By Wendy Hsu | December 6, 2012
In my last post, I introduced the idea of using webscraping for the purpose of acquiring relevant ethnographic data. In this second post, I will concentrate on the next step of the ethnographic process: data processing and interpreting. Remember The Hsu-nami, the band that I talked in the last post? The image above is a screenshot of their Myspace friend distribution, a map that I created for analyzing the geography of their community. This post is about the value of creating such maps.
Processing data in the digital age can be both exciting and daunting. Digitization of social and cultural life has generated for us more data than ever. We could now easily log geo-location data with the use of GPS. We could potentially tap into the world of APIs to acquire whatever social data relevant to our online research inquiry. But what do we do with all this data? How do we make sense of it? Jenna Burrell has raised some concerns regarding the quantity and quality of data in light of the recent big data trends. With those very critical notions in mind, I have been experimenting for a while a series of digital methods to play with data that I have gathered from my own field research of online and offline music-cultures.
What happens when we can see the geo-spatial patterns of our findings or hear the noises in our field recordings? A multimodal engagement with data will make visible patterns that are previously hidden to the eye, the ear, and the mind. Computers are really good at recontextualizing data and highlighting its materiality. In what follows, I will offer a few methodological ideas for locating patterns within data. Also, I will mostly focus on the use of open-source or, what I would like to call “vernacular” technologies. This discussion, by design, precludes expensive (and powerful!) software programs N’Vivo and Atlas Ti that qualitative researchers have used for decades.
I want to use this as an opportunity to move away from the current disciplinary focus on text (interview transcripts). Instead, I want to highlight the capacity of the digital to engage with data in sensory modes beyond the textual. We know that qualitative programs are great at the conventional machine-processing such as counting, sorting, and annotating. But information resides in many modes. In this post, I choose to focus on the spatial, geographical and positional as a context of data analysis; and I will devote the next post to talk about the the dimension of sound in field research.