Vorlesung 7; point & line data

WAS? Wo?

z.B. Windströme, Meeresströme

visual variables:

- size

- color hue

- color value

different lines for different magnitudes

absolute data/ total values

WIE VIEL? Was? Wo?

z.B. Verteilung Güter, Fliessrichtung, Bewegung -> Hauptströme ablesbar

- radial: klares Zentrum, nach aussen

- network

- distributiv: Verteilung über Raum, Abzweiger (eine Quelle spaltet sich auf)

Legende beinhaltet qualititive Farbunterschiede & quantitative Abstufung

- use color value/hue to avoid visual clutter instead of using line size

- increase figure/ground contrast: flow sits visually on top, small flow lines cut larger ones

- choose appropriate projection to support flow patterns

- include arrows if direction of flow is important

- include comprehensive and unambiguous legend

visual variables:

- shape

- color hue

- orientation

- texture

different points for different types of features

Was? Wo?

visual variables:

- size

- color value (hue)

different points for different magnitudes/features

absolute data/ total values

meistens Kreise, aber alle Formen möglich

Was? Wo? Wie?

unclassed (jeder Wert repräsentiert) / unclassed (Werte in Klassen)

- mimetic/ pictorial: characteristics of represented feature/ imitate as small picture/ graphically linked to represented feature

- associative: hints on nature of presented feature

- geometric: no similarity to represented feature, abstract, arbitrary

1. area of symbol proportional to data being mapped (circle: A = Pi * r² -> Stadt A 4x grösser -> Radius nur 2x grösser)

2. range graded scaling (data classed, each class with representative symbol, scaled to midpoint of class)

3. psychological scaling (Wahrnehmung, Unter-/Überschätzung -> Power Law Function (siehe Bild)

Streuungskarten, z.B. Bevölkerung

Fokus auf Dichte

- one-to-one: 1 Punkt = 1 Event

- one-to-many: 1 Punkt = Magnitude

+ einfach

+ zeigen Dichte

+ Zugriff auf Ursprungsdaten

- von Hand sehr mühsam

- mit Computer random