Existing user performance models of navigation for very large documents describe trends in movement time over the entire navigation task. However, these navigation tasks are in fact a combination of many sub-tasks, the details of which are lost when aggregated. Thus, existing models do not provide insight into the navigation choices implicit in a navigation task, nor into how strategy ultimately affects user performance. Focusing on the domain of data visualizations, the very large documents we investigate are very large data views. We present an algorithmic decision process and descriptive performance model of zooming and panning navigation strategy, parameterized to account for speed-accuracy trade-offs, using common mouse-based interaction techniques. Our model is fitted and validated against empirical data, and used to evaluate proposed optimal strategies. Further, we use our model to provide support for interaction design considerations for achieving performant interaction techniques for navigation of very large data views.