IS428 AY2018-19T1 Zhuo Yunying

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. 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. 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. Staff members are also given proximity cards which will register their movements in the building by fixed proximity sensors in each zone and Rosie, a robotic mail delivery system equipped with proximity sensors.

With the huge amount of data collected by the various sensors installed in the building, there is a need to build an interactive data visualization tool to help the management efficiently identify typical patterns and issues of concern in building operations.

The interactive visualization can be targeted at employees from the following departments:

• Security Department: To track movement of employees within the building so as to ensure that employees are not entering prohibited zones/areas
• Facilities Department: To understand building operations and the respective building data elements so that anomalies can be spotted quickly
• Executive Department: To understand power usage of the new building so as to properly plan ahead on possible policies to implement (e.g. campaigns to save power consumption)

Dataset Analysis & Transformation Process

Before the analysis began, the dataset given is analysed to identify its respective format and attributes. There were 6 different zip files provided in the assignment and each 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 in order to prepare the data for import and analysis on an interactive visualization.

Fixed/Mobile Proximity Data & Employee List

There are 2 main types of proximity detectors installed in the building to enhance the safety and security of its employees. These detectors include fixed proximity sensors installed in each zone of the building and a mobile proximity sensor that is attached to Rosie, a robotic mail delivery system. Data will be captured when employees, holding their own proximity card, pass a proximity sensor in the building.

The formats of the captured data by both the fixed proximity sensor and mobile proximity sensor are similar. However, the fixed proximity sensor provides the zone in which employees pass through while the mobile proximity sensor provides the exact coordinates of the employee’s location. The following shows the main differences between the two different types of data captured:

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The following section illustrates the issues faced in the data analysis phase leading to a need to transform the data into a specified format.

Issue: Because of the differences in the types of data captured, the data cannot be correlated with each other for display in the same chart. For example, one can plot employee’s exact location using the X/Y coordinates (from the mobile proximity sensor) but the zone coordinates (from the fixed proximity sensor) were not known and hence, could not be plotted. This will present an issue during the analysis since we are unable to clearly identify which area the employees are present on the map.

Solution: To resolve this issue, the coordinates for each zone has to be identified. This is done using an online tool that allow analysts to get coordinates for custom polygons on a map. By plotting these custom polygons on a map, we will be able to get the centre point coordinates of the zone. This will then allow us to plot the employees’ location on each zone. The following details the process of getting individual zone coordinates.

1. Download the online tool from the link: https://github.com/bryantbhowell/tableau-map-pack/blob/master/draw_tableau_polygons_on_background_image.html
2. Open the tool and choose the map to be plotted. In the zone maps given, there were white spaces and additional text that were not part of the map itself. As such, the map has to be cropped to get the actual map itself only. The following shows the cropping of maps and this process is repeated for each of the 3 levels.
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3. Open the map using the online tool and plot each of the custom polygons. During the process of plotting, the following assumptions were made:
   
   1. For some areas with multiple zones separated on the map, a small area of the zone is being selected. For example, zone 4 have 3 different segments in level 1. However, they all refers to the elevators and stairs. By plotting only one of the zones, we will still be able to know that the employee is taking the stairs/elevators. There isn’t a need to identify which stairs or elevators they took. As such, for zone 4, only one of the custom polygon is drawn.
   2. For areas that were too big, only a small segment of the zone was plotted. For example, zone 1 encompasses the entire corridor spaces. If all the corridor spaces are drawn, the centre point will not be situated in zone 1. Therefore, only one part of the zone is drawn. As long as employees are within the same zone, they are in the corridor areas.
      The outcome of plotting these polygons will result in the following display on the online tool. The same process is repeated for each of the 3 levels.
      500px

4. After the polygons have been plotted, the results can be exported into a .csv file format for analysis in Tableau. Once the data has been imported into Tableau, the centre coordinates of each zone can then be retrieved. The following shows the centre points that were plotted on the chart and the ability to export the data. This will then allow us to retrieve the coordinates for each zone to be plotted onto the map.
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5. However, in level 2 and 3, shapes of each zones were not consistent and this has resulted in errors in the centre point of the zone. The following shows the errors that has been encountered during the process.
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6. In order to resolve this error, a manual task has to be performed to get the coordinate point of each erroneous zone coordinate. This can be performed in Tableau using the annotate function. The following shows an example of how the annotate function can provide the X/Y coordinate values. The process is repeated for each of the erroneous centre points.
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7. After the completion of this process, the issue of lacking individual zone coordinates will be resolved.

Issue: Currently, the data for the fixed proximity sensors and mobile proximity sensors are in 2 separate spreadsheets. This makes the analysis process difficult with the need to blend or join data in Tableau.

