A spatial curve dynamical model framework is adopted for functional prediction of counts in a spatiotemporal log-Gaussian Cox process model. Our spatial functional estimation approach handles both wavelet-based heterogeneity analysis in time, and spectral analysis in space. Specifically, model fitting is achieved by minimising the information divergence or relative entropy between the multiscale model underlying the data, and the corresponding candidates in the spatial spectral domain. A simulation study is carried out within the family of log-Gaussian Spatial Autoregressive \(\ell ^{2}\)-valued processes (SAR\(\ell ^{2}\) processes) to illustrate the asymptotic properties of the proposed spatial functional estimators. We apply our modelling strategy to spatiotemporal prediction of respiratory disease mortality.

采用空间曲线动力学模型框架对时空对数-高斯Cox过程模型中的计数进行功能预测。我们的空间功能估计方法既处理基于时间的小波异质分析，又处理空间的频谱分析。具体而言，通过最小化数据基础下的多尺度模型与空间光谱域中的相应候选者之间的信息差异或相对熵来实现模型拟合。在对数-高斯空间自回归\（\ ell ^ {2} \）值过程（SAR \（\ ell ^ {2} \）的范围内进行仿真研究过程）以说明所提出的空间功能估计量的渐近性质。我们将我们的建模策略应用于呼吸道疾病死亡率的时空预测。