IS428 2017-18 T1 Assign Shi Xiaoyu

To be a Visual Detective: Detecting spatio-temporal patterns

Problem & Objective

Mistford is a mid-size city, located to the southwest of a large nature preserve. The city has a small industrial area with four factories. Recently, there is a significant decrease on the number of nesting pairs of the Rose-Crested Blue Pipit, a popular local bird. It is speculated that the downfall of the Rose-Crested Blue Pipit may be related to noxious gases from the factories near the preserve.

With the passage of the Mistford Pact of 2010, the city and the preserve have adopted certain safeguards to help ensure the safety of the people, animals, and vegetation of the area. With the aim in mind, air sampling sensors have been placed near the town and in the preserve to monitor air quality. In total, there are nine sensors to collect information on several substances of potential concern, such as Appluimonia, Chlorodinine and etc.

By using the data given, I aim to implement data visualization techniques to better analyse data and conduct tasks below:

• Characterize sensors’ performance and detect unexpected behaviors
• Identify the chemicals detected by the sensor group and the chemical release pattern
• Determine which factories are responsible for which chemical release with explanation. Identify operation pattern of the factories

Data Preparation

There are three types of data provided, location data of the four factories and nine sensors, air sampling data from sensors and meteorological data (wind direction & wind speed) from a weather station in proximity to the factories and sensors. To prepare data before importing into Tableau, I implement the following solutions to cope with different data issues.

Problem #1	Building Location Data of Factories and Sensors
Issue	Location data of the four factories and nine sensors are stored in the unstandardized format (word document). Tableau fails to load the data.
Solution	Create a spreadsheet named Location Data. Put the factory location data and sensor location data shown as below. 800px

Problem #2	Calculating Meteorological Data For Every Hour
Issue	Meteorological data is captured every 3 hours whereas sensor data is captured every hour. Hence, the meteorological information for some sensor data records are missing. In this case, wind direction and speed for certain time stamp cannot be shown in the chart. In addition, there is a blank line at row 460 in the meteorological data.
Solution	Remove the blank row. Add one column named DC/Hour, which represents the average direction change per hour till next timestamp. E.g. the D3 stands for the direction change per hour from 4/1/16 0:00 to 4/1/16 3:00. Similarly, add another column named SC/Hour (Average Speed Change Per Hour). Set the last record of April, August and December to 0. The new added columns will be used in the solution 3 to calculate wind direction and wind speed for every hour.Note: Here is an assumption that the the meteorological data is changed at an average speed during every three hours. 800px

Problem #3	Combine Sensor Reading and Meteorological Data in the Same File
Issue	Meteorological data and sensor data are in the separate files. It’s important to have the sensor readings with wind information at exact time stamp since we need tell which factory is responsible for the chemical release by using meteorological data of wind speed and direction.
Solution	Move the meteorological data and sensor data into the same file named Consolidated Data and combine the data using the formulas shown as below. 800px