Reflections week10 jcybull

week10.md

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+For this weeks reflection I will talk about this academic paper:
+
+"Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff" https://ieeexplore.ieee.org/document/8816695
+
+This paper starts of by showing that there can be a gap between developers and designers when creating a vizulizations. And this gap can be caused by differnt factors including the data and tools used when working together. They look at the problem and draw information from 5 differnt project working on differnt data visulizations, and evaluate what the challenges are across these projects.
+
+they then go into details about the process of creating a large data visualization, Starting of by having a group of people that share a set of skills and together, and as the project moves on it becomes more segmented. This process is comparable to the sofware development process inlvolving design and developlent pahses with multple iterations.
+
+They are able to identify 6 challenges that they faced when working on the 5 projects mentioned above:
+
+Adapting to data changes: This can affect the selection of graphics since they are data dependantl.
+Anticipating edge cases: Edge cases can cause the mapping of the data to break.
+Understanding technical constraints: This leads to uncertinty on the feasinility of the design.
+Articulating Data-Dependent Interactions: This adds a level of complexity and dimesions to the data.
+Communicating data mappings
+Preserving Data Mapping Integrity across iterations
+When working on complex projects data related problems can ocurre at all levels of the design process. And recomend the creation of mor powerfull tools that could be helpful during the developent process.

week11.md

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+"Mapping Color to Meaning in Colormap Data Visualizations"- https://schlosslab.discovery.wisc.edu/wp-content/uploads/2018/09/SchlossGramazioSilvermanParkerWanginPress.pdf
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+This paper focuses on how differnt colors affect the way people interpret colormaps. they argue that when a person reads a colormap such as weather maps, neural activity maps and so on, they have to form conceptual inferences from what they see. In order to test how people formed these inferences, they created a test where they used different color backgrounds. Finding the following assumptions:
+![plot](keypoints.png)
+
+They designed two experiments to analize their hypothesis:
+
+1. In experiment 1 they evaluated how the background color influenced
+  inferred mappings when colormaps were constructed using various
+  standard color scales for visualization.
+
+2. Experiment 2 directly tested our hypothesis that there is an opaque-ismore bias.
+
+They concluded that, if the opacity is consistent then there is no effect on bias caused by the background color, but if the opacity does change on the color map, then there is a relationship between opacity, bias and background color. 

week12.md

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+Paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7918044
+
+For this weeks research reflection I decided to look at big data ans what are some of the cahllenges and methods for vizualicing it. In the paper the author argue that visualizing data allows us to better understand trends in the data we are observing. One key point oberved in this study was that observing big data compared to using raw numbers improved decision making by 77%. 
+Among the challenged of big data visualizations they found:
+1. semi/non formated data
+2. Setting the dimensions of the data: when done wrong, patterns can be lost or missed
+3. Sacalability is usually an issue
+
+Some of the suggested tools that they found helpful when visualizing big data were:
+* Tableu
+* Power BI
+* Plotly
+* Gephi
+* Excel
+

week13.md

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+https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9495259
+
+In this weeks data viz, the researchers argue that data visualizations have become a data format that is heavily shared and reused. 
+They are planing to use AI to approach this. 
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+There are three areas of focus that using AI can help on: Generation, Enhancement and Analisys
+
+![Screenshot](screenshot.png)

week14.md

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+Title: Point grid map: a new type of thematic map for statistical data associated with geographic points
+
+This paper introduces a new method of representing multivariable data linked to geographical points, Traditional methods of representing the data often seem crowded and and difficult to read, instead they propose that a point grid is used to represent multiple variables. this method is more efficient and easier to read whhen multiple types of data are involved. This is tipically used for Social, economical and enviromental datapoints.