How might we... help children affected by natural disasters?
This post is the first in our "How Might We..." series, dedicated to re-imagining industries, organizations, and solutions to real-life problems. At Berkeley Innovation Group, we believe design thinking has a myriad of applications and want to share our Discoveries, Insights, Ideas, and Experiments with you.
Every so often, our eyes witness something our words cannot convey. Such is the case with Guy Waltam’s photograph from the North Bay fires, showing a six-year old girl staring at the charred remains of her bicycle. While adults struggle to reconstruct homes and businesses, children are left grasping for a sense of normalcy amidst the devastation.
Enter the North Bay Bike Project, a non-profit founded by a Bay Area mother who understood her children’s love of bikes and the positive impact a new bike has on a displaced child. Using the example of North Bay Bikes and following design thinking’s human-centered framework, we see a path from the charred remains of a home and to giving a child a sense of normalcy amidst the chaos of a disaster.
We begin with Discovery, the process of seeking out new and unusual information through desk research and ethnographic interviews.
When Clark volunteered in Santa Rosa one week into the crisis, he witnessed well-intentioned volunteers stressed to the breaking point, exhausted first responders tending to the most vulnerable, and adults shell-shocked from the complete loss of both their community and livelihood. Left clinging to the one stuffed animal that accompanied their evacuation, children shared amongst themselves and made informal connections. These observations, coupled with the outpouring of support surpassing the needs of existing evacuation centers, told us of an overwhelming desire to help, but a lack of empathy for everyone on the ground. This inspires us to go further in our work.
As Arthur Shoepenhauer observes, “the task is not so much to see what no one else has seen, but to think what no one else has thought about what everyone sees.” This is the core of Insight generation.
Design thinking is rooted in diversity of experience. No specific level of training or study qualifies someone as a design thinker. In the case of North Bay Bikes, it took a mother’s intuition to see past the adults’ challenges and notice the unmet needs of the children.
With these observations informing the Insight, which highlights the needs of children, we formed the following problem statement, “how might we empower play to restore a sense of normalcy to children displaced by natural disasters?”
Diverging on the problem statement, or emphasizing the quantity of ideas over the quality of ideas, inspires our Ideation phase.
In this phase, we dream up all possible solutions, emphasizing extreme use cases. Examples of extreme ideas around our problem statement might include: creating mobile play structures that move between evacuation centers, create a sports league comprised of teams from each evacuation center, and a Happy Meal-inspired theme of a free toy with every meal eaten in the center. North Bay Bikes combined Insights around children’s desire to play outside and explore to develop a bike donation program.
The final step of our initial iteration of the design thinking process involves Experimentation. There are three levels of experimentation as we continue to iterate: market research, minimum-viable product (MVP), and prototyping. We put out an informal request through our personal networks to identify families who lost their home and whose children needed a new bike. The response was overwhelming; over 100 families were identified!
Responding to the North Bay fires with the tools at our disposal, we used the design thinking process to: observe without judgment evacuees’ plight, look for unseen connections between those observations to develop insights, form a problem statement, then ideate on possible solutions before running our first experiment. In only a few weeks, we are designing an outreach program both for donations and distribution for maximum impact that is replicable when the next disaster strikes.