This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.
Explain how analytical methods are used to derive analytical results from geospatial data
Completed (GI-N2K)