The basic belief that ‘seeing’ is a good way of understanding and generating knowledge.
Humans have a very well developed sense of sight
More than 50% of a human brain’s neurons are used in vision.
Michael Zeitlin (1992) of Texaco: Michael Zeitlin (1992) of Texaco:
Geovisualization integrates approaches from visu- alization in scientific computing (ViSC), cartography, image analysis, informa- tion visualization, exploratory data analysis (EDA), and geographic information systems (GISystems) to provide theory, methods and tools for visual exploration, analysis, synthesis, and presentation of geospatial data
2001 research agenda of the International Cartographic Association (ICA) Commission on Visualization and Virtual Environments
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four visualization goals exploration, analysis, synthesis, and presentation
Subtopic 1
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Driving forces: graphics and display technologies, data & internet
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Nöllmann Cognitive
Visualization should not primarily focus on generating images but on using images to generate new ideas [194]. This also means that elaborate and highly realistic images are not necessarily required to generate valid hypotheses. Instead it is often abstraction, in the past achieved with pencil and paper, that helps to distinguish pattern from noise and thus makes a map or some other graphic useful
Essentially, hu- man vision produces abstractions from the complex input on the retina and these abstractions are matched to the mind’s vast collection of patterns (or schemata) from experience.
Abstraction
“...it was impossible to look at all the (geological) data at once
visualization lecture pdf
before the advent of high speed parallel processing, 3D graphics and ... visualization. Today we can grasp enormous amounts of information...In other words we see – and use – more of our data and process it with visual pattern recognition as the basis of our interpretation.”
Quoted in M. Wood. 1994. “The Traditional Map.” in Visualization in Geographic Information Systems, Edited by Hilary Hearnshaw & David Unwin. John Wiley & Sons.
Visualizing large data sets result in maps and other displays that are cluttered with overlapping items and small symbols such that properties of data items are hardly visible. Zooming is no remedy to these problems: it does help to avoid information overload and magnifies small symbols but at the cost of losing the overall view of the data which is just as important.
A Primer of GIS
maps filter out unnecessary details of the environment in order to highlight interesting infor- mation
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For example, a road map based on satellite images would be extremely
hard to use
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The challenge is to study the relative advantages and disadvantages of realism and abstraction in geovisualization and then, depending on the prob- lem context, potentially integrate both abstract and realistic displays in a single geovisualization environment.
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The second driving force for geovisualization is the need to analyze and ex- plore a dramatically increasing amount of geospatial data that are routinely collected these days by a multitude of scientific and governmental institutions, private companies, and individuals.
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Clutter reduction
/ visual clustering
Ellis and Dix’s [7] distinguish three main types of clutter reduction techniques: appearance (alter the look of the data items), spatial distortion (displace the data items in some ways) and temporal (animation).
Vizualizing Large Spatial Datasets in Interactive Maps.pdf
Clutter not only reduces the background visibility, but also hinders the users understanding of the structure and content of the data.
Vizualizing Large Spatial Datasets in Interactive Maps.pdf
Changes
In general, there has been a shift away from technology-driven visualization towards more human-centered approaches that base on usability engineering principles and apply theoretical results from cognitive research as demanded by Slocum et al.
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Future
The map has evolved from its traditional role as a presentational device to an interactive and highly dynamic interface to access and explore geospatial data.
On the other hand, portable devices like PDAs or mobile phones will provide location-based services, e.g., route finding in foreign places which build on some sort of map display. This means additional challenges for application developers in terms of e±cient memory and bandwidth use as well as in terms of visualization design for multi-platform, small-size displays.
Aspects
Nöllmann Graphic
On a map, information is usually represented by symbols, points, lines, and areas with diÆerent properties such as color, shape, etc. Bertin’s concept of fun- damental graphic variables for map and graphic design and rules for their use, published as “S ́emiologie graphique” [83] in 1967, has proposed a basic typology for map design. This work was based on his experience as geographer and cartog- rapher. For a discussion of Bertin’s graphic variables see also Section 4.1 in this book. Since then his original set of variables has been modified and extended, see MacEachren [521, 522]
Andrienko and Andrienko [21] described a selection of methods to rep- resent single and multiple attributes in a map. Depending on the type of the attributes (logical, numeric, or nominal), they used bar and pie diagrams com- mon in statistic visualization. Similarly, glyph-based techniques from visual data mining can also be combined with map displays
Combining Visual and Computational Exploration
The example of Guo et al. [315] shows that the integration of a computational data-mining technique into a geovisualization system allows the exploration of large geospatial data sets. The visual information load is reduced by automati- cally clustering the data and only displaying summary information while details are still available on demand. Users can explore the data and generate hypotheses by interacting with the system and by bringing in their expertise.
