Constructed functional marks for spatial point process intensity estimation

This paper aims to enhance the inference for spatial point processes' intensity function when complex interactions among points play a crucial role. We exploit local characteristics into the inferential procedure of maximising a regularised Poisson likelihood, penalised by the degree of interaction among points. The experiments conducted emphasize the importance of local second-order characteristics in improving inference for complex spatial point processes.