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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://direct.mit.edu/books/oa-monograph/5866/The-Connectivity-of-ThingsNetwork-Cultures-since"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2102.01974"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2101.03491"/>
	<rdf:li rdf:resource="https://www.mcgill.ca/oss/article/critical-thinking/dunning-kruger-effect-probably-not-real"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1904.10792"/>
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	<rdf:li rdf:resource="https://policyviz.com/product/john-snow-cholera-map-facemask/"/>
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	<rdf:li rdf:resource="https://theconversation.com/three-charts-that-show-where-the-coronavirus-death-rate-is-heading-137103"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1612.08468"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1908.06543"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1907.12879"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1908.02586"/>
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	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s11023-018-9484-3"/>
	<rdf:li rdf:resource="https://twitter.com/kjhealy/status/1117816055928373249"/>
	<rdf:li rdf:resource="https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368"/>
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	<rdf:li rdf:resource="https://blogs.lse.ac.uk/lsereviewofbooks/2019/01/10/book-review-mapping-society-the-spatial-dimensions-of-social-cartography-by-laura-vaughan/"/>
	<rdf:li rdf:resource="http://www.charlieseguin.com/dot_map.html"/>
	<rdf:li rdf:resource="https://www.frontiersin.org/articles/10.3389/fpsyg.2018.01742/full"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1705.02801"/>
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	<rdf:li rdf:resource="https://pudding.cool/process/regional_smoothing/"/>
	<rdf:li rdf:resource="https://www.washingtonpost.com/graphics/2018/investigations/unsolved-homicide-database/?city=pittsburgh"/>
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	<rdf:li rdf:resource="http://socviz.co/"/>
	<rdf:li rdf:resource="https://www.washingtonpost.com/news/monkey-cage/wp/2016/12/05/that-viral-graph-about-millennials-declining-support-for-democracy-its-very-misleading/"/>
	<rdf:li rdf:resource="http://www-personal.umich.edu/~mejn/election/2016/countycart30701024.png"/>
	<rdf:li rdf:resource="http://xkcd.com/1732/"/>
	<rdf:li rdf:resource="http://benfry.com/exd09/"/>
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	<rdf:li rdf:resource="https://rawgit.com/Groupe-ElementR/cartography/master/inst/doc/cartography.html"/>
	<rdf:li rdf:resource="http://www.omegahat.org/Rcartogram/"/>
	<rdf:li rdf:resource="http://kieranhealy.org/blog/archives/2015/06/12/americas-ur-choropleths/"/>
	<rdf:li rdf:resource="http://www.crcpress.com/product/isbn/9781466508910/des"/>
	<rdf:li rdf:resource="http://www.pitt.edu/~pittcntr/Events/All/Conferences/others/other_conf_2014-15/04-10-15_diagrams/diagrams-cfp.html"/>
	<rdf:li rdf:resource="http://mitpress.mit.edu/books/atlas-knowledge"/>
	<rdf:li rdf:resource="http://lombardi.cs.arizona.edu/"/>
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  </channel><item rdf:about="https://tkeskinturk.github.io/blog/publicopin/">
    <title>Visualizing the Dynamics of Opinion Change – Turgut Keskintürk</title>
    <dc:date>2026-03-18T13:26:14+00:00</dc:date>
    <link>https://tkeskinturk.github.io/blog/publicopin/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- I'm going to follow the references, but I will make two predictions just based on this.
1. It's pretending to solve the age-period-cohort non-identification problem by decreeing that one of those effects just doesn't exist.  (Advantages of theft over honest toil, etc.)
2. Having done so, it's the Kitagawa (-Oaxaca-Blinder) decomposition.  (Which is a cool thing I wish I had appreciated earlier, and no shame in having re-re-re-discovered.)

]]></description>
<dc:subject>have_read visual_display_of_quantitative_information social_measurement via:kjhealy public_opinion surveys cultural_evolution track_down_references</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bd8814cc4a6b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
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<item rdf:about="https://direct.mit.edu/books/oa-monograph/5866/The-Connectivity-of-ThingsNetwork-Cultures-since">
    <title>The Connectivity of Things: Network Cultures since 1832 | Books Gateway | MIT Press</title>
    <dc:date>2025-09-24T15:31:46+00:00</dc:date>
    <link>https://direct.mit.edu/books/oa-monograph/5866/The-Connectivity-of-ThingsNetwork-Cultures-since</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A media history of the material and infrastructural features of networking practices, a German classic translated for the first time into English.
"Nets hold, connect, and catch. They ensnare, bind, and entangle. Our social networks owe their name to a conceivably strange and ambivalent object. But how did the net get into the network? And how can it reasonably represent the connectedness of people, things, institutions, signs, infrastructures, and even nature? The Connectivity of Things by Sebastian Giessmann, the first media history that addresses the overwhelming diversity of networks, attempts to answer all these questions and more.
"Reconstructing the decisive moments in which networking turned into a veritable cultural technique, Giessmann takes readers below the street to the Parisian sewers and to the Suez Canal, into the telephone exchanges of Northeast America, and on to the London Underground. His brilliant history explains why social networks were discovered late, how the rapid rise of mathematical network theory was able to take place, how improbable the invention of the internet was, and even what diagrams and conspiracy theories have to do with it all. A primer on networking as a cultural technique, this translated German classic explains everything one ever could wish to know about networks."

--- I am most interested in this for the promised history of network diagrams.]]></description>
<dc:subject>to:NB books:noted networks history_of_technology visual_display_of_quantitative_information downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7041adc9e602/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
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<item rdf:about="https://www.degruyter.com/document/isbn/9781503642102/html">
    <title>Thinking Through Data: How Outliers, Aggregates, and Patterns Shape Perception</title>
    <dc:date>2025-03-14T17:49:16+00:00</dc:date>
    <link>https://www.degruyter.com/document/isbn/9781503642102/html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We encounter digital data processing on a range of platforms and in a multitude of contexts today: in the predictive algorithms of the financial sector, in drones, insurance, and risk management, in smart cities, biometrics, medicine, and more. This fascinating book explores the historical context of the current data-driven paradigm and explains how elusive yet crucial statistical concepts such as outliers, aggregates, and patterns form how we sense and make sense of data. From the sixteenth century's embodied measurements of the foot, through the blurred facial features of L'Homme Moyen, to the image aggregates of today's security systems, the examples collected in this book illustrate the central role of aesthetics throughout the history of statistical knowledge production. Taking its point of departure in analyses and discussions of contemporary artistic experiments by Rossella Biscotti, Stéphanie Solinas, and Adam Broomberg and Oliver Chanarin, the book broadens our understanding of the structures of knowledge and methods in statistical computation beyond optimistic narratives of calculative power. Venturing out into the tails of the distributions—to the systemically overlooked and excluded—this book challenges us to embrace an alternative view of modern data processing."

