Reading is a process that unfolds across space and time. Standard modeling approaches, however, overlook much of the spatio-temporal dynamics involved in reading by relying on aggregated reading measurements—typically only focusing on fixation durations—and employing models with strong simplifying assumptions. In this paper, we propose a generative model that captures not only how long fixations last, but also where they land and when they occur. To this end, we model reading scanpaths via two conditionally independent distributions: one for fixation location and timing, and another for fixation duration.The location (and timing) of fixation shifts, so-called saccades, are modeled using a spatio-temporal Hawkes process, which captures how each fixation excites the probability of a new fixation occurring near it in time and space. Empirically, our Hawkes process model exhibits higher likelihood on held-out reading data than baselines. The duration time of fixation events is modeled as a function of fixation-specific features convolved across time, thus capturing non-stationary delayed effects. We find that convolution-based approaches demonstrate weak predictive power when modeling disaggregated fixation durations. Similarly, our analysis of surprisal theory on disaggregated data reveals limited effectiveness in predicting both where fixations occur and how long they last.