My path through the beginning of the project was fairly clear to me. I wanted to use data and design research methods to show policy makers how they could rely on residents to help guide them in making decisions about the communities they worked for. So, a big first goal was to get in touch with people who worked in the public sector, and further my thinking based on their ideas and feedback. To aid this process, I consolidated my early thinking into a concept, or pitch deck, that I could discuss through an interview process.
Using this early concept deck I interviewed 6 public servants or civic technologists, working in various aspects of government. I synthesized these interviews and pulled out high level concepts, including overall structure of the methods, concerns to address, features to consider, and aspects of the ecosystem to be mindful of.
Loosely, here are those concepts, grouped by how they helped inform my thinking:
Notes on Methodological Structure
- How controlled are the environments? Start with controlled ones
- How will I be able to tell what people are reacting to?
- Don’t ask participants to articulate problems—ask them to describe the space or feel the space
- Scaling the tech to what’s reasonable
- Show outcomes
- articulation and identification versus gut feeling
- “more true to this project, you’re identifying variance”
- Increase civic engagement for the historically marginalized
- Reduce assumption in the interpretation of the biometric and video data by adding in additional data points—triangulation
- Think of direct interactions with policy versus indirect, e.g. filling out a section 8 form versus living in public housing
- Constituents who have the time and energy to give their voice are disproportionately represented
- Is there a government service that is already mostly a positive experience? What are some small pieces that can be improved through this kind of data?
- Consider the typing or characterization of the areas I’m looking at
Features to Consider
- Showing content and connections
- should I categorize the findings? How will I distill insight?
- SMS tree structures have been successfully used in public settings, such as throughout the Ferguson protests
- Bring in NYCHA housing data or narrow into one policy space, like NYCHA housing only
- If I do narrow into NYCHA—what are the attributes that affect emotional well-being?
- What are the baseline stressors to consider in a community?
- Think about the primary piece of data you want to convey—use that to help structure the visualization.
- I need a functional partner on the government side of things
- Look for ways to create a shared government service model around design research
Concerns to Address
- Diversity of opinion
- Working with OpenData requires extensive expertise
- All data can be manipulated—even raw emotional data (so be careful with my language)
- Who owns the data I’m collecting? What is the transparency around the output?
- Barrier around willingness to forsake privacy
- Don’t assume bureaucrats are doing something wrong
- Participation will be the biggest barrier
Ecosystem Aspects to be mindful of
- Qualitative data is exciting
- Use the data to create intrigue for civic decision makers
- Examine how participatory budgeting works, and consider fitting project to that context
- Community Boards and Townhall meetings are a type of competitor for my project
- Insight versus impact—each has it’s own connotations and I should consider opting for the former
- Government has a culture of expertise—integrating design research is part of a larger conversation
- Participatory Budgeting practices need more expertise in the room.
- Most civic innovation is in improving forms/paperwork
- Consider policies that are timely or might be in the news
- Policy decisions are driven by consultation
- Residents are at the forefront of asking for NYCHA safety features
- My proposed research could influence type/amount of contributors to safety implementations in NYCHA housing