Environmental Engineering Reference
In-Depth Information
interpretation of scenarios (Lin et al. 2013 ). Scenario modelling is helpful for
decision-makers, researchers and the public in order to understand consequences
coming from global change processes (e.g. impact of land use or water management
changes). A user-friendly web-based simulation and visualization tool is usable for
education purposes, because the public can explore the interconnection between the
past and future as well as their influential factors (e.g. water availability and water
usage for agriculture). In addition, experiencing this vivid geographic virtual reality
is easier than attempting to understand the numerous tables and graphs that aim to
explain the same issue (Lin et al. 2013 ).
2.6.2 Non-geospatial Visualization
Non-spatially related data visualization is used to provide insights into data rela-
tionships, making them accessible for decision-makers or the public. This is often
statistical information about number of people with access to water in the form of
histograms. Feedback relationships might be shown based on a scatter plot between
the variables, such as number of industrial plants and water quality in a speci
c region
or soil salinization in relation to irrigation and time of the year. Being aware of such
statistical relationships (backed up by further evidence for causal relationships),
decision-makers are able to act and to adjust support and policy measures. Looking at
the irrigation example, decision-makers can give recommendations to the farmers
about adjustments to their irrigation type in order to minimize soil salinization.
Traditional visualization methods comprise tables, graphs, histograms, treemaps,
voronoi maps, symbol maps, bar charts, dendrograms, contour plots, parallel
coordinates, boxplots, scatterplots, colour-encoded maps (choropleth maps), cor-
relation matrix and calendar charts. Correlation matrix plots as shown in Fig. 8 a are
useful to illustrate the relationships between variables and therefore are a way of
illustrating potential feedback systems. A straight line represents a high correlation
known for conductivity and water salinity. That mathematical relationship can be
used to predict water quality guiding decision-makers in water quality management
related issues (Kumar and Sinha 2010 ). Two other examples of time series repre-
sentations are shown in Fig. 8 b and c. A streamgraph is a type of stacked graph that
shows the time-dependent development of certain variables based on their relative
area change (Fig. 8 b). The area size of each colour-coded stack represents the
magnitude of the variables, such as the development of conductivity, pH or other
measures of water quality. The calendar chart, compared to the streamgraph, has the
advantage that colour coding is used to highlight days of special events (Fig. 8 c).
This makes it easier to see patterns quickly that reveal and relate, for instance, the
history of drought occurrence to wild
fires. This knowledge might be helpful in
drought and
fire prediction. Several open source tools for data mining, analysis and
visualization [e.g. Orange ( http://orange.biolab.si ), R ( www.r-project.org ) , StatNet
( http://statnet.org/ ) , PySal ( https://geodacenter.asu.edu/ )] are available, typically
with an active user community providing support. Being open source, these tools
should be particularly useful for developing countries.
Search WWH ::

Custom Search