A theme that resonated with me from this week’s readings was information loss, both through miscommunication between reader and content, and through lack of a voice in history translating to lack of a voice in documentation – which can also be switched to say that a lack of voice in documentation leads to an assumed lack of voice in history. I thought it was important that Drucker mentioned in “Humanities Approaches to Graphical Display,” that “the history of knowledge is the history of forms of expression of knowledge.” This to me meant that history is only as much as how it was documented and interpreted, and the miscommunications through information loss become very dangerous in this sense. This week we’ve been learning about different data visualization techniques to use in our Final Project, and these readings emphasize the importance of being smart about our techniques and tools. The first step is our data – choosing what to gather, how to gather it, then gathering it, and then thinking about how the reasoning behind why we gathered it can translate into knowledge through a visualization. Our data for our project is metadata about the most popular LA food trucks, looking at categories such as common words, food types, ingredients, names, and prices. We want to take this metadata, visualize it, and then use it to prove and analyze our findings about success and trends of food trucks. One of the first steps of visualizing could be done through a word cloud that makes common words bigger, and then links them to words that they are most commonly paired with. This would give insight into the main items or catchphrases that food trucks are using the drive business, and would also give insight into what consumers are consuming the most of. From here, I would take the most common ones and do further analysis on them, in order to get specific, accurate, detailed data. This could be a timeline, incorporating time frames into the visualization to show the rise of trucks and when the trends proved in the word cloud were realized. I think two different types of visualizations would allow for flexibility and accuracy with our data. It would also encourage the readers to interact with the data more and figure out how the two relate to one another. Of course, this could be a problem in itself. To make sure the readers don’t assume too much, our graphs would have short descriptions for accuracy, and then a further detailed “about” paragraph. In addition, we would address any data that doesn’t quite fit into the map. For example, uncertainties. In addition to addressing visualizations, Trucker’s article also points out some faults with “data.” Data assumes that it is a black and white fact that can be plotted onto a visualization, when in reality there are many uncertain pieces of data that don’t quite fit in. In order to not have to omit these pieces of data – which would result in the reader thinking they just don’t exist – a visualization tool has to be created with these humanities issues in mind. How our tool expresses the data, defines it. The representation of knowledge is just as important as the knowledge itself.