Monthly Archives: May 2013

Changes over time

Weekly phenological changes in a plant community

Weekly phenological changes in a plant community

Changes over time in environmental systems can be visualized. Depending on parameter settings EVO routines allow visualization of different system developments (scenarios) that are calculated on the fly.

Comparing compartments

Time (right compartment): 6:00, 8:00 and 12:00

Time (right compartment): 6:00, 8:00 and 12:00

With EVOs it is possible to compartmentalize environmental systems and vary system parameters independently of each other in the respective compartments for efficient side-by-side comparisons in visualizations. In this exmple a forest island is split into two compartments and sun height is varied (rising sun) in one compartment only. Specific scene arrangements and EVO routines have been developed for such compartmentalized visualizations.

Complex functional dependencies

ShopStrip_ComplexDepDevelopmental or plastic changes of environmental components may depend on complex functional dependencies. For believable visualization of such processes regression models have been employed together with Python control structures. For this case of a soybean plant EVO routines have been developed to visualize the plastic growth reponse to different light levels.

Compositing different environmental layers

Compositing clouds over wind flow makes it easy to hide or show internal processses.

Compositing clouds over wind flow makes it easy to hide or show internal processses.

Compositing is an efficient methodology for visualizing different layers of complexity in dynamic envrionmental scenes without the need to manipulate the total scenes before the next animation is produced. It works by separately producing animation channels for environmental processes like wind flow, cloud development, background dynamics like light/shade patterns cast by clouds etc. that all work together in the same system. By compositing these processes can easily be merged in any combination later on and one can even adjust effects like colour corrections or transparency for particular processses only or relative to the behaviour of the other processes.

Context-dependent visualization

ShopStrip_PosDepParticlesThere is a variety of ways objects or other phenomena in an environmental system could be visualized in a context-dependent manner. In this example particles (stylized plants) are differently coloured depending on local density: Plants that are surrounded by many neighbours are tinted red, whereas those that grow in low density are tinted green. EVO routines have been developed that combine kernel (image-analysis) functions with querying particle properties spatially explicitely to provide context-dependent colour patterns.

Cross sections

Shadow sampling (left) and temperature sampling (right) using a cross section through a tree crown. Tree crown invisible in the right image of each pair.

Shadow sampling (left) and temperature sampling (right) using a cross section through a tree crown. Tree crown invisible in the right image of each pair.

One of the great advantages of 3D visualization is the flexible handling of cross sections. Cross section visualization can be made dynamic, i.e. cross sections can be moved to desired positions within an environmental system with real-time updates of the cross section image. Furthermore, context objects around the section can be switched on and off for close inspection of local influences. This way, cross sections can be used as factor sampling planes. EVO routines have been developed for moving cross sections along different axes and for switching between sampling of different factors.

Data export (*.csv)

ShopStrip_ExportBasically there is no limit in terms of data that can be obtained from the visualized systems. The data may be based on actual ‚virtual measurements’, on calculations or may just be ‚designed data’ matching the virtual system. EVO routines have been developed to structure such data in tabular form and export the formatted data to a *.csv (comma separated value) file. Such files can be read and processed further by Excel or stats packages.

Diagram visualization: Dynamic diagrams

Dynamic HIstograms

Dynamic HIstograms

Changing conditions over time reflected in dynamic relationship (prototype!)

Changing conditions over time reflected in dynamic relationship (prototype!)

Alongside the environmental visualizations diagrams can be shown that present arbitrary data (e.g. histograms or curvilinear relationships). EVO routines allow connecting the diagrams with changing data of the visualization so that the diagram updates dynamically upon parameter changes.

Diagram visualization: Imported diagrams

ShopStrip_DiagImportedScientific diagrams that show relationships underlying the processes visualized in an EVO can be imported as images and can be shown alongside the environmental system. EVO routines have been developed that show dynamic markers within such diagrams to reveal the current position on the x-axis (value of the independent variable) or other cardinal points that change during the processes visualized.

Direct local manipulation

Wheat field with nutrient-poor patch and its development after local fertilizer addition

Wheat field with nutrient-poor patch and its development after local fertilizer addition

There are possibilities to directly manipulate a visualized environmental component at a particular point in time in system development and at specific locations. For that one can interact with the component using the mouse cursor. The environmental system can respond to such manipulations during further development.

In the case depicted here EVO routines have been developed that allow to switch to a special manipulation view of the system where a ‘mouse brush’ can be used to add (paint) environmental factor strength (here fertilizer) to an environmental component (here soil). Custom user interface elements allow changing the size and stroke intensity of the ‘factor brush’.

Experimental Design

Blocked vs. randomized block design affected by a light gradient

Blocked vs. randomized block design affected by a light gradient

EVO routines have been developed to visualize several standard experimental designs with different numbers of replications and including randomization procedures. Via these routines the visualized designs can be scaled relative to environmental gradients (confounding factors) with repercussions on the experimental outcome.

Factor gradients

Visualization of shade influence (left) and of a soil texture gradient (right)

Visualization of shade influence (left) and of a soil texture gradient (right)

Visualization of simple to complex factor gradients in three dimensional environmental systems is among the most useful and versatile capabilites of EVOs, even more so as spatio-temporal dynamics of such gradients can be visualized.

EVO routines have been developed for parameterizing and visualizing various dynamic environmental gradients such a light, water, nutrients and soil texture.

Head Up Display (HUD) of scene data

The HUD information display is independent of the view angle onto the scene.

The HUD information display is independent of the view angle onto the scene.

Any kind of data (e.g. state characterization, calculated data or scene «measurements») can be superimposed over the environmental visualization. The HUD information can dynamically adjust to changes in the visualization model. EVO routines have been developed for extracting data and showing them in form of a formatted HUD including text colouration.

Heterogeneity and scale

ShopStrip_HeterogeneityHeterogeneity and scale are two important aspects of environmental patterns. Both can be represented in EVOs, e.g. by using procedural textures. This way, smooth continual spatial gradients can be visualized that my change gradually over time. Environmental components that respond sensitively to factor strengths represented by these patterns can be visualized. For example, EVO routines have been developed to manipulate the heterogeneity and scale of nutrient availability in soil which affect the visualized crop yield accordingly.