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Unraveling Hurricane Ian’s impact

Wang, J., & Hu, Y. (2024). Unraveling Hurricane Ian’s impact: A multiscale analysis of mobility networks in Florida. Transportation Research Part D: Transport and Environment, 104482. ABSTRACT: Understanding how human mobility patterns respond to natural disasters is crucial. This study investigates Hurricane Ian’s impact on human mobility patterns and subsequent recovery in southwest Florida. Using […]

COVID-19, Social media, algorithms and the rise of indigenous movements in Southern Africa

Foyet, M. & Child, B. (2024). COVID-19, Social media, algorithms and the rise of indigenous movements in Southern Africa: Perspectives from activists, audiences and policymakers. Frontiers in Sociology, 9, 1433998. ABSTRACT: The paper employs a mixed-methods approach to investigate the influence of social media on social movements among indigenous communities in Southern Africa.  Findings reveal that […]

Hurricane Milton Updates

Last update: 10/18/2024 Climate change almost certainly made Hurricane Milton deluge worse explains Dr. David Keellings in this interview with the Miami Herald. Professor Corene Matyas was interviewed by the Australian Broadcasting Corporation’s News Breakfast and by ABC Radio News in Australia about Hurricane Milton on 10/8/2024.   Dr. Matyas was on the CBC Radio […]

Identifying Points of Interest as sentinels for infectious disease surveillance

Du, F., & Mao, L. (2024). Identifying Points of Interest (POIs) as sentinels for infectious disease surveillance: a COVID-19 study. Spatial and Spatio-temporal Epidemiology, 100691, https://doi.org/10.1016/j.sste.2024.10069. ABSTRACT: Traditional surveillance relies on medical facilities, such as clinics and laboratories, as sentinels to monitor disease activities. Few studies have investigated the feasibility of using Point of Interests (POIs) […]

Neha Kohli receives new awards to study in India

Neha Kohli received an American Institute of Indian Studies Junior Fellowship to carry out her PhD project “The Matter of Islands: Examining Island Narratives and Political Life in the Eastern Indian Ocean” in India in 2024-25. Early on, she also received a Graduate Student Research Fellowship from the American Association of Geographers’ Asian Geography Specialty […]

The timing, magnitude, and relative composition of extreme total water levels vary seasonally along the U.S. Atlantic coast

Quadrado, G., & Serafin, K. (2024). The timing, magnitude, and relative composition of extreme total water levels vary seasonally along the U.S. Atlantic coast. JGR Oceans, 136, 104482. ABSTRACT: This paper investigates when extreme total water levels (TWLs) occur during the year along the U.S. Atlantic coast and whether individual components, like waves, tides, and […]

Michelle Ruiz to join Bill Anderson Fund

Graduate Student Michelle Ruiz was selected to join the Fall 2024 cohort of the Bill Anderson Fund. The Bill Anderson Fund supports minority graduate students who are historically underrepresented in hazards and disaster-related careers. Since its establishment in 2014, the program has enrolled over 100 students from more than 20 disciplines, offering professional development through […]

Classification of tropical cyclone rain patterns using convolutional autoencoder

Kim, D., & Matyas, C. J. (2024). Classification of tropical cyclone rain patterns using convolutional autoencoder. Scientific Reports, 14(1), 791. ABSTRACT: Heavy rainfall produced by tropical cyclones (TCs) frequently causes wide-spread damage. TCs have different patterns of rain depending on their development stage, geographical location, and surrounding environmental conditions. However, an objective system for classifying […]

An ensemble deep learning approach to spatiotemporal tropospheric ozone forecasting in Tehran

Rezaali, M., Jahangir, M. S., Fouladi-Fard, R., & Keellings, D. (2024). An ensemble deep learning approach to spatiotemporal tropospheric ozone forecasting: A case study of Tehran, Iran. Urban Climate, 55, 101950. DOI: https://doi.org/10.1016/j.uclim.2024.101950. This study proposes a novel framework for spatiotemporal forecasting of Ground-level Ozone Concentration (GOC) using advanced machine learning techniques, including Artificial Neural […]