Originally, Bertin’s variables have been designed to describe information visu- alization on paper maps. Today, advances in graphics display technology provide a set of new graphic variables that can be utilized in geovisualization. Trans- parency and crispness are regarded as static graphic variables, the latter for ex- ample is suitable to represent uncertainty of some classification on a map [522]. However, geovisualization goes beyond static maps and therefore sets of tactile, dynamic, and sonic variables have been proposed, e.g., loudness, pitch, dura- tion, temporal position, rate-of-change, etc. Most of these variables are analogs of graphic variables in another dimension, e.g., duration corresponds to size and temporal position to spatial location. However, both dynamic and sonic variables need to be observed over time and thus require more user attention than static representations.
Visual variables
(Bertin)
Position
Size
Shape
Value
Orientation
Color
Color
Color is used to drive user’s attention on denser clusters. The color assigned to a polygon is determined using a hot-to- cold color ramp where hot colors are assigned to dense clusters and cold colors to sparse ones [18].
Vizualizing Large Spatial Datasets in Interactive Maps.pdf
According to cartographic research, between three and seven classes is ideal.
Size should be used only to show ordinal distinctions.
Symbolization should show enough reference to relevant and meaningful things and events without unnecessary exaggeration and conflict to other symbols. The whole “map” should balance cartographic representation needs with the underlying geographic representation.
Cartographic communication, in the most general sense, relies on dis- tortions. As Mark Monmonier (1991) writes:
A good map tells a multitude of little white lies; it suppresses truth to help the user see what needs to be seen. But the value of a map depends on how well its generalized geometry and generalized content reflect a chosen aspect of reality. (p. 25)
Symbolization
How do symbols exaggerate or minimize features on the map? How does the cartographic communication benefit from the chosen symbols? What is the best measurement framework? Nominal, ordinal, interval, ratio?
Generalization
How have irrelevant details for the map’s purpose been filtered out? How have details relevant to a
map’s purpose been emphasized? How have lines, points, areas, and content been handled?
Conventions (icons, colors, ...)
traffic light, road signs, ...
Cluster quality Measures
Purity
Entropy
NMI Normlized Mutual Information
Continous vs discrete data: Heatmap vs Cluster icons
Process
vis
Classes
classes
the data to be visualized
the visualization technique
the interaction technique used
Visual Data Mining of Large Spatial Data Sets.pdf
clutter reduction taxonomy
Ellis_&_Dix_clutter_reduction_taxonomy_5.3.pdf
Classification of Visual Data Mining Techniques
(Visual Data Mining of Large Spatial Data Sets.pdf)
The data type to be visualized [35] may be one-dimensional data, such as temporal (time-series) data, two-dimensional data, such as geographical maps, multidimensional data, such as relational tables, text and hypertext, such as news articles and web documents, hierarchies and graphs, such as telephone calls, and algorithms and software.
The visualization technique used may be classified as: Standard 2D/3D dis- plays, such as bar charts and x-y plots, Geometrically transformed displays, such as hyperbolic plane [40] and parallel coordinates [15], Icon-based displays, such as cherno↵ faces [6] and stick figures [29] [30], Dense pixel displays, such as the recursive pattern [2] and circle segments [3], and Stacked displays, such as treemaps [16] [34] and dimensional stacking [41]
The third dimension of the clas- sification is the interaction technique used. Interaction techniques allow users to directly navigate and modify the visualizations, as well as select subsets of the data for further operations. Examples include: Dynamic Projection, Interactive Filtering, Interactive Zooming, Interactive Distortion, Interactive Linking and Brushing. Note that the three dimensions of our classification - data type to be visualized, visualization technique, and interaction technique - can be assumed to be orthogonal. Orthogonality means that any of the visualization techniques may be used in conjunction with any of the interaction techniques for any data type. Note also that a specific system may be designed to support di↵erent data types and that it may use a combination of visualization and interaction tech- niques. More details can be found in [20].
Visual Data Mining Tools
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for multivariate data were broadly classified as geometric, glyph- or icon-based, pixel-oriented, and hierarchical by Schroeder [745] and Keim and Kriegel [438]. In a geovisualization context, geometric and glyph-based techniques are most common. Graph-drawing techniques that depict relationships between individ- ual data items are also covered in this section.
