A Strategy and Architecture for the Visualization of Complex Geographical Datasets

1997 ◽  
Vol 11 (02) ◽  
pp. 239-261 ◽  
Author(s):  
Mark Gahegan ◽  
David O'Brien
Keyword(s):  
Exploratory Data Analysis ◽  
Mineral Exploration ◽  
Environmental Data ◽  
Human Cognition ◽  
Problem Domain ◽  
Visual Attributes ◽  
Computer Visualization ◽  
Geographic Datasets ◽  
Exploratory Data ◽  
Use Of Knowledge

The use of computer visualization as a means to analyze complex geographic datasets is discussed. Visualization is a valuable tool for conducting exploratory data analysis on geographical data; making good use of the human eye's unparalleled ability to recognize structure and relationships that may be inherent within the data. Traditional GIS are extremely poor at visualization, being limited to a very restricted set of visual attributes with which to convey information (position, size, color). The use of a more sophisticated approach is discussed in detail. Specifically, a system to visualise complex environmental datasets is described, which makes use of knowledge concerning the problem domain as well as knowledge concerning human cognition. In the realizations produced, the most salient attributes in the data, for a particular task, are assigned to the most striking visual attributes. Assignments are controlled by heuristics that may be changed to alter system behavior. Results are presented showing the application of this approach on datasets involving several multi-dimensional thematic layers of environmental data, used in mineral exploration.

scholarly journals Statistical inference for exploratory data analysis and model diagnostics

2009 ◽  
Vol 367 (1906) ◽  
pp. 4361-4383 ◽  
Author(s):  
Andreas Buja ◽  
Dianne Cook ◽  
Heike Hofmann ◽  
Michael Lawrence ◽  
Eun-Kyung Lee ◽  
...  
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Statistical Testing ◽  
Human Cognition ◽  
Model Diagnostics ◽  
The Real ◽  
Exploratory Data

We propose to furnish visual statistical methods with an inferential framework and protocol, modelled on confirmatory statistical testing. In this framework, plots take on the role of test statistics, and human cognition the role of statistical tests. Statistical significance of ‘discoveries’ is measured by having the human viewer compare the plot of the real dataset with collections of plots of simulated datasets. A simple but rigorous protocol that provides inferential validity is modelled after the ‘lineup’ popular from criminal legal procedures. Another protocol modelled after the ‘Rorschach’ inkblot test, well known from (pop-)psychology, will help analysts acclimatize to random variability before being exposed to the plot of the real data. The proposed protocols will be useful for exploratory data analysis, with reference datasets simulated by using a null assumption that structure is absent. The framework is also useful for model diagnostics in which case reference datasets are simulated from the model in question. This latter point follows up on previous proposals. Adopting the protocols will mean an adjustment in working procedures for data analysts, adding more rigour, and teachers might find that incorporating these protocols into the curriculum improves their students’ statistical thinking.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

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