Spider plots

Strauss Plot R. Strauss (Chem. Eng., pp. 112-116, Mar. 25, 1968) developed a sensitivity plot, in Fig. 9-14, in which the ordinate is a measure of profitabihty and the abscissa is the change in a variable greater than (or less than) the value used in the base case. Where the abscissa crosses the ordinate is the result of the base case of NPW, return, annual worth, etc. The slope of a line on this spider plot is the degree of change in profitability resulting from a change in a... 

Among several possible methods for such multi objective decision-making, e.g. spider plots [1] or Principal Component Analysis (PCA) for comparing various Safety, Health and Environmental aspects [2], the present study uses an aggregation approach, i.e. 

Figure 3. Predicted DCS classification of Itraconazole (A) as well as the effect of solubility on fraction absorbed (B). The Spider plot suggest good oral bioavailability at solubility values above 100 pg/mL. The solubility of itraconazole at neutral pH is estimated at 1 ng/mL. Itraconazole can be solubilized in 2-hydroxypropyl-p-cyclodextrin to levels in excess of 10 mg/mL which suggests Class I behavior (C). (See color insert after Index.)...

Fig. 4. Spider-plot display of geochemical data corresponding to typical light and heavy oils that have correlation significance. The data have been multiplied or divided in this display to provide convenient magnitude for visual comparison. The averaged values with abbreviations are reported in Table 2. These results show that despite the difference in physical properties, the oils were sourced from the same source rock.

Fig. 4.59 Radical intensity of draglines prepared from N. clavata spiders plotted against UV-irradiation time for silks from juvenile and mature spiders and out of silkworm.

Figure 2.5 A sensory map or spider plot. The spokes are positioned equally spaced around the center point and represent the scales without anchors. Intensity values are measured from the center point (0) to that place where the line crosses that attribute for that product. Sampling product while observing a plot improves understanding of differences. Relationships among product attributes are best understood by examining the correlation values. (FI refers to flavor Mf refers to mouth feel.)...

Visualizing the multidimensional data sets inherent in lead generation is a challenge and often comes down to personal preference. Old-fashioned spreadsheets are the still the standard for some, while visual depictions like radar or spider plots appeal to others, as shown in Figure 17.10 for a comparison of two compoimds in different series. 

Figure 17.10 An example of a radar or "spider" plot to visualize the differences between compounds in two series across multiple parameters. Only seven parameters are shown, but this approach may be used to...

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