--- Sounds like "baby stats. for the Theory-brained, via pictures", but I actually think that would be amazing so I hope that's what this is and it's good. 
]]></description>
<dc:subject>to:NB books:noted statistics aesthetics history_of_statistics visual_display_of_quantitative_information downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:adb95a135c62/</dc:identifier>
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<item rdf:about="http://jakehofman.com/publication/visualizing-inferential-uncertainty/">
    <title>How visualizing inferential uncertainty can mislead readers about treatment effects in scientific results 🏆 | jakehofman.com</title>
    <dc:date>2023-02-13T14:57:39+00:00</dc:date>
    <link>http://jakehofman.com/publication/visualizing-inferential-uncertainty/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When presenting visualizations of experimental results, scientists often choose to display either inferential uncertainty (e.g., uncertainty in the estimate of a population mean) or outcome uncertainty (e.g., variation of outcomes around that mean) about their estimates. How does this choice impact readers’ beliefs about the size of treatment effects? We investigate this question in two experiments comparing 95% confidence intervals (means and standard errors) to 95% prediction intervals (means and standard deviations). The first experiment finds that participants are willing to pay more for and overestimate the effect of a treatment when shown confidence intervals relative to prediction intervals. The second experiment evaluates how alternative visualizations compare to standard visualizations for different effect sizes. We find that axis rescaling reduces error, but not as well as prediction intervals or animated hypothetical outcome plots (HOPs), and that depicting inferential uncertainty causes participants to underestimate variability in individual outcomes."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information confidence_sets prediction hofman.jake via:? to_teach:linear_models to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:00972a2320bb/</dc:identifier>
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<item rdf:about="https://journals.sagepub.com/doi/10.1177/2378023120960959">
    <title>Class Mobility and Reproduction for Black and White Adults in the United States: A Visualization - Daniel Laurison, Dawn Dow, Carolyn Chernoff, 2020</title>
    <dc:date>2023-01-17T02:29:24+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/10.1177/2378023120960959</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The relationship between where people start out in life (class origin) and where they are likely to end up (class destination) is central to any question about the fairness of contemporary society. Yet we often don’t have a good picture—literally or metaphorically—of the contours of that relationship. Further, work on class mobility in the United States often glosses over the large differences between white and Black Americans’ class positions and mobility trajectories. This visualization, based on data from the Panel Study of Income Dynamics, shows the association between occupational class origin and destination for Black and white employed Americans ages 25 to 69. Stark racial inequality, produced by the legacy and ongoing operation of white supremacy, is evident in each aspect of these figures."]]></description>
<dc:subject>transmission_of_inequality visual_display_of_quantitative_information to_teach:statistics_of_inequality_and_discrimination in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://www.prisonpolicy.org/reports/pie2020.html">
    <title>Mass Incarceration: The Whole Pie 2020 | Prison Policy Initiative</title>
    <dc:date>2022-12-29T03:06:22+00:00</dc:date>
    <link>https://www.prisonpolicy.org/reports/pie2020.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Always good at sparking class discussion.]]></description>
<dc:subject>prison crime visual_display_of_quantitative_information to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e47ac53db4af/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2109.12975">
    <title>[2109.12975] Towards a Theory of Bullshit Visualization</title>
    <dc:date>2021-10-30T03:24:16+00:00</dc:date>
    <link>https://arxiv.org/abs/2109.12975</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this unhinged rant, I lay out my suspicion that a lot of visualizations are bullshit: charts that do not have even the common decency to intentionally lie but are totally unconcerned about the state of the world or any practical utility. I suspect that bullshit charts take up a large fraction of the time and attention of actual visualization producers and consumers, and yet are seemingly absent from academic research into visualization design."]]></description>
<dc:subject>via:? visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c4bf1ff24ab9/</dc:identifier>
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<item rdf:about="https://mitpress.mit.edu/books/atlas-forecasts">
    <title>Atlas of Forecasts | The MIT Press</title>
    <dc:date>2021-09-23T17:57:15+00:00</dc:date>
    <link>https://mitpress.mit.edu/books/atlas-forecasts</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["To envision and create the futures we want, society needs an appropriate understanding of the likely impact of alternative actions. Data models and visualizations offer a way to understand and intelligently manage complex, interlinked systems in science and technology, education, and policymaking. Atlas of Forecasts, from the creator of Atlas of Science and Atlas of Knowledge, shows how we can use data to predict, communicate, and ultimately attain desirable futures.
"Using advanced data visualizations to introduce different types of computational models, Atlas of Forecasts demonstrates how models can inform effective decision-making in education, science, technology, and policymaking. The models and maps presented aim to help anyone understand key processes and outcomes of complex systems dynamics, including which human skills are needed in an artificial intelligence–empowered economy; what progress in science and technology is likely to be made; and how policymakers can future-proof regions or nations. This Atlas offers a driver's seat-perspective for a test-drive of the future."]]></description>
<dc:subject>to:NB books:noted coveted visual_display_of_quantitative_information pretty_pictures prediction popular_science borner.katy books:in_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5caec1738b81/</dc:identifier>
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<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/10618600.2021.1938586">
    <title>The q–q Boxplot: Journal of Computational and Graphical Statistics: Vol 0, No 0</title>
    <dc:date>2021-07-22T15:42:11+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/10618600.2021.1938586</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Boxplots have become an extremely popular display of distribution summaries for collections of data, especially when we need to visualize summaries for several collections simultaneously. The whiskers in the boxplot show only the extent of the tails for most of the data (with outside values denoted separately); more detailed information about the shape of the tails, such as skewness and “weight” relative to a standard reference distribution, is much better displayed via quantile–quantile (q-q) plots. We incorporate the q-q plot’s tail information into the traditional boxplot by replacing the boxplot’s whiskers with the tails from a q-q plot, and display these tails with confidence bands for the tails that would be expected from the tails of the reference distribution. We describe the construction of the “q-q boxplot” and demonstrate its advantages over earlier proposed boxplot modifications on data from economics and neuroscience, which illustrate the q-q boxplots’ effectiveness in showing important tail behavior especially for large datasets. The package qqboxplot (an extension to the ggplot2 package) is available for the R programming language. Supplementary files for this article are available online."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information statistics kafadar.karen</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bf5ad5438099/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kafadar.karen"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.07811">
    <title>[2105.07811] Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty in Election Polls</title>
    <dc:date>2021-05-18T17:59:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.07811</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Election poll reporting often focuses on mean values and only subordinately discusses the underlying uncertainty. Subsequent interpretations are too often phrased as certain. Moreover, media coverage rarely adequately takes into account the differences between now- and forecasts. These challenges were ubiquitous in the context of the 2016 and 2020 U.S. presidential elections, but are also present in multi-party systems like Germany. We discuss potential sources of bias in nowcasting and forecasting and review the current standards in the visual presentation of survey-based nowcasts. Concepts are presented to attenuate the issue of falsely perceived accuracy. We discuss multiple visual presentation techniques for central aspects in poll reporting. One key idea is the use of Probabilities of Events instead of party shares. The presented ideas offer modern and improved ways to communicate (changes in) the electoral mood for the general media."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information confidence_sets prediction statistics political_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:03eee10e15fb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2008.01779">
    <title>[2008.01779] Cumulative deviation of a subpopulation from the full population</title>
    <dc:date>2021-05-18T14:02:37+00:00</dc:date>
    <link>https://arxiv.org/abs/2008.01779</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Assessing equity in treatment of a subpopulation often involves assigning numerical "scores" to all individuals in the full population such that similar individuals get similar scores; matching via propensity scores or appropriate covariates is common, for example. Given such scores, individuals with similar scores may or may not attain similar outcomes independent of the individuals' memberships in the subpopulation. The traditional graphical methods for visualizing inequities are known as "reliability diagrams" or "calibrations plots," which bin the scores into a partition of all possible values, and for each bin plot both the average outcomes for only individuals in the subpopulation as well as the average outcomes for all individuals; comparing the graph for the subpopulation with that for the full population gives some sense of how the averages for the subpopulation deviate from the averages for the full population. Unfortunately, real data sets contain only finitely many observations, limiting the usable resolution of the bins, and so the conventional methods can obscure important variations due to the binning. Fortunately, plotting cumulative deviation of the subpopulation from the full population as proposed in this paper sidesteps the problematic coarse binning. The cumulative plots encode subpopulation deviation directly as the slopes of secant lines for the graphs. Slope is easy to perceive even when the constant offsets of the secant lines are irrelevant. The cumulative approach avoids binning that smooths over deviations of the subpopulation from the full population. Such cumulative aggregation furnishes both high-resolution graphical methods and simple scalar summary statistics (analogous to those of Kuiper and of Kolmogorov and Smirnov used in statistical significance testing for comparing probability distributions)."]]></description>
<dc:subject>to:NB calibration visual_display_of_quantitative_information matching to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:36a8567ee82a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:calibration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:matching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.08016">
    <title>[2104.08016] A Review of the State-of-the-Art on Tours for Dynamic Visualization of High-dimensional Data</title>
    <dc:date>2021-04-21T14:52:40+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.08016</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article discusses a high-dimensional visualization technique called the tour, which can be used to view data in more than three dimensions. We review the theory and history behind the technique, as well as modern software developments and applications of the tour that are being found across the sciences and machine learning."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1e68103ece27/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nytimes.com/interactive/2021/03/17/upshot/partisan-segregation-maps.html">
    <title>A Close-Up Picture of Partisan Segregation, Among 180 Million Voters - The New York Times</title>
    <dc:date>2021-03-18T22:22:47+00:00</dc:date>
    <link>https://www.nytimes.com/interactive/2021/03/17/upshot/partisan-segregation-maps.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Visually striking, even low-key gorgeous.

"Colors show estimates of partisanship using data collected from 2016 to 2018, based on party registration, participation in partisan primary elections, demographic information and precinct or county election results."

--- I don't see anything in this about how the model is validated, and the demographic component makes me leery about how much of this is just America's Ur-Choropleth No. 2 in high resolution.]]></description>
<dc:subject>us_politics visual_display_of_quantitative_information maps spatial_statistics partisanship_and_polarization to_teach:data_over_space_and_time pretty_pictures track_down_references have_read to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7f4705717225/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:partisanship_and_polarization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pretty_pictures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2102.01974">
    <title>[2102.01974] AttentionFlow: Visualising Influence in Networks of Time Series</title>
    <dc:date>2021-02-05T20:09:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.01974</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest. Moreover, attention trends of different items exhibit mutual influence via mechanisms such as hyperlinks or recommendations. Many visualisation tools exist for time series, network evolution, or network influence; however, few systems connect all three. In this work, we present AttentionFlow, a new system to visualise networks of time series and the dynamic influence they have on one another. Centred around an ego node, our system simultaneously presents the time series on each node using two visual encodings: a tree ring for an overview and a line chart for details. AttentionFlow supports interactions such as overlaying time series of influence and filtering neighbours by time or flux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and WikiTraffic. We show that attention spikes in songs can be explained by external events such as major awards, or changes in the network such as the release of a new song. Separate case studies also demonstrate how an artist's influence changes over their career, and that correlated Wikipedia traffic is driven by cultural interests. More broadly, AttentionFlow can be generalised to visualise networks of time series on physical infrastructures such as road networks, or natural phenomena such as weather and geological measurements."

