IS428 2016-17 Term1 Assign3 Joachim Fu Jun Hao

Problem & Motivation

After the successful resolution of the 2014 kidnapping at GAStech’s Abila, Kronos office, GAStech officials determined that Abila offices needed a significant upgrade. At the end of 2015, the growing company moved into a new, state-of-the-art three-story building near their previous location. Even though the employee morale rose somewhat with the excitement of the new building, there are still a few disgruntled employees in the company.

The new office is built to the highest energy efficiency standard, but as with any new building, there are still several HVAC issues to work out. The building is divided into several HVAC (heating, ventilation, and air conditioning) zones. Each zone is instrumented with sensors that report building temperatures, heating and cooling system status values, and concentration levels of various chemicals such as carbon dioxide (abbreviated CO2) and hazium (abbreviated Haz), a recently discovered and possibly dangerous chemical. CEO Sten Sanjorge Jr. has read about hazium and requested that these sensors be included. However, they are very new and very expensive, so GAStech can afford only a small number of sensors.

With their move into the new building, GAStech also introduced new security procedures, which staff members are not necessarily adopting consistently. Staff members are now required to wear proximity (prox) cards while in the building. The building is instrumented with passive prox card readers that cover individual building zones. The prox card zones do not generally correspond with the HVAC zones. When a prox card passes into a new zone, it is detected and recorded. Most, but not all, areas are still open to staff members even if they forget their prox cards. People are somewhat careless with their prox cards, but some diligent staff members will go to the security desk and pick up a new prox card if their old one is mislaid. As part of the deal to entice GAStech to move into this new building, the builders included a free robotic mail delivery system. This robot, nicknamed Rosie, travels the halls periodically, moving between floors in a specially designed chute. Rosie is equipped with a mobile prox sensor, which identifies the prox cards in the areas she travels through.

Data Transformation & Analysis

Given files

1. Employee List Excel File
2. Building CSV File
3. Floor 1 Zone 8 CSV File
4. Floor 2 Zone 2 CSV File
5. Floor 2 Zone 4 CSV File
6. Floor 3 Zone 1 CSV File
7. Mobile Prox CSV File: Measures the location of employee cards (by x,y coordinates) detected based on mobile sensors
8. Prox-out CSV file: Measure the location of employee cards (by zone area) detected based on fixed mobile sensors

Employee ID in Fixed + Mobile Proximity CSV File and Employee List Excel File

• Problem: In the employee list excel file given, I analysed the data and removed the index column. Since I felt that the numbers that denote the number of cards the employee has due to misplacement were not necessary, I removed them.
• Solution: Based on the Mobile Prox and Prox-out CSV files, the employee ID detected were in the format as follows: <first letter of the first name> + <last name> + <number of times replaced (i.e. 005 means replaced card 4 times)>. As such, I ensured that the employee list excel file, mobile prox and prox out CSV files had the identical employee ID meant for joining of tables in Tableau.

Prox-out (Fixed Sensor) CSV File Data Format

• Problem: Comparing the mobile and fixed sensors data sets, I found out that the fixed proximity data had zones depicted instead of the x,y coordinates as shown in the Mobile Proximity Sensor Data Format.

• Solution: Given the pictures (.jpeg files) on the floor and zone layout, there were dimensions given to each zone on each floor. As such, I had to organise the pictures provided.

1. Removed white spaces and text in the picture using 'Snipping Tool' as shown below
2. Import picture into Tableau and selected options to show the different floor maps based on selected floors filtered
3. Since the axis is well formulated in a similar ratio aspect as that of the dimensions stated in the picture, I added reference lines on both axis to approximate a point within the zone as shown below.

After getting a range for both axis for each zones, I find the mid-point and allocated to each value in the CSV file depending on the floor and zone the employee is in.

Observations & Patterns

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.