Visualizing spatially varying distribution data

Kao, David L.; Luo, A.; Dungan, Jennifer; Pang, Alex

doi:10.1109/iv.2002.1028780

Cited by 35 publications

(25 citation statements)

References 11 publications

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“…To this end, we employ a kernel density estimation [22]. Equation 1 defines the multivariate kernel density estimation, and this method has been used in other works [11,16,8]. To reduce the calculation time, we have chosen to employ the Epanechnikov kernel, Equation 2.f…”

Section: Heatmapsmentioning

confidence: 99%

Understanding syndromic hotspots - a visual analytics approach

Maciejewski

Rudolph

Hafen

et al. 2008

2008 IEEE Symposium on Visual Analytics Science and Technology

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When analyzing syndromic surveillance data, health care officials look for areas with unusually high cases of syndromes. Unfortunately, many outbreaks are difficult to detect because their signal is obscured by the statistical noise. Consequently, many detection algorithms have a high false positive rate. While many false alerts can be easily filtered by trained epidemiologists, others require health officials to drill down into the data, analyzing specific segments of the population and historical trends over time and space. Furthermore, the ability to accurately recognize meaningful patterns in the data becomes more challenging as these data sources increase in volume and complexity. To facilitate more accurate and efficient event detection, we have created a visual analytics tool that provides analysts with linked geo-spatiotemporal and statistical analytic views. We model syndromic hotspots by applying a kernel density estimation on the population sample. When an analyst selects a syndromic hotspot, temporal statistical graphs of the hotspot are created. Similarly, regions in the statistical plots may be selected to generate geospatial features specific to the current time period. Demographic filtering can then be combined to determine if certain populations are more affected than others. These tools allow analysts to perform real-time hypothesis testing and evaluation. MOTIVATIONRecently, the detection of adverse health events has focused on pre-diagnosis information to improve response time. This type of detection is more largely termed syndromic surveillance and involves the collection and analysis of statistical health trend data, most notably symptoms reported by individuals seeking care in emergency departments. Currently, the Indiana State Department of Health (ISDH) employs a state syndromic surveillance system called PHESS (Public Health Emergency Surveillance System) [9], which receives electronically transmitted patient data (in the form of emergency department chief complaints) from 73 hospitals around the state at an average rate of 7000 records per day.These complaints are then classified into nine categories (respiratory, gastro-intestinal, hemorrhagic, rash, fever, neurological, botulinic, shock/coma, and other) [4] and used as indicators to detect public health emergencies before such an event is confirmed by diagnoses or overt activity. Unfortunately, detection of events from these indicators is an extremely challenging issue. Figure 1 shows a typical month of emergency department visits for those complaints classified as neurological syndromes. During this time period, there was one event of carbon monoxide poisoning which happened to coincide with the largest peak on December 21st; however, this peak is not significantly higher than any other peak during this month. Obviously, the detection of such a small signal deviation can be extremely difficult. In order to facilitate enhanced syndromic surveillance and improve signal detection, we have developed a linked geospatiotemporal vi...

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Section: Heatmapsmentioning

confidence: 99%

Understanding syndromic hotspots - a visual analytics approach

Maciejewski

Rudolph

Hafen

et al. 2008

2008 IEEE Symposium on Visual Analytics Science and Technology

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show abstract

“…As can be seen, the visualization can get quite cluttered. Kao, Dungan & Pang (2002) present additional methods of visualizing 2D pdf data using density estimate volume visualization and shapebased descriptors for the pdfs. In contrast, our hierarchical spatial clustering gives a multi-resolution representation of the distribution data, and the user can interactively visualize a representative distribution or the pdfs for each cluster at various levels of detail.…”

Section: Related Workmentioning

confidence: 99%

“…For example, histogram is a crude and fast method of approximation. A more accurate estimate of the pdf can be obtained using the kernel estimator (Silverman, 1986) (Kao et al, 2002).…”

Section: Datasetsmentioning

confidence: 99%

“…The data set consists of 101 x 101 pixels and 250 realizations. Values range from 0 to 255, re-scaled from percentage forest-cover (Kao et al, 2002). A visualization of this dataset from (Kao et al, 2001) is shown in Figure 1.…”

Section: Datasetsmentioning

confidence: 99%

“…As mentioned before, the pdf at each pixel can be constructed from the realizations by using density estimation methods, e.g., histograms, kernel estimator etc. (Silverman, 1986) (Kao et al, 2002). Once the pdfs are generated, we can compare the distance between two pdfs using any of the established metrics: Kullback-Leibler distance, entropy etc.…”

Section: Distance Function For Clusteringmentioning

confidence: 99%

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Visualization techniques for spatial probability density function data

Bordoloi¹,

Kao

Shen

2004

Data Sci. J.

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A minimally invasive, efficient method for propagation of full‐field uncertainty in solid dynamics

Jones

Redle

Kolla

et al. 2021

Numerical Meth Engineering

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We present a minimally invasive method for forward propagation of material property uncertainty to full‐field quantities of interest in solid dynamics. Full‐field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known a priori . The method, motivated by the well‐known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the evolution equations based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the driving term of the evolution equations. The implementation is model independent, storage and communication efficient, and straightforward. We demonstrate its efficiency, accuracy, scaling with dimension of the parameter space, and convergence in distribution with two problems: a quasi‐one‐dimensional bar impact, and a two material notched plate impact. For the bar impact problem, we provide an analytical solution to PDF of the solution fields for method validation. With the notched plate problem, we also demonstrate good parallel efficiency and scaling of the method.

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Visualizing spatially varying distribution data

Cited by 35 publications

References 11 publications

Understanding syndromic hotspots - a visual analytics approach

Understanding syndromic hotspots - a visual analytics approach

Visualization techniques for spatial probability density function data

A minimally invasive, efficient method for propagation of full‐field uncertainty in solid dynamics

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