This set of exercises will introduce you to the concept of data classification and show you how to understand and use classification when both interpreting and creating maps. Data classification is a type of data representation in which data are generalized into discreet categories, or classes. This is especially helpful in visualizing spatial data, as it allows for unique values to to be shown (or "symbolized") as part of a category, rather than symbolized individually. Different categories of data in a classification scheme are often symbolized as colors in maps. 

When reading and creating maps, it is important to understand how a map portrays (or 'symbolizes') the underlying data. This understanding is demonstrated by the ability to describe the map in words, translating the visual information into verbal information, and, in the case of creating maps, making sure that the maps you make preserve the verbal message you would like to communicate regarding the data. 

This suite of tutorials and exercises will help you to understand and (verbally) interpret data classification schemes in maps that you find and use as an information consumer. It will also show you how to create a classification scheme when creating your own maps to be used and interpreted by others.

Here's a quick overview of the contents of this unit, with suggestions for framing assignments and discussions around each module:

   Looking Critically at Maps - This module introduces students to map analysis and suggests assignments that will guide students to developing guidelines for communicating effectively with maps.

map analysis

Histograms, Tendencies, and Data Types - This module provides an overview of tools for describing and exploring trends within datasets.

   Case Study - Explore the ramifications of classification and visual representation using data illustrating the impact of Hurricane Katrina on New Orleans.
New Orleans

Creating Your Own Map - Make your own map with data of interest and determine the best representation based on trends in the data and the story you're trying to tell.

Disaster Map