Manual touch interactions elicit widespread skin vibrations that excite spiking responses in tactile neurons distributed throughout the hand. The spatiotemporal structure of these population responses is not yet fully understood. Here, we evaluate how touch information is encoded in the spatiotemporal organization of simulated Pacinian corpuscle neuron (PC) population responses when driven by a vibrometry dataset of whole-hand skin motion during commonly performed gestures. We assess the amount of information preserved in these peripheral population responses at various spatiotemporal scales using several non-parametric classification methods. We find that retaining the spatial structure of the whole-hand population responses is important for encoding touch gestures while conserving the temporal structure becomes more consequential for gesture representation in the responses of PCs located in the palm. In addition, preserving spatial structure is more beneficial for capturing gestures involving single rather than multiple digits. This work contributes to further understanding the sense of touch by introducing novel measurement-driven computational methods for analyzing the population-level neural representations of natural touch gestures over multiple spatiotemporal scales.