China’s Urbanization: Dealing with the Complexity of Fieldwork Practices and Data Analysis

Thursday, 19 July 2018: 16:15
Oral Presentation
Valentina ANZOISE, Ca' Foscari University of Venice, Italy, Italy
Luca GHIROTTO, Department of Life Quality Studies, University of Bologna, Italy
Debora SLANZI, Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari of Venice,, Italy, European Centre for Living Technology, Ca' Foscari University of Venice, Italy
Irene POLI, European Centre for Living Technology, Italy, Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari of Venice, Italy
Integrating methods of data collection and analysis can be more effective to address complex issues, but at the same time it makes the research process increasingly demanding, in terms of time, competences and finances needed to collect, organize and analyze data, and constructively feedback research outcomes. A crucial issue then is to develop strategies to deal with the mountains of words, images and numbers that researchers track, collect or produce in the whole research path, so that social sciences can effectively contribute to enhance social understanding of complex phenomena not only by developing novel empirical techniques of inquiry, but by enhancing the debate on the kinds of knowledge we are producing, and the worlds we occupy as well as make (Back & Puwar, 2012).
The paper will draw on a two-years research about the perceptions of the transformations induced on landscape and imaginaries by the current patterns of urbanization in China, in particular those driven by the planning of high tech zones. A comparative research has been conducted within a Constructivist Grounded Theory framework (Charmaz, 2014). Data have been collected through several techniques and approaches (e.g. participatory workshops, photo-elicitation interviews, visual ethnography, etc.) and have been analyzed both qualitatively and quantitatively, combining visual and narrative analysis with the estimation of statistical models to discover complex latent structures in the data. Nonetheless, much information (e.g. spontaneous conversations, reports, social network posts and chats) and data collected (official statistics, maps, etc.) have been under-analyzed. The authors will discuss the strategies developed, together with the difficulties encountered and choices undertaken. To conclude some proposals will be advanced to deal with the above issues and “reduce the complexity” without giving away the richness and sensitiveness of data nor of the process initiated but achieve the engagement of multiple publics, in multiple ways (Buroway, 2005).