2. João Mourinho
Author:
Automated Generation of
Context-Aware Schematic Maps:
Design, Modeling and Interaction
João Falcão e Cunha
Supervisors:
Industrial Engineering
and Management
Doctoral Program
Teresa Galvão Dias
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1. Introduction
2. Research Objectives
3. Methodology
4. State of the Art
5. Spider Maps
6. Validation
7. Automated Generation
8. Tests
9. Conclusions
Index
4. Introduction1
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Motivation
Problem
• Create better and cheaper maps for Public Transportation
5. Introduction1
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Solution Proposal
Solution 1: Spider Maps
• Eliminate superfluous information and entropy | Improved
Context
Solution 2: Automate Spider Maps
• Approach to generate them automatically
7. Research Objectives2
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1. Describe the state of the art of the schematic maps and
related science areas
2. Define and systematize the set of features that comprise the
Spider Map
3. Test the validity of the Spider Map
4. Develop an effective approach to automate the production of
Spider Maps
5. Test and evaluate this approach
8. Methodology3
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1. Literature Revision
2. Define the Spider Map Concept | Integrate Knowledge
3. Validate the concept | Quantitative and Qualitative Validation
through test with real users
4. Develop an Approach for the Automated Generation |
Modelling The problem | Implement through a Spiral /
Incremental mixed model
5. Test and evaluate this approach | Real maps
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1. Introduction
2. Research Objectives
3. Methodology
4. State of the Art
5. Spider Maps
6. Validation
7. Automated Generation
8. Tests
9. Conclusions
Index
10. State of the Art4
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Schematic Maps
11. State of the Art4
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Automated Generation of Schematic Maps
Silvania Avelar, 2002 | Framework to Generate Schematic Maps
on Demand
12. State of the Art4
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Concept / Mind Maps
• Emulate the way human brain maps information
• Efficient context-based retrieval
Context Enhancement Techniques
• User Centered Design
• Focus + Context Techniques
• User-adapted Interaction
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1. Introduction
2. Research Objectives
3. Methodology
4. State of the Art
5. Spider Maps
6. Validation
7. Automated Generation
8. Tests
9. Conclusions
Index
18. Test Design and Methodology
• Phase 1 – Concept Testing
Concept Spider Maps vs Concept Diagrammatic Maps
• Phase 2 – Real Maps, Real use
Bus Spider Maps vs Bus Diagrammatic Maps
• 11 Users (Krug Method) | 4x4x3 test array
• Both Phases Include:
• Usability Tasks | Objective Measurement
• Open questionnaire | Subjective Assessment through Tag
Clouds
Validation6
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19. Phase 1 – Concept Spider vs DiagrammaticValidation6
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..Time -25%
..Correctness (concepts) +3%
..Correctness (relations) +8%
Memory Recall..
Attention Focus..
..On Diagrammatic Map
..On Spider Map Focused
Scattered
Subjective Opinion of users favourable to Spider Map
All users preferred the Spider Map
20. Phase 2 – Bus Spider vs DiagrammaticValidation6
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Self Location Time -94%
0%
Navigation Time
Real Use Tests
Subjective Opinion of users favourable to Spider Map
10 in 11 users preferred the Spider Map
Locate Notable Point Time
Searching Time
Stop Identification
-84%
-25%
-97%
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1. Introduction
2. Research Objectives
3. Methodology
4. State of the Art
5. Spider Maps
6. Validation
7. Automated Generation
8. Tests
9. Conclusions
Index
22. Automated Generation7
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Automation of the Schematization Process:
Normal Map (input)Phase I – Pre ProcessingOptimizationPost ProcessingSpider Map (Output)
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Problem Formulation:
• Decision Variables | Coordinates of Map Features
• Stops
• Lines
• Hub
• Geographical Accidents
24. Automated Generation7
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Objective Function
• Weighted Sum of Soft Constraint Scores | Based on Stott’s work
25. Automated Generation7
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Objective Function
• Weighted Sum of Soft Constraint Scores | Desirable Features
• Wide Adjacent Angles
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Objective Function
• Weighted Sum of Soft Constraint Scores | Desirable Features
• Wide Adjacent Angles
• Inter-vertex spacing
• Distance between Stops
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Objective Function
• Weighted Sum of Soft Constraint Scores | Desirable Features
• Wide Adjacent Angles
• Inter-vertex spacing
• Distance between Stops
• Reduce Edge Crossings
Contribution: An enhanced version of the
