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| − | <font size = 5; color="#FFFFFF">VAST Challenge: Mini Challenge 2</font> | + | <font size = 5; color="#FFFFFF">shinyNET: A web-based flight data visualisation toolkit using R Shiny and ggraph</font> |
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| − | <font size = 3; color="#FFFFFF">Overview</font> | + | <font size = 3; color="#FFFFFF">Abstract</font> |
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| − | Mistford is a mid-size city is located to the southwest of a large nature preserve. The city has a small industrial area with four light-manufacturing endeavors. Mitch Vogel is a post-doc student studying ornithology at Mistford College and has been discovering signs that the number of nesting pairs of the Rose-Crested Blue Pipit, a popular local bird due to its attractive plumage and pleasant songs, is decreasing! The decrease is sufficiently significant that the Pangera Ornithology Conservation Society is sponsoring Mitch to undertake additional studies to identify the possible reasons. Mitch is gaining access to several datasets that may help him in his work, and he has asked you (and your colleagues) as experts in visual analytics to help him analyze these datasets.
| + | shinyNET is a web-based visual analytics tool that allows users to visualise flights data as a network graph. It is built by using R Shiny framework to integrate a collection of R packages for data wrangling, data tidying , data visualisation and graph analysis. With the responsive interfaces of shinyNET, users can choose to visualise the entire airlines systems or to visualise the network graph of a selected airlines system. It also allows users to compute network geometrics such as betweenness, closeness and to use these newly derived measures to enhance the data discovery process. |
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| − | <div style=background:#0B5345 border:#A3BFB1>
| + | All these analysis and visualisation are performed without having the users to type a single line of code. |
| − | <font size = 3; color="#FFFFFF">Mini-Challenge 2</font>
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| − | </div>
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| − | Ornithology student Mitch Vogel was immediately suspicious of the noxious gases just pouring out of the smokestacks from the four manufacturing factories south of the nature preserve. He was almost certain that all of these companies are contributing to the downfall of the poor Rose-crested Blue Pipit bird. But when he talked to company representatives and workers, they all seem to be nice people and actually pretty respectful of the environment.
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| − | In fact, Mitch was surprised to learn that the factories had recently taken steps to make their processes more environmentally friendly, even though it raised their cost of production. Mitch discovered that the state government has been monitoring the gaseous effluents from the factories through a set of sensors, distributed around the factories, and set between the smokestacks, the city of Mistford and the nature preserve. The state has given Mitch access to their air sampler data, meteorological data, and locations map. Mitch is very good in Excel, but he knows that there are better tools for data discovery, and he knows that you are very clever at visual analytics and would be able to help perform an analysis.
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| − | Mini-Challenge 2 provides a three month set of data for you to analyze, covering April, August, and December 2016.
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| − | The primary job for Mitch is to determine which (if any) of the factories may be contributing to the problems of the Rose-crested Blue Pipit. Often, air sampling analysis deals with a single chemical being emitted by a single factory. In this case, though, there are four factories, potentially each emitting four chemicals, being monitored by nine different sensors. Further, some chemicals being emitted are more hazardous than others. Your task, as supported by visual analytics that you apply, is to detangle the data to help Mitch determine where problems may be. Use visual analytics to analyze the available data and develop responses to the questions below. In addition, prepare a video that shows how you used visual analytics to solve this challenge. Novel visualizations and analysis approaches are especially interesting for this mini-challenge. Please do not use any other data in your work (including other Internet-based sources or other mini-challenge data).
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| − | You may use tools you developed in other VAST Challenges in your efforts – please let us know when you do so!
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| − | Please visit [http://vacommunity.org/VAST+Challenge+2017+MC2 VAST Challenge 2017: Mini-Challenge 2].
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| | <div style=background:#0B5345 border:#A3BFB1> | | <div style=background:#0B5345 border:#A3BFB1> |
| − | <font size = 3; color="#FFFFFF">Questions</font> | + | <font size = 3; color="#FFFFFF">Objective</font> |
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| − | * Characterize the sensors’ performance and operation. Are they all working properly at all times? Can you detect any unexpected behaviors of the sensors through analyzing the readings they capture?Limit your response to no more than 9 images and 1000 words.
| + | Our main aim is to investigate the airport network infrastructure of India (ANI) to explore its various properties and its traffic dynamics. We propose to build a visual network exploration tool using R. This tool can be used not only to explore the airport network in India but also can be used to explore any kind of airport network. |
| − | * Now turn your attention to the chemicals themselves. Which chemicals are being detected by the sensor group? What patterns of chemical releases do you see, as being reported in the data? Limit your response to no more than 6 images and 500 words.
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| − | * Which factories are responsible for which chemical releases? Carefully describe how you determined this using all the data you have available. For the factories you identified, describe any observed patterns of operation revealed in the data. Limit your response to no more than 8 images and 1000 words.
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shinyNET: A web-based flight data visualisation toolkit using R Shiny and ggraph
shinyNET is a web-based visual analytics tool that allows users to visualise flights data as a network graph. It is built by using R Shiny framework to integrate a collection of R packages for data wrangling, data tidying , data visualisation and graph analysis. With the responsive interfaces of shinyNET, users can choose to visualise the entire airlines systems or to visualise the network graph of a selected airlines system. It also allows users to compute network geometrics such as betweenness, closeness and to use these newly derived measures to enhance the data discovery process.
All these analysis and visualisation are performed without having the users to type a single line of code.
Our main aim is to investigate the airport network infrastructure of India (ANI) to explore its various properties and its traffic dynamics. We propose to build a visual network exploration tool using R. This tool can be used not only to explore the airport network in India but also can be used to explore any kind of airport network.
shinyNET: A web-based flight data visualisation toolkit using R Shiny and ggraph