--- I am quite sure that "influenced by" here is just going to be cashed out as "predictable from", but that might still be interesting.]]></description>
<dc:subject>to:NB time_series network_data_analysis visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f8a5c08e5644/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2101.03491">
    <title>[2101.03491] gwpcorMapper: an interactive mapping tool for exploring geographically weighted correlation and partial correlation in high-dimensional geospatial datasets</title>
    <dc:date>2021-01-12T22:29:20+00:00</dc:date>
    <link>https://arxiv.org/abs/2101.03491</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Exploratory spatial data analysis (ESDA) plays a key role in research that includes geographic data. In ESDA, analysts often want to be able to visualize observations and local relationships on a map. However, software dedicated to visualizing local spatial relations be-tween multiple variables in high dimensional datasets remains undeveloped. This paper introduces gwpcorMapper, a newly developed software application for mapping geographically weighted correlation and partial correlation in large multivariate datasets. gwpcorMap-per facilitates ESDA by giving researchers the ability to interact with map components that describe local correlative relationships. We built gwpcorMapper using the R Shiny framework. The software inherits its core algorithm from GWpcor, an R library for calculating the geographically weighted correlation and partial correlation statistics. We demonstrate the application of gwpcorMapper by using it to explore census data in order to find meaningful relationships that describe the work-life environment in the 23 special wards of Tokyo, Japan. We show that gwpcorMapper is useful in both variable selection and parameter tuning for geographically weighted statistics. gwpcorMapper highlights that there are strong statistically clear local variations in the relationship between the number of commuters and the total number of hours worked when considering the total population in each district across the 23 special wards of Tokyo. Our application demonstrates that the ESDA process with high-dimensional geospatial data using gwpcorMapper has applications across multiple fields."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information spatial_statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:85635ec5b123/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mcgill.ca/oss/article/critical-thinking/dunning-kruger-effect-probably-not-real">
    <title>The Dunning-Kruger Effect Is Probably Not Real | Office for Science and Society - McGill University</title>
    <dc:date>2021-01-03T20:51:26+00:00</dc:date>
    <link>https://www.mcgill.ca/oss/article/critical-thinking/dunning-kruger-effect-probably-not-real</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- It's rather annoying that there was no link to the R code, but I reproduced the basics with a little bit of work (incorporated below for reference).  Setting the measurement standard deviation (s) to 1, as in my code, gives a modest but perceptible over-estimation at low percentiles and under-estimation at high percentiles; set it to 5 and marvel.



n <- 1000
s <- 1
actual.raw <- rnorm(n)
perceived.raw <- actual.raw+rnorm(n,sd=s)

buckets <- cut(actual.raw,
               breaks=quantile(actual.raw,
                               probs=c(0:4)/4))

perceived <- 100*ecdf(perceived.raw)(perceived.raw)
actual <- 100*ecdf(actual.raw)(actual.raw)


plot(x=actual, y=perceived, cex=0.1,
     xlab="Actual percentile", ylab="Perceived percentile")
points(y=aggregate(perceived~buckets,
                   FUN=mean)[,2],
       x=aggregate(actual~buckets,
                   FUN=mean)[,2],
       pch=16, col="blue")
abline(0,1, col="grey")
abline(lm(perceived~actual),col="blue")

# For contrast:
plot(x=actual.raw, y=perceived.raw, cex=0.1,
     xlab="Actual raw score", ylab="Perceived raw score")
abline(0,1, col="grey")
abline(lm(perceived.raw~actual.raw),col="blue")]]></description>
<dc:subject>debunking bad_data_analysis psychology dunning-kruger visual_display_of_quantitative_information to_teach:linear_models via:tsuomela</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:faac8efe5364/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:debunking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bad_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dunning-kruger"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:tsuomela"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nytimes.com/interactive/2020/12/20/us/politics/election-hispanics-asians-voting.html">
    <title>Where Immigrant Neighborhoods Swung Right in the Election - The New York Times</title>
    <dc:date>2020-12-22T04:59:21+00:00</dc:date>
    <link>https://www.nytimes.com/interactive/2020/12/20/us/politics/election-hispanics-asians-voting.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This is visually impressive, and clearly needs explanation.  But it's also the ecological fallacy from top to bottom.  ]]></description>
<dc:subject>us_politics visual_display_of_quantitative_information ecological_inference_and_the_ecological_fallacy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b8c9e814e1f4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecological_inference_and_the_ecological_fallacy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kieranhealy.org/blog/archives/2020/10/10/excess-deaths-overview/">
    <title>Excess Deaths Overview - kieranhealy.org</title>
    <dc:date>2020-12-19T04:19:32+00:00</dc:date>
    <link>https://kieranhealy.org/blog/archives/2020/10/10/excess-deaths-overview/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>coronavirus_pandemic_of_2019-- visual_display_of_quantitative_information healy.kieran</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:eb3757ec97ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coronavirus_pandemic_of_2019--"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/abs/10.1177/0049124120914943">
    <title>Clustered Iconography: A Resurrected Method for Representing Multidimensional Data - Olav Muurlink, Anthony M. Gould, Jean-Etienne Joullié, 2020</title>
    <dc:date>2020-12-16T21:30:54+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.1177/0049124120914943</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Development of graphical methods for representing data has not kept up with progress in statistical techniques. This article presents a brief history of graphical representations of research findings and makes the case for a revival of methods developed in the early and mid-twentieth century, notably ISOTYPE and Chernoff’s faces. It resurrects and improves a procedure, clustered iconography, which enables the presentation of multidimensional data through which readers engage more effectively with the presentation’s central message by way of an easier understanding of relationships between variables. The proposed technique is especially well adapted to the needs and protocols of open-source research."

--- ISOTYPE!]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information neurath.otto an_elegant_method_of_a_more_barbarous_age</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5f0d66052975/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neurath.otto"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:an_elegant_method_of_a_more_barbarous_age"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.10792">
    <title>[1904.10792] Trajectory Functional Boxplots</title>
    <dc:date>2020-12-09T15:21:58+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.10792</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["With the development of data-monitoring techniques in various fields of science, multivariate functional data are often observed. Consequently, an increasing number of methods have appeared to extend the general summary statistics of multivariate functional data. However, trajectory functional data, as an important sub-type, have not been studied very well. This article proposes two informative exploratory tools, the trajectory functional boxplot, and the modified simplicial band depth (MSBD) versus Wiggliness of Directional Outlyingness (WO) plot, to visualize the centrality of trajectory functional data. The newly defined WO index effectively measures the shape variation of curves and hence serves as a detector for shape outliers; additionally, MSBD provides a center-outward ranking result and works as a detector for magnitude outliers. Using the two measures, the functional boxplot of the trajectory reveals center-outward patterns and potential outliers using the raw curves, whereas the MSBD-WO plot illustrates such patterns and outliers in a space spanned by MSBD and WO. The proposed methods are validated on hurricane path data and migration trace data recorded from two types of birds."

--- Of course I taught functional boxplots _last_ week.]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information time_series functional_data_analysis statistics to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d83fc296dfb1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:functional_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.washingtonpost.com/graphics/2018/national/mass-shootings-in-america/">
    <title>Mass shooting statistics in the United States - Washington Post</title>
    <dc:date>2020-12-05T19:36:59+00:00</dc:date>
    <link>https://www.washingtonpost.com/graphics/2018/national/mass-shootings-in-america/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>violence data_sets mass_shootings re:statistics_of_muckers visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f1fb448513b9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mass_shootings"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:statistics_of_muckers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-031219-041252">
    <title>Testing Statistical Charts: What Makes a Good Graph? | Annual Review of Statistics and Its Application</title>
    <dc:date>2020-11-19T20:02:55+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-031219-041252</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It has been approximately 100 years since the very first formal experimental evaluations of statistical charts were conducted. In that time, technological changes have impacted both our charts and our testing methods, resulting in a dizzying array of charts, many different taxonomies to classify graphics, and several different philosophical approaches to testing the efficacy of charts and graphs experimentally. Once rare, charts and graphical displays are now everywhere—but do they help us understand? In this article we review the history of graphical testing across disciplines, discuss different direct approaches to testing graphics, and contrast direct tests with visual inference, which requires that the viewer determine both the question and the answer. Examining the past 100 years of graphical testing, we summarize best practices for creating effective graphics and discuss what the future holds for graphics and empirical testing of interactive statistical visualizations."

