The frequency and size of wildfires have been increasing since the 1950’s. Furthermore, the incidence of fires impacting an expanding wildland urban interface(WUI) is becoming more prominent. These factors make the need for proactive wildfire mitigation essential. One step toward this end is the application of spatial data science to identify areas with high wildfire susceptibility, hazard, and risk. This one-year project, titled Spatial Analysis Research and Course development (SPARC), is planned in three distinct phases. During the summer of 2026, phase one will use various satellite imagery and GIS datasets to develop a wildfire susceptibility model. A susceptibility model is the foundational data layer describing the intrinsic characteristics of a landscape that make it more or less prone to a wildfire. Factors include fuel load, fuel continuity, as well as topographic variables. The susceptibility model will be advanced into a hazard model, which takes into account the likelihood of fire ignition. This will be estimated using past fire events and lightning pattern analysis. Finally, the hazard model will be advanced into a risk model, which takes into account the presence of homes, structures, and critical infrastructure.