Project ISF: Analyzing Spatial-Temporal Dynamics of the Hearing-Impaired Population to Inform Sustainable Urban Development Program DUIRI - Discovery Undergraduate Interdisciplinary Research Internship Term Spring 2024 Status Accepted Research Area Sustainability and hearing disability Description Ensuring the development of cities that are safe, inclusive, resilient, and sustainable is pivotal to realizing the Sustainable Development Goals (SDGs) outlined by the United Nations. Also, the World Health Organization estimates that, by 2050, 10% of the world’s population will be diagnosed with hearing loss; it is vital to prioritize the needs of these marginalized groups to enhance social equity and forge truly inclusive and sustainable urban environments. As such, this project will focus on the hearing-impaired population within the United States, conducting a spatial-temporal analysis to deepen our understanding of both the distribution and temporal evolution of this demographic. The project inaugurates a series of research endeavors related to urban sustainability and human hearing matters, with findings poised to establish a solid groundwork for subsequent studies. These will aim at enhancing infrastructure systems and will guide planning and policy development through a lens of both inclusivity and sustainability. This is an interdisciplinary project and the potential undergraduate student will be co-supervised by Dr. Qingchun Li from the School of Construction Management Technology and Dr. Noori Kim from the School of Engineering Technology at the Polytechnic Institute. Dr. Qingchun Li’s research interests include urban science for urban resilience and sustainability. Dr. Noori Kim’s research interests include hearing mechanics, future healthcare, MEMS, electromagnetics, acoustics, and embedded systems (IoT). Supervisor Qingchun Li Mentor Qingchun Li Type of work required data analysis and visualization, conducting spatial-temporal analysis of hearing-impaired population data in the U.S., results visualization, and literature review. Websites N/A Qualifications Min 3.5 GPA and familiar with data analysis using Python or R. Credit Hours 2 Weekly Hours 6 (estimated)