Solution: To simplify the process of analysis, both the data from the fixed and mobile proximity sensors are combined into one single file. With the above process completed, we are also able to get the coordinates for each of the zone to be plotted on the chart. However, we do not have the zone information for the mobile proximity sensor. In this analysis, it does not matter as we are only interested in plotting the zone coordinates onto the chart. The following shows the data format of the transformed data:

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Issue: During the process of correlating the zone data and the coordinates, the dataset has a zone that stated “Server Room”. However, there is no point coordinate for this zone on our generated polygons.

Solution: To resolve this issue, the annotate function is also used to help us get the centre point coordinate of the server room. This result is then, updated into the file for analysis.

Issue: With the zone coordinates and the X/Y coordinates from the mobile proximity sensor, the data can be plotted onto a map to identify where employees are located. However, with only the proximity ID in the captured data, we are unable to correlate the employee with each proximity ID. The employee list provided in the dataset also does not show the mapping between their name and the proximity ID.

Solution: Upon close observation of the employee name and the proximity ID, a correlation can be identified. The proximity ID is derived from the first letter of the employee’s first name and the entire string of the last name. The last three numbers refer to the number of times each employee request for a new pass. By default, the count starts from 001. The following formula is then applied to identify a correlation between the employee and the proximity ID:

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Building Data Elements (HVAC Sensor Readings & Status Information)

The building data elements provide multiple readings collected from different sensors in the entire building. However, the data is not structured in a way that allows for flexibility in the choice of sensor readings to be analysed and to apply filters by different floors and zones. As a result, there is a need to reshape the data to provide for this flexibility.

There are 3 main categories of data in the building data dataset:

• Overall Building Data (e.g. Drybulb Temperature, Pump Power etc.)
• Floor Data (e.g. Air Loop Inlet Temperature, Cooling Coil Power etc.)
• Floor/Zone Data (e.g. Reheat Coil Power, CO2 Concentration etc.)

To better analyse the data using Tableau, the following is being done:

1. The entire building data dataset is grouped into different Excel Spreadsheet as follows:

   500px 500px

2. Tableau provides an Excel plugin that allow one to reshape data into a format for easy processing especially if multiple attributes were present for selection. With the use of the tool, the following is then performed to each of the grouped datasheet:
   1. For the building data spreadsheet, a data reshape is performed to obtain the following result:
      650px

   2. For the floor data spreadsheet, a data reshape is performed. The output of the reshaped data is similar to the building data, with the exception of the difference in attributes between the overall building and each floor attributes.
   3. For the floor/zone data spreadsheet, a data reshape is also performed on its attributes. However, due to the volume of the data generated, additional processing needs to be done as follows:
      650px

With the reshaped data, it can then be imported into Tableau for analysis.

Hazium Readings

The sensor readings for hazium concentration level were saved as 4 different files in the given dataset. These readings are consolidated into one single file. The result is similar to the building dataset elements. As such, similar processing techniques were used to process the data - Excel Tableau’s plugin for data reshape. The following illustrates the steps taken to achieve the final transformed dataset.

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Dataset Import Structure & Process

With the dataset analysis and transformation phase completed, the following files will have to be imported into Tableau for analysis:

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Each of the data file is added as a data source in Tableau. The relationships defined between each data source is the timestamp, floor and zone. This will allow analysis to be conducted across all the data sources at any one point.

Additional processing is performed to the first data source - Proximity Data and Employee List.

• Import the proximity data as a data source.
• Add new data connections to the proximity data source. The new data connection file will be the Employee List.
• Perform a left join between the proximity data and the employee list to correlate the employee ID together. This will allow us to identify the employee with the captured proximity card ID. The following shows the configuration of the join:

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To process and display the dates in a readable format for the analyst, each of the data sources will have a new calculated field to derive the date and day of the week. This will convert the date from the format “31/5/2016” to a format of “31 May (Tuesday)”.

The formula used is as follows:

STR(DATEPART('day', [Date/Time])) + " " + DATENAME('month',[Date/Time]) + " (" + DATENAME('weekday', [Date/Time]) + ")"

Interactive Visualization

The interactive visualization can be accessed here: https://public.tableau.com/views/Assignment3_145/Home?:embed=y&:display_count=yes

For the best experience, adjust your screen resolution to 1366x768 and enable full screen on the browser. Adjust the dashboard so that all elements can be clearly visible without the need to scroll up/down.

