Frías, María PilarTorres-Signes, AntoniRuiz-Medina, María Dolores2025-10-092025-10-092021-04-291133-0686https://doi.org/10.1007/s11749-021-00773-zhttps://hdl.handle.net/10953/6193We introduce a new class of spatial Cox processes driven by a Hilbert-valued random log-intensity. We adopt a parametric framework in the spectral domain, to estimate its spatial functional correlation structure. Specifically, we consider a spectral functional, approach based on the periodogram operator, inspired on Whittle estimation methodology. Strong consistency of the parametric estimator is proved in the linear case. We illustrate this property in a simulation study under a Gaussian first-order Spatial Autoregressive Hilbertian scenario for the log-intensity model. Our method is applied to the spatial functional prediction of respiratory disease mortality in the Spanish Iberian Peninsula, in the period 1980–2015.engIn fite-dimensional log-intensityPeriodogram operatorRespiratory disease mortalitySpatial Autoregressive Hilbertian processesSpatial Cox processesinfo:eu-repo/semantics/article60G2560G6062J0562J10info:eu-repo/semantics/openAccess