Working across the disciplines of computing, information systems, and design has been an interesting challenge and created tensions with regard to the further direction of my research. Most recently, it became necessary to make a choice and define key theories and assumptions which will form the basis of my research.
Based on a position paper I co-authored recently, my team of supervisors and I identified the following three ‘routes’ through the PhD:
- “Theoreticall PhD”: participatory decision making (with or without tech)
- Focus: looking at planning as a decision making process, exploration of what I call ‘community data’, i.e. data which supports local community decision making (here Surowicky’s notion of aggregation may play a role).
- Further comment: This approach would put the social process center-stage, technology as intermediary or facilitator could come in later once it is clear how it could be used in the particular context through reflecting how information communication technology could improve the process.
- Projects: Halton project as community-generated decision making, community-based action & comparable case study (e.g. another case where technology-played a role this process) // Rachel C’s work… // Paul Dourish?
- Methods: a case study approach could fit well; sample surveys involving policy makers, citizens as well as interviews could be the way to go and an intervention through application of digital technology could be planned for.
- PhD routed in CHI looking at front end of digital system / data usability
- Focus: look at the front end of the digital system (look at heritage of the data in such system and how data is being used to inform decisions… explores trust issues (data representation, looks at who’s dictating the design of the technology
- People: interesting people related to this area would be Drew Hemment, Thomas Erickson.
- Projects: Interesting sample technological systems include the Open311 dashboard & the Birmingham Civic Dashboards
- Methods: exploratory interviews with users of the systems mentioned above, experiments with regards to perceptions of decision makers given variation to how data is displayed (i.e. show data pieces, tweak contextual info & such as source (locally, originator, timeliness)
- Potential academic / industrial placements: Charnegie Mellon University, San Francisco (first open-data city), Standford D-School.
- PhD focused on Back-end & data flows:
- Focus: data integration, data extraction, and data collection / capture (community dataset) /// data architecture /// GIS tools.
- Comment: Compared to the previous routes, option 3 is the most risky option as it would demand much new learning of programming languages. It may not facilitate my existing skill set appropriately.
- Projects: i.b.d.
- Methods: Network analysis methodologies, ontologie generation from data.
A well informed friend of mine noted that there’s a relatively large field in computer science focusing on data capture, a somewhat smaller community focusing on data analysis, and a much underdeveloped theme, which focuses on the actual impact of data analysis in decision making contexts.