## 4 Discussion  
The spatiotemporal modeling approach adopted in this study has shown significant effectiveness in predicting shelter demand during natural disasters. By integrating vehicle trajectory data with modeling techniques such as GAMs and Gaussian radial basis functions, the study was able to capture spatial and temporal dynamics that traditional models often overlook. The high correlation between predicted and actual vehicle counts, particularly for shelters, highlights the accuracy and reliability of this approach. However, the slightly lower performance of the model in predicting demand at lodging facilities suggests that additional factors may need to be considered in future iterations of the model. The GAM-based approach presented in this study provides several key advantages in disaster management. First, it allows emergency planners to visually interpret how evacuation behaviors evolve over space and time, unlike black box machine learning models. Second, it enables real-time adjustments to disaster response strategies by highlighting which factors (e.g., school closures, day of the week, or location) have the most significant impact on shelter demand. For example, GAM plots (Figures 8-12) illustrate how demand changes across different locations, allowing authorities to pre-allocate resources to high-risk areas proactively. Additionally, decision-makers can use these insights to simulate scenarios, such as predicting how demand might shift if certain schools remain open or if evacuation timing changes. This level of interpretability ensures that the model is not only accurate but also actionable for real-world disaster response planning. _Application to New Regions or Hazards of Interest:_ It is important to note that the proposed modeling framework was developed using data specific to Escambia, Santa Rosa, and Oklahoma counties in Florida's Panhandle. Applying this model to new regions, (e.g., Central or Southern Florida) or other disaster conditions would require collecting region and period-specific data that captures the same key variables used in model development. This includes connected vehicle data in the region and period of interest and geographic coordinates and available capacities of shelter facilities, and relevant contextual factors such as school closure status. Furthermore, to preserve the accuracy of the spatiotemporal \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RMSE** & **MAE** & **MAPE** & **CORR** \\ \hline Training & 6.7778 & 3.7317 & 84.0176\% & 0.8126 (<0.001) \\ Test & 5.9453 & 3.6453 & 115.855\% & \(-\)0.0532 (0.7647) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: GAM Summary Statistics for Shelters 15-day training data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RMSE** & **MAE** & **MAPE** & **CORR** \\ \hline Training & 4.0464 & 2.8338 & 113.0784\% & 0.5326 (<0.001) \\ Test & 3.6256 & 2.6322 & 104.0038\% & 0.1511 (0.1169) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: GAM Summary Statistics for Lodging Facilities on 15-day training data. smoothing terms, the new data should match or exceed the spatial and temporal resolution of the training data. Based on the acceptable accuracy of our shelters GAM model, we recommend collecting data from at least 44 facilities for a study area of about size 3370 sq-miles (see Figure 14). In terms of temporal resolution, at least a 15-day data collection window is recommended. This highlights the importance of building region-specific data infrastructure that supports real-time updating and model adaptation as conditions evolve. _Implications:_ The findings of this study have significant implications for disaster response planning, evacuation coordination, and resource allocation. The model's ability to predict the demand for shelters and lodging facilities can assist decision-makers by forecasting demand fluctuations. This allows authorities to pre-position resources, adjust staffing levels, and identify potential overflow risks before disasters strike.