Throughout all the different dashboards, useful guides/tips are provided to help users navigate through the different filters and actions so that their analysis can be performed smoothly. The following interactivity elements are also used throughout all the dashboards to maintain consistency:

Interactive Technique	Rationale	Brief Implementation Steps
Filter dates with the use of time range slider 200px	To provide flexibility for analysts to choose the time period that they are interested to analyse. The use of checkboxes or dropdown list requires the analyst to check/uncheck each date manually which is time-consuming. As such, a time range slider is preferred.	The date/time field have to be duplicated with its data type set to “date” Add the new field to be filtered.
Filter each floor/zone using a single selection drop down list 200px	To allow analysts to concentrate on the data collected from each level with the use of a single selection. Use of a drop down list also allow analysts to easily choose the building level that they are interested to analyse.	Configure the filter selection to be a single selection drop down list
Change and zoom of floor plans based on each floor filter 200px	To allow for easy reference of mapping each building data elements with each zone. When a user filters from one floor to another, the floor plan also changes to provide for quick and easy reference. Due to the space constraint, each floor plan has to be zoomed for users to identify and see the zone areas clearly.	Create calculated fields for the x and y axis. Put the 2 calculated fields into the worksheet view. To hide the mark, set the colour as transparent. Navigate to Maps > Background Images. Add the floor plans into the background images and configure it according to the filter condition. Put the “floor” attribute as a filter.

The following sections elaborates on other interactivity techniques are integrated into each of the individual dashboard.

Home Dashboard

There is a large amount of data attributes captured in the dataset provided. As such, it will not be possible to display all the attributes for a proper analysis in a single dashboard. At the same time, although many of these attributes are interrelated to each other, there is no clear and correct order in which data attributes should be analysed. Therefore, use of a story point does not seem plausible in this context. To resolve this issue, flexibility has to be provided for users to navigate between different dashboards. To do so, a homepage is created with 3 different data categories in mind – employee movement, HVAC Control System/Chemical Levels and power consumption. Each of these categories are further broken down into its respective sub-categories for users to conduct their analysis. This allow users to choose the analysis that they are interested to look in.

The following shows the home dashboard:

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To allow for flexibility in navigation, the following interactive techniques have been employed:

Interactive Technique	Rationale	Brief Implementation Steps
Navigate across dashboards with buttons	To provide users the flexibility of moving from one dashboard to the other, with an easy and simple to use interface	Create images of the buttons and placed these images into the folder directory as follows: My Tableau Repository > Shapes. In Tableau, create a new calculated field. Drag the new calculated field into the “Shapes” mark. Configure the shapes that you want to use by selecting the Shape Palette loaded into the folder previously. In the dashboard, drag the worksheet with the button into an empty area. Configure the action, with the use of the filters action.
Display tooltips when users hover over each button	To provide users with contextual information about the dashboard and the expected charts that they will look at after the button click	Configure the tooltips information on the marks shelf

Employee Movement Dashboard

The following shows the employee movement dashboard:

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The following interactive techniques have been employed in this dashboard:

Interactive Technique	Rationale	Brief Implementation Steps
Traverse through an hourly time series by clicking on the arrows	To analyse and draw connections between employees and their movement in the building over a series of time	Drag the date/time field into the “Pages” shelf Change the field to “Hour” to enable the animation on an hourly basis The following shows a screenshot of the configuration: 150px
Filter data source types based on fixed or mobile proximity type	To allow the analyst to understand the fixed and mobile proximity data	Add a checkbox filter into the dashboard
Highlight department upon selection	To highlight departments that are of interest to the analyst	Add the shape legends for each department Select the new object and check “Highlight selected items”. The following shows a screenshot of the configuration: 150px

To help analysts get started with the analysis of the data, a typical pattern of GAStech employees have also been identified and included in the dashboard.

HVAC Control System & Chemical Levels (Heating)

The following shows the HVAC Control System (Heating) dashboard:

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The following interactive techniques have been employed in this dashboard:

Interactive Technique	Rationale	Brief Implementation Steps
Highlight temperature type, upon selection	To enable analysts to visualize the differences between the water temperature and its respective set-points, all 3 lines have been placed on the same chart. However, situation arises when there is a need to look at one single attribute only. Therefore, the use of highlighting will help analyst to perform its tasks better.	The implementation steps are similar to the use of highlighting in the “Employee Movement” dashboard.
Highlight across all the different charts	To allow analysts to see how one data point is correlated to another data point in another chart, given the same date/time	Navigate to Dashboard > Actions > select “highlight”. Configure the source and target sheets for the highlighting of charts in the dashboard.