Bentley-Ottmann algorithm
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Objective Function
• Weighted Sum of Soft Constraint Scores | Desirable Features
• Wide Adjacent Angles
• Inter-vertex spacing
• Distance between Stops
• Reduce Edge Crossings
• Line Straightness
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Objective Function
• Weighted Sum of Soft Constraint Scores | Desirable Features
• Wide Adjacent Angles
• Inter-vertex spacing
• Distance between Stops
• Reduce Edge Crossings
• Line Straightness
• Benefit Horizontal and
Vertical Lines
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Problem Formulation
• Constraints | Hard Constraints – needed for a feasible solution
• Vertices must respect
Octilinear embedding
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Problem Formulation
• Constraints | Hard Constraints – needed for a feasible solution
• Vertices must respect
Octilinear embedding
• Avoid Forbidden Areas
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Problem Formulation
• Constraints | Hard Constraints – needed for a feasible solution
• Vertices must respect
Octilinear embedding
• Avoid Forbidden Areas
• Avoid Vertex Occlusion
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Problem Formulation
• Constraints | Hard Constraints – needed for a feasible solution
• Vertices must respect
Octilinear embedding
• Avoid Forbidden Areas
• Avoid Vertex Occlusion
• Maximum Displacement
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Problem Formulation
• Constraints | Hard Constraints – needed for a feasible solution
• Vertices must respect
Octilinear embedding
• Avoid Forbidden Areas
• Avoid Vertex Occlusion
• Maximum Displacement
• Preserve Topological
Relations
Contributions: May be treated as soft
constraint, fast matrix comparison
36. Automated Generation7
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Pre Processing Optimization
Post
Processing
Normal Map Spider Map
• Objective | Find a Feasible Solution
• How:
• Align to grid | Discretize Space | Respect Constraints
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• Objective | Find a Feasible Solution
• How:
• Align to grid | Discretize Space | Respect Constraints
• Contributions:
• Intelligent grid granularity guessing (SmartFit + HPPO)
• Determine the best grid value without user intervention |
Obtain the best performant value automatically, while
respecting topological relations and solving vertice contentions
Automated Generation7
Pre Processing Optimization
Post
Processing
Normal Map Spider Map
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• Objective | Improve Solution
• How:
• Tabu Search
Tenure Time: 5
Automated Generation7
Pre Processing Optimization
Post
Processing
Normal Map Spider Map
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• Objective | Improve Solution
• How:
• Tabu Search
• Contributions: Spatial Distribution Analysis Algorithm
• De-clustering algorithm
• Improved Variability to escape local minima
• Runs automatically only when needed
Automated Generation7
Pre Processing Optimization
Post
Processing
Normal Map Spider Map
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• Objective | Prepare the Map to be output
• How:
• Deal with Geographical
Accidents
• Contributions:
• Dynamic Differential Grid Apperture Size
Algorithm
• Geographical accidents are considered
Automated Generation7
Pre Processing Optimization
Post
Processing
Normal Map Spider Map
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• Objective | Prepare the Map to be output
• How:
• Deal with geographical accidents
• Introduce inflection points
Automated Generation7
Pre Processing Optimization
Post
Processing
Normal Map Spider Map
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• Objective | Prepare the Map to be output
• How:
• Deal with geographical accidents
• Introduce inflection points
• Contributions:
• Improved A* algorithm version | Smooth Line Paths, Speed
Improvements
Automated Generation7
Pre Processing OptimizationNormal Map Spider Map
Post
Processing
43. Index
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1. Introduction
2. Research Objectives
3. Methodology
4. State of the Art
5. Spider Maps
6. Validation
7. Automated Generation
8. Tests
9. Conclusions
44. Tests7
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Test Enviroment
• GenX Framework in C# | developed in cooperation with OPT,
STCP and FWT
• Typical low spec low cost Laptop
• Visual Studio, Debug Mode | Up to 5x slower execution
Test Design
• 6 Bus Maps from Porto | Real data
• Two versions per map | with and without geographical
accidents | 12 Maps total
• 8 Tests | Algorithm performance | Map quality | Parameter
Sensitivity
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100 iterations for Map with 104 Stops and 155 Edges
10K iterations for Map with 104 Stops and 155 Edges
Maps versions without geographical accidents
Results - Overview
12 secs
2h 20 secs
20% faster
Average Quality Improvement After 1000 iterations
Average Quality Improvement from iteration 1000 to 10000
409%
22%
Algorithm can produce good quality solutions quickly!
