Designing For Context
Simplifying Data and Systems Thinking
Aila Dutt, Gram Vaani, July 2024
Status Quo
CoRE Stack deals with wicked problems that are solved for through systems thinking, data, and design.
Citizens and community stewards need to be able to understand, analyse, and apply various concepts and data around climate change to understand the intricacies of socio-ecological relationships and ensure that they are able to propose interventions in an informed manner.
How Might We simplify complex systems and data to encourage data driven decision making?
What Do We Do?
Our aim is to be as accessible as possible to ensure participation and a reclamation of agency
4W’s, 1H
Who: Community Stewards, Citizens
What: Data visualization + “knowledge packs”
Where: Visualization to simplify analysis; knowledge packs on app/other media platforms
Why:
Approach
Discover
Define
Ideate
Prototype
Systems Thinking For Knowledge Packs
What Do We Do?
Climate Change
Lack of Long Term and Short Term Planning
Lack of Participatory, Collective Decision making
?
?
These three ‘fault lines’ affect how power and resources are distributed within an ecological system. It was important for us to show how they are interdependent, and without understanding one, we couldn’t solve for the other.
What Do We Do?
Storytelling for Systems Thinking
Post our secondary research on environmental education and curriculum design, we felt that using storytelling through semi-fictional “case studies” could situate the user in the context of these problems was the ideal way to explain the system.
This would ensure:
Storytelling for Systems Thinking: How
What Do We Do?
“Long ago, farmers relied on good soil and natural water for modest harvests, providing sustenance and some profit. Commercial seeds later promised higher yields but required more water, leading to borewells and increased use of fertilizers and machinery. These advancements initially boosted yields and profits significantly.
However, heavy machinery compacted the soil, making it difficult for roots to penetrate and rainwater to replenish aquifers. Borewells over-extracted water faster than it could be naturally replenished. Overuse of fertilizers poisoned the soil and water. The prosperity from increased yields came at a high cost, as soil and water quality deteriorated. Farmers became dependent on constant fertilization and machinery, revealing the hidden costs of modern farming practices.”
How Might We use data to help participants understand their landscapes better?
How Do We Do It?
Our aim is to be as simple as possible to ensure application of concepts and using data as proof of conceptual understanding
Data Visualization
How Do We Do It?
What Data “describes” a landscape?
What data will be most useful to make an inference?
Can this data be verified?
Are people able to understand this data to make a decision?
Is it truly making decision making better?
How Do We Do It?
Challenges: How do you show changes in variables + changes in time + changes in geography?
Data Visualization
How Do We Do It?
Understand concept + complexity
Analyse Data in App
Apply Concepts
“Set a 15 min timer and think all the ways you can show data this in a simple form. Forget the technology for a minute. Why is this data important for them? Restart.”
Gurman Bhatia, Revisual Labs
Data Visualization
How Do We Do It?
So far we have settled on these descriptors with a fill-in-the-blanks approach to support the reading and and analysis of data.
Next Steps:
Aila Dutt, CoRE Stack, 2024
- One is to present knowledge packs to people to build their technical understanding on various NRM concepts plus possible action pathways. The way we've been discussing this is to build a system model and have a navigable way for people to consume small nuggets, coupled with case studies, and to specifically bring out key faultlines to do with individual good vs. collective good, short-term vs. long-term, and complexities arising from climate change, which hamper sustainability and equity. These faultlines are being actively experienced by people and will be a good way to make a connect.
- The other is to present data / visualizations to people to have them leverage the technical understanding to build better NRM plans. We discussed evaluating this at three levels: accuracy of the constituent outputs, usability/appropriateness of the presentation for people to be able to answer questions about it accurately, and usefulness of the whole exercise for people to be able to design better NRM plans than what they could do without the data.
Process where the community understands the technical aspects and complexity and how case studies are supporting this
How we have used the technical understanding and build on this w visualizations