Lost in visualization: using quantitative content analysis to identify, measure, and categorize political cartographic manipulations.

Abstract

All maps are biased. Yet, some maps appear more biased than others. Though we have enough research to answer how and why maps can be used as tools for political argumentation, geographers have been remiss in scrutinizing how and why some maps are presumed to be more biased than others. I argue the reason for this lapse is primarily due to methodological shortcoming. This dissertation is an attempt to establish a new methodological framework with which we can begin answering fundamental questions that have been ignored thus far in the literature on politically motivated cartography. First, is it possible to quantify and compare different map manipulations found on overtly political maps? Second, are there any techniques of cartographic and visual manipulation that make maps produced for political purposes appear more subjective or objective? If so, how do these techniques correlate or cluster with one another on maps? Finally, can we establish a non-anecdotal categorical framework that will allow us to compare the type of cartographic manipulations found in a sample of maps with those found in other maps? I use quantitative content analysis to answer these research questions while testing the method's applicability to increasing our understanding of how maps are used to make political arguments.