IS428 2017-18 T1 Assign Tan Song Kai
Contents
Assignment Details
IS428 Main Page: (https://wiki.smu.edu.sg/1718t1is428g1/Main_Page)
Assignment Overview: (https://wiki.smu.edu.sg/1718t1is428g1/Assignments)
Assignment Dropbox: (https://wiki.smu.edu.sg/1718t1is428g1/Assignment_Dropbox)
Problem & Motivation
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 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.
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 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 analyse these datasets. These datasets includes air sampler data, meteorological data, and locations maps provided by the state government, which has been monitoring the gaseous effluents from the factories through a set of sensors distributed around the factories.
Task
General Task
The dataset provided several months of meteorological data (wind speed and direction) and chemical data emitted by four industrial factories and captured by nine sensing stations. To explore the spatio-temporal chemical readings and wind data, specifically which factories emitted what chemicals and how the nine sensors in the area were performing, the team developed a web-based analytics tool with interactive visualizations and path line analysis to reveal sensor errors and chemical reading spikes, as well as pinpoint possible sources of chemical reading spikes. The goal was to help the local ornithologist determine whether or not the factories were compliant with environmental regulations.
Specific Task
Specifically, we are to provide visualisation to identify these issues:
• Sensors: To find out if all sensors’ performance and operations are working properly at all times, by detecting unexpected behaviours of sensors from the readings captured. [Characterize the sensors’ performance and operation. Are they all working properly at all times? Can you detect any unexpected behaviours of the sensors through analysing the readings they capture? Limit your response to no more than 9 images and 1000 words.]
• Chemicals: To find out which chemicals are being detected by the sensor group, by identifying patterns of chemical releases. [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.]
• Factories: To find out which factories are responsible for which chemical releases, to be able to pinpoint on the factories which are responsible for the Rose-Crested Blue Pipits. [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.]
Dataset Analysis & Transformation Process
Datasets Provided (Sensor Data, Sensor Location, Meteorological Data)
The datasets provided to Mitch from the state includes:
- Sensor Data (Sensor Data.xlsx)
- Contains three months of readings in the following format:
- Chemical: Which one of the four chemicals detected by the sensors
- Monitor: Which one of the nine sensors picking up the reading
- Reading: The air sensor detected amount in parts per million
- Date Time: The date and time of day of the reading, local time with no change for Daylight Savings
- Sensor Location (Sensor Location.xlsx)
- The factories and sensor locations are provided in terms of x,y coordinates on a 200x200 grid, with (0,0) at the lower left hand corner (southwest). The sensors map shows the locations of the sensors and factories by number for the sensors and by name for the factories.
- Meteorological Data (Meteorological Data.xlsx)
- Contains three months of readings in the following format:
- Date: The date and time of the readings, local time with no change for Daylight Savings
- Wind Direction: The compass directions where wind is originating from, using a north-referenced azimuth bearing where 360/000 is true north
- Wind Speed: The speed of the wind in meters per second
Before being able to commence on our analysis, the datasets provided were analysed to better understand how we could potentially link these sets of data together, and at the same time verify the field attributes provided. This is to ensure that Tableau (the visualisation tool that will be used in this analysis), is able to interpret the data as accurately and seamlessly possible.
From briefly looking through the datasets, we can derive that no data requires any immediate attention to solve, as the Date field for both the Meteorological Dataset and Sensor Dataset are in proper DateTime format. Additionally, the other fields provided are pure strings / integers, and there are no joint fields in any one column. The only possible field that may cause a bit of confusion for Tableau would be the Elevation (m) field in the Meteorological Dataset, where it only takes up one row within the entire column. Hence, we just remove it to ensure Tableau does not misinterpret it.
Additional Information Provided
Next, the documents provided within the Assignment Data (Backgrounder on Sensors in the Lekagul Wildlife Preserve Area.doc, Companies v2.doc, MC2 Data Descriptions.doc and MapLargeLabels.jpg) were carefully gone through to better understand the entire context that is provided.
From the information provided, it can be derived that the factories and sensors locations are provided in terms of x, y coordinates on a 200x200 grid (MapLargeLabels.jpg), with (0,0) at the lower left-hand corner (southwest). The location of the four factories are also given to us:
- Roadrunner Fitness Electronics: (89, 27)
- Kasios Office Furniture: (90, 21)
- Radiance ColourTek: (109, 26)
- Indigo Sol Boards: (120, 22)
The factory location coordinates are then placed into another excel file, knowing that this information / data is necessary for further analysis later when we start analysing the wind direction and wind speed to identify the chemicals released by the factories.
