Analysis and visualization of multidimensional time series: Particulate matter (PM10) from São Carlos-SP (Brazil)

Alexandrina, Eduardo Carlos; Ortigossa, Evandro S.; Lui, Elaine Schornobay; Gonçalves, José Antônio Silveira; Corrêa, Nivaldo Aparecido; Nonato, Luís Gustavo; Aguiar, Mônica Lopes

doi:10.1016/j.apr.2019.03.001

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…The advantage of a scatter plot is the consumption of only a few pixels to represent a single data object, standing out as a space-efficient chart. In addition, they are well-established metaphors commonly used in the scientific and business context [44].…”

Section: Cleveland and Mcgillmentioning

confidence: 99%

“…In this sense, scatter plots are straightforward; however, they have limitations and could suffer from visual scalability. Occlusion occurs as the number of points becomes larger than screen resolution [46]; overlapping occurs when there are very close dots on each other, obscuring the individual legibility of the symbols [44]. An important issue regarding the scatter plots in a multidimensional projection context is: that there are no coordinated axes.…”

Section: Cleveland and Mcgillmentioning

confidence: 99%

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Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science

2022

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The exploration and analysis of multidimensional data can be pretty complex tasks, requiring sophisticated tools able to transform large amounts of data bearing multiple parameters into helpful information. Multidimensional projection techniques figure as powerful tools for transforming multidimensional data into visual information according to similarity features. Integrating this class of methods into a framework devoted to data sciences can contribute to generating more expressive means of visual analytics. Although the Principal Component Analysis (PCA) is a well-known method in this context, it is not the only one, and, sometimes, its abilities and limitations are not adequately discussed or taken into consideration by users. Therefore, knowing in-depth multidimensional projection techniques, their strengths, and the possible distortions they can create is of significant importance for researchers developing knowledge-discovery systems. This research presents a comprehensive overview of current state-of-the-art multidimensional projection techniques and shows example codes in Python and R languages, all available on the internet. The survey segment discusses the different types of techniques applied to multidimensional projection tasks from their background, application processes, capabilities, and limitations, opening the internal processes of the methods and demystifying their concepts. We also illustrate two problems, from a genetic experiment (supervised) and text mining (non-supervised), presenting solutions through multidimensional projection application. Finally, we brought elements that reverberate the competitiveness of multidimensional projection techniques towards high-dimension data visualization, commonly needed in data sciences solutions.

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Section: Cleveland and Mcgillmentioning

confidence: 99%

Section: Cleveland and Mcgillmentioning

confidence: 99%

Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science

2022

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“…InfoVis approach graphically represent information through computer systems, which show alternative data visions to describe their structures [21]. However, a large gap lies between the generation and storage of data, and the ability of analytical tools to process, organize and properly display the extracted information [22,23].…”

Section: Plos Onementioning

confidence: 99%

Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning

et al. 2022

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Learning techniques involve unraveling regression structures, which aim to analyze in a probabilistic frame the associations across variables of interest. Thus, analyzing fraction and/or proportion data may not be adequate with standard regression procedures, since the linear regression models generally assume that the dependent (outcome) variable is normally distributed. In this manner, we propose a statistical model called unit-Lindley regression model, for the purpose of Statistical Process Control (SPC). As a result, a new control chart tool was proposed, which targets the water monitoring dynamic, as well as the monitoring of relative humidity, per minute, of Copiapó city, located in Atacama Desert (one of the driest non-polar places on Earth), north of Chile. Our results show that variables such as wind speed, 24-hour temperature variation, and solar radiation are useful to describe the amount of relative humidity in the air. Additionally, Information Visualization (InfoVis) tools help to understand the time seasonality of the water particle phenomenon of the region in near real-time analysis. The developed methodology also helps to label unusual events, such as Camanchaca, and other water monitoring-related events.

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Tiwari

Saxena

2021

Air Pollution and Its Complications

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Analysis and visualization of multidimensional time series: Particulate matter (PM10) from São Carlos-SP (Brazil)

Cited by 7 publications

References 25 publications

Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science

Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science

Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning

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