Vorlesung 7; point & line data
Universität Zürich GEO123, Frühlingssemester 2019 Tumasch Reichenbacher
Universität Zürich GEO123, Frühlingssemester 2019 Tumasch Reichenbacher
Set of flashcards Details
| Flashcards | 13 |
|---|---|
| Language | English |
| Category | Geography |
| Level | University |
| Created / Updated | 25.08.2019 / 25.08.2019 |
| Weblink |
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lines, qualitativ
visual variables:
- size (Autobahn vs. Quartierstrasse)
- color hue (z.B. aktuelle Verkehrslage, rot - grün)
- texture (Kantonsgrenze vs. Gemeindegrenze)
different lines for different types of roads
qualitative flow maps
WAS? Wo?
z.B. Windströme, Meeresströme
lines, quantitative
visual variables:
- size
- color hue
- color value
different lines for different magnitudes
absolute data/ total values
quantitative flow maps
WIE VIEL? Was? Wo?
z.B. Verteilung Güter, Fliessrichtung, Bewegung -> Hauptströme ablesbar
Flow Map Types
- radial: klares Zentrum, nach aussen
- network
- distributiv: Verteilung über Raum, Abzweiger (eine Quelle spaltet sich auf)
Legende beinhaltet qualititive Farbunterschiede & quantitative Abstufung
Flow maps, design issues
- 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
points, qualitative
visual variables:
- shape
- color hue
- orientation
- texture
different points for different types of features
Was? Wo?
points, quantitative
visual variables:
- size
- color value (hue)
different points for different magnitudes/features
absolute data/ total values
proportional point symbol map = graduated symbol map = Diagrammkarte
meistens Kreise, aber alle Formen möglich
Was? Wo? Wie?
unclassed (jeder Wert repräsentiert) / unclassed (Werte in Klassen)
point symbol types
- 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
point symbol scaling
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)
Dot density map
Streuungskarten, z.B. Bevölkerung
Fokus auf Dichte
- one-to-one: 1 Punkt = 1 Event
- one-to-many: 1 Punkt = Magnitude
Vor-/Nachteile dot density map
+ einfach
+ zeigen Dichte
+ Zugriff auf Ursprungsdaten
- von Hand sehr mühsam
- mit Computer random
