Generalization of geodata an der TU München

Karteikarten und Zusammenfassungen für Generalization of geodata an der TU München

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Lerne jetzt mit Karteikarten und Zusammenfassungen für den Kurs Generalization of geodata an der TU München.

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

What is generalization in cartography?

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

Name two applications for generalization.

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

Name 4 different generalization operations and explain each of them briefly.

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

Name 3 different line-based generalization algorithms and explain one of them briefly.

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

Explain two agent-based generalization models.

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

What are constraints for agent based generalization?

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

What is data matching in cartography?

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

Name two matching algorithms and explain one of them briefly.

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

Explain shortly the algorithm of Töpfer's radical law.

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

Name 2 different OGC Web Services and explain them shortly.

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

Which request is the same for all OGC Web Services.

Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

What are 3D generalization applications? Name 3 of them and describe them briefly.

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Beispielhafte Karteikarten für Generalization of geodata an der TU München auf StudySmarter:

Generalization of geodata

What is generalization in cartography?

Generalization is a processing step in the course of decreasing map scale. When decreasing the scale of a map without generalization, it becomes unclear, confusing, less readable and important information might be lost.

Generalization therefore increases map readability and eliminates superfluous/disturbing details within the target scale.


Every map needs to be generalized to fulfill the different criteria of map design:

  • Completeness
  • Visibility
  • Readability
  • Visual balance 
  • aesthetics
  • Correctness

Generalization of geodata

Name two applications for generalization.

  • Simplify data to reduce storage space
  • Generalize maps to increase their readability

Generalization of geodata

Name 4 different generalization operations and explain each of them briefly.

  • Simplification: Non-characteristic details are removed from shapes while preserving characteristic features
  • Aggregation: Aggregate multiple similar objects together
  • Exaggeration: Highlight important structures by increasing their size
  • Displacement: Move objects to increase readability and improve the map design
  • Selection: Select important information and eliminate unimportant information
  • Categorization: Put objects into categories and use the same signature for all objects of one category

Generalization of geodata

Name 3 different line-based generalization algorithms and explain one of them briefly.

  • Douglas-Peucker
    • P: Tolerance
    • Draw a straight line between starting- and end point
    • Check the orthogonal distance between all points and the straight line
    • If the distance of all points is within the tolerance, remove all intermediate points and terminate the algorithm
    • If at least one point is outside of the tolerance, pick the point which is the furthest away from the straight line
    • Then draw a new straight line between the previous starting point and the newly picked point and another line between the newly picked point and the previous end point
    • Repeat this process recursively until either all points of a segment lie within the tolerance or there are no more intermediate points
  • Perception based order
  • LiOpenshaw
    • P: Tolerance
    • Drawing a circle at point A with diameter 2*P wihich intersects the point C on the line
    • Draw a circle with diameter AC, the center is a point along the generalizated line
    • C is the new starting point, repeat 1 and 2
  • Visvalingham Williamson

Generalization of geodata

Explain two agent-based generalization models.

  • CartACom approach: Agents can communicate with each other to find the best solution for the entire system. Agents can communicate, cooperate and negotiate with each other.
  • GAEL: Agents are represented as point based structures and are allowed to be deformed to fit the constraints.

Generalization of geodata

What are constraints for agent based generalization?

A constraint is a function linked to object attributes. Conflicts occur if one object does not satisfy a specific constraint.

Examples of constraints:

  • Readability
  • Preservation of relations
  • Geographic coherence

Generalization of geodata

What is data matching in cartography?

Establishes logic connections of geographic objects in two comparable data sets.

Generalization of geodata

Name two matching algorithms and explain one of them briefly.

Buffer growing 

  1. Instantiation of reference polyline 
  2. Identification of possible matching candidates
  3. Exclusion of incorrect candidates 
  4. Exactness inspection of the matching candidates 

Multi-stage matching 

  1. Extract all of the nodes of a line network, represent the line-network as a matrix of nodes 
  2. Associate the corresponding nodes between different datasets 
  3. match corresponding line objects between different datasets 
  4. iterate to re-associate weak corresponding nodes, identify new matching pairs 

Delimited stroke-oriented matching

  1. Identify potential matching pairs 
  2. Exclude incorrect potential matching pairs 
  3. Exactness prove of promising matching pairs 
  4. network-based selection 
  5. matching-growing from the seeds 

Iterative hierarchical conflation (bottom up and top down) 

  1. Pre-processing 
  2. node matching 
  3. elementary matching 
  4. combined matching  

Generalization of geodata

Explain shortly the algorithm of Töpfer's radical law.

When deriving a map with a smaller scale from a base map, Töpfer's radical law gives an estimate about how many objects of a certain class shall be retained to get a readable map.

Generalization of geodata

Name 2 different OGC Web Services and explain them shortly.

  • WMS = Web Map Service
  • WFS = Web Feature Service
  • WMTS = Web Map Tile Service
  • WPS = Web Processing Service

Generalization of geodata

Which request is the same for all OGC Web Services.

They all use a GetCapabilities request.

Generalization of geodata

What are 3D generalization applications? Name 3 of them and describe them briefly.

  • Reducing computational costs in 3D renderings (e. g. VR): Objects in a large distance are generalized.
  • Navigation apps: Generalization can be used to show focus areas in a high level of detail whereas objects in areas without focus are represented in a generalized shape.
  • Large 3D-analyses (like flooding, solar potential, ...): Reduce computational cost by only retaining 3D features required for the analysis

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