San Francisco Dreams, Fast Food Nightmares

The IBM Visual Communication Lab has created an interesting website called Many Eyes that allows people to get an intuitive understanding of data through different visualization techniques. The site provides a wide array of formats for data display, including the recently developed format of Tree Maps that I’ve found useful for analyzing my hard disk content using SequoiaView.

The Many Eyes service is free and easy to use. One can both upload data and explore visualizations of data others have uploaded. I’ll show you two that I’ve created.

FA(S)T FOOD FUN
Someone uploaded to Many Eyes the nutritional content of items from McDonald’s menu, so I decided to create a chart that I thought would highlight the good, bad and ugly. Using a scatterplot, the horizontal axis shows trans fat content, the vertical axis shows saturated fat content, and the size of each data symbol shows the cholesterol content. Click on the figure to the right to go to the visualization I created that allows you to see which data points are for which products and to create your own visualizations from this data set.

Plotting the trans fat, saturated fat, and cholesterol of the McDonald’s menu in this way makes serveral facts obvious:

• The Deluxe Breakfasts should come with a defibrillator. They are the large-sized data points in the top right that max out all unhealthy categories.
• Beware of products that proclaim low trans fat. Look for the small dot at the top left which is the Double Quarter Pounder with Cheese. It has very low trans fat and very low cholesterol, but more saturated fat than any other product they sell.
• There are some products with small-size data points in the lower left corner indicating healthy goodness. Next time that I’m stuck at an airport and the only place to eat is McDonald’s, I’ll be eating an Asian Salad with Grilled Chicken or the Premium Grilled Chicken Sandwich. Well…more likely I’ll wait until I get home and use the visualization described below to decide where to eat.

SAN FRANCISCO TREATS
I love eating out, so I uploaded all of the ratings data for San Francisco restaurants from the San Francisco Chronicle’s Top 100 Bay Area Restaurants. Each restaurant was given ratings for Overall Quality, Food, Service, Price, Atmosphere, and Noise Level. Higher is better for all ratings except for Price where higher means more expensive, and Noise Level where higher means noisier (unless, of course, you like deafening crowds).

I created a visualization that readily displays the Overall Quality, Food and Price ratings. Click on the figure to the right to see which restaurants correspond to which data point. Restaurants further to the right in the plot have better food, ones higher up are pricier, and ones with larger data point sizes have better overall ratings. (Note that I had to add some randomness to each data point so that individual restaurants could be seen, otherwise too many restaurant data points fell on top of each other, making individual restaurants impossible to see as exhibited in my first attempt to visualize this data).

Comparing different aspects of restaurants using the Chronicle website is difficult and the advantages that some restaurants have over others are not obvious. This Many Eyes chart makes certain facts about these top restaurants very clear:

• La Folie and Fleur de Lys reign supreme. Personally, I’m a little surprised by this because my one visit to La Folie wasn’t nearly as impressive an experience as at Gary Danko or Masa’s.
• Ton Kiang provides the best value of all restaurants according to the Chronicle ratings. You’ll find it’s data point in the lower right: a high Food rating and a low Price rating. From personal experience, I can also add that it ranks high on the Sidewalk Waiting rating for the long waits on Sunday mornings to get in for dim sum.
• What are Kokkari and House of Prime Rib doing on this list? They are the data points to the far left indicating low Food ratings. A quick change of the axes using the Many Eyes tools show that they are also low on Service but have decent Atmosphere ratings. High prices, mediocre food, poor service and good atmosphere: not a great combination for Top 100 restaurants.

I did a little other manipulation of the data axes to pull out interesting information. The figure on the right shows Food rating along the horizontal axis and Overall Quality along the vertical axis (Again, click on the figure to explore the individual data points). You’ll notice that the Food rating has a strong relationship to the the Overall Quality rating. If the horizontal axis is changed to service, price, or atmosphere (go ahead, click on the figure and change the axes yourself), you’ll find that these categories are not so strongly related to the Overall Quality rating, indicating how strongly food quality impacts the overall rating of a restaurant (as well it should).

If you pay more at one of these restaurants, are you more likely to get better food? Nope, the figure on the right shows that there is a slight trend to getting better food the more that you pay, but not much. A similar plot of Price versus Service indicates a similar disconnection. What you mostly pay for, according to this data, is ambiance: plotting Price against Atmosphere does show that the Atmosphere rating tends to increase as the Price rating increases.

To understand these relationships more precisely, I decided to do some statistical analysis on my own (not an ability available on the IBM website). The chart below shows the correlation matrix for each of the restaurant ratings. Numbers vary from 0 to 1, where a 0 means that two factors are uncorrelated, while a 1 means that the two factors are perfectly correlated. The two factors being compared by each number shown are the categories associated with the row and column of each number.

Some interesting insights from the correlation matrix are:

• The rating category most correlated with Overall Quality is Food, with a correlation of 0.89.
• Price is more correlated with Atmosphere (0.6) than Food (0.45) . This means that by paying more, you have a better chance of increasing the look of the restaurant than the quality of the food.
• I’m very surprised by how little Price is correlated with Service (0.22). Paying more appears to have little effect on the quality of the wait staff. This may be due to the expectations of the reviewer: higher-priced restaurants might have gotten penalized more in the Service rating for service faux pas (such as not providing clean utensils between courses) than lower-priced restaurants.
• The noise level of a restaurant had next to no impact (-0.18) on the overall rating assigned to the restaurant.

That’s all I’ve got to say on San Francisco restaurants for now.

The service provided by Many Eyes is an interesting one and demonstrates how plotting data in the proper way can quickly pull out relationships and interesting features of a dataset. I’m sure the Edward Tufte would approve. I look forward to IBM allowing people to embed these visualization applets on their website, which would allow the SF Chronicle to provide this service on their own website.