**4. Different types of displays**

#### **4.1 Standard bubble charts with groups**

The simplest form of bubble charts has a single group of highlighted cases (see Fig. 1). This type of display can easily be generalized to displays in which two or more groups are

Visual Detection of Change Points and Trends Using Animated Bubble Charts 331

the content of chlorophyll-a changed very little. It should also be kept in mind that if different colours are used in the same panel, they may interfere with each other. Spatial patterns in strong colours may conceal patterns in light colours, if the background is

Fig. 2. Four consecutive frames from an animation of salinity and pH data for seawater samples collected in the Eastern Gotland Basin in the Baltic Proper (sampling site BY15) and analysed by the Swedish Meteorological and Hydrological Institute (SMHI) and the Finnish

Size-coding of bubble chart markers is another tool that should be employed with great caution, unless the user actually wants to suppress some data points or the dataset is so small that the markers can be inspected one by one. Furthermore, it is worth noticing that the (average) size of the markers has a strong impact on the perception of a pattern formed by a set of markers . Markers that are too small tend to blur the contours of a cloud of points, and large markers can make it difficult to comprehend the number of points in

Institute of Marine Research (FIMR).

different subsets of data.

white.

assigned different coloured markers. Theoretically, the red-green-blue (RGB) system enables colour coding of up to 224 groups. However, static bubble charts with more than eight colours are difficult to perceive (Gilmore et al., 1989), and animated charts are best perceived if no more than four groups of cases are simultaneously highlighted in the same display.

Fig. 1. Four consecutive frames from an animation of pH against alkalinity of seawater samples from the Eastern Gotland Basin in the Baltic Proper (sampling site BY15). Data source: the Swedish Meteorological and Hydrological Institute (SMHI).

Figure 2 shows how the interdependence between pH and salinity of seawater samples varied over time and between laboratories. In particular, it can be seen that in 1989–1993 the variability of pH for a given salinity was unusually large for one of the laboratories involved, which indicates data quality problems. Moreover, there are single outliers in the data that were collected more recently. Further studies are needed to determine whether these outliers represent flawed data or unusual water samples. It cannot be excluded that mixing of seawater due to strong winds can cause rather abrupt changes in pH.

We have already emphasized that multicoloured bubble charts should be used with caution. This advice is further motivated by Figure 3, in which the upper frames with group-specific coloured markers contain more information than the lower frames with black markers only. Nevertheless, the lower frames show more clearly that there was a level shift in the total volume of phytoplankton between the two time periods, although

assigned different coloured markers. Theoretically, the red-green-blue (RGB) system enables colour coding of up to 224 groups. However, static bubble charts with more than eight colours are difficult to perceive (Gilmore et al., 1989), and animated charts are best perceived if no more than four groups of cases are simultaneously highlighted in the same

Fig. 1. Four consecutive frames from an animation of pH against alkalinity of seawater samples from the Eastern Gotland Basin in the Baltic Proper (sampling site BY15). Data

mixing of seawater due to strong winds can cause rather abrupt changes in pH.

Figure 2 shows how the interdependence between pH and salinity of seawater samples varied over time and between laboratories. In particular, it can be seen that in 1989–1993 the variability of pH for a given salinity was unusually large for one of the laboratories involved, which indicates data quality problems. Moreover, there are single outliers in the data that were collected more recently. Further studies are needed to determine whether these outliers represent flawed data or unusual water samples. It cannot be excluded that

We have already emphasized that multicoloured bubble charts should be used with caution. This advice is further motivated by Figure 3, in which the upper frames with group-specific coloured markers contain more information than the lower frames with black markers only. Nevertheless, the lower frames show more clearly that there was a level shift in the total volume of phytoplankton between the two time periods, although

source: the Swedish Meteorological and Hydrological Institute (SMHI).

display.

the content of chlorophyll-a changed very little. It should also be kept in mind that if different colours are used in the same panel, they may interfere with each other. Spatial patterns in strong colours may conceal patterns in light colours, if the background is white.

Fig. 2. Four consecutive frames from an animation of salinity and pH data for seawater samples collected in the Eastern Gotland Basin in the Baltic Proper (sampling site BY15) and analysed by the Swedish Meteorological and Hydrological Institute (SMHI) and the Finnish Institute of Marine Research (FIMR).

Size-coding of bubble chart markers is another tool that should be employed with great caution, unless the user actually wants to suppress some data points or the dataset is so small that the markers can be inspected one by one. Furthermore, it is worth noticing that the (average) size of the markers has a strong impact on the perception of a pattern formed by a set of markers . Markers that are too small tend to blur the contours of a cloud of points, and large markers can make it difficult to comprehend the number of points in different subsets of data.

Visual Detection of Change Points and Trends Using Animated Bubble Charts 333

Fig. 4. Ordinary time series plot and jittered bubble charts of the difference in daily mean temperatures between the meteorological stations Protivanov and Jevičko in the Czech Republic. A small amount of noise has been added to the month number. Data source: the

When there is pronounced seasonal variation in the collected data, it may be of interest to animate changes in trend slopes by month. This can be achieved by using the month as animation variable and one of the built-in trend line options in Excel®. Figure 5 shows longterm temperature trends in central England, and the four panels draw attention to the fact that the trend slope gradually decreases from March to June. In principle, this pattern could have been revealed by producing a series of static plots. However, this process can be automated by using software for animation. In addition, animation can help to identify between which months of the year that the major changes in trend slopes occur. Such differences in slopes between adjacent months can be further accentuated by standardizing

Czech Hydrometeorological Institute, Brno.

the data so that differences in monthly means are eliminated.

