Shakhawat H. Tanim

Grants

• 2026 — Data-Driven Approaches for Opioid Use Disorder Treatment, Recovery, and Overdose Prevention in Rural Communities via Mobile Health Clinics. National Institutes of Health (NIH). Awarded — Co-Investigator.
• 2025 — Increasing Mobile Health Clinic Utilization in Rural Communities. South Carolina Rural Primary Healthcare. Awarded — Co-Investigator.
• 2023 — Dam Sector Regional Risk Resilience Program (DSR3P), Year 2. U.S. Army Corps of Engineers. Not awarded — Co-Investigator.
• 2022 — Trustworthy, Explainable, and Intelligent Digital Twin Framework for Geospatial Analytics. Taylor Geospatial Institute. Not awarded — Co-Investigator.

Research Interests

• Human-environment interactions — how people perceive, respond to, and are affected by environmental risk
• Geospatial data science and GeoAI — machine learning, deep learning, and spatial modeling applied to hazard and health problems
• Flood and storm risk assessment and decision-making — hurricane evacuation behavior, flood impact dynamics, and resilience assessment for community water systems
• Infrastructure interdependencies — cascading impacts across transportation, dam, and reservoir systems under normal and disrupted conditions
• Emergency and health response — mobile health clinic placement, disease forecasting, and geospatial decision-support tools for public health access

Publications

Most recent first

2026

• Tanim, S. H., White, D., Witrick, B., & Rennert, L. Optimizing mobile health clinic placement via geospatial modeling. Public Health in Practice, 100805.
• Kabir, M. E., Tanim, S. H., Sellnow, D. D., Luteria, G. L. P., & Rennert, L. Teaching AI to decode vaccine hesitancy narratives: A few-shot learning and topic modeling approach. Big Data and Cognitive Computing, 10(5), 159.

2025

• Hogewood, L., Tanim, S. H., Deegan, M., & Ladner, D. Resilience assessment matrix development for community water supply. Natural Hazards Review, 26(6), 101061.
• Wu, J., Tanim, S. H., & Rennert, L. A deep learning approach for enhancing pandemic prediction: A retrospective evaluation of transformer neural networks and multi-source data fusion for infectious disease forecasting. Epidemics, 53, 100865.

2024

• Tanim, S., Woo, M., & Rennert, L. A machine learning and GIS-based approach for mobile health clinic placement: Identifying factors for successful deployments in South Carolina. Proceedings of the 2024 IEEE International Conference on Big Data, Washington, DC.
• Bhatta, N., Tanim, S. H., & Murray-Tuite, P. Dynamics of link importance through normal conditions, flood response, and recovery. Sustainability, 16(2), 819.

2023

• Tanim, S. H., Reader, S., & Hu, Y. Predicting hurricane evacuation for local neighborhoods across a metropolitan region. International Journal of Disaster Risk Reduction, 99, 104117.

2022

• Tanim, S. H., Wiernik, B. M., Reader, S., & Hu, Y. Predictors of hurricane evacuation decisions: A meta-analysis. Journal of Environmental Psychology, 79, 101742.

2020

• Yu, Q., & Tanim, S. H. A preliminary exploration of the cooling effect of tree shade in urban landscapes. International Journal of Applied Earth Observation and Geoinformation, 92, 102161.

2019

• Acheampong, M., Tanim, S. H., & Ananga, E. Is Ghana ready to attain SDG 7: A comprehensive assessment of its renewable energy potential and pitfalls. Energies, 12(3).

2017

• Tanim, S. H., & Tobin, G. A. Social factors and evacuation vulnerability: An application in Pinellas County, Florida. Papers in Applied Geography, 4(2), 123–136.

Conference Presentations

Most recent first

• 2026 — Interactive Web Tool to Optimize Mobile Health Clinic Placement via Geospatial Modeling. CSTE 2026 Annual Conference, EpiVision Session, Boston, MA.
• 2024 — Strategic Placement of Mobile Health Clinics: Integrating Geospatial Machine Learning and Location-Allocation. American Geophysical Union (AGU) Fall Meeting, Washington, DC.
• 2024 — A Geospatial Machine Learning Approach to Explore Factors Driving Mobile Health Utilization. IEEE Workshop on Trustworthy Machine Learning for Healthcare, Washington, DC.
• 2021 — Effect of Predictor Heterogeneity on Evacuation-Demand Models. AAG Annual Meeting.
• 2020 — A Statistical Meta-Analysis of Hurricane Evacuation Decision Models. Southeastern Division of the American Association of Geographers (SEDAAG).
• 2020 — Modeling the Geospatial Demographics of Hurricane Evacuation for Tampa Bay, Florida. USF School of Geosciences Colloquium, Tampa, FL.
• 2017 — Modeling Vehicle Conversion to CNG-Driven Vehicles Using Mixed Logit and Latent Class Logit: A Case Study in Dhaka, Bangladesh. University Transportation Centers (UTC) Conference, Gainesville, FL.
• 2016 — Integrating Social and Transportation Aspects of Evacuation with GIS. SEDAAG, Columbia, SC.
• 2016 — Documenting Social Factors and Evacuation Vulnerability: Pinellas County, Florida. Applied Geography Conference, Louisville, KY.
• 2016 — Understanding How Urban Landscape Heterogeneity and Socioeconomic Structure Affect Urban Heat-Island Effects in Tampa. Research Symposium: Celebrating Geographies of Space, Tampa.