Ziyang_Xu_reflections-research

week10.md

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+Link: https://link.springer.com/chapter/10.1007/978-3-540-33037-0_2
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+It is common to think of statistical graphics and data visualization as relatively modern developments in statistics. In fact, the graphic representation of quantitative information has deep roots. These roots reach into the histories of the earliestmap making and visual depiction, and later into thematic cartography, statistics and statistical graphics, medicine and other fields. Along the way, developments in technologies (printing, reproduction), mathematical theory and practice, and empirical observation and recording enabled the wider use of graphics and new advances in form and content.

week11.md

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+Link： https://dl.acm.org/doi/pdf/10.1145/2721882.2721883?casa_token=9-kWdF-Iq2MAAAAA:dWllfmyTAVUcnwriff_mm-dD0fUgEPcnY1OJn25XW6OW74NHCxcE0WyLzF_S80IanqnMOnlo967t
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+Human mind is very visual; data visualization is an ancient need. Since humans strived for survival they depicted hunting strategies on caves walls, they also kept statistics of how many animals and of what kind they caught on dwellings. In the history there are of visuals alphabets, like Phaistos, Sumerian, Assyrian cuneiform, were based on visuals rather than on sounds. Then, Egyptians and Maya civilizations, created pictographic images to communicate within social classes and across generations. For strategically purposes, maps were used to depict a kingdoms´ richness. The most antique map dates from 2500 B.C. from the city of Ga Sur at Nuzi (Mesopotamian) which describes the Euphrates river sided by two mountains. Homero map (Homero, 900BC) or Ptolemy map (Ptolemy, 200AD) are also very well known as the most important maps of the ancient world. In the sixteen century (1502) in the Portuguese discoveries the kingdoms richness and territories were depicted as shown in the Cantino map. Later, in the eighteen century, William Playfair, created various types of diagrams to depict statistical information, he wrote a book applying those representations techniques (Playfair, 1805). In our era in the 70´s the first infographics appeared in journals and magazines in order to summarize information and create a great impact in a massive way. Several practitioners and academicians in diverse areas need to present data graphically. From geographers, to economists, military, statisticians, engineers, biologists, to many others fields, many professionals need to see and understand data graphically. In this context, it is virtually impossible to relate to a particular field. On the other hand, the use of several disciplines in the design of data visualization artifacts is a reality. In fact, the use of principles, concepts, techniques and theories come from multiple backgrounds: programming, web design, semiotic or psychology.   These areas give an important contribute to the process of transforming data into understandable information; these areas complement each other.

week12.md

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+Link：https://joss.theoj.org/papers/10.21105/joss.03021.pdf
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+seaborn is a library for making statistical graphics in Python. It provides a high-level interface to matplotlib and integrates closely with pandas data structures. Functions in the seaborn library expose a declarative, dataset-oriented API that makes it easy to translate questions about data into graphics that can answer them. When given a dataset and a specification of the plot to make, seaborn automatically maps the data values to visual attributes such as color, size, or style, internally computes statistical transformations, and decorates the plot with informative axis labels and a legend. Many seaborn functions can generate figures with multiple panels that elicit comparisons between conditional subsets of data or across different pairings of variables in a dataset. seaborn is designed to be useful throughout the lifecycle of a scientific project. By producing complete graphics from a single function call with minimal arguments, seaborn facilitates rapid prototyping and exploratory data analysis. And by offering extensive options for customization, along with exposing the underlying matplotlib objects, it can be used to create polished, publication-quality figures.

week13.md

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+LINK: https://www.researchgate.net/profile/Adebowale-Shadare/publication/311597028_DATA_VISUALIZATION/links/5851945608aef7d0309f20a7/DATA-VISUALIZATION.pdf
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+Data visualization involves presenting data in graphical or pictorial form which makes the information easy to understand. It helps to explain facts and determine courses of action. It will benefit any field of study that requires innovative ways of presenting large, complex information. The advent of computer graphics has shaped modern visualization. This paper presents a brief introduction to data visualization.
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+Data visualization is the process of representing data in a graphical or pictorial way in a clear and effective manner. It has emerged as a powerful and widely applicable tool for analyzing and interpreting large and complex data. It has become a quick, easy means of conveying concepts in a universal format. It must communicate complex ideas with clarity, accuracy, and efficiency. These benefits have allowed data visualization to be useful in many fields of study

week14.md

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+Link: https://link.springer.com/chapter/10.1007/978-1-4471-2804-5_6
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+Most visualization techniques have been designed on the assumption that the data to be represented are free from uncertainty. Yet this is rarely the case. Recently the visualization community has risen to the challenge of incorporating an indication of uncertainty into visual representations, and in this article we review their work. We place the work in the context of a reference model for data visualization, that sees data pass through a pipeline of processes. This allows us to distinguish the visualization of uncertainty—which considers how we depict uncertainty specified with the data—and the uncertainty of visualization—which considers how much inaccuracy occurs as we process data through the pipeline. It has taken some time for uncertain visualization methods to be developed, and we explore why uncertainty visualization is hard—one explanation is that we typically need to find another display dimension and we may have used these up already! To organize the material we return to a typology developed by one of us in the early days of visualization, and make use of this to present a catalog of visualization techniques describing the research that has been done to extend each method to handle uncertainty. Finally we note the responsibility on us all to incorporate any known uncertainty into a visualization, so that integrity of the discipline is maintained.

week15.md

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+Link: https://onlinelibrary.wiley.com/doi/abs/10.1002/ev.20065?casa_token=7dHjwPf3bv8AAAAA:a-4-pLAEw86PimgrWdufmQXxh2Da6CCyjOHEG_kGQ2jV6Hf6JQHlCNkdRROW-XBvJF9Ljdy3qAe6rqY
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+This chapter elaborates on the definition of data visualization, highlights its historical development, and offers examples of how data visualization has been used in evaluations to help aid understanding, collect data and information, conduct analysis, and communicate to a variety of stakeholders. This chapter also outlines future trends in data visualization and their potential influence on evaluation practice. The chapter concludes with some of the main limitations and cautions that are associated with data visualization. © Wiley Periodicals, Inc., and the American Evaluation Association.