Kartographie 9

range:

small scale maps: >1: 500 000 , least detail shown, greatest spatial extend

medium scale maps: between two, less spatial extend (e.g. town)

large scale maps: >1: 10 000, most details, leats spatial extend (e.g. campus map)

-areal scale: area size on the map vs reality

-linear scales: linear distance on the map

ratio/ representative fraction, verbal statement or graphic symbol (plus: withstands size changes)

might useful:

-known features (e.g. stadion)

-latitude/longitude

map comparison

generalisation to keep map readable!

all maps are to some degree generalizations!

Aspect of:

art (subjective), intuition

science (objective), laws, rules

"process of reducing amount of detail appropriate to the scale/purpose in a map in a meaningful way to improve the legibility of the map!"

2 approaches;

- cartographic (geometric) generalization-> graphic representation

- object (or semantic) generalisation ->classification and modelling

conceptual models exist

Why? (conceptual models)

to keep the map readable at different scales, and appropriate for its purpose

as a consequence of scale changes -> reduced comlexity/richness

as " " " map purpose and audience

distance changes affect area

scale shpold not exceed 10X

When? (conceptual models)

if there's a danger of congestion (Überfüllung), coalition (Verschmelzung), conflict or complication

below 30% ink

just noticeable difference JND

visual perceptual characteristics

map purpose, data quality or storage space

procedural measurse:

select simplification algorithm, modify tolerance value as complexity changes, assess density of set of polygons, determine wether a feature should change, compute the curvature of a line segment

-> quality assesment

How? (fundamental operations)

raster:

-structural (reduction, resampling)

-numerical (low-pass or high-pass filters)

-category merging

-cell aggregation (weigted or non-weighted)

vector:

-point features (aggregation, displacement(verdrängen))

-line features (simplifactin, smoothing, displacement, merging, enhancing)

-areal featues (fusion, collapse (zusammenlegen), displacement)

-volume feature (smoothing, enhancement, simplification)

-holistic (refinement)

generalisation steps:

A. simplifiation

selection(erhalten)

elimination (fortlassen)

displacement (verdängen)

B. classification

aggregation (gouping pint location and representing them as areal objects)

amalgamation (grouping of individual areal features into a larger element)

collapse (replacing an object's physical details with symbol representing the object)

merging (grouping of line features)

resampling (semantic generalisation; cells are redifined inraster data

B. classification (resumption)

partitioning (semantic generalisation; identifiying classes in data)

overlay (combining layers of data to create new data)

C. enhancement (verbesserung; geometric generalisation)

smoothing (shifts object position to improve appearance; relocates points)

filtering (blur or sharpen)

exaggeration in 2D (making map features larger than in reality, not to scale)

exaggeration in 3D (vertical exaggeration)

refinement (similar to collapse, selecting specific portions of an object to represent the entire object)

interpolation (generation of new detail; mathematical)

generation (make up detail; artistic)