2b Design Space - multiple dimensions and rationale.pdf

Design Space: multiple dimensions and design rationale 1 2  In any design there will be a very large number of decisions to make Each decision represents a dimension in multi- dimensional space We can’t draw more than two dimensions...

Design Space: multiple dimensions and design rationale 1 2  In any design there will be a very large number of decisions to make Each decision represents a dimension in multi- dimensional space We can’t draw more than two dimensions! Parellel co-ordinates is a common visualisation method for high-dimensional data We can use them to visualise our design space… 3  In any design there will be a very large number of decisions to make Each decision represents a dimension in multi- dimensional space We can’t draw more than two dimensions! Parallel co-ordinates is a common visualisation method for high-dimensional data We can use it to visualise our design space… 4  Parallel coordinates Used for visualising multidimensional data Each dimension (decision) is represented as a vertical axis, with its values equally spaced along it The dimensions are arranged horizonatally, equally spaced A single data point is a line that joins its values on each dimension Robert Kosara, “Parallel Coordinates (eagereyes)”, https://eagereyes.org/techniques/parallel-coordinates, 2010 (accessed 18/04/21) 5  Using Parallel coordinates to visualise Design Space choices Now we are not confined to representing only two design alternatives in two dimensions 6 7  Designing a classroom wall width, classroom offset, orientation, roof angle… daylight factor, view quality https://bernalm.gitbooks.io/design-space-construction/content/visualization_and_optimization.html 8  Questions Options Criteria (QOC) A more formal way of representing the Design Choice process and Design Rationale – Questions: the key issues/choices of the design – Options: possible answers to the questions – Criteria: reasons for arguing for or against the options 9  Design documentation: design rationale – an HCI example does not need flashing focussed light attention How should can be perceived we represent bell in a noisy an alarm? environment text can be perceived immediately PA system PA system: a ‘public announcement’ system, often using speakers in the walls of a building. 10  Design documentation: design rationale – an HCI example does not need flashing focussed visual light attention How should can be perceived we represent bell in a noisy an alarm? environment text can be perceived immediately PA system We add in solid lines for when there are positive links between options and criteria, as here in the case of a bell – which does not need focused visual attention to be perceived. Note that a bell might not be easily heard in a noisy environment, however… and so there is a dotted line there… representing a negative link. We are showing, with each link, a decision about whether each option is good or bad with regard to a criterion. By going through every combination of O and C, we can be thorough in our assessment of the design options. 11  Design documentation: design rationale – an HCI example does not need flashing focused visual light (2) attention (1) How should can be perceived we represent bell (2) in a noisy an alarm? environment (2) text (1) can be perceived immediately (3) PA system (1) Here I’ve added the number of positive links for each option, and for each criterion, e.g. a flashing light has two positive links to the criteria. Similarly, only one design option does not need focused visual attention. Based on this, the flashing light or the bell would be the best design option. Since there are two equally ranked options, however, we could think about further criteria, to help narrow down on one option… or we could think of other options, to find some better alternative. Either way, there are clear reasons for that next step in the design process. 12  AN PT HG ZL AN 385 0 100 15 Two dimensions of PT 109 323 45 23 choice HG 10 0 432 58 ZL 0 0 140 360 (A) clustered bar (B) 100% stacked bar (1) 1x4 (correct) (2) 1x4 (incorrect) (C) stacked bar (3) 1x4 (%correct) (D) stacked line (4) 1x4 (%incorrect) (5) 4x4 (all data) (E) line (6) 4x4 (all, as %) (F) pie (7) 2x2 (HG) (8) 1x1 (%correct) (G) radar 13  A 2D space of 56 design options clustered bar X X X X X 100% stacked bar X X X X X stacked bar X X X X X stacked line X X X X X X X X line X X X X X X X X pie 1. 1x4 (correct) X X X X 2. 1x4 (incorrect) radar X X X X X X 3. 1x4 (%correct) 4. 1x4 (%incorrect) 1 2 3 4 5 6 7 8 5. 4x4 (all data) 6. 4x4 (all, as %) 7. 2x2 (HG) 8. 1x1 (%correct) Remember this? 14  AN PT HG ZL Representing data choice (6) AN 77 0 20 3 PT 22 65 9 4 4x4 (represented as %) HG ZL 2 0 0 0 86 28 12 72 clustered can read the exact bar (A) values clearly which clearly shows extent visualisation? 100% stacked of successes vs bar (B) failures stacked familiar to readers bar (C) clearly shows which runner has most radar (G) successes 15 clustered bar (A) 100% stacked bar (B) stacked bar (C) radar (G) 16  Representing data choice (6) 4x4 (represented as %) can read the exact values clearly which clearly shows extent visualisation? of successes vs failures familiar to readers clearly shows which runner has most successes 18  Representing data choice (6) 4x4 (represented as %) can read the exact values clearly (2) which clearly shows extent visualisation? of successes vs failures (1) familiar to readers (4) clearly shows which runner has most successes (3) 19  Design process What are the design decisions? Which combinations are – possible – impossible – relevant – preferable – under-explored (gap-detection) Which options best satisfy our criteria? https://www.slideshare.net/StephenMacNeil1/cocreating-dimensions-and-examples-using-design-space-gaps (extract) 20  Design Space: multiple dimensions and rationale 21