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1. Spatiotemporal Modeling of Connected Vehicle Data: An Application to Non-Congregate Shelter Planning During Hurricane-Pandemics

   https://doi.org/10.3390/app15063185  

   Tsekeni, Davison Elijah; Alisan, Onur; Yang, Jieya; Vanli, O Arda; Ozguven, Eren Erman (March 2025, Applied Sciences)

   The growing complexity of natural disasters, intensified by climate change, has amplified the challenges of managing emergency shelter demand. Accurate shelter demand forecasting is crucial to optimize resource allocation, prevent overcrowding, and ensure evacuee safety, particularly during concurrent disasters like hurricanes and pandemics. Real-time decision-making during evacuations remains a significant challenge due to dynamic evacuation behaviors and evolving disaster conditions. This study introduces a spatiotemporal modeling framework that leverages connected vehicle data to predict shelter demand using data collected during Hurricane Sally (September 2020) across Santa Rosa, Escambia, and Okaloosa counties in Florida, USA. Using Generalized Additive Models (GAMs) with spatial and temporal smoothing, integrated with GIS tools, the framework captures non-linear evacuation patterns and predicts shelter demand. The GAM outperformed the baseline Generalized Linear Model (GLM), achieving a Root Mean Square Error (RMSE) of 6.7791 and a correlation coefficient (CORR) of 0.8593 for shelters on training data, compared to the GLM’s RMSE of 12.9735 and CORR of 0.1760. For lodging facilities, the GAM achieved an RMSE of 4.0368 and CORR of 0.5485, improving upon the GLM’s RMSE of 4.6103 and CORR of 0.2897. While test data showed moderate declines in performance, the GAM consistently offered more accurate and interpretable results across both facility types. This integration of connected vehicle data with spatiotemporal modeling enables real-time insights into evacuation dynamics. Visualization outputs, like spatial heat maps, provide actionable data for emergency planners to allocate resources efficiently, enhancing disaster resilience and public safety during complex emergencies. 

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2. Water in Geospatial Higher Education: A Discussion of Lessons Learned

   Ignatius, A.R.; Coleman, L.; Kurimo-Beechuk, E.; McDonald, J.; Milligan, R.; Mishra, D.R.; Page, M. (January 2021, Georgia Water Resources Conference)

   null (Ed.)

   Geospatial technologies and geographic methods are foundational skills in modern water resources monitoring, research, management, and policy-making. Understanding and sustaining healthy water resources depends on spatial awareness of watersheds, land use, hydrologic networks, and the communities that depend on these resources. Water professionals across disciplines are expected to have familiarity with hydrologic geospatial data. Proficiency in spatial thinking and competency reading hydrologic maps are essential skills. In addition, climate change and non-stationary ecological conditions require water specialists to utilize dynamic, time-enabled spatiotemporal datasets to examine shifting patterns and changing environments. Future water specialists will likely require even more advanced geospatial knowledge with the implementation of distributed internet-of-things sensor networks and the collection of mobility data. To support the success of future water professionals and increase hydrologic awareness in our broader communities, teachers in higher education must consider how their curriculum provides students with these vital geospatial skills. This paper considers pedagogical perspectives from educators with expertise in remote sensing, geomorphology, human geography, environmental science, ecology, and private industry. These individuals share a wealth of experience teaching geographic techniques such as GIS, remote sensing, and field methods to explore water resources. The reflections of these educators provide a snapshot of current approaches to teaching water and geospatial techniques. This commentary captures faculty experiences, ambitions, and suggestions for teaching at this moment in time. 

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3. Developing Math Knowledge and Computational Thinking Through Game Play and Design: A Professional Development Program.

   Smith, H. (October 2020, Contemporary issues in technology and teacher education)

   null (Ed.)

   The Game Play and Design Framework is a project-based instructional method to engage teachers and students with mathematics content by utilizing technology as a vehicle for game play and creation. In the authors’ prior work, they created a technology tool and game editing platform, the Wearable Learning Cloud Platform (WLCP), which enables teachers and students to play, create, and experience technology-augmented learning activities. This paper describes a 14-week Game Play and Design professional development program in which middle school teachers played, designed, tested, and implemented mathematics games in the classroom with their own students. Examples are included of teacher-created games, feedback from the students’ experience designing games, and evidence of student learning gains from playing teacher-created games. This work provides a pedagogical approach for educators and students that utilizes the benefits of mobile technologies and collaborative learning through games to develop students’ higher-level thinking in STEM classrooms. 

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4. Developing mathematics knowledge and computational thinking through game play and design: A professional development program.

   Smith, H. (October 2020, Contemporary issues in technology and teacher education)

   null (Ed.)

   The Game Play and Design Framework is a project-based instructional method to engage teachers and students with mathematics content by utilizing technology as a vehicle for game play and creation. In the authors’ prior work, they created a technology tool and game editing platform, the Wearable Learning Cloud Platform (WLCP), which enables teachers and students to play, create, and experience technology-augmented learning activities. This paper describes a 14-week Game Play and Design professional development program in which middle school teachers played, designed, tested, and implemented mathematics games in the classroom with their own students. Examples are included of teacher-created games, feedback from the students’ experience designing games, and evidence of student learning gains from playing teacher-created games. This work provides a pedagogical approach for educators and students that utilizes the benefits of mobile technologies and collaborative learning through games to develop students’ higher-level thinking in STEM classrooms. 

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5. Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

   https://doi.org/10.3390/rs13204128  

   Xu, Jinwen; Qiang, Yi (October 2021, Remote Sensing)

   Quantitative assessment of community resilience is a challenge due to the lack of empirical data about human dynamics in disasters. To fill the data gap, this study explores the utility of nighttime lights (NTL) remote sensing images in assessing community recovery and resilience in natural disasters. Specifically, this study utilized the newly-released NASA moonlight-adjusted SNPP-VIIRS daily images to analyze spatiotemporal changes of NTL radiance in Hurricane Sandy (2012). Based on the conceptual framework of recovery trajectory, NTL disturbance and recovery during the hurricane were calculated at different spatial units and analyzed using spatial analysis tools. Regression analysis was applied to explore relations between the observed NTL changes and explanatory variables, such as wind speed, housing damage, land cover, and Twitter keywords. The result indicates potential factors of NTL changes and urban-rural disparities of disaster impacts and recovery. This study shows that NTL remote sensing images are a low-cost instrument to collect near-real-time, large-scale, and high-resolution human dynamics data in disasters, which provide a novel insight into community recovery and resilience. The uncovered spatial disparities of community recovery help improve disaster awareness and preparation of local communities and promote resilience against future disasters. The systematical documentation of the analysis workflow provides a reference for future research in the application of SNPP-VIIRS daily images. 

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