HVAC Control System & Chemical Levels (Natural Ventilation)

The following shows the HVAC Control System (Natural Ventilation) dashboard:

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The following interactive techniques have been employed in this dashboard:

Interactive Technique	Rationale	Brief Implementation Steps
Highlight across all the different charts	To allow analysts to see how one data point is correlated to another data point in another chart, given the same date/time	The implementation steps are similar to the use of highlighting in the “HVAC Control System & Chemical Levels (Heating)” dashboard.
Link filter with “Mechanical Ventilation” dashboard	To allow analysts to conduct their analysis smoothly such that when one filters for level 2 data in mechanical ventilation dashboard, they will also use level 2 data when they go into the natural ventilation dashboard. The rationale is that both ventilation elements are related to each other and analysts might be interested to analyse both elements one after the other.	When configuring the filters, select the worksheets that will be affected by the filters. Other than selecting the worksheets in the dashboard, show all the worksheets in the workbook and check those that applies. The following shows an example of the configuration: 450px

HVAC Control System & Chemical Levels (Mechanical Ventilation)

The following shows the HVAC Control System (Mechanical Ventilation) dashboard:

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The following interactive techniques have been employed in this dashboard:

Interactive Technique	Rationale	Brief Implementation Steps
Highlight across all the different charts	To allow analysts to see how one data point is correlated to another data point in another chart, given the same date/time	The implementation steps are similar to the use of highlighting in the “HVAC Control System & Chemical Levels (Heating)” dashboard.

HVAC Control System & Chemical Levels (Air-Conditioning)

The following shows the HVAC Control System (Air-Conditioning) dashboard:

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The following interactive techniques have been employed in this dashboard:

Interactive Technique	Rationale	Brief Implementation Steps
Highlight temperature type, upon selection	To enable analysts to visualize the differences between the thermostat temperature and its respective set-points, all 3 lines have been placed on the same chart. However, situation arises when there is a need to look at one single attribute only. Therefore, the use of highlighting will help analyst to perform its tasks better.	The implementation steps are similar to the use of highlighting in the “Employee Movement” dashboard.

HVAC Control System & Chemical Levels (Chemicals)

The following shows the HVAC Control System (Chemicals) dashboard:

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The following interactive techniques have been employed in this dashboard:

Interactive Technique	Rationale	Brief Implementation Steps
Show different views of the Hazium data in the dashboard	To provide different perspectives for an analyst to conduct his investigation. For example, the line graph allows analysts to view the change in hazium concentration level overtime while the heatmap allow analysts to easily view the concentration levels of hazium based on colours representation.	-
Highlight across different charts	To show the correlation between the same set of data that has been represented in 2 different chart layouts	The implementation steps are similar to the use of highlighting in the “HVAC Control System & Chemical Levels (Heating)” dashboard.
Configure safe range of CO2 level	To provide a clear view for the analysts to see if the level of carbon dioxide concentration exceeds a safe range To provide flexibility for analysts to configure a safe range, as defined by the organization	Switch to the “Analytics” view and choose to add a “Reference Band” into the chart. For the value, choose to add a new custom parameter. The following shows a screenshot of the new parameter created: 250px To create a band, define two parameters that is the typical safe range level to a maximum safe range level. This will help to create a reference band. The following shows the configuration of a reference band: 250px Add both of these parameters onto the dashboard and analysts will be able to configure the values based on the organization’s preference.
Click on data point to explore ventilation elements that might lead to variations in CO2 level	To allow co-referencing of carbon dioxide levels with the ventilation elements and analyse possible reasons that led to the changes in carbon dioxide concentration	To ensure that the filter applied in the dashboard is linked with the “HVAC Control System Ventilation Elements” dashboard, similar steps were taken as mentioned previously to link filters across different dashboards. To allow clicking of the chart to another dashboard, actions were defined based on the following configuration: 350px

Power Consumption in Kronos Office Building

The following shows the Power Consumption dashboard:

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The following interactive techniques have been employed in this dashboard:

Interactive Technique	Rationale	Brief Implementation Steps
Click on data point to explore possible causes of variations in HVAC power demand	To allow analysts who are interested to identify reasons as to why the HVAC control system is consuming so much power with just a single click on a data point	The implementation steps are similar to the use of linking technique used in the “HVAC Control System & Chemical Levels (Chemicals)” dashboard.

Interesting & Anomalous Observations

Using the dashboard as a platform for investigation and analysis, the following aims to provide answers to the questions posed.

Q1: Typical Patterns In Proximity Card Data & Typical Day Of GAStech Employees

Typical Patterns in Proximity Card Data
Based on the data captured by the proximity sensors, the following shows a typical pattern in the proximity card data.

1. For the fixed proximity sensors, readings are collected for 24hours during the weekdays, except for 1am and 4am. From 12midnight to 6am, only level 1 fixed proximity sensor will collect data. This can possibly mean that there is no employee movement from 12midnight to 6am on level 2 and 3.
2. For the mobile proximity sensor, Rosie (the mobile robot) travels the halls at 9am and 2pm daily. On 4, 5, 11 and 12 June, the robot does not collect any readings. One possible reason is that these are weekends (Sat/Sun) and therefore, Rosie will not be required to travel along the hallways.

