<p>The event is said to have a time dependence if it is seen that the event occurs as a consequence of events that occurred in the time before the event. On the other hand, the event is said to have a spatial dependence if it takes place as a consequence of certain occurrences in the surrounding location. The spatial and time dependencies are combined in this paper. Spatial and temporal dependence is analyzed using the Generalized Space-Time Autoregressive (GSTAR) model. Adding an intervention effect to the GSTAR model is the most recent result of this research project’s findings. Previous research has only demonstrated the intervention impact in time series models. The intervention effect is a critical element that cannot be disregarded due to its ability to change the data pattern. The findings led to the development of a GSTAR model that included an intervention effect derived from the intervention effect included in the time series model. The model was subsequently applied to forest fire hotspot data in Ketapang Regency, with interventions in the form of dry season. According to the findings, a GSTAR model with an intervention effect was created, precisely representing the conditions of forest fires in numerous sites centred around Ketapang during the past five years. The accuracy of this model is 85%. Then, the model can be utilized to forecast the occurrence of forest fires during the next five months in 2024.</p>