A foundational element of my thinking about thesis came from my work with the SPACES program in Philadelphia. The SPACES Artist Residency brings artists in residence and community members together to nurture social impact projects that improves the quality of life for residents living in a neighborhood experiencing the effects of decades of disinvestment.
I was recruited to join the project via a Knight Foundation grant, with the goal of measuring the impact of the residency projects. Specifically, were residents experiencing a sense of belonging? Did they feel as if their community was tighter, more cohesive, as a result of the artists-in-residency projects? This was advocacy for the use of data in service design.
So, how does one measure a sense of belonging? I would argue that you need to account for the many dimensions that make up our connection to the world around us. For example, how might a place affect how you feel socially? Creatively? Economically? Aesthetically? Does it make you feel inspired to be there? Do you feel you can afford it? Do you simply like how a place looks? Feeling as if you belong somewhere is a complex emotion, multi-faceted, and deeply personal.
Earlier in my tenure at SVA, I developed a physical prototype and system for urban planners to understand the emotional effects of neighborhoods on residents, commuters, and passers-by.
To inform this system, I developed a research schema that asks users to respond emotionally to how aspects of a place make them feel. I considered the dimensions of Aesthetics, Creativity, Sociableness, and Economics, and then developed a simple scoring rubric for how a user might react to each of these dimensions as they consider whether they belong to a place.
For example, when standing in a financial district, surrounded by skyscrapers, a user might feel socially unwelcome, and economically unable to connect, but creatively at ease if the hustle and bustle of the area gets them thinking about their novel.
Using this method as a starting point, I brought this type of data-driven thinking into the process of crafting Civic View.