Typical Day for GAStech Employees

The following lists a typical day for GAStech employees, in all departments:

1. Employees start arriving in the office at 7am. By 9am, majority of the employees would have already arrived in the building and settle in their own office space.
2. Lunch time is usually between 12nn to 2pm.
3. At around 2pm, many employees will be back in their office and that’s when we can see lots of activities going on in the different levels.
4. Employees typically end work at around 5pm, though some may leave slightly earlier.
5. After 6pm, majority of the employees in the office are from the Engineering, Facilities and IT department.
6. After 7pm, employees working in level 3 would have left the office.
7. At 12midnight, most of the employees would have left the building and only people in the facilities team will be present in level 1.
8. People in the facilities department will be available in the building 24 hours and its always the same people patrolling around the area.
9. Other than the facilities personnel, IT and engineering employees often stay in the office and will only leave at around 12midnight.

The following lists some activities that is part of a typical day pattern for employees in a specific department:

Department	Activities
Engineering	Between 2pm and 3pm, employees seem to appear around the meeting and training room. After 6pm, only Clemencia Whaley, Penney Bueno and Twana Quiroz will remain in the building. They will leave around 11.40pm.
Executive	At around 12nn, CEO Sten Sanjorge Jr. will be moving around the building. At around 1pm, other executives start leaving for lunch.
Facilities	Shift work is being done throughout the day as we can see some pattern for employees in the facilities department: Varro Awelon and Emile Earpa will always be in level 1 from 12mn to around 7.30/8am. Dylan Scozzese and Chi Staley will always be in level 1 from 8am onwards. At approximately 9am, some employees from the facilities department will proceed to the deli.
Information Technology	At around 10.30am, all employees from the IT department will gather at the meeting room area on level 2. Out of all IT employees, Erminia Bello seems to be always staying in the office till late.
Security	Employees do not typically move around the building. The data captured had shown that they always stay within their own office.

Q2: Interesting Patterns & Its Significance In Building Data

The following identifies some interesting patterns and the significance of the pattern to GAStech.

