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Paving the Way for Geovisual Analytics

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Hotspots: The density of a specific value is mapped to an entire region, based on the discrete values in the region. A kernel density estimation function is used to compute the density at all points, and they are colored depending on the density values. The hotspots are regions which show how the density decreases as one goes away from the hotspot. The hotspot uses a standard color spectrum, where it is usually rendered in red, which subsequently fades into orange, yellow, and green.
Network visualization: Networks are ubiquitously represented in the form of twoor three-dimensional geometric primitives (e.g., circles, spheres, triangles, etc.) for nodes, and one-dimensional geometric primitives (e.g., lines and curves) for the links between the nodes2. Thus, networks are popularly represented using nodelink diagrams. The variations of the visualizations come from the differences in rendering of the nodes and links, and the graph layout of the network itself. In geospatial data, transport networks (road, rail, airlines, etc.) are represented using node-link diagrams. Map-based node-link diagrams constrain the location of the nodes to be rendered at its latitude-longitude location, as per the cartographic maps.
Isochrones: For transport networks, the visual representation of regions which can be arrived from a given point at the same time, show the reachability, and thus efficiency, of the network. One could thus mark the region reachable in, say dt amount of time, from a starting point. This can grow out to regions reachable in increments of dt time periods. Each of these regions are called an isochrone. Such visualizations can alternatively be constructed using distance as a measure, instead of time. Such visualizations are called iso-distance views.
Symbol map or glyph/icon-based visualization: Symbols, glyphs or icons are diagrammatic representations of associated attribute/property of the (discrete) point at which the glyph is placed. One of the most universally accepted glyph is an arrow for representing a vector or direction. Similarly, in the case of geospatial data, a specific attribute, say property taxes or population, can be represented as a circle, as a glyph, whose radius indicates the relative value. The transfer function used in choropleth map can be used for coloring the glyphs, thus encoding the values using color.
Three-dimensional visualization: Considering maps can be used as textures in computer graphics  applications, one could generate volumetric visualizations. A popular web-based earth observation visualization showing the global weather conditions and ocean surface currents is at http://earth.nullschool.net, samples of which are shown in Figure 3.
Even though a three-dimensional visualization requires extensive computations in comparison to its two-dimensional counterparts, it provides more intuition and dimensionality realism to geospatial data. Apart from the texture-based visualizations, this class also includes visualizations which show extrusions in the third dimension, from a plane with two-dimensional map texture, e.g. three-dimensional modeling of buildings on a flat/planar map of a city.

Fig. 3: Interactive three-dimensional visualization of global weather conditions and ocean surface currents at http://earth.nullschool.net which is updated every three hours using forecasts computed on supercomputers. Note the artistic rendering in the visualization of the map of the global wind directions and magnitude in

(a) Asia, Europe, and Africa;

(b) the Americas;

(c) a close-up of the Indian subcontinent; and

(d) a close-up of the Amazon basin in South America.

 

Fig. 4: Cartograms of population of the county map of California using (left) non-contiguous, (middle) contiguous, or (right) Dorling types. Image courtesy:
http://www.ncgia.ucsb.edu/projects/Cartogram_Central/types.html

[2] Network data is represented using graph data structures. Nodes of a network correspond to vertices of graph, and links between nodes correspond to edges in the graph. We distinguish

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