Jason Andersen, MGISSami Eria, PhD Geography
Department of GeographyUniversity of Minnesota
Critical Analysis of Brewer & Buttenfied (2010) – “Mastering Map Scale: Balancing Workloads Using Display and Geometry Change in Multi-scale Mapping”
CSCI 8715, Fall 2011
Week4: 29 Sept 2011
Problem Statement How can managers of
Multi Resolution Databases (MRDB) projects manage workloads incorporating tasks of varying complexities efficiently so as to produce a high quality cartographic product within budget and on time?
http://www.mapsofworld.com/usa/states/minnesota/
Problem Statement The significance of the
problem in context of spatial databases: managers of spatial databases at several national mapping agencies in the world face this problem while trying to produce cartographic map products at multiple scales
Brewer & Buttenfield contribute to the field of spatial databases by providing a conceptual model for understanding how managers can begin to start addressing the problem.
This problem is “hard” or challenging because the authors’ primary hypothesis in solving the problem is counter to expectations in the spatial databases community who expect a combination of tasks to increase overall workload
Major Contributions1. The creation of a new
conceptual model for determining the time and cost (workload) of producing a multi-scale map from multi-resolution spatial databases; in particular, the role played by the combination of symbol change and geometry change.
2. The extension of the model to further reduce the workload by the creation and incorporation of Level of Detail (LoD) spatial data into the map production process
Most significant Incorporation of LOD
Why Significant? LOD model was empirically tested
http://habib.wikidot.com/techniques
Key ConceptsSimple explanations Map Products often
needed for print media, e.g. paper maps, Web maps
National: Mapping Agencies (NMAs) compile data at specific resolutions for mapping at standard mapping scales
These mapping scales/resolutions are called “anchors”
Example NMA: Germany (uses ATKIS database)
Varying complexity as anchor data is used to map at varying scales
This has impacts on a mapping organization’s workload (includes various tasks)
Tasks include: symbolization (symbol
change)generalization (geometry
change)
Key Concepts Problems:
Time, cost, complexity
Database consistency across database versions after updates
Complicated workflows/workloads
A workload consists of multiple tasksLevel of difficulty of
each task determined by 1) length of time to complete task 2) Skill required 3) challenge in integrating changes into database
Key ConceptsProject managers Need to balance the three
parameters when carrying out multi-scale mapping projects, hence the concept of workload balancing.
What does this paper propose?
A model for managing the tasks in a multi-scale mapping project
Hypothesis A combination of
Symbol change tasksGeometry change tasks
will reduce the overall workload as compared to doing either one of these aloneThis is contrary to
expectations of most people in Spatial Databases
Further workload reduction by integration of LoD into the workflow
Exercise 1
Question: Which of the following is a smaller map scale and why?
A) 1:24k B) 1:250k
Question: Give a real life example of the use of LoD in a spatial database application?
Question: What is an example of 1. symbol /display
change2. geometry changewhen creating smaller
scale map products?
Key Concepts
Symbol + Geometry change
Include LoD
Symbol + Geometry change
Validation methodology
Strengths Simple
conceptualization of model
Good visualization of model
Empirical data used for case study 2
Weaknesses No empirical data for
case study 1
Assumptions of the model1. Data are produced at
one or more specific compilation resolutions anticipating the generation of varied map products.
2. The compiled data anchors the workload in the sense that it requires a minimum of work to create a product at the anchor’s mapping scale
3. Label changes not taken into consideration
Critique of an assumption that is unreasonable
Label changes cannot be left out of the modeling process because they are inherent to map production
Impact of removing this unreasonable assumption
Overall Workload is unreasonably low
Revisions
Preserve1. The conceptual
model for workload balancing involving symbol + Geometry change
2. The conceptual model for workload balancing involving LoD
Revise Carry out experiments
using empirical data to confirm the first conceptual model (symbol + Geometry change effects on workload)
Justification Without empirical data,
the model is weak and only hypothetical