business decision making in our user interviews. Therefore, we incorporated the map of Mapbox, which provided more information such as major buildings and communities.
4 user interviews were conducted during our design process, after prototype iterations. Every interview was followed by a usability test, which required users to use our visualization to finish a series of task and think out loud. For example, we asked them to use the visualization to find out the best location for a two dollar sign price range Japanese restaurant in Las Vegas, with a rating target of 3.5 stars.
- Location information is critical for business owners. A lot of research is done before business owners pick the location of their restaurants.
- As we mentioned earlier, one challenge restaurant owners have when using yelp data is that they would need to look into each restaurant individually to get detailed information. There is a major user need to see a high-level picture of all the restaurants of interest.
- Provide users with a map view of the distribution of restaurants within a city.
- Provide users with an overview, summative results, and the ability to compare restaurants, while still allowing users to dive in to see information about individual restaurants.
We used the Yelp Dataset given out for Yelp Academic Challenge. The dataset is a subset that consists of data from 10 cities around the world, including information about business names, locations, reviews, check-ins, attributes of the businesses, etc. To narrow the scope of the project, we focused on two cities only: Las Vegas, NV and Urbana-Champaign, IL, because we assumed these two cities had different characteristics as a tourist destination and a college town. However, the visualization was designed in a way where it could be extended to any city, provided that we can have access to the full dataset.
Wei-Hung Hsieh: Project Manager & User Researcher
Nikhil Venkatesh: Programmer & Designer
Xiaoshuo Lei: Designer
Ying Zheng: User Researcher & Usability Evaluator
Cecilia Aragon, Professor, HCDE, University of Washington
This visualization was created with the dataset provided by 2016 Yelp Dataset Challenge.