• Implicit Search | faster, less prone to premature convergence and
more capable to escape local mínima and higher quality maps in our
algorithm
Tests7
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• Soft Evaluation of Topological Relations | visually beautiful maps at
cost of user orientation and execution time
• Spatial Distribution Algorithm | +9% Map quality without significant
speed penalty
Results – Additional Comments
• Our A* implementation | very low overhead processing time of 2s
Tests7
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Algorithms and Framework already being used to produce Maps in
Portugal (Porto)
Tests7
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Algorithms and Framework already being used to produce Maps in
Portugal (Lisbon)
Tests7
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Algorithms and Framework already being used to produce Maps in
Portugal (Santo Tirso)
Tests7
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And Brazil, Spain…
Tests7
52. Index
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1. Introduction
2. Research Objectives
3. Methodology
4. State of the Art
5. Spider Maps
6. Validation
7. Automated Generation
8. Tests
9. Conclusions
53. Conclusions8
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Contributions of This Thesis
• Spider Map Concept
• Definition
• Modelling
• Concept validation
54. Conclusions8
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Contributions of This Thesis
• Spider Map Concept
• Automated Spider Map
Generation
• Data Modeling Improvement
• Algorithms with improvements
in all phases of the
schematization process
55. Conclusions8
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Contributions of This Thesis
• Spider Map Concept
• Automated Spider Map
Generation
• Value to Society
• Application of the Spider Map
Concept and Automated
Generation algorithms to real
world production of Spider
Maps
• Decrease in map production
costs and lead times
56. Conclusions8
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Contributions of This Thesis
• Spider Map Concept
• Automated Spider Map
Generation
• Value to Society
• Scientific Production
• 5 Conference Papers
• 2 Papers Sent to Scientific
Magazines: 1 approved, 1
pending approval
57. Conclusions8
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New Insights in the Horizon
• Spider Map Concept | Dynamic Spider Maps | User Centered
Design | Dynamic Adaptation
• New media types | Wearables | Augmented Reality | Device
Adaptation
• Algorithm Improvements | Machine and Pervasive Learning |
Multicore Architectures | Incremental Solutions
Complex Cities
Increasingly complex transportation networks
Need | better maps easy to read
Go from the use of these maps (difficult, crowded)
To these (easier to use and context enhanced)
Complex Cities
Increasingly complex transportation networks
Need | better maps easy to read
After more than 2000 years of evolution, Harry Beck did this
Pushed the transformation of “normal maps” into the creation of Schematic Maps
Bold Innovation
For the first time -> Line Orientation 0, 45, 90
Differential Scaling
4+ Decades of advances
Silvana Avelar integrated the roots of Line Schematization approaches to create a framework
4+ Decades of advances
Special Type of Schematic Map which features a Hub (spatial contexto depicting in detail where the user is) and a set of schematized lines
- Hub + Set of schematized Lines
Spider Architecture | Spider architecture is good for knowledge representation!
Context as a mean to enhance learning and direct information
Design techniques to reduce information overload
Inheritance of some of the best design techniques from map evolution
- Line Simplification | Grouping Stops into Map Points | Differential zoom in crowded areas | Line Grouping | Simplicity vs Completeness | User dire
