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The Promise and Practice of Teaching Data Literacy in Social Studies: A Companion Site A Taxonomy of Data Visualizations Information can be visualized in multiple ways, from bar graphs to scatterplots, choropleth maps to distribution maps, timelines to time series. Designers can choose from an array of graphical elements such as points, lines, or icons used to represent data, and multiple aesthetic attributes such as color, shape, and size. Furthermore, designers can apply multiple combinations of titles, legends, and explanatory text to provide context for a data visualization. Given the almost dizzying array of data visualizations students may encounter in social studies, it is helpful to place them in categories related to the types of questions they will help us answer. How do students learn with data visualizations? Reading data visualizations in print and online social studies texts can improve students' overall comprehension and quality of reasoning. And there is evidence to suggest that reading data visualizations helps students better understand historical and geographic context, multiple causation, and change over time — all important concepts for students to grasp in social studies subject areas. However, students may face significant challenges in trying to make sense of different kinds of data visualizations. This section provides insight into both benefits and challenges of reading timelines, maps, and graphs and charts. How should students analyze data visualizations in social studies? The challenges that data visualizations present, coupled with their prevalence in social studies texts, standardized assessments, in online social studies resources, and as sources of information in society, suggest that teaching with and about data visualizations in social studies is essential. This module provides guidance for how teachers can support students' data literacy for social studies. Index of Lesson Plans This page contains a list of the minimal manuals and lessons found on this website. Minimal manuals are designed to be adapted to different grade levels and do not have a grade designation associated with them. Lesson plans are organized according to the school level for which they are designed. However, many of these lessons can be adapted for different grade levels.

Ptolemy's Cosmographia (150 CE)

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What kinds of data visualizations will students encounter in social studies? In the previous module, you looked at a variety of data visualizations, including those you found in textbooks, online, and on this website. It is clear that information can be visualized in multiple ways, from bar graphs to scatterplots, choropleth maps to distribution maps, timelines to time series. Designers can choose from an array of graphical elements such as points, lines, or icons used to represent data, and multiple aesthetic attributes such as color, shape, and size. Furthermore, designers can apply multiple combinations of titles, legends, and explanatory text to provide context for a data visualization.

Given the almost dizzying array of data visualizations students may encounter in social studies, it is helpful to place them in categories related to the types of questions they will help us answer. For example, studying social studies often has us wondering where something happened, or how two places relate to each other in space. Spatial data visualizations can help us answer such questions. Of course, we also ask a lot of questions about when an event occurred in history. Temporal data visualizations — those that show us change or developments over time — can be helpful for such inquiries.

The following sections provide an overview of four broad categories of data visualizations that are useful in social studies. If you follow the link for each category, it will lead you to a page that provides further information about types and functions of data visualizations included in the category, and shows some examples related to social studies content.

Spatial Data Visualizations The events, phenomena, and changes that we study in social studies have all happened somewhere. Understanding ourselves, where we've been, where we are now, and how we got here requires us to orient ourselves spatially. Visualizing where events have unfolded, and being able to see the human and physical characteristics that distinguish one place from another, bring us closer to answering questions about why and how events happened the way that they did.

Maps are the most common type of spatial data visualization students will encounter in social studies. Maps allow us to discover and visualize spatial relationships and to make large scale movements or patterns -- those normally outside of human perception -- contained and visible. Historians and social scientists use them to show how people have moved over space and time, how societies have grown or dissolved, how diseases or languages spread, and how resources and people are distributed. Maps can show both the world and parts of the world at the same time, making it possible for us to make comparisons or see how events in one part of the globe relate to the whole. Maps have also played an important role in the human story. Way-finding maps have led travelers, explorers, and conquerors across waterways and to new lands, and facilitated the spread of ideas, technologies, foods, diseases, and people. When viewed as primary source evidence, maps can reveal the habits, thoughts, and perspectives of the people who created them, helping us understand how people of the past saw the world, where they had gone, where they thought they could go, who they thought they could conquer.

But land is not the only space humans have visualized. They have tried to visualize space beneath the earth, and in the sky. And they have visualized the spaces of the human body, often revealing prejudices tied to sex, race, or religion.

This page provides an overview of spatial data visualizations' different functions, and provides examples of the different kinds of data visualizations that fulfill these functions.

Temporal Data Visualizations

Spatiotemporal Data Visualizations

Categorical or Topical Data Visualizations