Data Description

Visual Analytics Science & Technology Challenge 2017 MC2

The Challenge

Data Description

Visual Findings

References and Acknowledgements

Feedback

Dataset

This data 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. The Meteorological data(Wind speed and Wind direction) represents 3 months of readings. The data from 9 sensors contains 3 months of chemical readings.

These sensors collect information on several substances of potential concern, including:

Appluimonia – An airborne odor is caused by a substance in the air that you can smell. Odors, or smells, can be either pleasant or unpleasant. In general, most substances that cause odors in the outdoor air are not at levels that can cause serious injury, long-term health effects, or death to humans or animals. However, odors may affect your quality of life and sense of well-being. Several odor-producing substances, including Appluimonia, are monitored under this program.

Chlorodinine – Corrosives are materials that can attack and chemically destroy exposed body tissues. Corrosives can also damage or even destroy metal. They begin to cause damage as soon as they touch the skin, eyes, respiratory tract, digestive tract, or the metal. 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.

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. Liquid forms of Methylosmolene are required by law to be chemically neutralized before disposal.

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.

Manufacturing Companies near Mistford

Roadrunner Fitness Electronics – Roadrunner produces personal fitness trackers, heart rate monitors, headlamps, GPS watches, and other sport-related consumer electronics. Roadrunner began as one of the region’s first fitness stores in 1962, with an eye toward outfitting the entire nation with appropriate outdoor gear.

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

Radiance ColourTek – Radiance produces solvent based optically variable metallic flake paints. Offering a new generation of paints in the 1970s, Radiance out marketed all competitors for three decades until manufacturing process issues began to tarnish their reputation.

Indigo Sol Boards – Indigo Sol produces skateboards and snowboards. Founder Billy Keys started off manufacturing wooden wine barrels for northwestern US wineries, but then navigated a course from decorative fiberglass wine barrels to making his first pair of fiberglass skis in 1971. Excellent product and sales decisions rocketed Keys Skis production to unexpected levels, until they were bought out by a large Denver, Colorado-based private investment group. Keys returned to making specialized snowboards in the 1980s, with a small company in Mistford called Indigo Sol.

Factory and Sensor locations

Data Preparation

The Datasets provided are regarding :

a) Sensor data : Chemical captured, data and time it was captured and the corresponding reading

b) Wind data : Wind speed and direction captured on a three-hourly basis

Sample Dataset for Wind

Sample Dataset for Sensors

For preparing the visualizations and analysis, the two datasets were merged using Date Time as the common column.

Pollution Plume model

Steps followed for pollution plume model , uaing the combined data set of wind and sensor, each reading was replicated three times for plotting points to form a triangle. Next, for the angle of the model, a user defined parameter was created. The user can input angle of the wind in this scenario.

Angle was created with the formula that generated 3 angles in equal gaps as denoted by the Angle of Fence Parameter. For example: For wind direction 190, the angles generated were 175,190,205 with angle of fence as 30.

Formula:

Radius_X IF [Path ID]=1 THEN [X] ELSE [X]+ [Wind_Converted]*SIN(RADIANS([Angle])) END

Radius_Y IF [Path ID]=1 THEN [Y] ELSE [Y]+[Wind_Converted]*COS(RADIANS([Angle])) END

Angle IF [Path ID]=1 THEN [Wind.Direction]- 15 ELSEIF [Path ID]=2 THEN [Wind.Direction] ELSE [Wind.Direction]+ 15 END

Wind mph [Wind.Speed..m.s.]*2.23

Wind_Converted [Wind mph]*(200/12)