]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information statistics evidence_based</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c981b79fa1ae/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evidence_based"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://statisticalatlas.com/United-States/Overview">
    <title>The Demographic Statistical Atlas of the United States - Statistical Atlas</title>
    <dc:date>2020-09-28T15:03:43+00:00</dc:date>
    <link>https://statisticalatlas.com/United-States/Overview</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>visual_display_of_quantitative_information demography via:arthegall to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cd5c089755d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:demography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arthegall"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://mitpress.mit.edu/books/visualization-and-interpretation">
    <title>Visualization and Interpretation | The MIT Press</title>
    <dc:date>2020-09-21T03:54:35+00:00</dc:date>
    <link>https://mitpress.mit.edu/books/visualization-and-interpretation</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the several decades since humanists have taken up computational tools, they have borrowed many techniques from other fields, including visualization methods to create charts, graphs, diagrams, maps, and other graphic displays of information. But are these visualizations actually adequate for the interpretive approach that distinguishes much of the work in the humanities? Information visualization, as practiced today, lacks the interpretive frameworks required for humanities-oriented methodologies. In this book, Johanna Drucker continues her interrogation of visual epistemology in the digital humanities, reorienting the creation of digital tools within humanities contexts.
"Drucker examines various theoretical understandings of visual images and their relation to knowledge and how the specifics of the graphical are to be engaged directly as a primary means of knowledge production for digital humanities. She draws on ideas from aesthetics, critical theory, and formal study of graphical systems, addressing them within the specific framework of computational and digital activity as they apply to digital humanities. Finally, she presents a series of standard problems in visualization for the humanities (including time/temporality, space/spatial relations, and data analysis), posing the investigation in terms of innovative graphical systems informed by probabilistic critical hermeneutics. She concludes with a brief sketch of discovery tools as an additional interface into which modeling can be worked."]]></description>
<dc:subject>to:NB books:noted visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4695f6cd2ab1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ieeexplore.ieee.org/abstract/document/6875964?casa_token=xmlHoSs-zCQAAAAA:KApgss8hoV85rUJFz7PCabJtDmlzk4a8MF4c3JG01L6d8YMaELOUJcRz7YnhOxufqDZHTocV">
    <title>Curve Boxplot: Generalization of Boxplot for Ensembles of Curves - IEEE Journals &amp; Magazine</title>
    <dc:date>2020-07-13T17:46:07+00:00</dc:date>
    <link>https://ieeexplore.ieee.org/abstract/document/6875964?casa_token=xmlHoSs-zCQAAAAA:KApgss8hoV85rUJFz7PCabJtDmlzk4a8MF4c3JG01L6d8YMaELOUJcRz7YnhOxufqDZHTocV</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In simulation science, computational scientists often study the behavior of their simulations by repeated solutions with variations in parameters and/or boundary values or initial conditions. Through such simulation ensembles, one can try to understand or quantify the variability or uncertainty in a solution as a function of the various inputs or model assumptions. In response to a growing interest in simulation ensembles, the visualization community has developed a suite of methods for allowing users to observe and understand the properties of these ensembles in an efficient and effective manner. An important aspect of visualizing simulations is the analysis of derived features, often represented as points, surfaces, or curves. In this paper, we present a novel, nonparametric method for summarizing ensembles of 2D and 3D curves. We propose an extension of a method from descriptive statistics, data depth, to curves. We also demonstrate a set of rendering and visualization strategies for showing rank statistics of an ensemble of curves, which is a generalization of traditional whisker plots or boxplots to multidimensional curves. Results are presented for applications in neuroimaging, hurricane forecasting and fluid dynamics."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information confidence_sets statistics spatio-temporal_statistics to_teach:data_over_space_and_time simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:234cf85ca14f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/abs/10.1198/jcgs.2011.09224">
    <title>Functional Boxplots: Journal of Computational and Graphical Statistics: Vol 20, No 2</title>
    <dc:date>2020-07-13T17:43:39+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/abs/10.1198/jcgs.2011.09224</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article proposes an informative exploratory tool, the functional boxplot, for visualizing functional data, as well as its generalization, the enhanced functional boxplot. Based on the center outward ordering induced by band depth for functional data, the descriptive statistics of a functional boxplot are: the envelope of the 50% central region, the median curve, and the maximum non-outlying envelope. In addition, outliers can be detected in a functional boxplot by the 1.5 times the 50% central region empirical rule, analogous to the rule for classical boxplots. The construction of a functional boxplot is illustrated on a series of sea surface temperatures related to the El Niño phenomenon and its outlier detection performance is explored by simulations. As applications, the functional boxplot and enhanced functional boxplot are demonstrated on children growth data and spatio-temporal U.S. precipitation data for nine climatic regions, respectively."]]></description>
<dc:subject>to:NB to_read visual_display_of_quantitative_information spatio-temporal_statistics confidence_sets simulation statistics to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a14fd1ea247f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.05035">
    <title>[2007.05035] Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles</title>
    <dc:date>2020-07-13T16:52:47+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.05035</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Across the world, scholars are racing to predict the spread of the novel coronavirus, COVID-19. Such predictions are often pursued by numerically simulating epidemics with a large number of plausible combinations of relevant parameters. It is essential that any forecast of the epidemic trajectory derived from the resulting ensemble of simulated curves is presented with confidence intervals that communicate the uncertainty associated with the forecast. Here we argue that the state-of-the-art approach for summarizing ensemble statistics does not capture crucial epidemiological information. In particular, the current approach systematically suppresses information about the projected trajectory peaks. The fundamental problem is that each time step is treated separately in the statistical analysis. We suggest using curve-based descriptive statistics to summarize trajectory ensembles. The results presented allow researchers to report more representative confidence intervals, resulting in more realistic projections of epidemic trajectories and -- in turn -- enable better decision making in the face of the current and future pandemics."

--- To think through: how is this related to the difficulties with putting valid confidence sets on nonparametric curve estimates?]]></description>
<dc:subject>simulation to_teach:data_over_space_and_time confidence_sets statistics via:carl_bergstrom visual_display_of_quantitative_information have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d741cb8bd8f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:carl_bergstrom"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://policyviz.com/product/john-snow-cholera-map-facemask/">
    <title>John Snow Cholera Map FaceMask - Policy Viz</title>
    <dc:date>2020-05-15T20:52:23+00:00</dc:date>
    <link>https://policyviz.com/product/john-snow-cholera-map-facemask/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Vetoed by the wife, but perhaps of interest to readers.]]></description>
<dc:subject>funny:geeky funny:pointed coronavirus_pandemic_of_2019-- epidemiology spatial_statistics visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:53f0f5a3ff26/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:funny:geeky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:funny:pointed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coronavirus_pandemic_of_2019--"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://yalebooks.yale.edu/book/9780300243406/slowdown">
    <title>Slowdown | Yale University Press</title>
    <dc:date>2020-05-05T15:17:03+00:00</dc:date>
    <link>https://yalebooks.yale.edu/book/9780300243406/slowdown</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Drawing from an incredibly rich trove of global data, this groundbreaking book reveals that human progress has been slowing down since the early 1970s. Danny Dorling uses compelling visualizations to illustrate how fertility rates, growth in GDP per person, and even the frequency of new social movements have all steadily declined over the last few generations.
"Perhaps most surprising of all is the fact that even as new technologies frequently reshape our everyday lives and are widely believed to be propelling our civilization into new and uncharted waters, the rate of technological progress is also rapidly dropping. Rather than lament this turn of events, Dorling embraces it as a moment of promise and a move toward stability, and he notes that many of the older great strides in progress that have defined recent history also brought with them widespread warfare, divided societies, and massive inequality."