S/N	Interesting Pattern	Significance
1	The water heater temperature set-point is set at around 60C. This is approximately similar to the water heater outlet temperature. From this, we can deduce that water entering the heater is always heated to the set-point temperature, regardless of the temperature of water entering the heater. During weekdays, the temperature of the water entering the heater slowly falls after 5am but will increase slowly after 12nn. Similar pattern is observed over the weekends, but at a smaller difference in the inlet temperature. However, the supply inlet mass flow rate remains the same. [Source: HVAC Control System & Chemical Levels (Heating) Dashboard]	A consistent pattern of a change in inlet temperature is observed over the different days. However, the flow rate of water remains unchanged. This meant that there are other reasons as to why the inlet temperature always changes throughout the day. The management needs to identify the reasons for the temperature difference to prevent wastage of energy in heating up the water, especially during the weekdays where temperature difference is vast.
2	The lower the supply inlet temperature, the higher the rate at which the water heater burns natural gas. From this, we can infer that the burning of natural gas is used as a way to heat up the water so as to maintain the outlet temperature. The building also has a hot water system pump to heat up the water. Despite of the difference in the inlet temperature throughout each day, the amount of power used by the hot water system pump remains constant throughout the 2 weeks. [Source: HVAC Control System & Chemical Levels (Heating) Dashboard]	Based on the data collected, we can deduce that the building provides for 2 different methods to heat up the water – burning of natural gas and using the hot water system pump. Through the pattern identified, we can see that the burning of natural gas is used more often than the hot water system pump to heat up the water. Natural gas is a non-renewable resource. As such, burning of natural gas for water heating is a poor practice and does not adhere to energy efficiency standards, as claimed by the company. It is important for the management to look into the issue and make use of the hot water system pump for heating of water instead.
3	The supply fan seems to consume higher levels of power from 7am to 10pm. The outlet mass flow rate also seems to be consistent across the days, when the supply fan is operating correctly. The timing in which the supply fan is consuming higher levels of power is similar to employees’ working hours, including the IT/Engineering department. [Source: HVAC Control System & Chemical Levels (Mechanical Ventilation) Dashboard]	Through this pattern, we can infer that the building seems to be following energy efficiency standards by using more energy only when required, such as only during working hours.
4	On level 1 and 2, the bathroom exhaust fan seems to be consistently turned on throughout the days. However, on level 3, the bathroom exhaust fan power is only consuming power between 7am to 10pm. [Source: HVAC Control System & Chemical Levels (Mechanical Ventilation) Dashboard]	Through this pattern, we can see that the use of the bathroom exhaust fan on level 1 and 2 does not seem to adhere to the energy efficiency standards. The practice of using energy only when required can be seen on level 3. However, as mentioned previously, employees in level 3 will be gone by 7pm. Despite so, the bathroom exhaust fan continues to generate power until late at night. This shows that the building is not following energy efficiency standards, as claimed.
5	The level of carbon dioxide concentration typically falls within a safe range of 350 to 1000 ppm for both level 2 and 3. However, the level of carbon dioxide concentration in level 1, especially zone 5 (Conference room) and zone 7 (loading area/server room) often falls outside of the safe range. [Source: HVAC Control System & Chemical Levels (Chemicals) Dashboard]	The air ventilation system in the building is performing its expected functions in level 2 and 3, on a normal day. However, more needs to be done to improve the ventilation on level 1, to ensure that it remains a conducive working environment for all employees. This is especially important in the conference room, where employees conduct meetings. If the level of carbon dioxide falls in the unhealthy range, employees may experience discomfort and may not be able to perform up to standards.
6	In the afternoon, the outdoor air mass flow rate is generally higher. However, during this period of time, the outside air temperature is generally warmer. When the outside air temperature is warmer, the percentage of outside air delivered by the HVAC system is generally lower. This pattern is generally observed across all the 3 levels. [Source: HVAC Control System & Chemical Levels (Natural Ventilation) Dashboard]	This is a good sign that the building is adhering to the energy efficiency standards. When the outside air is warmer, it takes more power to cool down the air so as to maintain a conducive temperature. As such, by restricting flow of warmer outdoor air, it helps the company to save on energy and power.
7	Level 1 has the highest outdoor air mass flow fraction. However, it has the lowest flow rate of air returning back to the HVAC system. On level 3, the outdoor air mass flow fraction can go as low as 6%. However, it has high flow rate of air returning back to the HVAC system. With the difference in outdoor air mass flow rate, the temperature of the air returning back to the HVAC system is higher in level 1 as compared to level 3. However, more energy is used on level 3 as compared to level 1. [Source: HVAC Control System & Chemical Levels (Natural Ventilation) Dashboard]	From this pattern, we can clearly see the exchange of air between the outside air and the internal building. When the amount of outdoor air flowing into the floor is high, the rate of air returning back to the system is lower. This shows that the building is adopting good air-conditioning/ventilation system. However, with higher flow rate of warmer outside air, there is a need for the cooling coil to use more energy in level 1 as compared to level 3. This will help to maintain a conducive temperature by cooling the warmer air. Despite so, the cooling coil in level 3 used more power than level 1 instead. This made the already cool level 3 cooler but level 1, which is hotter remains at a higher overall temperature. This indicates possible issues with the HVAC control system which needs the attention of the operations staff.
8	The night cycle control status in level 3 is always off. In contrast to the night cycle control status in level 1, the status will be turned on after normal working hours of the company and during Sunday. [Source: HVAC Control System & Chemical Levels (Air Conditioning) Dashboard]	There is a need for the operations staff to check as to why the night cycle manager is not working on level 3. Although there is no direct evidence to show the consequences of the night cycle control manager being off, it may potentially cause wastage of energy especially when the set-point temperature is not set appropriately.
9	Across all the three levels, the corridor spaces have constantly been using high levels of power. [Source: Power Consumption in Kronos Office Building Dashboard]	This is bad for the company as the corridor spaces do not contribute greatly to improving employees’ productivity or efficiency. Spending 80% of their energy in these areas are considered as energy wastage and potential measures have to be employed to reduce energy use in these areas.
10	Level 3 Zone 9 (server room) has been constantly using large amounts of equipment power. [Source: Power Consumption in Kronos Office Building Dashboard] Temperature of the air in this zone is also constantly kept at a lower temperature than other zones. [Source: HVAC Control System & Chemical Levels (Air Conditioning) Dashboard]	It is normal for the server to consume the largest amount of equipment power. At the same time, knowing that the server room have to be turned on 24 hours a day, measures have been taken to ensure a cooler temperature in the zone. This is notable for the company as it shows that they did deployed their resources properly.

Q3: Notable Anomalies/Unusual Events In Data

The following lists some of the anomalies and unusual events identified in the data. Events that are possibly related to each other are grouped as one anomaly.

