Engaging in Exploratory Data Analysis, Visualization, and Hypothesis Testing ............................................................................................. Exploratory Data Analysis, Geovisualization, and Data

Given the cognitive and inherently subjective nature of synthesizing and interpreting the graphical displays, it is often best to validate the visual findings through hypothesis testing. There are also times when the results derived from hypothesis testing and statistical validation are best depicted through visual plots, charts, graphs, and maps to communicate the findings to the intended audience. As such, the processes of data exploration and visualization are closely aligned with hypothesis testing methods, a linkage that forms an integral part of spatial analysis and one that is clearly recognized and valued by geographers. Our plan in this chapter therefore is twofold. First, we will explore the emerging field of data visualization and the contributory role of cartography and GIS in the development of these tools. This discussion will be accompanied by examples of how standard plots are derived and the interpretation of the derived images. The second half of the chapter will be devoted to the key steps in hypothesis testing. For hypothesis testing, our focus will be on Student’s t-test and chi-square (χ 2) statistics, which are among the most commonly used significance tests. The examples presented in the chapter will be foundational, with the primary goal of introducing the reader to the core concepts and tools in data visualization. Thereafter, in subsequent chapters of the book, we will share examples that entail the use of more advanced visualization tools, and statistical validation methods.