Spatial analysis and visualization of global data on multi-resolution hexagonal grids.

In this article, computation for the purpose of spatial visualization is presented in the context of understanding the variability in global environmental processes. Here, we generate synthetic but realistic global data sets and input them into computational algorithms that have a visualization capability; we call this a simulation-visualization system. Visualization is key here, because the algorithms which we are evaluating must respect the spatial structure of the input. We modify, augment, and integrate four existing component technologies: statistical conditional simulation, Discrete Global Grids (DGGs), Array Set Addressing, and a visualization platform for displaying our results on a globe. The internal representation of the data to be visualized is built around the need for efficient storage and computation as well as the need to move up and downresolutions in a mutually consistent way. In effect, we have constructed a Geographic Information System that is based on a DGG and has desirable data storage, computation, and visualization capabilities. We provide an example of how our simulation-visualization system may be used, by evaluating a computational algorithm called Spatial Statistical Data Fusion that was developed for use on big, remote-sensing data sets.

Profile of HIV seropositive inmates diagnosed in Maryland's state correctional system.

Correctional systems increasingly serve as the health care nexus for the initial diagnosis and treatment of human immunodeficiency virus (HIV) infection, particularly among traditionally underserved populations. A survey was conducted to describe the clinical profile of inmates in a State correctional system diagnosed with HIV infection by various testing strategies. Approximately 50 percent of the inmates diagnosed were potential candidates for anti-retroviral therapy, and 17 percent were severely immunocompromised. Implementation of voluntary HIV testing at prison entry increased the number of persons identified with HIV infection; however, since volunteers at entry had higher CD4 cell counts compared with infected inmates diagnosed by other methods, there was not a parallel increase in the percentage requiring immediate medical treatment. These data are important for planning medical resources in the correctional setting and underscore the opportunity to provide prevention and therapy for a vulnerable population with HIV infection. Public health interventions within the correctional setting have a broader societal impact, since most infected inmates serve short sentences (median, 3 years). Clinical case management is critical for inmates with HIV infection released to the community so that linkages with primary care providers and support services can be established.

Focusing attention on objects of interest using multiple matched filters.

In order to be of use to scientists, large image databases need to be analyzed to create a catalog of the objects of interest. One approach is to apply a multiple tiered search algorithm that uses reduction techniques of increasing computational complexity to select the desired objects from the database. The first tier of this type of algorithm, often called a focus of attention (FOA) algorithm, selects candidate regions from the image data and passes them to the next tier of the algorithm. In this paper we present a new approach to FOA that employs multiple matched filters (MMF), one for each object prototype, to detect the regions of interest. The MMFs are formed using k-means clustering on a set of image patches identified by domain experts as positive examples of objects of interest. An innovation of the approach is to radically reduce the dimensionality of the feature space, used by the k-means algorithm, by taking block averages (spoiling) the sample image patches. The process of spoiling is analyzed and its applicability to other domains is discussed. The combination of the output of the MMFs is achieved through the projection of the detections back into an empty image and then thresholding. This research was motivated by the need to detect small volcanos in the Magellan probe data from Venus. An empirical evaluation of the approach illustrates that a combination of the MMF plus the average filter results in a higher likelihood of 100% detection of the objects of interest at a lower false positive rate than a single matched filter alone.