[Sections of Chapter 2 of PhD Dissertation 'Simulation, Kriging, and Visualization of Circular-Spatial Data']

Portions of a dissertation describing the circular-spatial data of the Space Shuttle Rocket Motor Nozzle is presented

Social Movements and International Relations: A Relational Framework

Social movements are increasingly recognized as significant features of contemporary world politics, yet to date their treatment in international relations theory has tended to obfuscate the considerable diversity of these social formations, and the variegated interactions they may establish with state actors and different structures of world order. Highlighting the difficulties conventional liberal and critical approaches have in transcending conceptions of movements as moral entities, the article draws from two under-exploited literatures in the study of social movements in international relations, the English School and Social Systems Theory, to specify a wider range of analytical interactions between different categories of social movements and of world political structures. Moreover, by casting social movement phenomena as communications, the article opens international relations to consideration of the increasingly diverse trajectories and second-order effects produced by social movements as they interact with states, intergovernmental institutions, and transnational actors

Simulation, Kriging, and Visualization of Circular-Spatial Data

The circular dataimage is defined by displaying direction as the color at the same direction in a color wheel composed of a sequence of two-color gradients with color continuity between gradients. The resulting image of circular-spatial data is continuous with high resolution. Examples include ocean wind direction, Earth\u27s main magnetic field, and rocket nozzle internal combustion flow. The cosineogram is defined as the mean cosine of the angle between random components of direction as a function of distance between observation locations. It expresses the spatial correlation of circular-spatial data. A circular kriging solution is developed based on a model fitted to the cosineogram. A method for simulating circular random fields is given based on a transformation of a Gaussian random field. It is adaptable to any continuous probability distribution. Circular random fields were implemented for selected circular probability distributions. An R software package was created with functions and documentation