Assignments

ISSS608 Visual Analytics and Applications

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To be a Visual Detective

The assignments require you to put the concepts, methods and techniques you had learned in class to solve real world problem using visual analytics techniques. Students should also use the assignments to gain hands-on experience on using the data visualisation toolkits I had shared with you to complate the assignment.

The assignment topics are based on VAST Challenge 2017. You are required to choose one of the challenge topic provided below and work out the solution.

Overview

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.

Mini-Challenge 1

The Boonsong Lekagul Nature Preserve is used by local residents and tourists for day-trips, overnight camping or sometimes just passing through to access main thoroughfares on the opposite sides of the preserve. The entrance booths of the preserve are monitored in order to generate revenue as well as monitor usage. Vehicles entering and exiting the preserve must pay a fee based on their number of axles (personal auto, recreational trailer, semi-trailer, etc.). This generates a data stream with entry/exit timestamps and vehicle type. There are also other locations in the part that register traffic passing through. While hiking through the various parts of the preserve, Mitch has noticed some odd behaviors of vehicles that he doesn’t think are consistent with the kinds of park visitors he would expect. If there were some way that Mitch could analyze the behaviors of vehicles through the park over time, this may assist him in his investigations.

Please visit VAST Challenge 2017: Mini-Challenge 1 for more information and to download the data.

Mini-Challenge 2

The four factories in the industrial area are subjected to higher-than-usual environmental assessment, due to their proximity to both the city and the preserve. Gaseous effluent data from several sampling stations has been collected over several months, along with meteorological data (wind speed and direction), that could help Mitch understand what impact these factories may be having on the Rose-Crested Blue Pipit. These factories are supposed to be quite compliant with recent years’ environmental regulations, but Mitch has his doubts that the actual data has been closely reviewed. Could visual analytics help him understand the real situation?

Please visit VAST Challenge 2017: Mini-Challenge 2.

Mini-Challenge 3

As Mitch works independently, he realizes that he cannot continually visit all areas of the preserve to inspect for environmental impacts as well as he would like to. He realizes that his analysis would be incomplete without thorough surveillance and knowledge of the preserve health over time. Fortunately, Mitch has acquired data from some commercial multi-spectral imagers that have been routinely covering the nature preserve every few weeks. Mitch believes that a visual analytics approach can help him achieve an understanding of the preserve health and alert him to possible conditions that may be impacting his birds.

Please visit VAST Challenge 2017: Mini-Challenge 3 for more information and to download the data.

Grand Challenge

Mitch realizes that explorations into each of these three areas (covered by the mini-challenges) will reveal important, enlightening information. However, could there be relationships among discoveries made across two or even all of the investigations that could reveal even more about what is happening across the nature preserve and how it is happening. Mitch remembers that you mentioned to him how important it is to analyze not only what is happening, but the entire range of “who-what-where-why-when- and –how”. This understanding will enable him to pursue positive steps in helping to save the Rose-Crested Blue Pipit.

Please visit VAST Challenge 2017: Grand Challenge for more information and to download the data.

The Task

As an expert in visual analytics, you have been hired to help GAStech understand its operations data. In this assignment, you are given two weeks of building and prox sensor data. Can you use visual analytics to identify typical patterns of and issues of concern?

You will be asked to answer the following types of questions:

1. What are the typical patterns in the prox card data? What does a typical day look like for GAStech employees?
2. Describe up to ten of the most interesting patterns that appear in the building data. Describe what is notable about the pattern and explain its possible significance.
3. Describe up to ten notable anomalies or unusual events you see in the data. Prioritize those issues that are most likely to represent a danger or a serious issue for building operations.
4. Describe up to five observed relationships between the proximity card data and building data elements. If you find a causal relationship (for example, a building event or condition leading to personnel behavior changes or personnel activity leading to building operations changes), describe your discovered cause and effect, the evidence you found to support it, and your level of confidence in your assessment of the relationship.

The data

You will have the following data and supporting information at your disposal:

• A building layout for the GAStech offices, including the maps of the prox zones and the HVAC zones
• A current list of employees, roles, and office assignments
• A description of the data formats and fields provided
• Proximity sensor data for each of the prox zone regions
• Proximity sensor data from Rosie the mobile robot
• HVAC sensor readings and status information from each of the building’s HVAC zones
• Hazium readings from four sensors.

The datasets have been zipped and uploaded into the dropbox of e-learn (LMS).

Visualisation Software

To perform the visual analysis, students are encouraged to explore any one or a combination of the following software:

• Tableau
• JMP Pro
• Qlik Sense
• Microsoft Power BI

One of the goals of this assignment is for you to learn to use and evaluate the effectiveness of these visual analytics tools.

Submission details

This is an individual assignment. You are required to work on the assignment and prepare submission individually. Your completed assignment is due on 24th October 2016, by 12.00 noon.

You need to edit your assignment in the appropriate wiki page of the Assignment Dropbox. The title of the wiki page should be in the form of: IS428_2016-17_T1_Assign3_FullName.

The assignment 3 wiki page should include the URL link to the web-based interactive data visualization system prepared.

Assignment 3 Q&A

Need more clarification, please feel free to pen down your questions.

1. What is Hazium? Hazium is a (fictitious) chemical that has become a recent concern on the island of Kronos. Not much is known about its effects, but it is suspected that Hazium is not good for people.
2. There are a few extra building file data fields in the .json dataset that do not appear in the .csv data. These extra data fields are actually valid for the building for the dates and times they were recorded, but they will not add significantly to your analysis. So for this assignment, please just use the data fields included in the .csv file.
3. Can you provide more info on the data provided in the mobile proximity card data? Are the x,y coordinates bound to a normal (x,y) plane, where in this case the plane is the floor maps? The (x,y) coordinates are bound to a normal plane. The (x,y) plus the floor number would identify a specific location. The lower left of the provided map is (0,0) and the upper right is (189,111).
4. In some cases, data is reported for some sensors and not others, or it is documented but not reported. Where can we find this data? Please use the data fields you have available to perform your investigation. In general, the documented set of attributes may not be reported for all zones.
5. What does the (x,y) coordinates represent for the mobile robot sensor? The (x,y) coordinates for these reading represent the location of the mobile sensor.
6. Sometimes, mobile prox data for a prox card repeats multiple times in a minute. Does this indicate the number of seconds that the prox card was within range of the sensor? No. Multiple readings do not indicate what fraction of the minute that the mobile sensor was in proximity of the prox card.
7. In some cases, the value of the VAV Availability Manager Night Cycle On/Off is 2. Is this a valid value? Yes.
8. Does F_3_Z_9 VAV Damper Position mean F_3_Z_9 VAV REHEAT Damper Position? Yes.
9. Can we consider all unlabeled rooms in the floor plans to be offices (it is only mentioned in Floor 2)?