Bare hand pointing on touch screens both benefits and suffers from the nature of direct input. This work explores techniques to overcome its limitations. Our goal is to design interaction tools allowing pixel level pointing in a fast and efficient manner. Based on several cycles of iterative design and testing, we propose two techniques: Cross-Keys that uses discrete taps on virtual keys integrated with a crosshair cursor, and an analog Precision-Handle that uses a leverage (gain) effect to amplify movement precision from the user’s finger tip to the end cursor. We conducted a formal experiment with these two techniques, in addition to the previously known Zoom-Pointing and Take-Off as baseline anchors. Both subjective and performance measurements indicate that Precision-Handle and Cross-Keys complement existing techniques for touch screen interaction.

ZoneZoom is an input technique that lets users traverse large information spaces on smartphones. Our technique ZoneZoom, segments a given view of an information space into nine sub-segments, each of which is mapped to a key on the number keypad of the smartphone. This segmentation can be hand-crafted by the information space author or dynamically created at run-time. ZoneZoom supports “spring-loaded ” view shifting which allows users to easily “glance ” at nearby areas and then quickly return to their current view. Our ZoneZoom technique lets users gain an overview and compare information from different parts of a dataset. SmartPhlow is an optimized application for browsing a map of local-area road traffic conditions.

There are many interface schemes that allow users to work at, and move between, focused and contextual views of a data set. We review and categorise these schemes according to the interface mechanisms used to separate and blend views. The four approaches are overview+detail, which uses a spatial separation between focused and contextual views; zooming, which uses a temporal separation; focus+context, which minimizes the seam between views by displaying the focus within the context; and cue-based techniques which selectively highlight or suppress items within the information space. Critical features of these categories, and empirical evidence of their success, are discussed. The aim is to provide a succinct summary of the state-of-the-art, to illuminate successful and unsuccessful interface strategies, and to identify potentially fruitful areas for further work.

In order to investigate large information spaces effectively, it is often necessary to employ navigation mechanisms that allow users to view information at different scales. Some tasks require frequent movements and scale changes to search for details and compare them. We present a model that makes predictions about user performance on such comparison tasks with different interface options. A critical factor embodied in this model is the limited capacity of visual working memory, allowing for the cost of visits via fixating eye movements to be compared to the cost of visits that require user interaction with the mouse. This model is tested with an experiment that compares a zooming user interface with a multi-window interface for a multiscale pattern matching task. The results closely matched predictions in task performance times; however error rates were much higher with zooming than with multiple windows. We hypothesized that subjects made more visits in the multi-window condition, and ran a second experiment using an eye tracker to record the pattern of fixations. This revealed that subjects made far more visits back and forth between pattern locations when able to use eye movements than they made with the zooming interface. The results suggest that only a single graphical object was held in visual working memory for comparisons mediated by eye movements, reducing errors by reducing the load on visual working memory. Finally we propose a design heuristic: extra windows are needed when visual comparisons must be made involving patterns of a greater complexity than can be held in visual working memory.

Applications supporting navigation in large networks are used every days by millions of people. They include road map navigators, ight route visualization systems, and network visualization systems using node-link diagrams. These applications currently provide generic interaction methods for navigation: pan-and-zoom and sometimes bird’s eye views. This article explores the idea of exploiting the connection information provided by the network to help navigate these large spaces. We visually augment two traditional navigation methods, and develop two special-purpose techniques. The first new technique, called “Link Sliding”, provides guided panning when continuously dragging along a visible link. The second technique, called “Bring & Go”, brings adjacent nodes nearby when pointing to a node. We compare the performance of these techniques in both an adjacency exploration task and a node revisiting task. This comparison illustrates the various advantages of content-aware network navigation techniques. A significant speed advantage is found for the Bring & Go technique over other methods.

