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**11 - 15**of**15**### author = {M. Friendly}, title = {A.-M. Guerry’s Moral Statistics of France: Challenges for Multivariable Spatial Analysis},

, 2007

"... journal = {Statistical Science}, year = {2007}, volume = {22}, pages = {}, note = {(in press)}, © copyright by the author(s) ..."

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journal = {Statistical Science}, year = {2007}, volume = {22}, pages = {}, note = {(in press)}, © copyright by the author(s)

### Interest Driven Navigation in Visualization

"... Abstract—This paper describes a new method to explore and discover within a large data set. We apply techniques from preference elicitation to automatically identify data elements that are of potential interest to the viewer. These “elements of interest (EOI) ” are bundled into spatially local clust ..."

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Abstract—This paper describes a new method to explore and discover within a large data set. We apply techniques from preference elicitation to automatically identify data elements that are of potential interest to the viewer. These “elements of interest (EOI) ” are bundled into spatially local clusters, and connected together to form a graph. The graph is used to build camera paths that allow viewers to “tour ” areas of interest (AOI) within their data. It is also visualized to provide wayfinding cues. Our preference model uses Bayesian classification to tag elements in a data set as interesting or not interesting to the viewer. The model responds in real time, updating the elements of interest based on a viewer’s actions. This allows us to track a viewer’s interests as they change during exploration and analysis. Viewers can also interact directly with interest rules the preference model defines. We demonstrate our theoretical results by visualizing historical climatology data collected at locations throughout the world.

### The Visible Perimeter of an Arrangement of Disks

"... Abstract. Given a collection of n opaque unit disks in the plane, we want to find a stacking order for them that maximizes their visible perimeter, the total length of all pieces of their boundaries visible from above. We prove that if the centers of the disks form a dense point set, i.e., the ratio ..."

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Abstract. Given a collection of n opaque unit disks in the plane, we want to find a stacking order for them that maximizes their visible perimeter, the total length of all pieces of their boundaries visible from above. We prove that if the centers of the disks form a dense point set, i.e., the ratio of their maximum to their minimum distance is O(n 1/2), then there is a stacking order for which the visible perimeter is Ω(n 2/3). We also show that this bound cannot be improved in the case of the n 1/2 × n 1/2 piece of a sufficiently small square grid. On the other hand, if the set of centers is dense and the maximum distance between them is small, then the visible perimeter is O(n 3/4) with respect to any stacking order. This latter bound cannot be improved either. These results partially answer some questions of Cabello, Haverkort, van Kreveld, and Speckmann.

### The Use of Geographic Information Systems in Analyzing the Spatial Distribution of People at Risk for Thyroid Cancer

"... An increase in thyroid cancer incidence rates in the past decade has recently brought this disease to public attention. Unfortunately, much about the nature of this disease is unknown. This project used thyroid cancer incidence data from the National Cancer Institute and compared it with a risk fact ..."

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An increase in thyroid cancer incidence rates in the past decade has recently brought this disease to public attention. Unfortunately, much about the nature of this disease is unknown. This project used thyroid cancer incidence data from the National Cancer Institute and compared it with a risk factor analysis, completed using the Spatial Analyst extension in ESRI’s ArcMap software. In addition, this risk factor analysis shows how Geographic Information Systems (GIS) can be an important tool in the analysis of this disease. The risk factor analysis used in this comparison identified at-risk populations based on the commonly recognized risk factors of radiation, gender, age and race. A statistical analysis of these two datasets found that there was no significant linear correlation between a risk factor analysis and incidence rate. However, it was able to provide some important information that was useful in future analyses. When the incidence rates and risk factor analysis data were spatially compared, the West and Midwest were found to have the largest difference. These results suggest that future analysis should be focused in these areas to find which risk factors play a smaller or larger role in incidence rates. Eventually, this information could help researchers identify factors that seem to have the largest affect on thyroid cancer to help people most at-risk for getting this disease by allowing them to obtain the information, treatment, and hopefully the proactive prevention methods they need.

### Ljubljana, February 22, 2008Algorithmic Aspects of Proportional Symbol Maps ∗

, 2008

"... Proportional symbol maps visualize numerical data associated with point locations by placing a scaled symbol—typically an opaque disk or square—at the corresponding point on a map. The area of each symbol is proportional to the numerical value associated with its location. Every visually meaningful ..."

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Proportional symbol maps visualize numerical data associated with point locations by placing a scaled symbol—typically an opaque disk or square—at the corresponding point on a map. The area of each symbol is proportional to the numerical value associated with its location. Every visually meaningful proportional symbol map will contain at least some overlapping symbols. These need to be drawn in such a way that the user can still judge their relative sizes accurately. We identify two types of suitable drawings: physically realizable drawings and stacking drawings. For these we study the following two problems: Max-Min—maximize the minimum visible boundary length of each symbol—and Max-Total—maximize the total visible boundary length over all symbols. We show that both problems are NP-hard for physically realizable drawings. Max-Min can be solved in O(n 2 log n) time for stacking drawings, which can be improved to O(n log n) time when the input has certain properties. We also implemented several methods to compute stacking drawings: our solution to the Max-Min problem performs best on the data sets considered. 1