News articles contain a wealth of implicit geographic content that if exposed to readers improves understanding of today’s news. However, most articles are not explicitly geotagged with their geographic content, and few news aggregation systems expose this content to users. A new system named NewsStand is presented that collects, analyzes, and displays news stories in a map interface, thus leveraging on their implicit geographic content. NewsStand monitors RSS feeds from thousands of online news sources and retrieves articles within minutes of publication. It then extracts geographic content from articles using a custom-built geotagger, and groups articles into story clusters using a fast online clustering algorithm. By panning and zooming in NewsStand’s map interface, users can retrieve stories based on both topical significance and geographic region, and see substantially different stories depending on position and zoom level.

This paper presents a new and very efficient technique for automatic labeling of dynamically generated screen maps. Such screen maps are frequently created in the course of the interactive navigation in a geographic information system. Since screen maps have to support the functions zooming and scrolling, the label placement must be achieved very fast. Therefore we developed a concept which separates the labeling process into two phases. Calculations are shifted in a socalled preprocessing-phase as far as possible. Thus, the number of time consuming operations in the time critical interaction-phase is reduced and the operation zooming and scrolling can be realized very fast. Our concept supports labeling of point objects as well as labeling of line and area objects. The separation in the two phases is achieved by a new reactive data-structure which is called reactive conflict graph. In the preprocessing phase, it stores information about all potential conflicts. During the interaction-phase this structure is optimally exploited and leads to a labeling in an efficient and a cartographic adequate way.

We address the problem of filtering, selecting and placing labels on a dynamic map, which is characterized by continuous zooming and panning capabilities. This consists of two interrelated issues. The first is to avoid label popping and other artifacts that cause confusion and interrupt navigation, and the second is to label at interactive speed. In most formulations the static map labeling problem is NP-hard, and a fast approximation might have O(nlogn) complexity. Even this is too slow during interaction, when the number of labels shown can be several orders of magnitude less than the number in the map. In this paper we introduce a set of desiderata for “consistent ” dynamic map labeling, which has qualities desirable for navigation. We develop a new framework for dynamic labeling that achieves the desiderata and allows for fast interactive display by moving all of the selection and placement decisions into the preprocessing phase. This framework is general enough to accommodate a variety of selection and placement algorithms. It does not appear possible to achieve our desiderata using previous frameworks. Prior to this paper, there were no formal models of dynamic maps or of dynamic labels; our paper introduces both. We formulate a general optimization problem for dynamic map labeling and give a solution to a simple version of the problem. The simple version is based on label priorities and a versatile and intuitive class of dynamic label placements we call “invariant point placements”. Despite these restrictions, our approach gives a useful and practical solution. Our implementation is incorporated into the G-Vis system which is a full-detail dynamic map of the continental USA. This demo is available through any browser.

Abstract. A set of points shown on the map usually represents special sites like cities or towns in a country. If the map in the interactive geographical information system (GIS) is browsed by users on the computer screen or on the web, the points and their labels can be viewed in a query window at different resolutions by zooming in or out according to the users ’ requirements. How can we make use of the information obtained from different resolutions to avoid doing the whole labeling from scratch every time the zooming factor changes? We investigate this important issue in the interactive GIS system. In this paper, we build low-height hierarchies for one and two dimensions so that optimal and approximating solutions for adaptive zooming queries can be answered efficiently. To the best of our knowledge, no previous results have been known on this issue with theoretical guarantees.

According to the multimedia design principle of spatial contiguity, presenting text explanations for visualizations within the image space improves the users' ability to make referential links between the text and its corresponding objects. In this paper we introduce a concept of Dual-Use of Image Space (DUIS) and we show how the concept presents text explanations for visualizations within the image space without obstructing the image. In DUIS the pixels are used both as shading information for the objects as well as text which can be read.

Map labeling encounters unique issues in the context of dynamic maps with continuous zooming and panning—an application with increasing practical importance. In consistent dynamic map labeling, distracting behavior such as popping and jumping is avoided. In our model a dynamic label placement is a continuous function that assigns a 2d-label to each scale. This defines a 3d-solid, with scale as the third dimension. To avoid popping, we truncate each solid to a single scale range, called its active range. This range corresponds to the interval of scales at which the label is visible. The active range optimization (ARO) problem is to select active ranges so that no two truncated solids overlap and the sum of the active ranges is maximized. We show that the ARO problem is NPcomplete, even for quite simple solid shapes, and we present constant-factor approximations for different variants of the problem.

Map labeling is one of the most time-consuming and complex tasks during map generation. Additionally, if real-time map labeling is demanded like for screen maps, traditional automatic labeling approaches are not sufficient since they also do not support user interaction methods like zooming and scrolling – change of scale and map clip. Thus, we developed a new approach considering these specific demands for screen maps. The core of our approach is the modeling of possible label conflicts. This includes the identification of such conflicts depending upon their scale of occurrence and the storage of this information. Therefore, we designed a purpose-build data-structure, the so-called reactive conflict graph. This data-structure is built up before a user interacts with the system in the so-called preprocessing phase. It will be exploited during the interaction phase and ensures real-time labeling in a cartographic adequate way. The developed approach supports the labeling of point, line and area objects. The feasibility is successfully demonstrated in a component-based prototype.

This paper describes a fast approach to automatic point label de-confliction on interactive maps. The general Map Labeling problem is NP-hard and has been the subject of much study for decades. Computerized maps have introduced interactive zooming and panning which has intensified the problem. Providing dynamic labels for such maps typically requires a timeconsuming pre-processing phase. In the realm of visual analytics, however, the labeling of interactive maps is further complicated by the use of massive datasets laid out in arbitrary configurations, thus rendering reliance on a pre-processing phase untenable. This paper offers a method for labeling point-features on dynamic maps in real time without pre-processing. The algorithm presented is efficient, scalable, and exceptionally fast; it can label interactive charts and diagrams at speeds of multiple frames per second on maps with tens of thousands of nodes. To accomplish this, the algorithm employs a novel geometric de-confliction approach, the "trellis strategy, " along with a unique label candidate cost analysis to determine the “least expensive” label configuration. The speed and scalability of this approach make it well-suited for visual