Information navigation techniques for handheld devices support interacting with large virtual spaces on small displays, for example finding targets on a large-scale map. Since only a small part of the virtual space can be shown on the screen at once, typical interfaces allow for scrolling and panning to reach off-screen content. Spatially aware handheld displays sense their position and orientation in physical space in order to provide a corresponding view in virtual space. We implemented various one-handed navigation techniques for camera-tracked spatially aware displays. The techniques are compared in a series of abstract selection tasks that require the investigation of different levels of detail. The tasks are relevant for interfaces that enable navigating large scale maps and finding contextual information on them. The results show that halo is significantly faster than other techniques. In complex situations zoom and halo show comparable performance. Surprisingly, the combination of halo and zooming is detrimental to user performance.

While zoomable user interfaces can improve the usability of applications by easing data access, a drawback is that some users tend to become lost after they have zoomed in. Previous studies indicate that this effect could be related to individual differences in spatial ability. To overcome such orientation problems, many desktop applications feature an additional overview window showing a miniature of the entire information space. Small devices, however, have a very limited screen real estate and incorporating an overview window often means pruning the size of the detail view considerably. Given this context, we report the results of a user study in which 24 participants solved search tasks by using two zoomable scatterplot applications on a PDA- one of the applications featured an overview, the other relied solely on the detail view. In contrast to similar studies for desktop applications, there was no significant difference in user preference between the interfaces. On the other hand, participants solved search tasks faster without the overview. This indicates that, on small screens, a larger detail view can outweigh the benefits gained from an overview window. Individual differences in spatial ability did not have a significant effect on task-completion times although results suggest that participants with higher spatial ability were slowed down by the overview more than low spatial-ability users.

We present a study that evaluates conventional Pan and Zoom Navigation and Rubber Sheet Navigation, a rectilinear Focus+Context technique. Each of the two navigation techniques was evaluated both with and without an overview. All interfaces guaranteed that regions of interest would remain visible, at least as a compressed landmark, independent of navigation actions. Interfaces implementing these techniques were used by 40 subjects to perform a task that involved navigating a large hierarchical tree dataset and making topological comparisons between nodes in the tree. Our results show that Pan and Zoom Navigation was significantly faster and required less mental effort than Rubber Sheet Navigation, independent of the presence or absence of an overview. Also, overviews did not appear to improve performance, but were still perceived as beneficial by users. We discuss the implications of our task and guaranteed visibility on the results and the limitations of our study, and we propose preliminary design guidelines and recommendations for future work.

There have been several studies that compare sequential search results versus clustered search results, and graphical presentations versus textual presentations. These studies have resulted in confirmed efficiency and preference of clustering over sequential lists. The studies between graphical and textual presentations have usually shown to be task dependant. This study shows a systematic evaluation of zoomable versus textual clustered search results. A controlled experiment with repeated measures design and within-subjects differences was performed with fifteen subjects, comparing Groxis, Inc.’s Grokker – their clustering product – a zoomable user interface, their textual clustering product and Vivisimo’s textual clustering product. No significant differences were found for objective measures. However, there were significant differences for subjective measures. The textual clustering interfaces was preferred and elicited major satisfaction among the users. Results are summarized in both a quantitative and qualitative format. Author Keywords Information retrieval, textual clusters, graphical clusters,

Abstract. We present a highly accurate method for classifying web pages based on link percentage, which is the percentage of text characters that are parts of links normalized by the number of all text characters on a web page. We also present a novel link grouping algorithm using agglomerative hierarchical clustering that groups links in the same spatial neighborhood together while preserving link structure. Grouping allows users with severe disabilities to use a scan-based mechanism to tab through a web page and select items. In experiments, we saw up to a 40-fold reduction in the number of commands needed to click on a link with a scan-based interface. Our classification method consistently outperformed a baseline classifier even when using minimal data to generate article and index clusters, and achieved classification accuracy of 94.0 % on web sites with well-formed or slightly malformed HTML, compared with 80.1 % accuracy for the baseline classifier.