agent-based and system dynamic simulations (e.g., MASON, Construct, Dynamo), with particular attention to the diffusion of information and the dispersion of beliefs and activities; and (5) qualitative ethnographic assessment, sociolinguistic characterization, sentiment analysis, text mining, and questionnaires. These elements are rarely taught at the undergraduate or master’s level. Most of the education is at the doctorate level (e.g., Table A.8 in Appendix A) or is offered through professional development or specialized training programs such as the Center for Computational Analysis of Social and Organizational Systems (CASOS) Summer Institute. Although many universities cover one or two of these elements in their doctorate programs, only two (Carnegie Mellon University and the University of Arizona) cover all five.
In addition, a number of universities are adding courses in the human-geography area to their doctorate programs. For example, the sociology programs at Cornell and the University of California, Irvine, and the computer science program at the University of Arizona all cover network analysis with courses related to geo-enabled network analysis. The George Mason University Center for Social Complexity and the University of Michigan Center for Complex Systems cover agent-based modeling that takes account of the spatial aspects of human behavior.
Programs that teach social network analysis (Box 3.1) are beginning to cover geo-enabled network analysis. Some programs that teach computer modeling are beginning to teach the programming and data acquisition techniques needed to create and use maps as a way of displaying human behavior. Two-year and community colleges have been among the first academic institutions to teach some of the basic skills needed to use and develop social networking tools and, to some extent, basic tools necessary for network analysis of social data, such as reading GPS signals. These programs are loosely based in media studies and computer science programs and are widespread across the nation.
Visual analytics is the science of analytic reasoning, facilitated by interactive visual interfaces integrated with computational power and database capacity (Thomas and Cook, 2005). Analytical reasoning is central to the analyst’s task of drawing conclusions from a disparate set of evidence and assumptions. The objective of visual analytics is to derive insight from voluminous, changing, vague, and often contradictory geospatial data and other information while avoiding human information overload (van Wijk, 2011). Some examples of information graphics used in visual analytics are shown in Figure 3.5.
The growth in the quantities of information that require visual representation and analysis by humans and the increasing complexity of the associated data and analytical problems have given rise to visual analytics as a new scientific discipline (Andrienko et al., 2010). Visual analytics has formalized only recently, with a key publication in 2005 (Thomas and Cook, 2005) and more recently a series of special issues in journals (e.g., Keim et al., 2008; Stapleton et al., 2011).
Visual analytics has origins in cartography, geographic information science, computer vision, information visualization, and scientific visualization. In general, cartography deals with maps and geospatial data, geographic information science deals with spatial relations and spatial query and analysis, scientific visualization deals with data that have a natural physical or geometric structure (e.g., wind flows), and information visualization deals with abstract data structures (e.g., trees, graphs). Choice and reasoning are central to visual analytics.
Research and new directions in visual analytics include creating new information visualization methods, virtual imaging, semantic search, data fusion, dynamic network visualization, and user testing. In particular, methods that focus on how to integrate graphics into the problem-solving process itself has become a key research interest.
Knowledge and Skills
Visual analytics deals with amplifying human cognitive capabilities
• by increasing cognitive capacities and resources, such as memory;