The priority level of issues will be ordered based on the following criteria:

• (Highest Priority) An issue leading to potential health risks or death to employees
• An issue leading to a decrease in employees’ morale
• An issue undermining the claim of Kronos Office as an energy efficiency building
• (Lowest Priority) An issue causing the financial losses to the company

Priority	Anomaly/Unusual Events	Possible Danger/Serious Issue
1	There is a high hazium gas concentration level across all the levels in the building on 11 June. This takes place in the afternoon from 1pm till the wee hours, the next day. [Source: HVAC Control System & Chemical Levels (Chemicals) Dashboard]	Not much is known about Hazium gas, except for the fact that it is dangerous. With the high levels of Hazium gas concentration throughout the entire building, it may become unsafe for the employees to work in. In the worst case scenario, all employees may be subjected to health ailments or death in the building. If the issue is not resolved, similar incidents may happen in future. This will put all employees in harm’s way and potentially, bringing health risks to the employees.
2	On 3 and 9 June, there is a rise in hazium gas concentration across all zones in the building. However, it was the highest in level 3, zone 1. This corresponds to the GAStech CEO’s office. The gas concentration level remains high throughout the entire day. [Source: HVAC Control System & Chemical Levels (Chemicals) Dashboard]	With a rise in hazium gas concentration in the CEO’s office, it may pose serious harm to the CEO. Furthermore, it was also mentioned that there are disgruntled employees in the company. It is possible that these employees may attempt to harm the CEO. If the issue is not resolved, similar incidents may happen in future. This will threaten the safety of the CEO and other employees working within the area.
3	In level 3 zone 1 (CEO’s office), there is a constant high level of temperature (ranging between 32 to 40C) from 1pm to 4am the next day. This happens every day from 2 June onwards. [Source: HVAC Control System & Chemical Levels (Air Conditioning) Dashboard]	It is important to check the cause of the regular high level of temperatures in the CEO’s office. This might be due to acts of disgruntled employees in the company or for other unknown happenings in the CEO’s office. If the issue is not resolved, the temperature in the zone may always be higher than normal and more energy might be consumed in an attempt to lower the temperature. In the worst case scenario, the situation may further worsen causing harm to the CEO, especially when the temperature is extremely high.
4	Between 5 and 6 June, there is a sudden increase in carbon dioxide concentration in level 1 zone 3 (main entrance), zone 5 (conference room) and zone 7 (loading area and server room). However, this pattern was not observed in level 2 and 3. [Source: HVAC Control System & Chemical Levels (Chemicals) Dashboard] To investigate the potential cause of the issue, I attempted to analyse the ventilation elements. It was discovered that the supply fan is generating lower amounts of electricity on 5 June as compared to normal days. The outlet mass flow rate was also low on level 1, during that day. From this pattern, it is possible that there is poor ventilation in the building that results in the spike in carbon dioxide concentration. [Source: HVAC Control System & Chemical Levels (Natural & Mechanical Ventilation) Dashboard]	It is important to identify the potential cause of the sudden spike in carbon dioxide concentration, especially during the weekends. Furthermore, the areas in which the spike is observed are working zones in the building. If the issue is not resolved, similar cases may happen in future and this may threaten the level of safety in the building.
5	On 7 and 8 June, the flow rate of outside air entering the HVAC system dropped. However, the percentage of outside air delivered by the HVAC system is actually higher than usual. This is abnormal because a lower flow rate of outside air should translate to a lower percentage of outside air delivered by the HVAC system. However, this was not the case observed on 7 and 8 June. Furthermore, the total flow rate of air returning to the HVAC system is also extremely low. Adding on to that, the temperature of the air returning back to the HVAC system is also higher than usual. This is observed across all the 3 levels. [Source: HVAC Control System & Chemical Levels (Natural Ventilation) Dashboard] On the same days, the supply fan also consumes extremely low levels of fan power from 7am to 10pm. This has led to a sharp drop in the supply fan outlet mass flow rate and a sudden rise in temperature in level 3. After 10pm, the supply fan starts to generate abnormally high amount of fan power until 12am. [Source: HVAC Control System & Chemical Levels (Mechanical Ventilation) Dashboard] The reheat damper position in almost closed within the same period of time. The flow rate of air entering the zone is also lower than usual and the temperature of the air for the zones are also higher than normal. This pattern is also reflected through the natural ventilation elements, as mentioned previously. [Source: HVAC Control System & Chemical Levels (Air Conditioning) Dashboard] On the same days, there were sudden increases in the carbon dioxide concentration across all the 3 levels in the building. This could be brought about by the events occurring in the building. [Source: HVAC Control System & Chemical Levels (Chemicals) Dashboard]	It is important to identify possible factors that had led to the poor circulation of air and an extremely low levels of fan power generated to all floors in the building. Possible explanations could include a breakdown in the HVAC system. Of course, further investigation has to be performed to confirm the cause. If the issue is not resolved, the higher temperature and carbon dioxide concentration in each floor may cause employees to feel uncomfortable in the building which further decreases the employee morale. Also, level 3 houses a server room and high temperature may cause the servers to break down, resulting in high costs for the company.
6	On level 3, the HVAC system fan consumes higher amounts of power every weekends as compared to the weekdays. This is unusual as employees are not typically working during the weekends. Therefore, the need for high usage of supply fan in the level is not required. [Source: HVAC Control System & Chemical Levels (Mechanical Ventilation) Dashboard]	There is a need to check the issue as to why the HVAC system fan is consuming so much fan power on level 3 during the weekends. If the issue is not resolved, large amounts of energy will be wasted every weekend on level 3. This translates to large amounts of money wasted and this also means that the company is not adhering to energy efficiency standards.
7	There is an abnormally high supply fan outlet mass flow rate on 11 and 12 June for all 3 levels. This is possibly supplied by the HVAC system fan, which has consumed abnormally high amounts of fan power on both days. This pattern continues until the early morning of 13 June. [Source: HVAC Control System & Chemical Levels (Mechanical Ventilation) Dashboard] The above pattern is supported with an abnormally high amounts of HVAC power consumed on 11 and 12 June. [Source: Power Consumption in Kronos Office Building Dashboard]	There is a need to check the issue as to why the HVAC system fan was triggered to generate abnormally high amounts of power. If the issue is not resolved, the company could potentially lose money from the high wastage of power consumed especially when no one is expected to be in the building during the weekends. The company is also not adhering to energy efficiency standards by allowing the wastage to occur.
8	On 11 and 12 June, the temperature of the air and the cooling/heating set-point is the same for all 3 levels. The reheat damper position is almost open throughout the entire day and this has brought about high levels of air flow rate into the different zones. The temperature of the air entering the zone is also higher than normal. A similar pattern is observed across all 3 levels in the building. The amount of energy used by the cooling coil power is also shown to be higher during these 2 days. [Source: HVAC Control System & Chemical Levels (Air Conditioning) Dashboard]	There is a need to check the cooling/heating set-point temperature in the different levels and the cause of the reheat damper position being fully opened throughout the days. If the issue is not resolved, there will be wastage of energy in trying to cool the building.

