This visualization is an interactive slideshow which takes the user through the Dataset of the 9th Round of the Yelp Academic Dataset Challenge. The dataset itself contains the Yelp reviews and aggregated check-ins over time of 144 thousand businesses located throughout various metropolitans across the globe. This visualization focuses on the data from the United States which includes the following cities: Pittsburgh, Charlotte, Urbana-Champaign, Phoenix, Las Vegas, Madison, Cleveland.
As the visualizations use a preset static canvas of 1024x768 pixels, the viewing size of the interactive visualization is at least 1280x800 pixels to accommodate the surrounding text and scene elements.
With such a large dataset, creating a visualization with the raw data turned out to be very difficult. The first step for created this visualization was to process the dataset and focus on the data pertaining restaurants in the United States only.
The use of CSS and FullPage.js allows for a cohesive template look for the interactive visualization. Each page is transitioned using the same method and text elements are kept the same (font size and family). Additionally there is a page navigation bar on the right side which allows users to skip between different slides. The tooltips which are triggered when a user moves their mouse over the navigation alters the display parameters for each navigation menu item.
Annotations have been used in all three visualizations with a similar process of using triggers to change the hidden paramenter of the annotation. For example, the Line Chart Visualization has an initial state of the hidden parameter which controls the display of the annotations set to false. As a user uses the brush bar to trigger changes to the line chart visualization x-axis parameters, it also changes this hidden parameter to true. In turn the annotations disappear until the user resets the brush bar to zero - thus zooming out back to the default visualization parameters.
Both parameters and triggers are used in all of the three visualizations. For the Bubble Chart Visualization, parameters for the x,y co-ordinates of each bubble are set to an initial central position for the All Reviews Visualization. As a user selects menu items to chose between All Reviews, Reviews by State and Reviews by Stars, it triggers the change of bubble’s x,y co-ordinates parameter to their respective groupings.
In Zoomable Sunburst Visualization, each mouse click on a region is a trigger for the path and arc parameters. By clicking within a region, you can zoom into the data to take a look at the information underneath. Clicking the centre circle will trigger the parameters to return to its values one up in the hierarchy.
For Line Chart Visualization, parameters are set for each Restaurant Category. These parameters are triggered to be updated as the mouse moves over the visualization, providing the user with a snapshot of the amount of check-ins in a given time. A secondary parameter and trigger set is the brush bar below the line chart. This bar allows the user to zoom into the data to show a closer look of the data. By clicking a set space on the brush bar, it triggers the x-axis parameter of the line chart to be updated to a ratio of the same selected section of the brush bar.
The restaurant business is an enormous industry where understanding your clients and their behavioural patterns may give prospective new restaurant owners a much needed head start. This interactive slideshow dives into the publicly available Yelp Academic Dataset which includes a large set of reviews client interactions across multiple industries and regions. We will focus on restaurants within the United States in order to better understand client interactions.
Assumptions are made in the positive correlation between the number of reviews and the popularity of a business. This is then tied with the assumption of the positive correlation between number of check-ins and the popularity of a business. By focusing on restaurants which have large number of reviews, check-ins and high star rating, we will be able to understand the type of restaurant to invest into.
We are able to see that the bulk of the reviews are from NV with a star rating between 2-4.
When the categories are consolidated into the Top 24 + Others, we can see that American New is the most reviewed restaurant category. The spread between states do not seem to affect the ranking of restaurant categories either.
Aggregating the hourly check-ins per restaurant categories for NV reveals a consistent trend. Regardless of the day of the week, there is a cyclic relationship between the number of check-ins and the time of the day. The amount of check-ins peaks out near the end of the day with a general increase over the weekend.
The initial analysis based on the Yelp Academic Dataset shows that a prospective restaurant owner should choose to open an American New styled restaurant. The location should be in NV where there were the most amount of reviews logged. The restaurant should focus of the evening and weekend crowds with possibly reducing staffing during the day as the amount of people tapers off.