• Assignments,  Course Details

    Final Project Guidelines

    Your final project should be quantitative, argument-driven and interactive. The details are up to you. A project which is more heavily qualitative might only have one or two interactive visual elements; a heavily interactive project might have less essay text.

    At a minimum, your project proposal should identify the dataset/s you’ll be working with, your team, and your goals. Your proposal must include:

    • a data critique
    • an outline of data cleaning you anticipate might be necessary
    • a brief secondary bibliography of other works related to your data
    • an outline of possible research questions or anticipated argument (think grant proposal)
    • an outline of goals for possible data visualizations and reader interactions
    • an outline of each persons’ responsibilities and roles if proposing a team project
    • a timeline of project milestones, in addition to the April 9 wireframe due date and May 16 final due date
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    Heat Maps and Dendrograms

    There’s a weird amount of heatmap memes on the Internet

    Heat Maps

    A heat map is a way to visualize data that is reliant on color variation. All heat maps require a legend to show what each color means. It can show labeled categorical data or qualitative data so long as it corresponds with a color. While some he maps may have specific colors, others can cover a range. This kind of heat map works best with quantitative, continuous data that allows for a range of possibilities, such as temperature.

  • Visualization Guide

    Data Visualization: Histograms and Arc Network Diagrams

    Histograms

    Histograms are a good way of displaying information as they are very intuitive. It is easy to understand histograms without having much experience with visualization. They are good for showing the relationships between quantitative measures, especially single distributions Histograms show the frequency of specific data types . Visualizing multiple distributions can be harder to do with histograms. It can be difficult to tell where each bar begins and ends when using a stacked histogram. However, histograms can be misleading. The parameters on the axes depend on the data, meaning that looking at different trends within the same data can be misleading.

    This image was posted on the DataViz project website. The histograms above show how much certain kind of food were eaten in a month. Each bar per month represents a different kind of food, with the foods being listed at the bottom of all the charts. This chart is useful for seeing the relationships between each food in an individual month. However, it is difficult to compare the trends over the entire year based on the setup of the image.

    Arc Network Diagrams

    Arc network diagrams are good for finding co-occurrence in datasets. However, they don’t show connections between nodes as easily as 2D models do. They can also become easily cluttered and hard to read with an increasing amount of data. Arc network diagrams are good for showing the relationships between qualitative and quantitative measures. They are primarily used to show correlation in data.

    This image is an arc network diagram showing the frequency of correspondence regarding a certain topic by Thomas Jefferson. It is from “The Image of Absence: Archival Silence, Data Visualization, and James Hemings.” by Laura F. Klein.  It shows the names of individuals as well as specific groups they were a part of, for example, the Jefferson Family or Free plantation staff. This is helpful because then the visualization shows that correspondence was more frequent between different groups of people.

  • Visualization Guide

    Matrix Plots and Node-link Diagrams/Dendrograms

    Historians looking to analyze trends and connections between several scatterplots and compile clusters will need to utilize both matrix plot graphs and node-link diagrams/dendrograms to represent their datasets. Node-link diagrams, dendrograms, and matrix plot graphs are all ways of compiling smaller samples of data to analyze broad trends.

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    Choropleth and Heat Maps

    Choropleth map uses shading and color variation to display data on a specific geographic location. This type of data visualization is most often associated with the display of population densities, income distribution, and election results. Choropleth maps are best at displaying density. By using either varying shades of a single color, transitions along a color scale, or discrete color blocks, Choropleth maps take advantage of contrast to display data on a geographic boundary.

  • Visualization Guide

    Data Visualization: Line vs. Area Charts

    When seeking to visualize data consisting of discrete points that can be measured continuously to some extent, or data that is entirely continuous, two options for data visualization are area and line charts. Though both of these formats serve similar purposes, there are notable differences in their optimal usage, particularly when attempting to represent multiple sets of data within one visualization.

  • Visualization Guide

    Arc Network Diagrams/Stacked Area Graphs

    Owen McCarty

    Arc network diagrams are an interesting way of displaying information for a reader. With arc network diagrams, nodes are placed on a single line x axis along with arc that connects the nodes to establish a relationship. The main draw of arc network diagrams is it typically uses thickness as a way to represent the relationship between nodes on the axis. The thicker the arc, the higher the frequency. Where this type of chart can be useful is displaying information that you want the reader to understand connections between various aspects of something. In looking at examples for this type of graph, words and people were frequently displayed in this chart. This included looking at correspondences between politicians and how frequently they showed up in each other’s archives and the frequency of related words that popped up in journal articles. Showing relationships in linguistics and individuals seems to be the best usage for this type of graph.

  • Visualization Guide

    Stream graphs and Line Charts

    Source: Gerd Altmann/PublicDomainPictures.net/CCO Public Domain

    With the advent of new technologies in recent decades, historians have changed how they present their data. From simple bar graphs and pie charts to the eye-catching designs of Stream graphs, layers of information can now be dissected in one visualization. But, like their predecessors, these new methods of data visualization have their own problems which can scare users away for more traditional graphs.

  • Visualization Guide

    Trees and Circles

    Imagine that you walk into a pet shop and you are tasked with organizing all the animals into categories. How would you represent that data visually? Maybe bar graphs to show there are more dogs than cats. However, what does each bar represent an animal or a general type of animal? And you want to show your readers how the proportions of mammals and reptiles, something a simple bar graph cannot easily express.

  • Visualization Guide

    Data Visualization: Stacked Area/Stream Graphs and Matrix Plot Graphs

    Stream Graphs and Stacked Area Graphs

    Stream graphs and stacked area graphs both represent data in similar ways. A stacked area chart represents data on an x- and y-axis, and each item is represented by a band of color. In these visualizations, the areas are “stacked,” allowing the viewer to compare the areas and see the totals. For example, the chart below shows the revenue per capita – essentially, how much a single person might spent in each given year – on each music format.

  • Visualization Guide

    There exists a plethora of ways to visualize data beyond simple bar graphs, pie charts, and basic line graphs. Historians often shy away from using visualization tools, partly because of their lack of use or maybe an inherent fear of statistics.