**4.3 Bubble charts with trend lines** 

Fig. 3. Bubble charts of phytoplankton data from three sites in Lake Vänern (D, Dagskärsgrund N; M, Megrundet N; T, Tärnan SSO) and two sites in Lake Vättern (E, Edeskvarnaån NV; J, Jungfrun NV) in Sweden. The coloured markers in the upper panels have been changed to black markers in the lower panels. Data source: the Swedish University of Agricultural Sciences (SLU).

#### **4.2 Jittered bubble charts**

A jittered plot adds some random noise to the x or the y coordinate, or both. Such plots are particularly useful for categorical and ordinal data, because they can give a realistic visual impression of the number of cases in different parts of the chart. In environmental monitoring, jittered plots are particularly useful when the x coordinate represents a class variable such as month or season, or the y coordinate represents a count variable such as the number of species found in the analysed sample.

Figure 4 illustrates a suspected artificial level shift in temperature data from the Czech Republic. The time series plot indicates that the temperature difference between the two investigated meteorological stations increased in 1998. By using a jittered plot to visualize the differences by month, it can be seen that the level shift was present during all seasons and was particularly pronounced during the warmer months.

Fig. 3. Bubble charts of phytoplankton data from three sites in Lake Vänern (D, Dagskärsgrund N; M, Megrundet N; T, Tärnan SSO) and two sites in Lake Vättern (E, Edeskvarnaån NV; J, Jungfrun NV) in Sweden. The coloured markers in the upper panels have been changed to black markers in the lower panels. Data source: the Swedish

A jittered plot adds some random noise to the x or the y coordinate, or both. Such plots are particularly useful for categorical and ordinal data, because they can give a realistic visual impression of the number of cases in different parts of the chart. In environmental monitoring, jittered plots are particularly useful when the x coordinate represents a class variable such as month or season, or the y coordinate represents a count variable such as the

Figure 4 illustrates a suspected artificial level shift in temperature data from the Czech Republic. The time series plot indicates that the temperature difference between the two investigated meteorological stations increased in 1998. By using a jittered plot to visualize the differences by month, it can be seen that the level shift was present during all seasons

University of Agricultural Sciences (SLU).

number of species found in the analysed sample.

and was particularly pronounced during the warmer months.

**4.2 Jittered bubble charts** 

#### **4.3 Bubble charts with trend lines**

When there is pronounced seasonal variation in the collected data, it may be of interest to animate changes in trend slopes by month. This can be achieved by using the month as animation variable and one of the built-in trend line options in Excel®. Figure 5 shows longterm temperature trends in central England, and the four panels draw attention to the fact that the trend slope gradually decreases from March to June. In principle, this pattern could have been revealed by producing a series of static plots. However, this process can be automated by using software for animation. In addition, animation can help to identify between which months of the year that the major changes in trend slopes occur. Such differences in slopes between adjacent months can be further accentuated by standardizing the data so that differences in monthly means are eliminated.

Visual Detection of Change Points and Trends Using Animated Bubble Charts 335

Fig. 6. Relationship between the concentrations of phosphorus and suspended matter in stream water from an agriculture-dominated catchment in southern Sweden. Data source:

When the collected data are multivariate and the coordinates are strongly correlated, important information can be obtained from score charts in the coordinate system determined by the first two principal components. An animation can refine such information by highlighting data points by time or group. As in the gradient plots in the previous section, the advantage of an animated display is that there is no perceptual

Figure 7 shows an animation of regional differences in the chemical composition of olive oil from different regions in Italy. The score charts draw attention to the fact that some groups of objects are more heterogeneous than others. By ordering the regions from south to north, or according to some characteristic of the areas, this type of animations can also highlight

the Swedish University of Agricultural Sciences (SLU), catchment code N33.

**4.5 Score charts for a pair of principal components** 

interference between the different subsets of data.

various gradients in the chemical composition.

Fig. 5. Four consecutive frames from an animation of trends by month for the Central England Temperature series compiled by the Hadley Centre, UK.

#### **4.4 Gradient charts**

In many environmental monitoring programmes, the sampling sites have a natural order. For example, samples from the marine environment are often taken along salinity or depth gradients, air pollutants are measured at different distances from a point source, and river water quality can be measured at different runoff levels. This calls for techniques that can efficiently visualize how relationships between two or more variables change along a gradient.

Figure 6 illustrates in two different manners how the relationship between the concentrations of phosphorus and suspended matter in a small stream varies with the runoff level. It is obvious that, compared to a static chart in which colour- and shapecoded markers are used to indicate runoff levels, an animated display has two advantages. First, there is no perceptual interference between the different subsets of data. Second, the analyst can inspect one highlighted subset while the previous subset is still fresh in memory.

Fig. 5. Four consecutive frames from an animation of trends by month for the Central

In many environmental monitoring programmes, the sampling sites have a natural order. For example, samples from the marine environment are often taken along salinity or depth gradients, air pollutants are measured at different distances from a point source, and river water quality can be measured at different runoff levels. This calls for techniques that can efficiently visualize how relationships between two or more variables change along a

Figure 6 illustrates in two different manners how the relationship between the concentrations of phosphorus and suspended matter in a small stream varies with the runoff level. It is obvious that, compared to a static chart in which colour- and shapecoded markers are used to indicate runoff levels, an animated display has two advantages. First, there is no perceptual interference between the different subsets of data. Second, the analyst can inspect one highlighted subset while the previous subset is still

England Temperature series compiled by the Hadley Centre, UK.

**4.4 Gradient charts** 

gradient.

fresh in memory.

Fig. 6. Relationship between the concentrations of phosphorus and suspended matter in stream water from an agriculture-dominated catchment in southern Sweden. Data source: the Swedish University of Agricultural Sciences (SLU), catchment code N33.
