Place matters, and whether or not we explicitly or consciously use the term “place-based initiative,” it describes much of the work we are undertaking right now. In essence, a place-based initiative is an effort to address an issue in a specific geographic area; instead of trying to solve the issue for everyone, everywhere, you focus on solving it in one place (at least, to start!). Not only do place-based initiatives provide a testing ground for larger solutions, they are also a fundamental part of driving effective systems change: local community change fundamentally affects larger systems by directly addressing community needs, empowering residents, and building community capacity and resilience.
But being a place-based change agent and finding solutions to complex problems isn’t just about having skills, knowledge, and vision to tackle change. It’s also about knowing where to look for information – and knowing what tools are out there – that can help you understand the problem and find solutions. This blog kicks off a series on some of the data tools and resources Spark has used to support place-based efforts. Over the next few months, we’ll look at online tools such as GIS, Google Fusion Tables, Kumu, and more, all of which can help support place-based initiatives by providing a visual map of needs, relationships, and complex interactions.
First Up, GIS
While data in itself is helpful – and necessary – to start to wrap your arms around a problem, geographically mapping data creates a compelling story, helping illustrate community needs and disparities in a clear, visual manner. Geographic Information Systems (or GIS) can be a powerful tool when geographic location is an important part of the problem you are hoping to solve and the solution you want to implement. For example, through GIS, you can create a visual depiction of where the highest-need populations in your community are, or look at where service providers are compared to those in need or where jobs are compared to areas with high concentrations of poverty.
Why use GIS? Maps are a type of visual storytelling, allowing us to quickly see and digest large volumes of data that would otherwise by overwhelming. They also allow us to make spatial connections, such as the location of low-income residents to job centers or highlighting the intensity of a problem in one area compared to another, in a way that would be significantly more difficult in words. Similarly, they allow for layering data to see the interactions or commonalities across multiple indicators. Have you heard the term “disaggregating data”? It’s an important part of digging down into where equity issues exist and is accomplished by unpacking patterns among different subgroups within the data. Geographic distinctions are an important way to disaggregate data. This is the type of analysis that helped surface the reality of the zip-code effect!
Need another reason to use GIS? Maps are visually appealing, making them engaging and memorable in a way many other forms of data presentation just aren’t.
What does it look like in action?
One example of how GIS supports place-based initiatives is equity mapping, which helps make connections between “areas of opportunity” and high-need communities, highlighting the disparities that exist between the two. The Regional Equity Atlas, originally released in 2007, helped change the conversation about equity in the Portland-Vancouver region, providing clear information that policymakers and stakeholders used for advocacy and policy-making efforts, such as improving transit access for low-income residents. Here are some other cool examples of how GIS can highlight disparities in a place-based effort:
- The Community Commons Maps and Data Center is a free, online GIS resource that includes data maps, along with data sets you can use to create your own map (login required).
- The Food Desert Locator lets you zoom in on your community (by census track) to identify food deserts and learn about the population in those areas.
Up next, we’re taking a look at Google Fusion tables, using examples from two recent Spark projects where we needed to take data visualization to the next level.
Have you used GIS to support your place-based effort? What was the result? Tell us in the comments!