## 1 Introduction
Within the context of shelter demand analysis, the interpretability of Generalized Additive Models (GAMs) allows one to explicitly quantify each predictor's effect on the response variable from spatio-temporal connected vehicle data. Specifically, from the coefficients of the GAM model it is possible to visualize the effect of spatial location (latitude, longitude), time (day effects), as well as policy related factors (school closures) on shelter demand. This transparency enables emergency planners to extract actionable insights, such as identifying high-demand shelter regions, understanding the influence of school closures on evacuation patterns, and forecasting demand trends with high interpretability. The smooth functions used in GAMs ensure that relationships remain flexible yet comprehensible, avoiding rigid assumptions inherent in linear models while maintaining the ability to assess variable importance. Spatial analysis and Geographic Information System (GIS) applications have been pivotal in urban emergency management, particularly in modeling and predicting disaster impacts. The availability of Geographic Positioning System (GPS) vehicle movement data resulted in the development of effective disaster response and situational awareness technologies. [PERSON] [27] highlights the use of spatial analysis techniques, including risk and hazard assessment, what-if scenario modeling, and resource allocation optimization. GIS-based mapping, spatial interpolation techniques (e.g., Kriging and Inverse Distance Weighting), and network analysis are used to assess risks, simulate evacuation routes, and allocate resources effectively. Visualization further supports real-time decision-making by creating dynamic maps that enhance situational awareness during emergencies. These spatial modeling techniques are instrumental in urban centers, allowing for optimized response strategies and reducing vulnerability during extreme events [27]. For example, [PERSON] [28] utilized high-speed GPS data to create time-evolving Origin-Destination (O-D) matrices, enabling more precise and adaptable evacuation planning. Similarly, [PERSON] et al. [29] employed GPS movement data within a metapopulation SIR model to explore the dynamics of disease spread during the COVID-19 pandemic, demonstrating the potential of spatiotemporal data to inform public health interventions during concurrent disasters. These studies illustrate how integrating GPS movement data with spatiotemporal predictions can significantly improve decision-making and resource distribution, ultimately enhancing the resilience and effectiveness of emergency response operations [30]. Current literature on evacuation modeling and shelter demand forecasting often relies on static datasets or simulated scenarios, lacking the real-time dynamics necessary to capture evacuee behavior during concurrent disasters and pandemics accurately. Existing studies primarily focus on congregate sheltering and overlook the unique challenges posed by non-congregate shelters in such complex scenarios. Moreover, few studies utilize connected vehicle data to analyze actual evacuation movements, limiting their ability to provide detailed, data-driven insights for emergency planning. This study addresses these gaps by leveraging connected vehicle data to estimate sheltering demand during hurricanes coinciding with pandemics. By integrating real-time vehicle trajectories with spatiotemporal models and GIS tools, we introduce an interpretable statistical model to forecast non-congregate shelter demand and analyze impact of interventions such as school closures. This method enhances demand estimation accuracy and supports more informed resource allocation and emergency planning decisions. Unlike prior studies, which rely on static datasets or simulated scenarios, our approach uses actual vehicle trajectories to dynamically forecast shelter demand. The study enhances demand forecasting accuracy by leveraging GIS tools and spatiotemporal analytics.