Play with the data from Looted Heritage

Visualizing the patterns of topic composition in 208 reports from Looted Heritage, first quarter of 2012.

Rob Blades (my student) and I are in the process of submitting an article concerning our Looted Heritage project. The gist of the article is a discussion of our workflow and the kinds of patterns that may be observed when data is available freely & openly. Ideally, this would include academic papers in journals. For the time being though, we focus on social media. We also try in the paper to include our reader in the exploration of the data. Rather than presenting static images, tables, graphs, and statistics, we put the onus on the reader to check our data for his or herself. Perhaps the reader will spot important patterns, which can then be discussed in another paper. Rather than the paper being the final end-point for our data, we want it to become a jumping off point instead.

In which case, to get the conversation started, you may find links to our dataset and our analytical tools below:

Looted Heritage: Monitoring the Illicit Antiquities Trade
Full Corpus of Reports loaded into Voyant Tools
A guide to the Voyant interface
Full output from MALLET Topic Modeling algorithm in csv and html format
Full Corpus of Reports loaded into Voyant Tools Frequency Tool
Visualizing the patterns of social connections in the corpus of reports

3d Models & Augmented Reality

A longer post will follow with details, but I’m so pleased with the results I’m putting some stuff up right now. In my first year seminar class on digital antiquity which just ended, we’ve been experimenting with 123D Catch to make models of materials conserved at the Canadian Museum of Civilization (thanks Terry & Matt!). Our end of term project was to take these models, and think through ways of using them to open up the hidden museum to a wider public. We wondered if we could get these models onto people’s smartphones, as a kind of augmented reality (we settled on Junaio).

The students researched the artefacts, wrote up a booklet, and had it printed. They made the models, taking the photos, cleaning up in Meshlab, making videos and all the other sundry tasks necessary to the project. We ran out of time though with regard to the augmented reality part. By the end of term, we only had one model that was viewable on a smartphone. Today I added the rest of the materials to our ‘channel’ on Junaio, and tested it on the booklet.

It was magic. I was so excited, I ran around campus, trying to find people who I could show it to, who would appreciate it (nothing kills a buzz like showing off work to people who don’t really appreciate it, yet smile politely as you trail off…)

More about our workflow and the tacit knowledge necessary to make this all work will follow. In the image below, a model sits on the booklet on my desk. Handsome devil, eh?

Converting 2 mode networks with Multimodal plugin for Gephi

Scott Weingart drew my attention this morning to a new plugin for Gephi by Jaroslav Kuchar that converts multimodal networks to one mode networks.

This plugin allows multimode networks projection. For example: you can project your bipartite (2-mode) graph to monopartite (one-mode) graph. The projection/transformation is based on the matrix multiplication approach and allows different types of transformations. Not only bipartite graphs. The limitation is matrix multiplication – large matrix multiplication takes time and memory.

After some playing around, and some emails & tweets with Scott, we determined that it does not seem to work at the moment for directed graphs. But if you’ve got a bimodal undirected graph, it works very well indeed! It does require some massaging though. I assume you can already download and install the plugin.

1. Make sure your initial csv file with your data has a column called ‘type’. Fill that column with ‘undirected’. The plugin doesn’t work correctly with directed graphs.

2. Then, once your csv file is imported, create a new column on the nodes table, call it ‘node-type’ – here you specify what the thing is. Fill it up accordingly. (cheese, crackers, for instance).

3. I thank Scott for talking me through this step. First, save your network; this next step will irrevocably change your data. Click ‘load attributes’. Under attribute type, select your column you created for step 2. Then, for left matrix, select select Cheese – Crackers; for right matrix, select Crackers – Cheese. Hit ‘run’. This gets you a new Cheese-Cheese network (select the inverse to get a crackers – crackers network).  You can then remove any isolates or dangly bits by ticking ‘remove edges’ or ‘remove nodes’ as appropriate.

4. Save your new 1 mode network. Go back to the beginning to create the other 1 mode network.