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Aubin Vertueux Dzossa Bontse is a Physics major and Data Analytics minor at Furman University deeply committed to sustainability and climate resilience. As a Climate Resilience Fellow at the Shi Institute, he supports South Carolina municipalities in developing data-driven climate adaptation plans, including heat vulnerability mapping and resilience plan assessments. His leadership extends to consulting on local climate strategies and designing rubrics to evaluate plan quality and effectiveness. Through research and community engagement, Aubin explores innovative approaches for integrating science, policy, and design to build more resilient communities.
SESSIONS
Poster Session: From Data to Action: Analyzing and Building Resilience to Heat Vulnerability in South Carolina
Problem Identification and Project Aim
Over the past few decades, the impacts of extreme heat, driven by climate change, have become increasingly visible. According to NOAA’s scientists, 2024 was in fact the world’s warmest year on record, surpassing 1.5°C above pre-industrial levels set by the Paris Agreement. While some remain indifferent to these temperature increases, certain populations cannot afford to do so. Prior research has shown that groups such as seniors aged 65 and above, children aged 0-5, and renters are disproportionately affected by extreme heat. These are not just remote groups of people. These are people who are loved and cared for in our communities. This intersection of social and environmental factors creates what we call composite heat vulnerability, a challenge this project addresses through an innovative data-driven approach applied in Fountain Inn, Bluffton town, and Goose Creek, SC.Methodology
This project employs an innovative method that emphasizes two main aspects of heat vulnerability. For social vulnerability, we focused on elderly people aged 65+ and living alone, children aged 0-5, renters, people living alone, Black residents, and Hispanic residents. For physical vulnerability, we focused on average land surface temperature. We developed a python algorithm to automatically carry out all steps of this heat vulnerability analysis as follows:- Data Collection: The algorithm retrieves census data (from U.S. Census Bureau servers) and processes relevant bands of Landsat temperature data to generate a land surface temperature map.
- Maxp_tabu optimization: Census blocks are clustered into homogenous micro regions (~450 people) using maxp_tabu, which are then populated with census data in ways that respect standard geographic and demographic boundaries.
- Spatial Analysis: After spatially joining the temperature data to micro regions, the algorithm calculates the median surface temperature and the percentage of chosen social groups per region, categorizing them as low, medium, and high. Using bivariate symbology, it then outputs exportable, interactive web maps showing the interplay between these factors across study areas.
Project Deliverables and Contributions to Climate Resilience
- The maps created by this project allow municipalities to identify areas most at risk for extreme heat with unprecedented accuracy, as the optimization step screened out statistical noise from the Census Bureau. This ensures accuracy and efficiency in resilience-building efforts and resource allocation.
- For Fountain Inn, the project makes recommendations for area-specific solutions for addressing extreme heat more efficiently, facilitating implementation of project findings.
- To help guide ongoing Bluffton climate resilience planning efforts, a subproject was initiated, creating an evaluation scorecard for climate resilience plan which was used to identify a model plan, along with recommendations for improvement. These recommendations provided an avenue for the Bluffton heat vulnerability analysis (conducted within the main part of the project) to inform the vulnerability analysis and, therefore, the solutions of their soon-to-be-released plan.
- The identification of areas most at risk from extreme heat in Goose Creek allowed the municipality to develop a tree-planting strategy using its tree funds.
- While previous research has focused on the census tract level analysis, this project studies heat vulnerability at the hyper-local level, making the findings more actionable.
- The methodology also provides a replicable model for other municipalities seeking to enhance climate adaptation strategies efficiently.