California has experienced increasingly frequent and severe wildfires in recent decades, of- ten attributed to a combination of climate change and land management practices. Public discussions sometimes claim that counties with stricter vegetation or “brush” policies experience lower wildfire risk, yet these claims are rarely evaluated using formal spatial–temporal statistical models. This thesis examines the relationship between wildfire occurrence in California and county- level brush policies using a Stoyan–Grabarnik (SG) objective framework for spatial–temporal point processes. The analysis combines wildfire ignition records from the U.S. Forest Service with daily temperature data from Google Earth Engine, precipitation observations from NOAA stations, and county-level brush policy indicators collected from California county fire agency sources. After cleaning and merging the datasets, the modeling sample contains 35,500 wildfire ignition records between 1987 and 2021, along with climate covariates and two policy indicators: whether a county requires defensible-space inspections at the time of property sale (INSPE REQ FOR SALES) and whether local vegetation ordinances extend beyond minimum state Public Resources Code requirements (ORDINANCE BEYOND PRC). California is discretized into a 10 km by 10 km grid using the California Albers projection, and time is partitioned into four- and six-month intervals. Within each space–time cell, wildfire counts are modeled using an SG-optimized intensity proxy that incorporates baseline spatial fire activity, time since the last fire, temperature, precipitation, and policy indicators. Comparisons of models with and without brush policy variables show that adding the policy indicators produces almost no improvement in the SG objective function. In contrast, climate-related variables and baseline spatial intensity consistently explain variation in wildfire occurrence. Precipitation enters the models with a positive coefficient, which is clarified by a daily analysis showing that the busiest wildfire days are disproportionately associated with non-zero precipitation at nearby stations, consistent with storm systems that produce both lightning ignitions and rainfall. Overall, the results suggest that large-scale climate conditions, historical spatial fire patterns, and fuel accumulation dynamics play a much larger role in explaining wildfire occurrence than the coarse county-level brush policy indicators used here. These findings highlight the importance of incorporating richer climate, fuel, and policy implementation data when evaluating wildfire mitigation strategies.