Integrating Building Survey Data with Geospatial Data: A Cluster-Based Ethical Approach (Papers Track)

Vidisha Chowdhury (University of Pennsylvania); Gabriela Gongora-Svartzman (Carnegie Mellon University); Erin D Trochim (University of Alaska Fairbanks); Philippe Schicker (Carnegie Mellon University)

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Climate Justice Interpretable ML


This research paper delves into the unique energy challenges faced by Alaska, arising from its remote geographical location, severe climatic conditions, and heavy reliance on fossil fuels while emphasizing the shortage of comprehensive building energy data. The study introduces an ethical framework that leverages machine learning and geospatial techniques to enable the large-scale integration of data, facilitating the mapping of energy consumption data at the individual building level. Utilizing the Alaska Retrofit Information System (ARIS) and the USA Structures datasets, this framework not only identifies and acknowledges limitations inherent in existing datasets but also establishes a robust ethical foundation for data integration. This framework innovation sets a noteworthy precedent for the responsible utilization of data in the domain of climate justice research, ultimately informing the development of sustainable energy policies through an enhanced understanding of building data and advancing ongoing research agendas. Future research directions involve the incorporation of recently released datasets, which provide precise building location data, thereby further validating the proposed ethical framework and advancing efforts in addressing Alaska's intricate energy challenges.