Tag Archives: datasets

Apigee – Fashion Studies Dataset project #stansmith

Instagram —> # Stan Smith —> APIGEE to create a collection of data —> pictures and hashtag —> mapping or story-line

Scarlett and I decided to deepen fashion studies through DH tools that we learned during the semester. Since we are studying fashion, we noticed two different aspects that we would like to develop and that both are necessary in our field. The first one is visualization, while the second one is connectivity. For the first, we decided to start from Instagram. Between all social media we have today, Instagram is based on hashtag and images. Fashion is a visual thing, and the absence of pictures wouldn’t allow its growth. The second aspect would be connectivity: through a specific detail, like Stan Smith shoes, we can trace the visual story of fashion, declined in that specific detail. Marketing strategies, level of interests all over the world, connect people with same passions and interests, etc.… It could not be appealing for someone, but we think fashion owns this power of connection. This is what we consider a tool that has the power of social mobility, etc.…

We started with Apigee, the leading provider of API technology and services for enterprises and developers. Hundreds of companies including Walgreens, Bechtel, eBay, Pearson, and Gilt Group as well as tens of thousands of developers use Apigee to simplify the delivery, management and analysis of APIs and apps (http://apigee.com/docs/api-services/content/what-apigee-edge). Our classmates in the Fashion Studies Track have suggested this program to us because it would have helped for a good but simple data project; so let’s see how it works.

Basically we wanted to collect images, find tags with StanSmith, locations all over the world, to see what relationship exists between the world and the shoes. I guess this could also be a good project to keep track of marketing movements in the entire fashion world, and with all the items, not only one specific.

We typed https://apigee.com/console in the search bar and in the page that popped out we chose “Instagram” in the column API. The next step is to select OAuth2 under the column “Authentication” because to interact with data through Apigee is necessary to authorize your Instagram account.

At this point you will have three options (Query, Template and Headers). We chose “Template” (for Instagram) and in the “tag name*” slot we typed our tag “stansmith”. Right after this step the authentication is complete, authorizing Apigee to use your social media account.

It’s necessary to select an API method and it’s important to select the second choice under the “Tags” section of the list.

We only had to click on “send” and the response came: Instagram has pagination, so the data we got were divided in pages. Copying the URL in the picture, and pasting it in a new searching bar we obtained a weird data page in order to see the next page.

Our friend told us that the process was almost complete, but the last step was to download “JSONview” (with Safari it doesn’t work, so we used Chrome), to see the data in an organized form. This step is specifically for an easier visualization of images, profile pictures, username, etc.…and we also found the numbers for “created_time”. This part is very important because converting this numbers from Unix epoch time to GMT is necessary for the visualization of images.

With Epoch Converter we were able to convert everything and the result is a list of data, where every “attribution” is a post. We collapsed the posts, having the chance to look at posted pictures in different resolutions!

For the presentation we’ll provide a Power Point with images step by step of the process to reach this data that we will probably use for a mapping or a timeline of the item.


Thanks for your time,


Nico and Scarlett

Terrorism Data

Hi all,

I am interested in studying the history of domestic terrorism in the U.S., so I went looking for datasets to that effect. What I was hoping to find was a comprehensive repository that covered most, if not all of U.S. history, and included incidents like the Oklahoma city bombing, alongside attacks on abortionists, hate crimes, and lynchings in the post-Civil War era (In the best of all possible databases, the decimation of Native American communities and cultural practices would also be included, even though the FBI defines terrorism as requiring illegal acts, and often enough the crimes against these communities were legally-sanctioned). Additionally, I was hoping to find basic information on who the victims and perpetrators were (at least with regards to incidents from the last few decades), as well as historical context for each incident. Perhaps I went looking in the wrong place, but as of now it appears that such a comprehensive database doesn’t exist, which is odd and upsetting. However, I did find smaller, though still formidable, datasets that tackle parts of the problem. One of these is from the Global Terrorism Database, maintained by the University of Maryland, which provides information on terror attacks worldwide. Granted, their information only goes back as far as 1970 and it neither includes hate crimes nor legally-sanctioned terror against ethnic groups. That said, their dataset is still the most informative and usable of what I’ve found so far, because it consists of the raw data behind their charts (i.e. information on each and every incident counted). More often than not in my search I found well-meaning organizations that provided aggregate counts rather than the specific data that went into those aggregates, so I was very grateful to find Maryland’s GTD.

I am going to use it to a) mine its listings of domestic terrorism in the U.S. and b) to compare incidences of terror – both domestic and international – in the U.S. with terror in the Middle East. Over the course of this semester, I would like to work towards making animated visualizations of what I find in here. I am excluding events from the rest of the globe outside these two areas just for the sake of manageability.

— Ashleigh