--- On the one hand, this accords well with my prejudices, and Dorling is a co-author of a co-author (and friend).  On the other hand, the justly-mocked zig-zag-and-swirl visualization making the rounds the other day [https://theconversation.com/three-charts-that-show-where-the-coronavirus-death-rate-is-heading-137103] is apparently the type of graph his graphic designer came up with for this book, which, well...]]></description>
<dc:subject>books:noted to:NB visual_display_of_quantitative_information modernity our_decrepit_institutions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7503a87f14f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modernity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:our_decrepit_institutions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://theconversation.com/three-charts-that-show-where-the-coronavirus-death-rate-is-heading-137103">
    <title>Three charts that show where the coronavirus death rate is heading</title>
    <dc:date>2020-04-27T17:34:51+00:00</dc:date>
    <link>https://theconversation.com/three-charts-that-show-where-the-coronavirus-death-rate-is-heading-137103</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[On the one hand: Figure 2 is really one of the worst statistical graphics I've ever seen, and I am bookmarking it largely to offer as comic relief the next time I teach spatio-temporal statistics.  (The horizontal axis is the first derivative of the vertical axis; everything here would be conveyed by a simple plot of quantity vs. time, or at most of 2nd derivative of quantity vs. time.)  _Of course_ it was invented by a graphic designer trying to pretty up the author's Excel charts.
On the other hand: this is the co-author of a co-author.  There but for the grace of God, etc.]]></description>
<dc:subject>visual_display_of_quantitative_information bad_data_analysis to_teach:data_over_space_and_time trapped_in_plutos_republic</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ea86d8082f67/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bad_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:trapped_in_plutos_republic"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://eagereyes.org/blog/2020/the-visual-evolution-of-the-flattening-the-curve-information-graphic">
    <title>The Visual Evolution of the “Flattening the Curve” Information Graphic</title>
    <dc:date>2020-03-26T13:31:32+00:00</dc:date>
    <link>https://eagereyes.org/blog/2020/the-visual-evolution-of-the-flattening-the-curve-information-graphic</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>visual_display_of_quantitative_information epidemiology_of_representations via:? epidemiology coronavirus_pandemic_of_2019--</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:564a1b1d439d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology_of_representations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coronavirus_pandemic_of_2019--"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.12902">
    <title>[1909.12902] Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs</title>
    <dc:date>2019-10-01T17:36:07+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.12902</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["To perform visual data exploration, many dimensionality reduction methods have been developed. These tools allow data analysts to represent multidimensional data in a 2D or 3D space, while preserving as much relevant information as possible. Yet, they cannot preserve all structures simultaneously and they induce some unavoidable distortions. Hence, many criteria have been introduced to evaluate a map's overall quality, mostly based on the preservation of neighbourhoods. Such global indicators are currently used to compare several maps, which helps to choose the most appropriate mapping method and its hyperparameters. However, those aggregated indicators tend to hide the local repartition of distortions. Thereby, they need to be supplemented by local evaluation to ensure correct interpretation of maps. In this paper, we describe a new method, called MING, for `Map Interpretation using Neighbourhood Graphs'. It offers a graphical interpretation of pairs of map quality indicators, as well as local evaluation of the distortions. This is done by displaying on the map the nearest neighbours graphs computed in the data space and in the embedding. Shared and unshared edges exhibit reliable and unreliable neighbourhood information conveyed by the mapping. By this mean, analysts may determine whether proximity (or remoteness) of points on the map faithfully represents similarity (or dissimilarity) of original data, within the meaning of a chosen map quality criteria. We apply this approach to two pairs of widespread indicators: precision/recall and trustworthiness/continuity, chosen for their wide use in the community, which will allow an easy handling by users."

