Many 2D visual spaces have a virtually one-dimensional nature with very high aspect ratio between the dimensions: examples include time-series data, multimedia data such as sound or video, text documents, and bipartite graphs. Common among these is that the space can become very large, e.g., temperature measurements could span a long time period, surveillance video could cover entire days or weeks, and documents can have thousands of pages. Many analysis tasks for such spaces require several foci while retaining context and distance awareness. In this extended version of our IEEE PacificVis 2010 paper, we introduce a method for supporting this kind of multifocus interaction that we call stack zooming. The approach is based on building hierarchies of 1D strips stacked on top of each other, where each subsequent stack represents a higher zoom level, and sibling strips represent branches in the exploration. Correlation graphics show the relation between stacks and strips of different levels, providing context and distance awareness for the foci. The zoom hierarchies can also be used as graphical histories and for communicating insights to stakeholders and can be further extended with annotation and integrated statistics.