Q4: Observed Relationships Between Proximity Card Data & Building Data Elements

There is an attempt to identify possible relationships between the proximity card data and the building data elements. However, after much searching, only one relationship can be spotted, as elaborated below:

Orhan Strum is a suspicious employee who may have attempted to bring about an increase in hazium gas concentration in the CEO’s office.

The following evidence was observed:

1. An abnormally high hazium gas concentration is observed in the CEO’s office on 11 June afternoon, till the next day. Using this as a potential lead, it was discovered that Orhan Strum had appeared in level 3 on 11 June between 8.30am to 1pm. After 1pm, the hazium gas concentration in the CEO’s office gradually increases. Orhan Strum was also not observed to be present in the office, as the hazium gas concentration rises.
2. On days when the hazium gas concentration was high (3 and 9 June), Orhan Strum will be in the building before it happens. Once the hazium gas concentration gradually increases, he will not be in the office anymore. This pattern was observed both on 2 June and 8 June, before the hazium gas concentration increases. On 9 June, when the hazium gas concentration went higher than normal, he was not detected to be present in the office building.

With the above evidence, I attempted to analyse whether a similar cause and effect can be identified. However, it was found out that on days when the hazium gas concentration is slightly higher in level 3, Orhan Strum will still be present in the office building. This reduces the probability that Orhan Strum is a potential culprit, since one will not harm itself with the dangerous gas. At the same time, the activities occur on a weekday and this increases the difficulty of finding a suspect. On the other hand, this could be a deliberate activity that Orhan Strum took to reduce his chances of being identified.

Taking into account the evidence and evaluation of similar patterns, the level of confidence in the assessment of the relationship is still moderately high.

References

In the completion of the analysis, the following references have been extremely useful:

• Tableau Training Video - Polygon Maps (http://www.tableau.com/learn/tutorials/on-demand/polygon-maps-8.2)
• Tableau Training Video - Background Images (http://www.tableau.com/learn/tutorials/on-demand/background-images-8)
• Creating Custom Polygons On A Background Image (https://tableauandbehold.com/2015/04/13/creating-custom-polygons-on-a-background-image/)
• Dynamically Switch Images Using Filters (https://community.tableau.com/message/182926#182926)
• Tableau Tip Week - Dashboard Navigation Buttons (http://www.thedataschool.co.uk/niccolo-cirone/tableau-tip-week-wednesday-creating-dashboard-navigator-buttons/)