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” 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?

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 Contributions 1. 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

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)

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

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

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 change 2. geometry change when 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 model 1. 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 Preserve 1. 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