McCourt School of Public Policy
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MDI’s Spatial Data Toolkit Critical to Identify Communities Vulnerable to Impacts of Climate Change and Environmental Injustice

As part of the Environmental Impact Data Collaborative (EIDC), the Massive Data Institute (MDI) is partnering with nonprofits including the Environmental Policy Innovation Center (EPIC) and Dream.Org to support efforts to ensure that more than $35 billion in funding for safe drinking water and other environmental equity initiatives goes to those who need it the most. The Biden Administration’s Justice40 Initiative mandates that at least 40% of federal benefits in climate and clean energy flow to communities more vulnerable to the effects of climate change and environmental injustice. MDI and its partners are working to identify these at-risk communities across various fields such as access to safe drinking water and job opportunities in “green” industries, among others.

When trying to identify which at-risk populations are eligible for important investments at the community-level, many organizations face the same issue: accurate, geocoded demographic data is not available at the geographic scale where funding or policy decisions are made. Thus, before any analysis can be done, the first major hurdle to overcome is to somehow take data from one set of geographic boundaries and realign it with another.

The process involves using digital shape files to either subdivide or join data according to boundaries which are different from the original data.  For example, the Census Bureau may have demographic data at the county level, but the data needs to be organized according to water utility boundaries. Such work is currently time-consuming, computationally-intensive, and requires experience working with geospatial data.

Best practices for realigning populations and data along unconventional boundaries in this way remain limited. Many of the most widely used tools are locked behind subscription-based solutions, such as ArcGIS, or require extensive and specific coding knowledge, like Earth Engine. This obstacle can make results more difficult to reproduce and distribute. Other available methods are highly tailored to specific use cases, as opposed to generalizable across multiple types of problems and projects. Even when addressing disparate challenges (like analyzing green jobs across counties or community water systems across census tracts), the technology and data needs are often not only overlapping, but complementary. With better tools, we can tackle a wide range of environmental data issues without wasted effort or duplicated work.

To address these challenges and current limitations, MDI has developed a Spatial Data Toolkit to more easily combine, transform, and analyze geospatial data. This growing set of tools is offered as a more accessible, flexible, and efficient way to analyze data across different geographies. 

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Written by Phil Cork and Elise Rust