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Jason Andersen, MGIS Sami Eria , PhD Geography Department of Geography University of Minnesota

CSCI 8715, Fall 2011. Jason Andersen, MGIS Sami Eria , PhD Geography Department of Geography University of Minnesota. Critical Analysis of Brewer & Buttenfied (2010) – “Mastering Map Scale: Balancing Workloads Using Display and Geometry Change in Multi-scale Mapping”. Week4: 29 Sept 2011.

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Jason Andersen, MGIS Sami Eria , PhD Geography Department of Geography University of Minnesota

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  1. CSCI 8715, Fall 2011 Jason Andersen, MGISSami Eria, PhD GeographyDepartment of GeographyUniversity of Minnesota Critical Analysis of Brewer & Buttenfied (2010) – “Mastering Map Scale: Balancing Workloads Using Display and Geometry Change in Multi-scale Mapping” Week4: 29 Sept 2011

  2. 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/

  3. 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

  4. Major Contributions • 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. • 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

  5. Key Concepts Simple 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)

  6. Key Concepts • Problems: • Time, cost, complexity • Database consistency across database versions after updates • Complicated workflows/workloads • A workload consists of multiple tasks • Level of difficulty of each task determined by 1) length of time to complete task 2) Skill required 3) challenge in integrating changes into database

  7. Key Concepts Project 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 tasks • Geometry change tasks will reduce the overall workload as compared to doing either one of these alone • This is contrary to expectations of most people in Spatial Databases • Further workload reduction by integration of LoD into the workflow

  8. 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 • symbol /display change • geometry change when creating smaller scale map products?

  9. Key Concepts Symbol + Geometry change Symbol + Geometry change Include LoD

  10. 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

  11. Assumptions of the model • Data are produced at one or more specific compilation resolutions anticipating the generation of varied map products. • 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 • 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

  12. Revisions Preserve • The conceptual model for workload balancing involving symbol + Geometry change • 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

  13. END Thanks!

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