Computational environmental ethnography: combining collective sensing and ethnographic inquiries to advance means for reducing environmental footprints

• Authors: Henrik Blunck;Niels Olof Bouvin;Johanne Mose Entwistle;Kaj Grønbæk;Mikkel B. Kjærgaard;Matthias Nielsen;Marianne Graves Petersen;Majken K. Rasmussen;Markus Wüstenberg
• Venue: Proceedings of the fourth international conference on Future energy systems
• Year: 2013

We lack an understanding of human values, motivations and behavior in regards to new means for changing people's behavior towards more sustainable choices in their everyday life. Previous anthropological and sociological studies have identified these objects of study to be quite complex and to require new methods to be unfolded further. Especially behavior within the privacy of people's homes has proven challenging to uncover through the use of traditional qualitative and quantitative social scientific methods (e.g. interviews, participatory observations and questionnaires). Furthermore, many research experiments are attempting to motivate environmental improvements through feedback via, e.g., room displays, web pages or smart phones, based on (smart) metering of energy usage, or for saving energy by automatic control of, e.g., heating, lighting or appliances. However, existing evaluation methods are primarily unilateral by opting for either a quantitative or a qualitative method or for a simple combination and therefore do not provide detailed insight into the potentials and impacts of such solutions. This paper therefore proposes a combined quantitative and qualitative collective sensing and anthropologic investigation methodology we term Computational Environmental Ethnography, which provides quantitative sensing data that document behavior while facilitating qualitative investigations to link the data to explanations and ideas for further sensing. We propose this methodology to include the establishment of base lines, comparative experimental feedback, traceable sensor data with respect for different privacy levels, visualization of sensor data, qualitative explanations of recurrent and exceptional patterns in sensor data, taking place as part of an innovative process and in an iterative interplay among complementing disciplines, potentially including also partners from industry. Experiences from using the methodology in a zero-emission home setting, as well as an ongoing case investigating transportation habits are discussed.