Foods of Abu Dhabi

The Foods of Abu Dhabi was a pilot group project me and my class have started during the Introduction to Digital Humanities class. The aim of the project was to create a map of as many dining places in Abu Dhabi as possible and create a map visualization of them. Our professor has designed a form on Fulcrum where all of us could introduce the data gathered.

Screenshot of the Fulcrum form.

The form included the following questions:

  • place of the name
  • food origin
  • food subvariety
  • location (either by using GPS or typing in the coordinates from Google maps)
  • date of establishment
  • average price (AED)
  • number of tables
  • comments
  • delivery to saadiyat (yes/ no)
  • last delivery time

In the beginning of the data collection process, we have ventured out in the city and went to restaurants, cafeterias, and cafes to ask the owners or the workers details about their dining places. The interactions were interesting as we would come to learn more details about the place than what we were mainly interested for the Fulcrum form and also “get a feel” of the place which a map cannot recreate. Then, however, we learnt that there is a much easier way to “collect” spatial data by using the coordinates from Google Maps. Using and other similar websites, we continued to add entries to our data set.

Our next job was to create and export a map using CARTO, a map that would surprise a certain aspect of dining in Abu Dhabi. I chose to focus on the Khalifa City and the dining areas it provides. My map can be consulted at the link below:

Foods of Abu Dhabi

The reason why I chose to focus on Khalifa City is because the area is known to be one of the richest in the entire city so I had my expectations set quite high regarding the prices of the food. It turned out that it was not the case and that there are many affordable dining places in Khalifa City. The viewer of the map can hover over the points on the map and see the name, the origin, and the average price of the dishes served at the respective place.


  • many of the dining areas have very affordable average prices (10-25 AED)
  • there is a variety of cuisines in Khalifa city: Middle Eastern (Emirati, Lebanese, Saudi), European (Spanish, French, Italian, British), Asian (Indian, Japanese, Chinese), American, and other International.
  • the average prices vary from 10-15 to 80-90 AED
  • there are extremely expensive dining places in Khalifa City (which was initially my expectation)
  • there are many Indian restaurants
  • many of the restaurants are centered around the Etihad building (center of the city)
  • none of the places deliver to the faraway land of Saadiyat

This project will be restarted during the following semester at NYU Abu Dhabi by professor Wrisley’s class on mapping. For the students in that class, I would like to give the following pieces of advice:

  • try to collect data by going out and interacting with the owners and workers of the restaurant; it is very difficult (hence, very little data about that) to gather information regarding the number of tables and the date of establishment (and maybe even others) about the place without getting to talk to someone who works there. Plus, it is very fun to do it although you might need to pretend you are organizing a big party when you are asking/ counting the number of tables!
  • try to add more about fields to the Fulcrum form: opening hours, special menu, general rankings on different websites. Some research on opening hours would really be interesting!
  • set a “national” granularity and try to add as many “national” restaurants. It would be very interesting in the end to see how many and where are different nation-representative restaurants located. Also, a comparison between the demographics and the origins of the food would definitely say something!
  • Good luck with the project and do add as many entries as possible!

Networks, Maps, and NodeGoat

I haven’t been around in a while. The reason for that is that many changes have been taking place and a lot of information had to be sunk in before I wrote this post. Last time I was discussing the presentations on corpus analysis, and little did I know that it was only the beginning of this field called digital humanities.

There is a whole lot more. During my last couple of classes, I have learned about networks and maps – and what I learnt is only a small part from what I learnt is to be learnt. But I am still glad I have a starting point now.

Let’s begin with networks.

Networks – in digital humanities – is an amazing concept. It allows one to visualize all what they read about in a book, on Wikipedia, or in a table. It visually connects the information and provides an easy, logical form for understanding its content. Before I move on to its use in different types of projects, I would like to give a short definition and description of a network.

A network consists of nodes, edges, and relationships. A node is any item in our data that we are focusing our analysis on, it is the person/ object/ phenomenon of our study. Edges are the links/ connections between two nodes and show that those nodes have a common characteristic. Lastly, the relationship is the idea behind the edge, the criteria used in determining the connections between the nodes before the visuals are displayed. An easy way to picture a network is by thinking about one’s genealogical three: each member of the family represents a node, each arrow (or double arrow) from one to the other is an edge, and the meaning of the arrow (as well as the position of one node in comparison with another – since the genealogical three is a hierarchical structured network).

An example of what one can do with networks (and a bit of programming knowledge!) in digital humanities is the amazing project by Silvia Gutiérrez, New Maps for the Lettered City. A few blog posts ago I referenced to the Mapping the Republic of Letters project created by a team of researchers at Stanford University, which offered a visualization of Republic of Letters writers’ travels – which could be further analyzed and interpreted. This new map, which looks at members of the 19th century salons in Mexico, is doing even more than what the Stanford project did. For example, the generations’ problem shows who met whom and where, and what literary movement(s) and salon(s) were each of them part of. This is extremely helpful for thinking about the human relationships that were formed in each salon and what new ideas might have each of them brought it from other salons and/ or other literary movements.

Coming back to the Digital Humanities class, me and my classmates, together with our professor, have started a network project on our own! We have used nodegoat to introduce our data and create a network visualization. The subject we chose was Egyptian Cinema and the categories of nodes we created were multiple: title of the film, author/ other authors/ main cast, release year (the very first, despite the country/ region). Then, for each person we have decided to attach some information which would help us study the social relationships between them. Thus, we added their spouse, date and place of birth and of death. Towards the end, our database looked like this, with 44 entries:


The network project had two distinct parts: data gathering and network visualization. In order to gather all the information on each film, we created an excel table where we introduced most of the required details (title, author, year) and then introduced them in the Film or Person forms on Nodegoat. Once we filled in all the data for around 10 films each classmate, we were ready to test the network visualization functionalities of the website! In the picture below you can see a social relationship network visualization of the connections between different film directors. Where they are more connected (the example of Chahine Youssef, Mazar Ahmed, Kamal Hussein are relevant in this sense). Other authors, such as Mohamed Khan, Abouseif Salah and others seem to be less-connected with the rest of the Egyptian film industry.


But wait! Nodegoat only displays the user’s input. The reason why some authors look to be less well-connected than others is simply because the “Person” category for each film was not filled accordingly, which is an important detail to remember. If the data collection would’ve been done more in depth, then our social network would have looked terribly different! (And we might have discovered that, in reality, they are all inter-connected with each other).


Above, you can see the films – out of all the 44 entries – that were associated with the film director Mohamed Khan. In a specific research project (on the themes addressed in various films) – such a network would serve the analysis and exploration of the research subject by providing an easy visual mean of analyzing it.

All in all, NodeGoat was interesting to use. After also checking out Palladio, I believe both websites are handy and can generate basic good-quality visualizations. Some shortcomings of NodeGoat would be:

  • the large amount of manual labor one still has to put in gathering and introducing the data (instead of simply giving it a .csv file to read we have to manually introduce data on each film which takes up quite a lot of time). In comparison with a web-scraping software it looks terribly inefficient to have to google the information and organize it;
  • its limited visualization options (only geographical and social);
  • its unnecessarily pop-up menus and tabs which slow down the process of introducing data.

However, considering the fact that it is a start-up website and my class purpose was only learning through experience, NodeGoat helped in showing us the “behind-the-scenes” of network visualization.