--- Isn't this the "false nearest neighbors" method of the old geometry-from-a-time-series literature?]]></description>
<dc:subject>to:NB dimension_reduction visual_display_of_quantitative_information statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5414d42b3487/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dimension_reduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1612.08468">
    <title>[1612.08468] Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models</title>
    <dc:date>2019-08-21T13:12:57+00:00</dc:date>
    <link>https://arxiv.org/abs/1612.08468</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When fitting black box supervised learning models (e.g., complex trees, neural networks, boosted trees, random forests, nearest neighbors, local kernel-weighted methods, etc.), visualizing the main effects of the individual predictor variables and their low-order interaction effects is often important, and partial dependence (PD) plots are the most popular approach for accomplishing this. However, PD plots involve a serious pitfall if the predictor variables are far from independent, which is quite common with large observational data sets. Namely, PD plots require extrapolation of the response at predictor values that are far outside the multivariate envelope of the training data, which can render the PD plots unreliable. Although marginal plots (M plots) do not require such extrapolation, they produce substantially biased and misleading results when the predictors are dependent, analogous to the omitted variable bias in regression. We present a new visualization approach that we term accumulated local effects (ALE) plots, which inherits the desirable characteristics of PD and M plots, without inheriting their preceding shortcomings. Like M plots, ALE plots do not require extrapolation; and like PD plots, they are not biased by the omitted variable phenomenon. Moreover, ALE plots are far less computationally expensive than PD plots."]]></description>
<dc:subject>to:NB variable_selection visual_display_of_quantitative_information statistics regression</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9eb5cd7d3c48/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:variable_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.06081">
    <title>[1908.06081] Analyzing the Fine Structure of Distributions</title>
    <dc:date>2019-08-20T15:29:50+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.06081</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["One aim of data mining is the identification of interesting structures in data. Basic properties of the empirical distribution, such as skewness and an eventual clipping, i.e., hard limits in value ranges, need to be assessed. Of particular interest is the question, whether the data originates from one process, or contains subsets related to different states of the data producing process. Data visualization tools should deliver a sensitive picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs are typically kernel density estimates and range from the classical histogram to modern tools like bean or violin plots. Conventional methods have difficulties in visualizing the pdf in case of uniform, multimodal, skewed and clipped data if density estimation parameters remain in a default setting. As a consequence, a new visualization tool called Mirrored Density plot (MD plot) is proposed which is particularly designed to discover interesting structures in continuous features. The MD plot does not require any adjustments of parameters of density estimation which makes the usage compelling for non-experts. The visualization tools are evaluated in comparison to statistical tests for the typical challenges of explorative distribution analysis. The results are presented on bimodal Gaussian and skewed distributions as well as several features with published pdfs. In exploratory data analysis of 12 features describing the quarterly financial statements, when statistical testing becomes a demanding task, only the MD plots can identify the structure of their pdfs. Overall, the MD plot can outperform the methods mentioned above."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information density_estimation statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8c6b9fda1aed/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:density_estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.06543">
    <title>[1908.06543] Benchmarks for Graph Embedding Evaluation</title>
    <dc:date>2019-08-20T14:21:27+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.06543</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Graph embedding is the task of representing nodes of a graph in a low-dimensional space and its applications for graph tasks have gained significant traction in academia and industry. The primary difference among the many recently proposed graph embedding methods is the way they preserve the inherent properties of the graphs. However, in practice, comparing these methods is very challenging. The majority of methods report performance boosts on few selected real graphs. Therefore, it is difficult to generalize these performance improvements to other types of graphs. Given a graph, it is currently impossible to quantify the advantages of one approach over another. In this work, we introduce a principled framework to compare graph embedding methods. Our goal is threefold: (i) provide a unifying framework for comparing the performance of various graph embedding methods, (ii) establish a benchmark with real-world graphs that exhibit different structural properties, and (iii) provide users with a tool to identify the best graph embedding method for their data. This paper evaluates 4 of the most influential graph embedding methods and 4 traditional link prediction methods against a corpus of 100 real-world networks with varying properties. We organize the 100 networks in terms of their properties to get a better understanding of the embedding performance of these popular methods. We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance. We rank the state-of-the-art embedding approaches using the GFS-score and show that it can be used to understand and evaluate novel embedding approaches. We envision that the proposed framework (this https URL) will serve the community as a benchmarking platform to test and compare the performance of future graph embedding techniques."]]></description>
<dc:subject>to:NB graph_embedding visual_display_of_quantitative_information network_data_analysis to_teach:baby-nets re:hyperbolic_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a29251928d7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_embedding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:hyperbolic_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://newleftreview.org/issues/II118/articles/franco-moretti-oleg-sobchuk-hidden-in-plain-sight?pc=126">
    <title>Franco Moretti &amp; Oleg Sobchuk, Hidden In Plain Sight, NLR 118, July–August 2019</title>
    <dc:date>2019-08-14T19:29:00+00:00</dc:date>
    <link>https://newleftreview.org/issues/II118/articles/franco-moretti-oleg-sobchuk-hidden-in-plain-sight?pc=126</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["If there is one feature that immediately distinguishes the digital humanities (dh) from the ‘other’ humanities, data visualization has to be it. Histograms, scatterplots, time series, diagrams, networks . . . ten, fifteen years ago, studies of film, music, literature or art didn’t use any of these. Now they do, and here we examine some premises (unspoken, and often probably unconscious) of this field-defining practice. Field-defining, because visualization is never just visualization: it involves the formation of corpora, the definition of data, their elaboration, and often some sort of preliminary interpretation as well. Whence the idea of this article: to gather sixty-odd studies that have had a significant impact on dh, and analyse how they visually present their data.footnote1 What interests us is visualization as a practice, in the conviction that practices—what we learn to do by doing, by professional habit, without being fully aware of what we are doing—often have larger theoretical implications than theoretical statements themselves. Whether this has indeed been the case for dh, is for readers to decide."]]></description>
<dc:subject>to:NB to_read visual_display_of_quantitative_information humanities moretti.franco</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b717ee5bcb68/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:humanities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:moretti.franco"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.12879">
    <title>[1907.12879] Visual Entropy and the Visualization of Uncertainty</title>
    <dc:date>2019-08-08T14:24:20+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.12879</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Background: It is possible to find many different visual representations of data values in visualizations, it is less common to see visual representations that include uncertainty, especially in visualizations intended for non-technical audiences. Objective: our aim is to rigorously define and evaluate the novel use of visual entropy as a measure of shape that allows us to construct an ordered scale of glyphs for use in representing both uncertainty and value in 2D and 3D environments. Method: We use sample entropy as a numerical measure of visual entropy to construct a set of glyphs using R and Blender which vary in their complexity. Results: A Bradley-Terry analysis of a pairwise comparison of the glyphs shows participants (n=19) ordered the glyphs as predicted by the visual entropy score (linear regression R2 >0.97, p<0.001). We also evaluate whether the glyphs can effectively represent uncertainty using a signal detection method, participants (n=15) were able to search for glyphs representing uncertainty with high sensitivity and low error rates. Conclusion: visual entropy is a novel cue for representing ordered data and provides a channel that allows the uncertainty of a measure to be presented alongside its mean value."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information entropy information_theory statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1293f72f4535/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:entropy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.02586">
    <title>[1908.02586] A modelling methodology for social interaction experiments</title>
    <dc:date>2019-08-08T13:20:15+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.02586</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Analysis of temporal network data arising from online interactive social experiments is not possible with standard statistical methods because the assumptions of these models, such as independence of observations, are not satisfied. In this paper, we outline a modelling methodology for such experiments where, as an example, we analyse data collected using the Virtual Interaction Application (VIAPPL) --- a software platform for conducting experiments that reveal how social norms and identities emerge through social interaction. We apply our model to show that ingroup favouritism and reciprocity are present in the experiments, and to quantify the strengthening of these behaviours over time. Our method enables us to identify participants whose behaviour is markedly different from the norm. We use the method to provide a visualisation of the data that highlights the level of ingroup favouritism, the strong reciprocal relationships, and the different behaviour of participants in the game. While our methodology was developed with VIAPPL in mind, its usage extends to any type of social interaction data."]]></description>
<dc:subject>to:NB network_data_analysis time_series statistics visual_display_of_quantitative_information to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7702c92b833f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1629942">
    <title>Scalable Visualization Methods for Modern Generalized Additive Models: Journal of Computational and Graphical Statistics: Vol 0, No 0</title>
    <dc:date>2019-07-24T14:15:06+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1629942</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the last two decades, the growth of computational resources has made it possible to handle generalized additive models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualizations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify the areas where the lack of modern visualization tools for GAMs is particularly severe, and we address the shortcomings of existing methods by proposing a set of visual tools that (a) are fast enough for interactive use, (b) exploit the additive structure of GAMs, (c) scale to large data sets, and (d) can be used in conjunction with a wide range of response distributions. The new visual methods proposed here are implemented by the mgcViz R package, available on the Comprehensive R Archive Network. Supplementary materials for this article are available online."]]></description>
<dc:subject>to:NB additive_models visual_display_of_quantitative_information computational_statistics statistics R to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4954db6bc43a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:additive_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11023-018-9484-3">
    <title>Peeking Inside the Black Box: A New Kind of Scientific Visualization | SpringerLink</title>
    <dc:date>2019-05-14T16:11:42+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11023-018-9484-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization (observed in a qualitative study of a systems biology laboratory) that was developed to address just this sort of epistemic opacity. The visualization is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information modeling simulation sociology_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:16794a8e7e35/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/kjhealy/status/1117816055928373249">
    <title>Kieran Healy on Twitter: &quot;Meanwhile, at the Citadel, Charles Joseph Minard figures out the problem with a Zombie army that can revivify itself and recruit anyone it kills.… https://t.co/2VUnEWxZ8K&quot;</title>
    <dc:date>2019-04-17T01:27:00+00:00</dc:date>
    <link>https://twitter.com/kjhealy/status/1117816055928373249</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>funny:geeky fantasy zombies visual_display_of_quantitative_information healy.kieran</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9d17240f63ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:funny:geeky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fantasy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:zombies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368">
    <title>Mistakes, we’ve drawn a few – The Economist</title>
    <dc:date>2019-03-30T03:00:09+00:00</dc:date>
    <link>https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>visual_display_of_quantitative_information self-criticism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c7bc276477c2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-criticism"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kieranhealy.org/blog/archives/2019/03/22/a-quick-and-tidy-look-at-the-2018-gss/">
    <title>A Quick and Tidy Look at the 2018 GSS</title>
    <dc:date>2019-03-26T22:30:09+00:00</dc:date>
    <link>https://kieranhealy.org/blog/archives/2019/03/22/a-quick-and-tidy-look-at-the-2018-gss/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Where by "to_teach" I mean "to work through myself".]]></description>
<dc:subject>R visual_display_of_quantitative_information to_teach</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2d414ea6cf1b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://blogs.lse.ac.uk/lsereviewofbooks/2019/01/10/book-review-mapping-society-the-spatial-dimensions-of-social-cartography-by-laura-vaughan/">
    <title>Book Review: Mapping Society: The Spatial Dimensions of Social Cartography by Laura Vaughan | LSE Review of Books</title>
    <dc:date>2019-02-20T18:02:08+00:00</dc:date>
    <link>https://blogs.lse.ac.uk/lsereviewofbooks/2019/01/10/book-review-mapping-society-the-spatial-dimensions-of-social-cartography-by-laura-vaughan/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In Mapping Society: The Spatial Dimensions of Social Cartography – available to download here for free – Laura Vaughan offers an analysis of how maps have both described and shaped social phenomena. This is a scholarly and thoroughly researched book that unpicks the context behind many of the foremost examples of social cartography, finds Inderbir Bhullar, and reveals how the layout of cities can exacerbate or ameliorate social ills."]]></description>
<dc:subject>to:NB books:noted book_reviews maps visual_display_of_quantitative_information statistics to_teach:data_over_space_and_time track_down_references</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4ffa51e3ea89/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.charlieseguin.com/dot_map.html">
    <title>The Lynching Dot Map: One dot for every lynching victim in the US 1883-1930</title>
    <dc:date>2018-10-21T23:37:52+00:00</dc:date>
    <link>http://www.charlieseguin.com/dot_map.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I can't decide if using this to teach about spatial point processes be good, or grossly insensitive.]]></description>
<dc:subject>the_american_dilemma violence american_history something_about_america lynching visual_display_of_quantitative_information to_teach:data_over_space_and_time via:gabriel_rossman</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8577df6e6698/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_american_dilemma"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:american_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:something_about_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lynching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:gabriel_rossman"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.frontiersin.org/articles/10.3389/fpsyg.2018.01742/full">
    <title>Frontiers | Visualizing Psychological Networks: A Tutorial in R | Psychology</title>
    <dc:date>2018-09-30T13:47:59+00:00</dc:date>
    <link>https://www.frontiersin.org/articles/10.3389/fpsyg.2018.01742/full</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>visual_display_of_quantitative_information network_data_analysis to_teach:baby-nets via:kjhealy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1ecca3ed34c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.02801">
    <title>[1705.02801] Graph Embedding Techniques, Applications, and Performance: A Survey</title>
    <dc:date>2018-08-14T15:55:59+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.02801</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library we developed, named GEM (Graph Embedding Methods, available at this https URL), which provides all presented algorithms within a unified interface to foster and facilitate research on the topic."]]></description>
<dc:subject>graph_theory network_data_analysis visual_display_of_quantitative_information geometry re:hyperbolic_networks via:rvenkat in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9c85a77742f4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:hyperbolic_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ggdag.netlify.com/">
    <title>Analyze and Create Elegant Directed Acyclic Graphs • ggdag</title>
    <dc:date>2018-08-09T18:28:25+00:00</dc:date>
    <link>https://ggdag.netlify.com/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["ggdag: An R Package for visualizing and analyzing directed acyclic graphs"]]></description>
<dc:subject>R graphical_models visual_display_of_quantitative_information via:arsyed to_teach:undergrad-ADA re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3545bb27c7de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://xkcd.com/1939/">
    <title>xkcd: 2016 Election Map</title>
    <dc:date>2018-08-09T18:26:51+00:00</dc:date>
    <link>https://xkcd.com/1939/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>visual_display_of_quantitative_information us_politics xkcd to_teach:data_over_space_and_time cartograms cartoons</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a55fc7976abb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:xkcd"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cartograms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cartoons"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://mitpress.mit.edu/books/picturing-science-and-engineering">
    <title>Picturing Science and Engineering | The MIT Press</title>
    <dc:date>2018-07-13T12:54:17+00:00</dc:date>
    <link>https://mitpress.mit.edu/books/picturing-science-and-engineering</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["One of the most powerful ways for scientists to document and communicate their work is through photography. Unfortunately, most scientists have little or no training in that craft. In this book, celebrated science photographer Felice Frankel offers a guide for creating science images that are both accurate and visually stunning. Picturing Science and Engineering provides detailed instructions for making science photographs using the DSLR camera, the flatbed scanner, and the phone camera. The book includes a series of step-by-step case studies, describing how final images were designed for cover submissions and other kinds of visualizations. Lavishly illustrated in color throughout, the book encourages the reader to learn by doing, following Frankel as she recreates the stages of discovery that lead to a good science visual. Frankel shows readers how to present their work with graphics—how to tell a visual story—and considers issues of image adjustment and enhancement. She describes how developing the right visual to express a concept not only helps make science accessible to nonspecialists, but also informs the science itself, helping scientists clarify their thinking. Within the book are specific URLs where readers can view Frankel's online tutorials—visual “punctuations” of this printed edition. Additional materials, including tutorials and videos, can be found online at the book's website."