Glyph-Based Techniques Glyph-based or icon-based techniques use a map- ping of multiple attribute values to a set of diÆerent visual features of a glyph which in turn represents one data object. Two examples of such techniques are ChernoÆ faces [154] and star plots [253].
Multidimensional Data Visualization
Kebing Thesis
Icon-based, Pixel-oriented, Geometric
Icon-based
Chernoff Faces
visual mappings.pdf
Stick Figures
Icons
One of the main drawbacks of using icons as aggregation symbols is that they do not show the area covered by the clusters.
Vizualizing Large Spatial Datasets in Interactive Maps.pdf
Many other icon-based systems have also been proposed, such as Shape-Coding [Bed90], Color Icons [Lev91, KeK94], and TileBars [Hea95]. Icon-based techniques can display multidimensional properties of data, however, with the amount of data increasing, the user hardly makes any sense of most properties of data intuitively, this is because the user cannot focus on the details of each icon when the data scale is very large.
kebing
[Bed90] J. Beddow, ‘Shape Coding of Multidimensional Data on a Mircocomputer Display”, Proceedings of Visualization ‘90, San Francisco, CA, 1990, pp. 238-246.
[Lev91] H. Levkowitz, “Color icons: Merging color and texture perception for integrated visualization of multiple parameters”, Proceedings of the 2nd conference on Visualization '91, San Diego, CA, pp. 164-170 (1991)
[Hea95] M. Hearst, “TileBars: Visualization of Term Distribution Information in Full Text
Information Access”, Proceedings of ACM Human Factors in Computing Systems
Conference, (CHI'95), pp.59-66 (1995)
Pixel-oriented
Color-based (variation of brightness, maxum variation of hue (color), constant maximum saturation
Recursive Pattern Technique
[KKA95] D.A. Keim, H.-P. Kriegel, M. Ankerst, “Recursive Pattern: A Technique for
Visualizing Very Large Amounts of Data”, Proceedings of Visualization ‘95, Atlanta, GA,
pp. 279-286 (1995)
Circle Segments Technique
[AKK96] M. Ankerst, D. A. Keim, H.-P. Kriegel, “Circle Segments: A Technique for
Visually Exploring Large Multidimensional Data Sets”, Proceedings of Visualization ‘96, Hot
Topic Session, San Francisco, CA, 1996.
Spiral
Axes
Keim D. A., Kriegel H.-P.: ‘VisDB: Database Exploration using Multidimensional Visualization’, Computer Graphics & Applications, Sept. 1994, pp. 40-49.
Heatmap
Clustering and Visualizing Geographic Data Using Geo-tree
http://en.wikipedia.org/wiki/Heat_map
Heatmap
However, a problem with this approach is that users interactions with the data items are harder to deal with, as they need to be handled at the pixel level.
Vizualizing Large Spatial Datasets in Interactive Maps.pdf
Vizualizing Large Spatial Datasets in Interactive Maps.pdf
if points are concentrated in small visible areas, polygons will cover a significantly wider area than the area of their points. In that case, using bounding boxes or hulls, as cluster footprints could be more effective.
can effectively be used with datasets of up to 1000 items.
– Flat Tree Viewer, 2D Matrix or Heat Map, Hyperbolic Lens
Viewer, Table Viewer
Choosing one (Heatmap against others)
After clustering geographic data, visualizing these clusters is another research issue. Reference [4] proposes a visualization method using voronoi polygons. However, its cost in time is too high to be applied in an interactive system (e.g. more than 2000 seconds for rendering 5000 points). Other solutions such as icons [2], cells [7], bounding boxes, or convex hull [3] also appear. But the drawback of these solutions is that users cannot see the data’s distribution within a cluster. On the contrary, paper [8, 9] focuses on visualizing geographic tendency without considers the latent clusters of data.
hierarchical aggregation.
Hierarchical aggregation is a common visualization technique to make visual representations more visually scalable and less visu- ally cluttered [1]. In particular, hierarchical aggregation tech- niques have been proposed for exploring spatial data sets [2], [3].
Vizualizing Large Spatial Datasets in Interactive Maps.pdf
Vizualizing Large Spatial Datasets in Interactive Maps.pdf
if points are concentrated in small visible areas, polygons will cover a significantly wider area than the area of their points. In that case, using bounding boxes or hulls, as cluster footprints could be more effective.
can effectively be used with datasets of up to 1000 items.
