IS428 2017-18 T1 Assign Wu Jianhua

Problem & Motivation

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?

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

1. Characterize the sensors’ performance and operation. Are they all working properly at all times? Can you detect any unexpected behaviours of the sensors through analyzing the readings they capture?
2. 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?
3. 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.

Background Information

The four manufacturing factories south of the nature preserve which Mitch is suspicious of the chemical releases and these are their coordinates:

Roadrunner Fitness Electronics (89,27) – Roadrunner produces personal fitness trackers, heart rate monitors, headlamps, GPS watches, and other sport-related consumer electronics.

Kasios Office Furniture (90,21) – Kasios Office Furniture manufactures metal and composite-wood office furniture including desks, tables, and chairs.

Radiance ColourTek (109,26) – Radiance produces solvent based optically variable metallic flake paints.

Indigo Sol Boards (120,22) – Indigo Sol produces skateboards and snowboards.

These sensors collect information on several substances of potential concern, the chemicals are order by their harmfulness level to the human and environment:

1. Methylosmolene – This is a trade name for a family of volatile organic solvents. After the publication of several studies documenting the toxic side effects of Methylosmolene in vertebrates, the chemical was strictly regulated in the manufacturing sector.
2. Chlorodinine – Corrosives are materials that can attack and chemically destroy exposed body tissues. They might be hazardous in other ways too, depending on the particular corrosive material. An example is the chemical Chlorodinine. It has been used as a disinfectant and sterilizing agent as well as other uses. It is harmful if inhaled or swallowed.
3. Appluimonia – An airborne odor is caused by a substance in the air that you can smell. However, odors may affect your quality of life and sense of well-being. Several odor-producing substances, including Appluimonia, are monitored under this program.
4. AGOC-3A – New environmental regulations, and consumer demand, have led to the development of low-VOC and zero-VOC solvents. Most manufacturers now use one or more low-VOC substances and Mistford’s plants have wholeheartedly signed on. These new solvents, including AGOC-3A, are less harmful to human and environmental health.

Dataset Analysis & Transformation Process

The data available consists of sensor readings from a set of air-sampling sensors and meteorological data from a weather station in proximity to the factories and sensors. Each of the dataset provided has its own unique ways to process and make sense of the data to bring value to the analysis. This section will elaborate on the dataset analysis and transformation process for each dataset to prepare the data for import and analysis on an interactive visualization.

Location Data

The factories and sensors 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.

In the given dataset (Sensor Location.xlsx), the data only consist of the location details for the 9 sensors. It’s is missing the data for the factor location.