- Frankel's photography is great, so I'm looking forward to this.]]></description>
<dc:subject>to:NB books:noted visual_display_of_quantitative_information photography pretty_pictures frankel.felice coveted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8eb32b706ce4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:photography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pretty_pictures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:frankel.felice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coveted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pudding.cool/process/regional_smoothing/">
    <title>Regional Smoothing in R</title>
    <dc:date>2018-07-05T14:02:30+00:00</dc:date>
    <link>https://pudding.cool/process/regional_smoothing/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to_read smoothing spatial_statistics maps visual_display_of_quantitative_information via:tslumley to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fc89d431122a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:smoothing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:tslumley"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.washingtonpost.com/graphics/2018/investigations/unsolved-homicide-database/?city=pittsburgh">
    <title>Homicide database: Mapping unsolved murders in major U.S. cities - Washington Post</title>
    <dc:date>2018-06-07T02:03:13+00:00</dc:date>
    <link>https://www.washingtonpost.com/graphics/2018/investigations/unsolved-homicide-database/?city=pittsburgh</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I've bookmarked the Pittsburgh sub-page, for teaching purposes, but the whole thing looks great (for insanely depressing values of "great').

]]></description>
<dc:subject>crime violence visual_display_of_quantitative_information to_teach:data_over_space_and_time crime_and_space</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:224b0858665e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crime"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crime_and_space"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/115/10/E2156">
    <title>Fast flow-based algorithm for creating density-equalizing map projections | PNAS</title>
    <dc:date>2018-05-05T14:51:13+00:00</dc:date>
    <link>http://www.pnas.org/content/115/10/E2156</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cartograms are maps that rescale geographic regions (e.g., countries, districts) such that their areas are proportional to quantitative demographic data (e.g., population size, gross domestic product). Unlike conventional bar or pie charts, cartograms can represent correctly which regions share common borders, resulting in insightful visualizations that can be the basis for further spatial statistical analysis. Computer programs can assist data scientists in preparing cartograms, but developing an algorithm that can quickly transform every coordinate on the map (including points that are not exactly on a border) while generating recognizable images has remained a challenge. Methods that translate the cartographic deformations into physics-inspired equations of motion have become popular, but solving these equations with sufficient accuracy can still take several minutes on current hardware. Here we introduce a flow-based algorithm whose equations of motion are numerically easier to solve compared with previous methods. The equations allow straightforward parallelization so that the calculation takes only a few seconds even for complex and detailed input. Despite the speedup, the proposed algorithm still keeps the advantages of previous techniques: With comparable quantitative measures of shape distortion, it accurately scales all areas, correctly fits the regions together, and generates a map projection for every point. We demonstrate the use of our algorithm with applications to the 2016 US election results, the gross domestic products of Indian states and Chinese provinces, and the spatial distribution of deaths in the London borough of Kensington and Chelsea between 2011 and 2014."]]></description>
<dc:subject>to:NB cartograms visual_display_of_quantitative_information kith_and_kin gastner.michael to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0e607177f4cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cartograms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gastner.michael"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mitpressjournals.org/doi/abs/10.1162/POSC_a_00273">
    <title>Explaining with Simulations: Why Visual Representations Matter | Perspectives on Science | MIT Press Journals</title>
    <dc:date>2018-04-04T14:05:32+00:00</dc:date>
    <link>https://www.mitpressjournals.org/doi/abs/10.1162/POSC_a_00273</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Computer simulations are often expected to provide explanations about target phenomena. However there is a gap between the simulation outputs and the underlying model, which prevents users finding the relevant explanatory components within the model. I contend that visual representations which adequately display the simulation outputs can nevertheless be used to get explanations. In order to do so, I elaborate on the way graphs and pictures can help one to explain the behavior of a flow past a cylinder. I then specify the reasons that make more generally visual representations particularly suitable for explanatory tasks in a computer-assisted context."]]></description>
<dc:subject>to:NB simulation modeling explanation philosophy_of_science visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2004b56035a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://socviz.co/">
    <title>Data Visualization for Social Science</title>
    <dc:date>2017-09-13T21:46:38+00:00</dc:date>
    <link>http://socviz.co/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Publisher's website for the book:
https://press.princeton.edu/titles/13826.html]]></description>
<dc:subject>visual_display_of_quantitative_information R healy.kieran to_teach:undergrad-research books:owned have_read books:recommended in_NB kith_and_kin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aaf89c535ccf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.washingtonpost.com/news/monkey-cage/wp/2016/12/05/that-viral-graph-about-millennials-declining-support-for-democracy-its-very-misleading/">
    <title>That viral graph about millennials’ declining support for democracy? It’s very misleading. - The Washington Post</title>
    <dc:date>2016-12-05T22:48:59+00:00</dc:date>
    <link>https://www.washingtonpost.com/news/monkey-cage/wp/2016/12/05/that-viral-graph-about-millennials-declining-support-for-democracy-its-very-misleading/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>democracy surveys visual_display_of_quantitative_information bad_data_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c26873869ab3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:surveys"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bad_data_analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www-personal.umich.edu/~mejn/election/2016/countycart30701024.png">
    <title>2016 county-level election cartogram</title>
    <dc:date>2016-11-15T02:59:26+00:00</dc:date>
    <link>http://www-personal.umich.edu/~mejn/election/2016/countycart30701024.png</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Shorter MEJN: We're fucked.]]></description>
<dc:subject>us_politics visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2e5c4c243cea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://xkcd.com/1732/">
    <title>xkcd: Earth Temperature Timeline</title>
    <dc:date>2016-09-14T12:49:57+00:00</dc:date>
    <link>http://xkcd.com/1732/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Listen, your car's temperature has changed before".]]></description>
<dc:subject>climate_change xkcd visual_display_of_quantitative_information history</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fb6541588ab9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climate_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:xkcd"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://benfry.com/exd09/">
    <title>Learning from Lombardi | Ben Fry</title>
    <dc:date>2016-04-25T17:43:35+00:00</dc:date>
    <link>http://benfry.com/exd09/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>fry.ben lombardi.mark visual_display_of_quantitative_information design network_visualization to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b57097700250/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fry.ben"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lombardi.mark"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.propublica.org/nerds/item/infographics-in-the-time-of-cholera">
    <title>Infographics in the Time of Cholera - ProPublica</title>
    <dc:date>2016-03-17T17:04:25+00:00</dc:date>
    <link>https://www.propublica.org/nerds/item/infographics-in-the-time-of-cholera</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>history_of_statistics visual_display_of_quantitative_information the_present_before_it_was_widely_distributed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:14badc2b698d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_present_before_it_was_widely_distributed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.uchicago.edu/ucp/books/book/distributed/S/bo22264670">
    <title>Semantic Properties of Diagrams and Their Cognitive Potentials, Shimojima</title>
    <dc:date>2016-01-06T06:21:14+00:00</dc:date>
    <link>http://press.uchicago.edu/ucp/books/book/distributed/S/bo22264670</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Why are diagrams sometimes so useful, facilitating our understanding and thinking, while at other times they can be unhelpful and even misleading? Drawing on a comprehensive survey of modern research in philosophy, logic, artificial intelligence, cognitive psychology, and graphic design, Semantic Properties of Diagrams and Their Cognitive Potentials reveals the systematic reasons for this dichotomy, showing that the cognitive functions of diagrams are rooted in the characteristic ways they carry information. In analyzing the logical mechanisms behind the relative efficacy of diagrammatic representation, Atsushi Shimojima provides deep insight into the crucial question: What makes a diagram a diagram?"]]></description>
<dc:subject>books:noted visual_display_of_quantitative_information cognition diagrams</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1708701c8aa0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diagrams"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://civilstat.com/2015/10/statistical-graphics-and-visualization-course-materials/">
    <title>Statistical Graphics and Visualization course materials | Civil Statistician</title>
    <dc:date>2015-11-01T02:35:49+00:00</dc:date>
    <link>http://civilstat.com/2015/10/statistical-graphics-and-visualization-course-materials/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>statistics visual_display_of_quantitative_information kith_and_kin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:28103e09e1b5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://rawgit.com/Groupe-ElementR/cartography/master/inst/doc/cartography.html">
