A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization

Xiao, Yi; Ai, Tinghua; Yang, Min; Zhang, Xiang

doi:10.3390/ijgi9040239

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…There are extensive studies focusing on the topic of multi-scale characteristics analysis in geography [22,23], which have explored in detail about the multi-scale characteristics of spatial-temporal objects [24][25][26]. The trajectory, as the representation of the spatial-temporal characteristics of physical objects, exhibits multi-scale characteristics.…”

Section: Multi-scale Modeling Of Trajectorymentioning

confidence: 99%

Semantic Modeling of Ship Behavior in Cognitive Space

Song

Wen

Tao

et al. 2022

JMSE

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Ship behavior is the semantic expression of corresponding trajectory in spatial-temporal space. The intelligent identification of ship behavior is critical for safety supervision in the waterborne transport. In particular, the complicated behavior reflects the long-term intentions of a ship, but it is challenging to recognize it automatically for computers without a proper understanding. For this purpose, this study provides a method to model the behavior for computers from the perspective of knowledge modeling that is explainable. Based on our previous work, a semantic model for ship behavior representation is given considering the multi-scale features of ship behavior in cognitive space. Firstly, the multi-scale features of ship behavior are analyzed in spatial-temporal dimension and semantic dimension individually. Then, a method for multi-scale behaviors modeling from the perspective of semantics is determined, which divides the behavior scale into four sub-scales in cognitive space, considering spatial and temporal dimensions: action, activity, process, and event. Furthermore, an ontology model is introduced to construct the multi-scale semantic model for ship behavior, where behaviors with different semantic scales are expressed using the functions of ontology from a microscopic perspective to a macroscopic perspective consecutively. To validate the model, a case study is conducted in which ship behavior with different scales occurred in port water areas. Typical behaviors, which include leveraging the axioms expression and semantic web rule language (SWRL) of the ontology, are then deduced using a reasoner, such as Pellet. The results show that the model is reasonable and feasible to represent multi-scale ship behavior in various scenarios and provides the potential to construct a smart supervision network for maritime authorities.

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Section: Multi-scale Modeling Of Trajectorymentioning

confidence: 99%

Semantic Modeling of Ship Behavior in Cognitive Space

Song

Wen

Tao

et al. 2022

JMSE

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“…Whether it is the integration of real-time road conditions [1], street view data [2], 3D models [3], or mobile cartography [4], each period of web map rendering technology has invariably facilitated the ubiquity of web maps [5][6][7]. Meanwhile, the tile pyramid technique has been increasingly adopted for various web maps for its advantages of storage and fast transmission speed [8][9][10]. The tile map is effectively represented by several small images of the same size (usually 256 × 256 pixels) seamlessly stitched following defined rules [11].…”

Section: Introductionmentioning

confidence: 99%

Inconsistency Detection in Cross-Layer Tile Maps with Super-Pixel Segmentation

et al. 2023

IJGI

Self Cite

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The consistency of geospatial data is of great significance for the application and updating of geographic information in web maps. Due to the multiple data sources and different temporal versions, the tile web maps usually meet the inconsistency question across different layers. This study tries to develop a method to detect this kind of inconsistency utilizing a raster-based scaling approach. Compared with vector-based handling, this method can be directly available for multi-level tile images in a pixel representation form. The proposed cross-layer raster tile map rendering method (CRTMRM) consists of four primary aspects: geographic object separation, consistency rendering rules, data scaling and derivation with super-pixel segmentation, and inconsistency detection. The scale transformation strategy with the super-pixel attempts to obtain a simplified representation. Taking the scale lifespan variation and geometric consistency rules into account, the inconsistency detection of tile maps is conducted between temporal versions, multi-sources, and different scales through actual and derived data overlay analysis. The experiment focuses on features of cross-layer water or vegetation areas with Level 9 to Level 14 in Baidu Maps, Amap, and Google Maps. This method is able to serve as a basis for massive unstructured web map data inconsistency detection and support intelligent web map rendering.

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“…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%

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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A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization

Cited by 7 publications

References 38 publications

Semantic Modeling of Ship Behavior in Cognitive Space

Semantic Modeling of Ship Behavior in Cognitive Space

Inconsistency Detection in Cross-Layer Tile Maps with Super-Pixel Segmentation

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

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