Techniques for the Visualization of Positional Geospatial Uncertainty

Geospatial data almost always contains some amount of uncertainty due to inaccuracies in its acquisition and transformation. While the data is commonly visualized (e.g. on digital maps), there are unanswered needs for visualizing uncertainty along with it. Most research on effectively doing this addresses uncertainty in data values at geospatial positions, e.g. water depth, human population, or land-cover classification. Uncertainty in the data’s geospatial positions themselves (positional uncertainty) has not been previously focused on in this regard. In this thesis, techniques were created for visualizing positional uncertainty using World Vector Shoreline as an example dataset. The techniques consist of a shoreline buffer zone to which visual effects such as gradients, transparency, and randomized dots were applied. They are viewed interactively via Web Map Service (WMS). In clutter testing with human subjects, a transparency-gradient technique performed the best, followed by a solid-fill technique, with a dots-density-gradient technique performing worst

Positional information, positional error, and read-out precision in morphogenesis: a mathematical framework

The concept of positional information is central to our understanding of how cells in a multicellular structure determine their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine which features of expression patterns increase or decrease positional information. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with single cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail

Complexity, Pedagogy and the Economics of Muddling Through

This paper was first presented at the AEA meetings on complexity. It was later published in a book edited by Massima Alszano and Alan Kirman, Economics: Complex Windows, Springer Publishers.

GIS Tools for Cartographic Representation of Spatial Data Uncertainty

Maps created in geographic information systems (GIS) are rendered with precisely defined features, but experienced GIS practitioners recognize that spatial data have relative error that is not always apparent to map readers. Limited awareness among many users regarding data error leads users to view and analyze data without regard for relative uncertainty. Tools and methods supporting map designer abilities to graphically communicate uncertainty associated with spatial data have not been readily available. There exists a need for users to display quantifiable characteristics of relative uncertainty associated with spatial data affected via cartographic representation. Development for this project synthesized prominent research recommendations to provide map designers’ with methods for conveying data uncertainty with scientifically tested symbolization within the ArcGIS software. The ultimate goal of this development project is to increase map designers efficiency in illustrating data uncertainty, and stimulate conversation about GIS tools for representing this uncertainty to a wider audience

Artificial Intelligence and Statistics

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algorithms, and evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and research. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors' collaborative research

Implementing system simulation of C3 systems using autonomous objects

The basis of all conflict recognition in simulation is a common frame of reference. Synchronous discrete-event simulation relies on the fixed points in time as the basic frame of reference. Asynchronous discrete-event simulation relies on fixed-points in the model space as the basic frame of reference. Neither approach provides sufficient support for autonomous objects. The use of a spatial template as a frame of reference is proposed to address these insufficiencies. The concept of a spatial template is defined and an implementation approach offered. Discussed are the uses of this approach to analyze the integration of sensor data associated with Command, Control, and Communication systems

Dissociating visuo-spatial and verbal working memory: It’s all in the features

Echoing many of the themes of the seminal work of Atkinson and Shiffrin (1968), this paper uses the Feature Model (Nairne, 1988, 1990; Neath & Nairne, 1995) to account for performance in working memory tasks. The Brooks verbal and visuo-spatial matrix tasks were performed alone, with articulatory suppression, or with a spatial suppression task; the results produced the expected dissociation. We used Approximate Bayesian Computation techniques to fit the Feature Model to the data and showed that the similarity-based interference process implemented in the model accounted for the data patterns well. We then fit the model to data from Guérard and Tremblay (2008); the latter study produced a double dissociation while calling upon more typical order reconstruction tasks. Again, the model performed well. The findings show that a double dissociation can be modelled without appealing to separate systems for verbal and visuo-spatial processing. The latter findings are significant as the Feature Model had not been used to model this type of dissociation before; importantly, this is also the first time the model is quantitatively fit to data. For the demonstration provided here, modularity was unnecessary if two assumptions were made: (1) the main difference between spatial and verbal working memory tasks is the features that are encoded; (2) secondary tasks selectively interfere with primary tasks to the extent that both tasks involve similar features. It is argued that a feature-based view is more parsimonious (see Morey, 2018) and offers flexibility in accounting for multiple benchmark effects in the field