IS428 AY2019-20T1 Assign Linus Cheng Xin Wei

VAST 2019 MC2: Citizen Science to the Rescue

Overview

One of St. Himark’s largest employers is the Always Safe nuclear power plant. The pride of the city, it produces power for St. Himark’s needs and exports the excess to the mainland providing a steady revenue stream. However, the plant was not compliant with international standards when it was constructed and is now aging. As part of its outreach to the broader community, Always Safe agreed to provide funding for a set of carefully calibrated professional radiation monitors at fixed locations throughout the city. Additionally, a group of citizen scientists led by the members of the Himark Science Society started an education initiative to build and deploy lower cost homemade sensors, which people can attach to their cars. The sensors upload data to the web by connecting through the user’s cell phone. The goal of the project was to engage the community and demonstrate that the nuclear plant’s operations were not significantly changing the region’s natural background levels of radiation.

When an earthquake strikes St. Himark, the nuclear power plant suffers damage resulting in a leak of radioactive contamination. Further, a coolant leak sprayed employees’ cars and contaminated them at varying levels. Now, the city’s government and emergency management officials are trying to understand if there is a risk to the public while also responding to other emerging crises related to the earthquake as well as satisfying the public’s concern over radiation.

Data

Data Files

Name	Info
VAST 2019 - St. Himark - About Our City.docx	A document describing the scenario, as well as general information about St.Himark
MC2_datadescription.docx	A document that provides descriptions for data files provided for MC2
MobileSensorReadings.csv	Contains readings from 50 mobile sensors that are attached to cars. Data fields include: Timestamp, Sensor-id, Long, Lat, Value, Units, User-id. The timestamps are reported in 5 second intervals, though poor data connectivity can result in missing data. Each sensor has a unique identifier that is a number from 1 to 50. Location of the sensor is reported as longitude and latitude values (see map description below). The radiation measurement is provided in the Value field. Radiation is reported with units of counts per minute (cpm). Each measurement is independent and does not represent a summation over the previous minute. Some users have chosen to attach a user ID to their measurements while some others chose with a default name.
StaticSensorReadings.csv	Contains readings from a set of carefully calibrated professional radiation monitors at fixed locations throughout the city. Data fields include: Timestamp, Sensor-id, Value, Units.
StaticSensorLocations.csv	Contains locations of the static sensors
StHimarkMapBlank.png	A picture containing the outline of the whole area of St Himark
StHimarkNeighborhoodMapNoLabels.png	A picture containing the outline of neighbourhoods in St Himark, without any labels
StHimarkNeighborhoodMap.png	A picture containing the outline of neighbourhoods in St Himark, including neighbourhood name, Nuclear plant and hospital labels
StHimarkLabeledMap.png	A fully labelled picture of St Himark, including main roads and bridges
StHimarkNeighborhoodShapefile	A folder containing a map of the neighbourhoods provided as a shapefile. Geometry of the polygons is reported in meters.

Data Preprocessing and Cleaning

Creation of POI.csv

In 'MC2_datadescription.docx', a list of coordinates pertaining to the hospitals and Always Safe Nuclear plant located in St Hilmark was given. These are possible point of interests that might be useful for analysis, so I decided to create a data file to contain the information.

Step	Info
1.	Open up MC2_datadescription.docx and scroll to the bottom to find coordinate information
2.	Input the information into excel to create a .csv file. The resulting file contains the following data columns: Latitude - latitude value Longitude - longitude value Type_of_POI - The kind of POI pertaining to the row

Creation of StaticSensorConcat.csv, appending geographical coordinates to Static readers

As 'StaticSensorLocations.csv' and 'StaticSensorReadings.csv' share the common column, sensor-id, I am able to extract the geographical information from 'StaticSensorLocations.csv' and tie it to each individual reading.

