After much planning on how to carry out data collection and visualization for a project about the restaurants in Abu Dhabi, our class completed the dataset of over 80 restaurant entries that we gathered from across the city or online (with the help of TripAdvisor, Zomato, Talabat, and Google Maps) and organized in the app Fulcrum. These restaurants conform the Foods of Abu Dhabi project, which gave way to maps created by us, the students, using CARTO.
In terms of data collection, I experienced some obstacles that I believe are not uncommon in research of this kind. To begin with, when I went around the city with another classmate, asking restaurants about their information (and explaining what the project was about), several of the employess gave us phone numbers to contact the restaurants’ main offices, because they couldn’t disclose all the data we were asking for. On the other hand, several restaurant owners were thrilled to have us feature them in the maps, and even offered us discounts for dinner (as we were walking around the city in the evening). I couldn’t forget that restaurant are, after all, businesses, and their commercial aspect can’t be ignored, even if we’re viewing them as representatives of culture. In fact, this project began with the idea of helping NYUAD students know where to eat, and where to order from, and at what price. After all, this is why websites like Zomato and Talabat are so successful, and provide such detailed information about restaurants. This worked at my advantage, for I couldn’t go the city to create all of my records.
Nevertheless, searching for the information online was also time-consuming, and required curating the content. Our entries asked for very particular and specific details, such as the number of tables and the date of establishment. We figured out a way to count the njumber of tables, by using Street View on Google Maps inside retaurants that pay for this service as advertisement. This was helpful in some instances, but not all locations had this feature. For this reason, many of my entries lack the number of tables. In terms of the date of establishment, I looked online for this information specifically. I used articles such as 11 New Restaurants in Abu Dhabi (some are not open anymore though, something I had to be careful about and double-check), Dishing Up the Past: A Time Before the U.A.E’s Michelin Restaurants and Celebrity Chefs, and a life-saver blog about finding spicy food in the city.
The following are three maps created on CARTO, with the dataset from Fulcrum. I chose only some of the data we created for all records, given I think these are the variables that are most relevant to the project when viewed in light of the humanities. Restaurants, as I mentioned before, are symbols of culture, and food allows the connection to one’s home and to other’s traditions. I think that the date of establishment of restaurants, as we had already discussed in class, might help trace a history of migration into the city, both regarding which nationalities are moving into the U.A.E. and when (origin of the food), and also concerning the luxurious lifestyle that characterize the Emirates in the world’s eyes (are the most expensive restaurants the newest? Or are the cheapiest restaurants more common now, because of the increased flow of incoming workers?). Average price comes into play here.
- The first one presents only the records that have a date of establishment associated to them (records where the date of establishment appeared as 0 were eliminated). This points appear with a timeline, that moves from 1968 to 2016. The map points to slow growth until around 2007, when various of the restaurants in the dataset began to open, until 2016.
- The second map and third maps show all the entries as points,with pop-ups that indicate the restaurants’ names, origin of the food, food subvariety, and average price in dirhams. The second map has a widget that allows the user to select specific categoried for the origin of the food, just as we did with the categories we set up in Fulcrum. The third map, instead of filtering the food origin, filters the average price range for the restaurants.