This paper concerns space-sphere point processes, that is, point processes on the product space of $\mathbb{R}^d$ (the $d$-dimensional Euclidean space) and $\mathbb{S}^k$ (the $k$-dimensional sphere). We consider specific classes of models for space-sphere point processes, which are adaptations of existing models for either spherical or spatial point processes. For model checking or fitting, we present the space-sphere $K$-function which is a natural extension of the inhomogeneous $K$-function for point processes on $\mathbb{R}^d$ to the case of space-sphere point processes. Under the assumption that the intensity and pair correlation function both have a certain separable structure, the space-sphere $K$-function is shown to be proportional to the product of the inhomogeneous spatial and spherical $K$-functions. For the presented space-sphere point process models, we discuss cases where such a separable structure can be obtained. The usefulness of the space-sphere $K$-function is illustrated for real and simulated datasets with varying dimensions $d$ and $k$.

Keywords: First and second order separability, Functional summary statistic, Log Gaussian Cox process, Pair correlation function, Shot noise Cox process.