Micro diagrams: visualization of categorical point data from location-based social media

Gröbe, Mathias; Burghardt, Dirk

doi:10.1080/15230406.2020.1733438

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…While the generalization operation of aggregation also reduces the number of points on the map Burigat and Chittaro (2008), it could be used in the same way to represent quantitative data-and therefore also point densitythrough point symbolization (Brewer and Campbell 1998). An approach that tackled the drawback of overlapping VGI points in this way was by arranging micro-diagrams in a regular grid on a map (Gröbe and Burghardt 2020). This approach achieved high diagram readability and a high-level overview of the data spread because of avoided overlaps.…”

Section: Density Visualizationmentioning

confidence: 99%

Visualizing Point Density on Geometry Objects: Application in an Urban Area Using Social Media VGI

Zahtila

Knura

2022

KN J. Cartogr. Geogr. Inf.

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Point datasets that relate to highly populated places, such as ones retrieved from social media or volunteered geographic information in general, can often result in dense point clusters when presented on maps. Therefore, it can be useful to visualize the relevant point density information directly on the urban geometry to tackle the problem of point counting and density range identification in highly cluttered areas. One solution is to relate each point to the nearest geometry object. While this is a straightforward approach, its major drawback is that local point clusters could disappear by assigning them to larger objects, e.g., long roads. To address this issue, we introduce two new point density visualization approaches by which points are related to the underlying geometry objects. In this process, we use grid cells and heatmap contour lines to divide roads, squares, and pedestrian zones into subgeometry units. Comparison of our visualization approaches with conventional density visualization methods shows that our approaches provide a more comprehensive insight into the point distribution over space, i.e., over existing urban geometry.

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Section: Density Visualizationmentioning

confidence: 99%

Visualizing Point Density on Geometry Objects: Application in an Urban Area Using Social Media VGI

Zahtila

Knura

2022

KN J. Cartogr. Geogr. Inf.

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“…When the points are located along a road network, visual marks can be encoded along the streets with bristle maps [KMM * 13]. If no natural regularization is available, it can be enforced with quadtrees [CM17], grids [GB20] or merged areas [ML19]. Finally, Phoenixmap [ZLG * 21] uses concave hulls for each category and encodes density along the outline.…”

Section: Related Workmentioning

confidence: 99%

Visual Parameter Selection for Spatial Blind Source Separation

Piccolotto

Bögl

Muehlmann

et al. 2022

Computer Graphics Forum

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Analysis of spatial multivariate data, i.e., measurements at irregularly‐spaced locations, is a challenging topic in visualization and statistics alike. Such data are inteGral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non‐expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data.

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“…When the points are located along a road network, visual marks can be encoded along the streets with bristle maps [KMM*13]. If no natural regularization is available, it can be enforced with quadtrees [CM17], grids [GB20] or merged areas [ML19]. Finally, Phoenixmap [ZLG*21] uses concave hulls for each category and encodes density along the outline.…”

Section: Visualization Of Geospatial Point Datamentioning

confidence: 99%

Visual Parameter Selection for Spatial Blind Source Separation

Piccolotto,

Bögl,

Muehlmann

et al. 2021

Preprint

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Multivariate measurements at irregularly-spaced points and their analysis are integral to many domains. For example, indicators of valuable minerals are measured for mine prospecting. Dimension reduction (DR) methods, like Principal Component Analysis, are indispensable tools for multivariate data analysis. Their applicability to spatial data is, however, limited. They do not account for Tobler's first law of geography, which states that "near things are more related than distant things." Spatial blind source separation (SBSS) is a data analysis and DR method developed specifically for spatial multivariate point data and hence accounts for such spatial dependence. SBSS requires analysts to set tuning parameters, which are themselves complex spatial objects: A partition of the spatial domain into regions and a point neighbourhood configuration. Their setting is dependent on each other, on domain knowledge of the analyst and data-driven considerations. We present a visual analytics prototype that guides and supports analysts in this process. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non-expert led to remarkable and surprising insights for a domain expert.

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Micro diagrams: visualization of categorical point data from location-based social media

Cited by 10 publications

References 29 publications

Visualizing Point Density on Geometry Objects: Application in an Urban Area Using Social Media VGI

Visualizing Point Density on Geometry Objects: Application in an Urban Area Using Social Media VGI

Visual Parameter Selection for Spatial Blind Source Separation

Visual Parameter Selection for Spatial Blind Source Separation

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