Most of us can relate to eating something wonderful at home, or away from home, and taking a photo of the food to share with others or remember for later. For whatever reason, this food was meaningful to us. Maybe it was a new experience, maybe we were surprised we had the skills to make it, maybe the dish received a lot of compliments, or maybe the food just tasted good to us.
Since the beginning of the internet, people have been uploading food photos to a myriad of websites. This is one of the earliest forms of Social Media. Going through the effort to snap the photo, post it somewhere online, and give it a title and description suggests that food was meaningful.
At QuantiView, we count what we view. We thought it would be interesting to gather food images posted online by people around the world and quantitatively determine what was in the images. Our methodology is unlike other quantitative research since there is no questionnaire or script. We create our data structure from scratch as we analyze the images, like when research companies code open-ended responses.
As we analyzed images from around the world, we logged country of origin when it was openly shared by the uploader. Several countries had more than 500 images which was enough to warrant deeper investigation.
For this report, we analyzed 971 food images submitted by people who identified themselves as residents of the USA. Our team of data entry specialists tagged each image for several key attributes, from both visual inspection of the images, as well as reading any related information given as context. Our rules for how we created the data are given in detail in the report.
The main variables we defined for each photo are:
General food forms (e.g., sandwich, pie, cookie, salad, etc.)
Meat and fish
Fruits and vegetables
Grains and starches
Oils and fats
Nuts and seeds
Spices and flavorings
Other keywords given
Each of these main variables were defined for each image. Once QuantiView data is created and cross-checked, it looks like normal quantitative data. Our reports have charts and cross-tabs. There are rankings from most common to least common. And so on.
One especially unique feature of QuantiView reports is image collages. For all of our main conclusions, we assemble a collage of images that illustrate and prove that result. For example, if pie is the #1 most common element of the meaningful food images submitted by people in the USA (it is not by the way), we create a collage containing images from the pool of 971 that contain pie. What this means is you can page through the report, never read a word, look at all the image collages, and instantly understand the results just by looking at the pictures.
In the end, by viewing this report, you will learn about food habits of people in the USA, in a fresh, enjoyable, and easy way.