Knowledge about the spatial and temporal distribution of fire ignitions provides relevant information for assessing the potential risk of fire, offering opportunities for improving fire prevention measures. For example, the distribution of ignitions can be used to allocate resources for early fire suppression on the high risk areas [1], especially when combined with Fire Danger Rating Systems (FDRS). By combining the results of models for predicting ignition occurrence with FDRS predictions we would include those socioeconomic factors behind human-caused ignitions to the weather and fuel moisture components that basically define FDRS. The number and spatial distribution of ignitions can also be integrated into fire spread models to generate spatially continuous information on probability of fire occurrence for landscape planning purposes when reducing the negative impact of fires is a goal [2,3]. Finally, by understanding the human behavior that triggers human-caused ignitions, it is possible to implement measures for reducing the number of ignitions and the subsequent fires. However, it has to be mentioned that predicting where and when they will the ignitions take place implies a high degree of uncertainty, as both natural events and human activities leading to their occurrence are often difficult to predict or even to measure.

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