Kartographie

Missing Data: prediction, guessing
- phenomena has to be well-known

In Science: Based on sample

predicting values at a location between two known values

predicting values at a location between two known values

predicting a value that falls beyond the location of known values

Raw information manipulated for mapping

Average, ratios, indexes, deviations (Abweichungen), regressions (Rückgang)

- easier to handle when searching for patterns

- check documentation (metadata)
- accuracy and precision?
- Age
- What sort of Modifications where made?
 

Production of touchable or untouchable maps

- Learning how to design and produce maps which effectively communicate what is known
 

- maps
- plans
- charts and sections
- three- dimensional models and globes representing the Earth or any other celestial body at any scale
 

- the use of graphics to facilitate thinking, problem solving and decision making (GIS)

- means in the process of understaning rather than the communication of what is finally understood (GIS)

- presenting what is known (cartography)
- Revealing unknowns (insights) (geovisualization)

- Low (cartography)
- High (geovisualization)

- public (cartography)
- Private (personal) realm (geovisualization)

- Symbols (Signaturen)
- Labels
- Orientation
- Perspective (Abildungsart)
- Scale bar
- Titles
- Notes (Kartenrandangaben)
- Inset/ locator map
- Legend
- Graticule (Kartennetz)
- Author/ cartographer
- Data Sources
- Dates

- Reference Map
- Thematic Map
- Navigational Charts

- storage of spatial information - General purpose: dictionary of features - Includes geographic features (land, water, landmarks) - Gives an answer to spatial questions (Where?, What?, When?) - E.g.: atlas, globes (road maps, satellite image maps)

- storage of spatial information
- General purpose: dictionary of features
- Includes geographic features (land, water, landmarks)
- Gives an answer to spatial questions (Where?, What?, When?)
- E.g.: atlas, globes (road maps, satellite image maps)

- tells a story
- Qualitative information: distribution maps (What, when, where?, Category membership; illustrate spatial patterns)
- Quantitative information: statistical maps (What, when, where, how much?, Category membership, sequence, ranks, magnitude; illustrate and quantify spatial patterns)
- e.g.: statistical maps, graphs, diagrams (weather maps, satellite image maps)

- Help to guide movement in space: Where, How far, How to get there?
- Aid in decision- maing, guide spatial behaviour
- distinguish between maps FOR and maps OF navigation
- e.g.: digital way- finder, GPS, aero/ hydronautical charts (weather maps, road maps)
 

- terrestrial (land) surveying
- photogrammetric recording (tterrestrial and aerial)
- laser scanning (terrestrial and aerial)
- satellite remote sensing
- enquiries and statistics
- paper maps
- census data
 

Shape of the Earth
- "if the sea covered the earth"
- surface of equal gravitational potential
 

High data quality

data collection is tedious and can take a long time & money

data comes from different sources, formats (vector, rasters), scales, resolutions, sensors, time spans, processing methods...

-point- like

-line- like

-area- like

- volumes

representations, abstraction of reality, field based, entity based

storage of spatial models in computer (raster, vector, objects)

-natural/ environmental phenomena
- points: one pixel
- lines: a sequence of pixels
- areas: contiguous group of pixels
 

Fast, simple, easy analysis, constant improvements of technology, easy display

high volume of data, redundancy of data, hard to represent small objects, limited to resolution, less adequate to represent human- made features

- Points: coordinate pair
- Lines: a sequence of pixels connected by vectors
- areas: points and vectors with a closure to form an area (polygon)

Extremely fne resolution, small or large format, compact and efficient, reduced redundancy, topology (GIS), vector data looks better for detailed features

cumbersome iin data capturing stage, complex for analysis, computing data is intensive, display can be expensive (high resolution)

Nominal

Ordinal

Interval

Ratio

What?

How much?

Qualitative, what kind?

- name labels, categories, no order

qualitative, what rank?

- first, second, third... bad, medium, good

- implicit order: e.g. from low to high

- distances between ranks not quantified