SUPPORTING EQUITABLE CLIMATE CHANGE DECISIONS IN A RURAL COMMUNITY THROUGH EXPANDED NOTIONS OF CLIMATE DATA: USING CRITICAL DATA PERSPECTIVES AND PRACTICES TO SUPPORT CLIMATE LEARNING WHILE CO-DESIGNING AN ONLINE, MAP-BASED, EDUCATIONAL RESOURCE

Abstract

Climate change threats are ever increasing, forcing communities to ask: what do they value and how are they going to protect it? Community-based climate education should play a central role in supporting equitable local decisions regarding local responses to climate challenges. However, there is little research about how to best support communities, especially rural communities that may be skeptical of climate change, to see how climate change is affecting their landscapes. In my dissertation I explore a community-based effort to build a map representing a valued local landscape feature and how this effort can act to convene knowledge about local landscape and climate, ratify that knowledge through inclusion onto a map, and ultimately inform community decision making. Guided by the perspectives and practices of critical data science and storylistening I frame my research around data and story. Prior work has considered the role of climate data within environmental education and story within community scholarship, but there is still a need to explore expanded notions of data within community learning and the role of community-held stories in local decision making. My dissertation focuses on how local, personally held landscape and climate data might complement and extend local, institutionally held data and how map building might support data-rich storytelling and listening. Working within a conservative-leaning, rural community and using the ArcGIS StoryMap web application, I engaged six community members over six design sessions to collaboratively design an online, public map of a creek and associated nature trail at the center of their town. I find that participants engaged in six key map-building design processes as they interacted with their local landscape in new ways. I also find that participants used the knowledge they brought into the design space to collaboratively expand, challenge, and occasionally transform their shared understanding. Together these processes allowed local, often generationally held, climate and landscape knowledge to become community-held understanding that could be included as data within the map. Using this analysis, I present my Evidentiary Landscape Learning (ELL) framework, placing my insights into a community-based learning context. The ELL framework demonstrates a pathway for engaging community members to understand how local and beyond-local socio-cultural values and systems are physically embodied in their local landscapes.