    <title>Commented Scripts to Build Maps with cartography</title>
    <dc:date>2015-10-07T13:35:49+00:00</dc:date>
    <link>https://rawgit.com/Groupe-ElementR/cartography/master/inst/doc/cartography.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[These look nice.  Maybe for the spatial data examples in the book?]]></description>
<dc:subject>R visual_display_of_quantitative_information maps to_teach:statcomp re:ADAfaEPoV via:phnk</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:216b86c63a0b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:phnk"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.omegahat.org/Rcartogram/">
    <title>Rcartogram</title>
    <dc:date>2015-07-06T21:28:17+00:00</dc:date>
    <link>http://www.omegahat.org/Rcartogram/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[R interface to the Gastner-Newman cartogram code.  I haven't tried it out yet.]]></description>
<dc:subject>R visual_display_of_quantitative_information cartograms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d18f97c3441f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cartograms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://kieranhealy.org/blog/archives/2015/06/12/americas-ur-choropleths/">
    <title>America's Ur-Choropleths</title>
    <dc:date>2015-06-15T17:25:43+00:00</dc:date>
    <link>http://kieranhealy.org/blog/archives/2015/06/12/americas-ur-choropleths/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Gabriel Rossman remarked to me a while ago that most choropleth maps of the U.S. for whatever variable in effect show population density more than anything else. ... The other big variable, in the U.S. case, is Percent Black. Between the two of them, population density and percent black will do a lot to obliterate many a suggestively-patterned map of the United States. Those two variables aren’t explanations of anything in isolation, but if it turns out it’s more useful to know one or both of them instead of the thing you’re plotting, you probably want to reconsider your theory.
"So as a public service, here are America’s two ur-chorolpeths, by county."]]></description>
<dc:subject>have_read visual_display_of_quantitative_information something_about_america healy.kieran to:blog the_american_dilemma to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:917ce03451fa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:something_about_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_american_dilemma"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.crcpress.com/product/isbn/9781466508910/des">
    <title>Visualization Analysis and Design - CRC Press Book</title>
    <dc:date>2015-01-02T18:10:47+00:00</dc:date>
    <link>http://www.crcpress.com/product/isbn/9781466508910/des</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Visualization Analysis and Design provides a systematic, comprehensive framework for thinking about visualization in terms of principles and design choices. The book features a unified approach encompassing information visualization techniques for abstract data, scientific visualization techniques for spatial data, and visual analytics techniques for interweaving data transformation and analysis with interactive visual exploration. It emphasizes the careful validation of effectiveness and the consideration of function before form.
"The book breaks down visualization design according to three questions: what data users need to see, why users need to carry out their tasks, and how the visual representations proposed can be constructed and manipulated. It walks readers through the use of space and color to visually encode data in a view, the trade-offs between changing a single view and using multiple linked views, and the ways to reduce the amount of data shown in each view. The book concludes with six case studies analyzed in detail with the full framework.
"The book is suitable for a broad set of readers, from beginners to more experienced visualization designers. It does not assume any previous experience in programming, mathematics, human–computer interaction, or graphic design and can be used in an introductory visualization course at the graduate or undergraduate level."]]></description>
<dc:subject>to:NB books:noted visual_display_of_quantitative_information design via:arthegall</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:14e3ecee8f51/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arthegall"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pitt.edu/~pittcntr/Events/All/Conferences/others/other_conf_2014-15/04-10-15_diagrams/diagrams-cfp.html">
    <title>Diagrams as Vehicles of Scientific Reasoning</title>
    <dc:date>2014-12-01T16:57:40+00:00</dc:date>
    <link>http://www.pitt.edu/~pittcntr/Events/All/Conferences/others/other_conf_2014-15/04-10-15_diagrams/diagrams-cfp.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Diagrams are ubiquitous in scientific talks, papers, and textbooks. Although diagrams are clearly a tool for communicating experimental procedures, empirical results, relations between causal factors, and mechanistic explanations, they are also key vehicles of reasoning—diagrams provide essential tools for exploring variations in experimental design, identifying new explanatory relations in experimental data, and advancing and revising mechanistic models. They also play a crucial role in the design of computational models that show how an identified mechanism would behave under a variety of conditions (including alterations to the environment and to the mechanism).
"This interdisciplinary workshop seeks to expand our understanding of the ways in which diagrams contribute to science through analysis of diagrams used in actual scientific research and theoretical accounts and experimental investigations of the ways scientists construct or reason with diagrams. We invite papers from both philosophers and historians of science, cognitive scientists who study diagrams, and scientists who use them. Hotel accommodations for three nights will be provided for those whose papers are accepted, but we are not able to cover travel costs. "]]></description>
<dc:subject>conferences visual_display_of_quantitative_information cognition natural_born_cyborgs</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3858f513eb3f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:conferences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:natural_born_cyborgs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mitpress.mit.edu/books/atlas-knowledge">
    <title>Atlas of Knowledge | The MIT Press</title>
    <dc:date>2014-11-19T19:39:27+00:00</dc:date>
    <link>http://mitpress.mit.edu/books/atlas-knowledge</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Maps of physical spaces locate us in the world and help us navigate unfamiliar routes. Maps of topical spaces help us visualize the extent and structure of our collective knowledge; they reveal bursts of activity, pathways of ideas, and borders that beg to be crossed. This book, from the author of Atlas of Science, describes the power of topical maps, providing readers with principles for visualizing knowledge and offering as examples forty large-scale and more than 100 small-scale full-color maps.
"Today, data literacy is becoming as important as language literacy. Well-designed visualizations can rescue us from a sea of data, helping us to make sense of information, connect ideas, and make better decisions in real time. In Atlas of Knowledge, leading visualization expert Katy Börner makes the case for a systems science approach to science and technology studies and explains different types and levels of analysis. Drawing on fifteen years of teaching and tool development, she introduces a theoretical framework meant to guide readers through user and task analysis; data preparation, analysis, and visualization; visualization deployment; and the interpretation of science maps. To exemplify the framework, the Atlas features striking and enlightening new maps from the popular “Places & Spaces: Mapping Science” exhibit that range from “Key Events in the Development of the Video Tape Recorder” to “Mobile Landscapes: Location Data from Cell Phones for Urban Analysis” to “Literary Empires: Mapping Temporal and Spatial Settings of Victorian Poetry” to “Seeing Standards: A Visualization of the Metadata Universe.” She also discusses the possible effect of science maps on the practice of science."]]></description>
<dc:subject>to:NB books:noted visual_display_of_quantitative_information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:05cc3a5cf783/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lombardi.cs.arizona.edu/">
    <title>Lombardi Spring Embedder</title>
    <dc:date>2014-11-17T15:29:25+00:00</dc:date>
    <link>http://lombardi.cs.arizona.edu/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Now, how do I get this working in R?]]></description>
<dc:subject>network_data_analysis visual_display_of_quantitative_information lombardi.mark via:? to_teach:baby-nets network_visualization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9368603e5075/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lombardi.mark"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_visualization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.powells.com/biblio/62-9780822357445-1">
    <title>Beautiful Data: A History of Vision and Reason Since 1945 (Experimental Futures) by Orit Halpern - Powell's Books</title>
    <dc:date>2014-10-30T00:28:32+00:00</dc:date>
    <link>http://www.powells.com/biblio/62-9780822357445-1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Beautiful Data is both a history of big data and interactivity, and a sophisticated meditation on ideas about vision and cognition in the second half of the twentieth century. Contending that our forms of attention, observation, and truth are contingent and contested, Orit Halpern historicizes the ways that we are trained, and train ourselves, to observe and analyze the world. Tracing the postwar impact of cybernetics and the communication sciences on the social and human sciences, design, arts, and urban planning, she finds a radical shift in attitudes toward recording and displaying information. These changed attitudes produced what she calls communicative objectivity: new forms of observation, rationality, and economy based on the management and analysis of data. Halpern complicates assumptions about the value of data and visualization, arguing that changes in how we manage and train perception, and define reason and intelligence, are also transformations in governmentality. She also challenges the paradoxical belief that we are experiencing a crisis of attention caused by digital media, a crisis that can be resolved only through intensified media consumption."]]></description>
<dc:subject>books:noted visual_display_of_quantitative_information data_analysis history_of_ideas in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:57f638cc0e1c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_ideas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>