An efficient aggregation and overlap removal algorithm for circle maps

Beilschmidt, Christian; Mattig, Michael; Fober, Thomas; Seeger, Bernhard

doi:10.1007/s10707-019-00342-5

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…However, for a large number of points, map legibility is often plagued by problems such as con-gestion and occlusion. Some studies introduced methods in the field of map generalization such as selection [27], label optimization [28], aggregation [12], and displacement [13] to increase legibility, but such methods focused on the spatial information of the POI and gave less consideration to the attribute information. There are also studies converting POIs into continuous density surfaces or statistical diagrams [8] to obtain their spatial distribution, but these methods also ignore the individual attribute information of the POI.…”

Section: Visualization Of Point Datamentioning

confidence: 99%

“…While map zooming alleviates these issues, it results in a loss of map context and can also cause users to get lost in zooming [11]. Researchers have designed a variety of methods, such as selection, aggregation [12], displacement [13], density surface fitting [14], pagination design [15], and multi-level structure [16], to achieve legible visualization of POIs, but such methods lead to a very limited number of POIs that are visualized simultaneously on the map, which is not conducive to the user's complete understanding of the surrounding environment. Therefore, it is of great research significance and application value to design a new visualization method that can integrate global distribution and local details and help users quickly and efficiently understand the surrounding POIs from a global perspective.…”

Section: Introductionmentioning

confidence: 99%

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LBS Tag Cloud: A Centralized Tag Cloud for Visualization of Points of Interest in Location-Based Services

Cheng,

Liu,

et al. 2023

IJGI

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Taking location-based service (LBS) as the research scenario and aiming at the limitation of visualizing LBS points of interest (POI) in conventional web maps, this article proposes a visualization method of LBS-POI based on tag cloud, which is called “LBS tag cloud”. In this method, the user location is taken as the layout center, and the name of the POI is converted into a text tag and then placed around the center. The tags’ size, color, and placement location are calculated based on other attributes of the POI. The calculation of placement location is at the core of the LBS tag cloud. Firstly, the tag’s initial placement position and layout priority are calculated based on polar coordinates, and the tags are placed in the initial placement position in the order of layout priority. Then, based on the force-directed model, a repulsive force is applied to the tag from the layout center to make it move to a position without overlapping with other tags. During the move, the quadtree partition of the text glyph is used to optimize the detection of overlaps between tags. Taking scenic spots as an example, the experimental results show that the LBS tag cloud can present the attributes and distribution of POIs completely and intuitively and can effectively represent the relationship between the POIs and user location, which is a new visualization form suitable for spatial cognition.

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Section: Visualization Of Point Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LBS Tag Cloud: A Centralized Tag Cloud for Visualization of Points of Interest in Location-Based Services

Cheng,

Liu,

et al. 2023

IJGI

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“…Mapping supports heterogeneous spatio-temporal data and offers composable operators for transformation and analysis tasks. For instance, it supports geographical projections, spatio-temporal joins of different data types and aggregation mechanisms [7]. For performance reasons, operators are designed to use parallel processing on manycore systems and GPUs [4].…”

Section: General Architecturementioning

confidence: 99%

VAT to the Future: Extrapolating Visual Complex Event Processing

Beilschmidt¹,

Drönner²,

Glombiewski³

et al.

EPiC Series in Computing

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A key technology to analyze high volume spatio-temporal data streams is complex event processing (CEP). CEP is unique in its ability to not only continuously process data as it arrives through common operations such as aggregations, but also to support pattern matching queries. Pattern Matching allows to detect a user-defined sequence of temporal predicates on event streams. The high volume flight data as provided by the OpenSky Network has a lot of characteristics that make it a perfect match for CEP. In particular, pattern matching operators can be utilized to detect a plethora of movement (landing, starting, evasion) and group patterns (airplanes closing in on each other) in a timely manner. However, CEP queries can be complex in nature and may require a combination of domain expertise and historical data analysis in order to deliver the desired results. In order to address these issues, we have combined a database-backed CEP system (ChronicleDB) with a scientific toolbox for interactive data exploration and geo visualization (Vat System). This allows users to interactively execute CEP queries and visually confirm the validity of their results, thus, simplifying the parameter tuning considerably.In addition, our solution supports efficient and interactive time travel queries. It allows to combine event streams with additional data sources (e.g., remote sensing images) and processing technologies (e.g., machine learning models) to extract higher level knowledge. Finally, our ongoing work on visual analytics explores extrapolating query results to provide more timely feedback for critical situations and multi-query optimization techniques to allow for an even more efficient system in general.

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Dual Space Coupling Model Guided Overlap-Free Scatterplot

Shi

Liu

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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An efficient aggregation and overlap removal algorithm for circle maps

Cited by 4 publications

References 20 publications

LBS Tag Cloud: A Centralized Tag Cloud for Visualization of Points of Interest in Location-Based Services

LBS Tag Cloud: A Centralized Tag Cloud for Visualization of Points of Interest in Location-Based Services

VAT to the Future: Extrapolating Visual Complex Event Processing

Dual Space Coupling Model Guided Overlap-Free Scatterplot

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