3d
3-dim
Kartograph
http://kartograph.org/showcase/3d/
compare
d3
animated wind chart
http://prcweb.co.uk/lab/ukwind/
Information landscapes
ex
Visualizing Geospatial data pdf
Interaction
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Interaction is paramount in geovisualization, especially for visual exploration, recall the map-use cube in Figure 6.1. The communication aspect of geovisu- alization is also shifting towards higher levels of interaction. Dykes introduced guided discovery as a communication task [220]. For example, consider a student who is learning by interactively (re-)discovering known relationships in a data set.
according to MacEachren [522] interaction is a key factor distinguishing geovisualization from traditional cartography.
“is an active process in which an individual engages in sorting, highlighting, filtering, and otherwise transforming data in a search for patterns and relationships” [522]
Buja et al. [126] introduced a taxonomy for general interactive multivari- ate data visualization. In the following, interactive visualization techniques are grouped using this taxonomy. Their two main classes are focusing individual views and linking multiple views. For a detailed description of interaction meth- ods see Chapter 3 of this book.
to operating a camera: choosing a perspective, deciding about magnification and detail, etc.
Two- and three-dimensional maps (see Section 6.4.2 and 6.4.3) usually come along with a set of navigational controls to move within the map space either by scrolling, shifting, or rotating a map or by walking or flying through a virtual 3D environment.
the way that the actual data items are displayed.
Deselected attributes as well as the actual numeric values of selected attributes should be accessible, e.g., as tool tip information when moving the mouse over a symbol or area on the map.
Linking and Brushing The full potential of interaction in geovisualization lies in linking multiple views
DiÆerent views should be linked in a geovisualization system. Highlighting a point cluster in a scatter plot or a cluster of PCP profiles thus shows the spatial pattern of the corresponding objects in the associated map view.
Primer of GIS
Map controls etc provided by mapping library
Icons, Tooltips, Popups
Icons HD-Eye Visual mining.pdf
In addition to the visualization technique, effective data exploration requires using some interaction and distortion techniques. The interaction techniques let the user directly interact with the visualization.
Examples of interaction techniques include interactive mapping, projection,11 filtering,12 zooming,13 and interactive linking and brushing.14
Interaction techniques allow dynamic changes of the visualizations according to the exploration objectives, but they also make it possible to relate and
combine multiple independent visualizations. Note that connecting multiple visualizations by linking and brushing, for example, provides more information than considering the component visualizations independently.
Summary
Pixel
An alternative approach for visualizing large data sets is to create a bitmap representing the data, and to superpose it on the map as a layer of translucent tiles [14]. However, a problem with this approach is that user’s interactions with the data items are harder to deal with, as they need to be handled at the pixel level.
UTFGrid: Scalable interaction
http://mapbox.com/developers/utfgrid/
Important: no overlap
Summary
Hierarchical Cluster Visualization in Web Mapping Systems.pdf
Animation
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Animation
nimation can also be used to display spatial features, e.g., animations of flights over the terrain. In other the temporal dimension is used to display quantitative attributes by map- ping their values to the blinking frequency of symbols or to highlight classes in a choropleth map by blinking
Bertin’s notion of visual variables [83], as introduced in Section 6.3.2, has been extended to dynamic animated displays, see MacEachren [522] and Chap- ter 6 of MacEachren [521]. Six dynamic variables are suggested: (1) temporal position, i.e., when something is displayed, (2) duration, i.e., how long something is displayed, (3) order, i.e., the temporal sequence of events, (4) rate of change, e.g., the magnitude of change per time unit, (5) frequency, i.e., the speed of the animation, and (6) synchronization, e.g., the temporal correspondence of two events.
OpenLayers Animated Cluster
http://acuriousanimal.com/code/animatedCluster/
Leaflet
Usability
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Usability
Currently, this scope is shifting away from innovators and early adopters towards a broader audience of prag- matic and conservative users [274]. Hence, it becomes increasingly important to provide geovisualization systems that are useful and usable for their target users. As Slocum et al. [776] put it, “the most sophisticated technology will be of little use if people cannot utilize it eÆectively”.
in geovisualization well-defined tasks are hard to identify when it comes to data exploration and knowledge discovery.
Scientific visualization methods such as scatter plots or PCPs are hard to understand for the average user. Simple interfaces and displays are paramount to keep the required training as low as possible.
Usability
Usability in the HCI community refers to the eÆectiveness, e±ciency, and sat- isfaction with which specified users can achieve specified goals in a software system.