Wrangling data for CNN
When you use a 2D-convolutional layer, you assume that there is some 2D topological order in your data. E.g. images have 2D topological order: it's important that some pixel is at position (x, y) - if we move that pixel to some other position, the image would be wrong. The number of channels can be arbitrary in images (1 for grayscale, 3 for RGB), but we still consider this as a 2D topological order.

Check the Table 9.1. at chapter 9.7. Data Types (Deep Learning book by Ian Goodfellow and Yoshua Bengio and Aaron Courville).

So, since your data doesn't have 2D topological order, but 1D, you should use 1D-convolutional layer with multiple channels.

You could have 4 channels: 1 for time, and 3 other for space coordinates.