Visualization of Water Services in Africa: Data Applications for Nexus Governance - Governing the Nexus: Water, Soil and Waste Resources Considering Global Change - page 195

c data are
available (e.g. Bonneau et al. 2006 ; Mirkin 2011 ; Dzemyda et al. 2013 ). Kelleher
and Wagener ( 2011 ) summarize in a short communication ten general guidelines
for good practices for scienti

Innumerous publications about visualization techniques for scienti

c data visualization along with references to related
key literature. The ten guidelines are:

Create the simplest graph that conveys the information you want to convey;
2. Consider the type of encoding object and attribute used to create a plot;
3. Focus on visualizing patterns or on visualizing details depending on the pur-
pose of the plot;
4. Select meaningful axis ranges;
5. Data transformations and carefully chosen graph aspect ratios can be used to
emphasize rates of change for time series data;
6. Plot overlapping points in a way that density differences become apparent in
scatter plots;
7. Use lines when connecting sequential data in time series plots;
8. Aggregate larger datasets in meaningful ways;
9. Keep axis ranges as similar as possible to compare variables;
10. Select an appropriate colour scheme based on the type of data.

1. '

'

In order to inform decision-makers best, the selection of visualization method
should be guided by the general workflow (Fig. 1 ) to assure it clearly meets the
purpose of the visualization. The type of visualization should be chosen in a way
that it is able to answer questions of interest. Water-related questions might be:
What happens if a water usage policy will be changed? Which are the water-related
implications on agriculture for future climate change predictions? For instance, if
one wants to answer the latter question, then a map could show a (interactive) time
series of a drought risk index and the expected agricultural yield /productivity in a
colour-coded format.

2.6.1 Geospatial Visualization

If we consider the visualization of water services, data is mostly related to a
geographic position.
Data integration that makes data usable for visualization is not a trial issue. The
problem arises of how to derive continuous maps from point data. Various methods
and approaches exist to upscale point information collected from an actually con-
tinuous feature (e.g. land use) to continuous map visualizations. Those methods
include interpolation, geostatistics (kriging) or the creation of homogeneous map
units based on image classi cations of meso- and large-scale measurement results

(see Sect. 2.3.3 ). The basis is a simpli

cation of reality assuming that single point
information represents a larger scale unit. As an example, water samples are taken
from different locations of a lake and averaged to a mean value that afterwards is
used to represent the water quality of that single lake. In sequence, based on remote
sensing, lakes with similar characteristics are assigned to the same water quality