Published
Edited
Apr 29, 2020
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md`1) Level one is domain problem characterization. The top level is to characterize the problems and data
of a particular domain. In this case domain is the field of study, and the problems and data are in reference to the target users. The output of this level is a set of questions.`
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md`2) Level two is data/operating abstraction design. the next level is to map those into abstract operations
and data types. These mappings are for sorting data into relevant bins depending on the domain and the questions being asked. `
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md`3) Level three is encoding/interaction technique design. the third level is to design the visual encoding
and interaction to support those operations. Essentially getting the data cleaned and ready to be visualized by our algorithm in the next level. At this point we have the right data for the questions and all thats left is deciding the most efficient way to present the data.`
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md`4) Level 4 is algorithm design. and the innermost fourth
level is to create an algorithm to carry out that design automatically
and efficiently. Algorithm design problems can include sacrificing speed for accuracy. `
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md`The outputs of each level are required to complete the next. Like good recursion, if a function (level) fails you have to go back up a level and try again, or search again for a solution. `
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md`At the first level a threat can be asking the wrong questions, or being concerned about the wrong problem in reference toyour target users, maybe people don't care about when to shop during Covid-19 pandemic becasue everyone gets food delivered. At the second level the breakdown continues because now we will be showing the wrong thing, maybe our users want to see number of people in a store not a percentage comparison. At the third level is about the visual aspect maybe the colors used in the groups are too close together or it is not apparent where the mean is or why it is important. At the fourth level it is all about speed and efficiency. `
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import { chart as world } from '@d3/radial-stacked-bar-chart-ii'
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world
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md`
<p class="subheader">Domain Problem and Data Characterization</p>
1. What **domain** is the visualization coming from?
This visualization could fall under epidemeology or political science depending on its use.
2. What domain specific **vocabulary** is used in the work?
Not much honestly, possibly for americans it uses state acronyms.
3. Who is the pertinent **target audience** within this domain?
Probably American political groups, or curious american citizens. The main purpose of this visualization is actually to demenonstrate the effectiveness of the encoding model.
4. What **specific questions** does the target audience hope to answer?
Population breakdown of states by age.
Based on those questions you've identified (based on the _audience_, in a particular _domain_), you are ready to move into the next stage in the design model.

<p class="subheader">Operation and Data Type Abstraction</p>
1. What generic _operations_ are required to answer the question(s) identified above? Indicate whether each operation is a _high-level_ or _low-level_ task, as described by Amar and Stasko (in the Munzner paper).
Operations include retrieving population data, and state names both are low level tasks. There is not much processing or high level tasks besides dividing population by age per state, but that does nto satisfy cause and effect for high level.

2. What are the data types present in the data (e.g., categorical, continous, etc.)?
The data is categorical (age).
Based on the required operations and data type, you are ready to consider available encodings.

<p class="subheader">Visual Encoding and Interaction Design</p>
1. Which visual encodings and interactions were selected to support the tasks identified in the previous step?
A circular diagram (Radial Stacked Bar Chart) was picked, with different colors to represent age ranges.
2. We'll discuss this more in next week's notebook, but based on your current knowledge, do you believe these encodings and interactions allow users to sufficiently accomplish their tasks?
Yes the differences in population are evident, however it is not easy to compare populations that differ alot.
<p class="subheader">Algorithm Design</p>
If any information is available on the algorithm design used to build the visualization, please make note of it here (if not, no worries).`

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import { chart as vis } from '@johnburnmurdoch/bar-chart-race-the-most-populous-cities-in-the-world'
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vis
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md`<p class="subheader">Domain Problem and Data Characterization</p>
1. What **domain** is the visualization coming from?
Epidemeology or world demographics
2. What domain specific **vocabulary** is used in the work?
Commonly adopte spellings of cities, example Kalkatta is on here.
3. Who is the pertinent **target audience** within this domain?
Curious citizens, more practically probably any individual who wants to use this data academically.
4. What **specific questions** does the target audience hope to answer?
What are the most populous cities from 1500 - 2018, how does the population of various cities grow over time?
Based on those questions you've identified (based on the _audience_, in a particular _domain_), you are ready to move into the next stage in the design model.

<p class="subheader">Operation and Data Type Abstraction</p>
1. What generic _operations_ are required to answer the question(s) identified above? Indicate whether each operation is a _high-level_ or _low-level_ task, as described by Amar and Stasko (in the Munzner paper).

Operations including gathering data, dividing it up by city and time period. According to Amar and Stasko, those are all low-level tasks.

2. What are the data types present in the data (e.g., categorical, continous, etc.)?

The data is continuous.

Based on the required operations and data type, you are ready to consider available encodings.

<p class="subheader">Visual Encoding and Interaction Design</p>
1. Which visual encodings and interactions were selected to support the tasks identified in the previous step?

A bar graph was used to show the differences in population, as well as, various colors to highlight different cities to distinguish region. The most populous city is placed at the top, with the next highest population underneath it.

2. We'll discuss this more in next week's notebook, but based on your current knowledge, do you believe these encodings and interactions allow users to sufficiently accomplish their tasks?

Yes and no, having every city from the same region the same color causes conflict for the user when cities from the same region are next to eachother. This conflict can lead to misconceptions.

<p class="subheader">Algorithm Design</p>
If any information is available on the algorithm design used to build the visualization, please make note of it here (if not, no worries).`
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