Modern cities are experiencing a unique historic moment. There is no shortage of tools available to the civic hackers among us, with more available each day. It’s possible for cities to understand and plan for shifting transit patterns, to communicate to citizens in the moment, and to serve up the exact right piece of information to the specific constituent at the exact right moment.
What’s often lacking, however, is a suitable framework within which we can examine data, understand impacts and outcomes, and tailor future actions.
When the Boston Mayor’s Office of New Urban Mechanics (MONUM) first collaborated with Emerson University’s Engagement Lab, the shared goal was to foster research-based innovation in cities across the country. In part, their motivation was the explosion of tools available to urban planners and city administrators around the country.
It also was clear that the combination of open data and the continued democratization of technology – in which seemingly anyone with an idea and the ability to copypasta code snippets can roll out an app – would only further the proliferation of tools, approaches, and data. At some point – whether we’re already there, long since passed that point, or so short of it that it barely is visible on the horizon – the unfathomable ocean of data becomes just noise.
What is needed, then, is a framework within which civic innovators and researchers can create, analyze, and iterate. Such an approach to the delivery of public services, which is heavily influenced by Lean Startup principles, aims to create a virtuous feedback cycle.
This feedback cycle helps ensure that the efforts of civic hacker brigades are focused where they can have the greatest impact:
- It serves as a check, a way to confirm that the impact they are having is in line with the end goal of the project or organization;
- It allows for the quick recognition of unforeseen interdependencies in a way that simply is not possible in more restrictive development environments; and,
- Contained within the approach itself is the implicit acknowledgment that local, practical knowledge is as important as formal, epistemic knowledge.
Ingredients of A Successful Collaboration
Questions are a reflection of priorities. If you were to ask, “How many houses in my neighborhood are owner-occupied?”, we would assume that your goal is to effect the percent of owner-occupied households in your neighborhood. And that may be true, in some sense.
What if you were to then say, “Why, no, in fact my goal is to fight neighborhood displacement that occurs as a result of gentrification”? In that case, you might ask several additional research questions about housing choices, the network effects of current and planned transportation infrastructure, or even about recreation activities and nightlife options.
Your goal wouldn’t be simply to identify the percent of households living in subsidized or some other below-market-rate rental arrangement. You would want to achieve a deeper engagement with a neighborhood, its current and former residents, and those people who might move there for their own individual motivations.
Effective, fully-invested partners
When New Urban Mechanics and The Engagement Lab first laid out design-action research as a framework for creating a more engaged civic life, a key tenet was the idea of collaboration. And not what many see as the traditional public-private, “Lets both grit our teeth and hold our nose” form of cooperation. Rather, it’s paramount that both sides be true partners.
Finding effective partners means breaking out of silos. And just to be clear, silos are not unique to government agencies. They are a common complaint among data analysts and generalist knowledge workers the world over.
There are a lot of ways to approach the search for partner organizations. The key is to find community organizations, non-profits, traditional businesses, and/or universities and academic researchers who want to research, develop, and iterate, together. Finding appropriate partners early on will allow your project to benefit from everyone’s expertise – meaning better, more relevant iterations; more reliable findings; and, more clearly articulated goals.
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