un collab 2010.09.17_presentation_02

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Document structure & problems: Too much information with no information hierarchy Including all supporting information to justify their recommendations/conclusions Key points are lost in text Too much micro-level data Framed deductively (argument first, then conclusion) Excessive data and explanations remove focus on important points

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Page 1: Un collab 2010.09.17_presentation_02

• Document structure & problems:– Too much information with no information hierarchy

• Including all supporting information to justify their recommendations/conclusions

• Key points are lost in text• Too much micro-level data• Framed deductively (argument first, then conclusion)• Excessive data and explanations remove focus on important points

Page 2: Un collab 2010.09.17_presentation_02

• Document structure & problems:– static page layout makes it difficult to see relevant/referred to

diagrams– uses lots of acronyms without easily accessible definitions– using old techniques, not making use of technology and its

affordance

Page 3: Un collab 2010.09.17_presentation_02

• Document structure & problems:– Confusing statements

“Common statistical techniques, as employed in this study, are suitable for estimating average patterns and trends but are not able to predict extreme events, given the data limitations described (in particular limitations in the use of country-level vulnerability indicators) and the unpredictability of individual hazard events. Th is m e a n s t h a t

i f t h e m o d e ls in t h is a n a ly s is p r e d ic t a n 1 ,0 0 0 a n n u a l a v e r a g e o f p e o p le k il le d b y a

, g iv e n h a z a r d t y p e g lo b a lly t h e r e c o u ld b e 1 0 ,0 0 0 o n e e v e n t k il l in g p e o p le f o l lo w e d b y

9 y e a r s o f a lm o s t n o c a s u a lt ie s .” (p. 25)

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• Examples:

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• Examples:

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• Examples:

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• Recommendations:– Have simplified version of the GAR with just key summarized

points (both in written and graphic form)• supporting information can be found in appendix as needed

– present key information in a short linear format so it’s clear what needs to be heard/read and then allow person to navigate information in non-linear way

– show supporting diagrams alongside its referring text– use source data to create the graphics and animation - pulls live

data keeping the report accurate/up to date (static graphs are a snapshot in time)

– the maps are all different scale/depiction - having standard views would help maintain scale for data comparison

Page 9: Un collab 2010.09.17_presentation_02

• Recommendations:– Data-driven visualizations– Show data relationally and dynamically: by region, by disaster,

etc.• regional view with summary of its main issues and statistics • create lists that show the rankings for different disasters, regions,

economic/social measurements, etc. with the country/region as links that take you to a view of that country/region's data

• include supporting case studies that apply to that region/country

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http://www.time.com/time/covers/20061030/where_we_live/

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http://behance.vo.llnwd.net/profiles3/166636/projects/495912/1666361272585579.jpg

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• Recommendations:– allow for comparisons:

• ex. based on Z ranking, country X and Y have the highest ranking. I want to see the mortality data for X and Y country side by side– the rankings give cues about priorities, but seeing the more complete picture for each

country/region could help prioritize monetary aid.

– make it accessible (in language, form, presentation) to the communities so they can get more involved/be more prepared• i.e. GAR could facilitate community preparedness (something already

measured in the GAR and therefore important)

– interactive glossary (every reference to SID as unintrusive link/pop up to its definition)

Page 13: Un collab 2010.09.17_presentation_02

• Recommendations:– Animation/Time-Based

• chapter 5 is “progress” of implementation so use animation/time-based to show progress

• maps illustrate disasters in motion - so use motion to illustrate them

– Use data to drive algorithmic animations that are interactive

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http://www.behance.net/gallery/Information-Graphic--Bus-Ride-37-Mintutes/215694

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• Recommendations:– game play (start of idea)

• simulation where you play regions and your game pieces are resource amounts are equivalents to the countries (# people, economic resources. etc.); more at risk you are, the more aid they get - monetary or infrastructure etc.

• events happen (actual natural disasters using real probabilities) that deplete your people/economic/infrastructure/financial resources

• goal of the game is for all regions to stay "alive" (have enough people still alive, still have all kinds of resources...) - it's not just about saving your region but doing the best to keep all regions afloat, so it's cooperative play

Page 17: Un collab 2010.09.17_presentation_02

• Thoughts:– focus on disaster management: economic, social impact– how to approach each level of information hierarchy (key points v.

supporting info)– show information regionally– found chapter 2 most interesting in terms of information design– chapter 5 is much clearer and better constructed in the way it

organizes information although it’s still lots of text and overwhelming

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• Questions:– Who is the audience exactly?

• How do they assess this info (regionally, disaster-based, etc.)? • What access to technology will they/could they have (laptop, internet,

etc.)?

– Where is this data?• Is it all in a relatable format or would intergration be required? Would

we have access to datasets in relational form?

– What is the information hierarchy? • What is most important to communicate 1st, 2nd, 3rd? • Does this hierarchy change within the target audience?

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• Questions:– How important is the portability of the content to regional

communities or other interested parties? ( for region-based “packaged” information)

– Is it useful to see macro information geographically as they try to do currently?

– For each set of data, there's a ranking. Would an ultimate ranking be helpful to UN?• ex. given probability of all natural disasters and the economic loss

and mortality combined, these are the most at risk places