On the 12th of December I attended a workshop conducted by Angeliki Chrysanthi on the geotagging of images. The ability to link image to space is an extremely powerful one which opens up a large scope to what is possible in using geospatial data. I was particularly interested in this workshop as I haven’t had much experience with mapping geographical locations before but was interested in the questions and results that can arise from such processes. I find the kind of quantitative and qualitative data produced by such applications interesting. Particularly my interest was peaked after reading some of Hochman’s work on tracing the dissemination of Banksy’s street art online. In this Hochman explores “what would it means in contemporary conditions to maintain the socio-cultural and political specificity of a place?”(Hochman, 2015) and eventually showing through the data that it “mutates the artistic object into the sum of its interactions with all other viewers of the same object, and also with all other visitors to the location of the object”(Hochman, 2015)
Angeliki began the workshop with a discussion of metadata. I’ve discussed in previous blog posts the importance of metadata and maintaining standards and much can be said for the confusion one can encounter when trying to locate the source and place of an image. Even through digital collections metadata can be scare as we were shown with an example from the Stackleberg collection hosted on the Travelogues website. It’s an interesting question when faced with trying to figure out the localities of photos and images. The rising increase in GPS embedded phones has aided greatly in embedded metadata and continuing existence of space within the digital image. One only need to view an Instgram account with location setting enabled to see the scope and level of detail GPS enabled smart phones have given us. The same can not be said for most modern day DLSRs and especially not analogue photos taken in the past. We first learned to add GPS details to a image by manually inputting values taken from geonames. This requires a lot of manual data entry. It showed the usefulness however of the capabilities of GeoSetter in editing embedded metadata not just location based.
It’s difficult when trying to link photos and images from the past to a geolocation. As stated previously this manual method in not scalable. Obviously with large image collection manual input becomes more and more impractical. How do we then map these collections? There is probably some way we could automate this activity with some query parsing but I would need to explore this further. Another method discussed was publishing images online in order to be identified by a user base. We used geonames to gather longitude and latitude for the images we were tagging. The website seemed like a useful tool and could be used to parse information too but as stated I would also need to explore this a a viable source. We were next shown what I feel is the main use of the geosetter software which it the matching of GPS data to recently captured images by a digital camera.
When viewing EXIF data on a digital camera and the metadata generated from a GPS system what unique identifier do they both have in common? The answer is time and I found it a rather simple but very effective solution in matching location to image. This sadly does not help in the mass automation of finding place in historic images and can only help with images taking with a synced GPS tracker. Angeliki used this to generate data to examine how visitors interact with cultural heritage sites. Specifically in her paper she referred to Gournia in Crete. As I said previously I’m interested in what we can learn from geotagging in how people interpret space. In her work Angeliki generated line density and the linear directional mean maps in order to examine visitor movement and interest in a cultural site. In linking these GPS routes with images she could then explore particular points of interest and see what visitors deemed engaging enough to capture on their cameras. Visitors also did not find this process invasive as it did not interrupt their normal activities in a site.
The linking of GPS data to images is a rather simple process in Geosetter. Once the time zones are set and the times between the captures and the GPS data are synced one can match mass amounts of images to location. The software presents a powerful potential for how we interact with space. The potential to give individuals the tools to record they’re visual and geographical interpretations of space. One could very easily make a guided tour of a historical site using a 360 camera synced to a GPS system or explore how people traverse and interpret other “non places” outside of the heritage site. I really enjoyed getting an introduction to Geosetter and am looking forward to playing around with it more. The great thing about Geosetter is being able to easily tether images to geographical data which can then be manipulated through R or Python to generate more diverse and complex representation of the data.
Chrysanthi, A., Papadopoulos, C., Frankland, T., Earl, G. “‘Tangible Pasts’: User-Centred Design of a Mixed Reality Application for Cultural Heritage.” In Archaeology in the Digital Era. CAA 2012 Proceedings of Computer Applications & Quantitative Methods in Archaeology 2012. (2013)
Hochman, Nadav. “The Social Media Image: Modes of Visual Ordering on Social Media” University of Pittsburgh, The Dietrich School of Arts and Sciences: (2015)