Step	Info
1.	I opened up Tableau prep and loaded both 'StaticSensorLocations.csv' and 'StaticSensorReadings.csv'. I created a Join clause, with the common field being Sensor-id. I used a left join in this case because it sorts your data nicely at the output stage.
2.	There is a redundant column called 'Sensor-id-1', which is a repeat of the 'Sensor-id' column. As it provides no utility, I decided to drop it in the Join function.
3.	After inspecting the output and ensuring it is correct, I exported the file to a csv named 'StaticSensorConcat.csv'.
4.	The resulting file contains the following data columns: Sensor-id - ID of each static sensor Lat- latitude value Long - longitude value Timestamp - time of reading Value - absolute value of measurement Units - unit of measurement Note, I left the units in place so that I could remember and understand what I'm dealing with. As well as providing some context if I ever view the project again.

Creation of AllSensorData.csv

To make analysis easier, I want to combine both Static and Mobile sensors together into one file 'AllSensorData.csv'

Step	Info
1.	I created a new column named *Sensor_Type in order to differentiate between the static and mobile sensors. I did this by using creating a new calculated field, and filling in the value 'Static' or 'Mobile accordingly.
2.	Upon inspection of the 'User-id' column in 'MobileSensorReadings.csv', I realized that it does not provide any substantial amount of information to be used for analysis and decide to drop that column. As stated by a paragraph in 'MC2_datadescription.docx', the naming of the user's are not standardized, thus looking at the id number of each sensor would be better.
3.	I used a union function in order to join the two datasets together. Since they have the exact same columns, there are 0 mismatching fields and I am able to perform the function with ease. I dropped the created column 'Table Names', as we have the column 'Sensor_Type' to differentiate between the 'Mobile' and 'Static sensors.
4.	The resulting file contains the following data columns: Sensor_Type - Type of Sensor, Mobile or Static Sensor-id - ID of each static sensor Lat- latitude value Long - longitude value Timestamp - time of reading Value - absolute value of measurement Units - unit of measurement

Extracting Neighbourhood to data from StHimark.shp

Ever since Tableau 2019.2 came out, they have added the ability to manipulate spatial data. As there is a shapefile provided, I decided to combine some features into the main dataset, 'AllSensorData.csv'

Step	Info
1.	I changed the connection type to 'Extract', as it allows Tableau to function faster, and to use all the data available.
2.	Referencing https://www.tableau.com/about/blog/2016/4/tableau-online-tips-extracts-live-connections-cloud-data-53351, I used the Tableau function 'MAKEPOINT', which makes points so that we can join 'AllSensorData.csv' and 'StHimark.shp' based on their spatial relationship, Geometry.
3.	The resulting file contains the following data columns: Sensor_Type - Type of Sensor, Mobile or Static Sensor-id - ID of each static sensor Lat- latitude value Long - longitude value Timestamp - time of reading Value - absolute value of measurement Units - unit of measurement Id - Numerical Id corrsponding to the Neighbourhood Nbrhood - Neighbourhood name Geometry - Type of Geometry

Creation of full_data.csv

My computer is pretty old and outdated, so to save on computational time and processing, I have removed more columns after Tableau runs too slowly, and have saved the data to 'full_data.csv'.

Steps	Info
1.	The resulting file contains the following data columns: Sensor_Type - Type of Sensor, Mobile or Static Sensor-id - ID of each static sensor Lat- latitude value Long - longitude value Timestamp - time of reading Value - absolute value of measurement Nbrhood - Neighbourhood name

Tasks

Q1) Visualize radiation measurements over time from both static and mobile sensors to identify areas where radiation over background is detected. Characterize changes over time.

Note About Radiation

For this particular question, I am referencing https://wattsupwiththat.com/2011/03/17/live-real-time-monitoring-map-of-radiation-counts-in-the-usa/, which states that the alert level of cpm is >= 100 counts per minute (cpm). Thus I will only be looking at values >= 100 cpm.

Static

Step	Info
1.	The above figure shows mobile sensor readings that are >= 100 CPM in 7 different neighbourhoods, Broadview Cheddarford Downtown Old town Palace Hills Safe Town Southwest The peak of readings seems to happen sometime after the 8th April 2020, as highlighted by the blue box. We can observe an increased number of readings represented by the circles in the figure.
2.	If we were to break down the figure by day, we can see that there is an increase of readings >= 100 CPM daily, as evidenced by comparing the number of circles highlighted by the brown box and comparing the numbers highlighted in the blue box. Moreover, there is an increase in the number of neighbourhoods being affected, from 4->7->6->7-> 7, which can be observed by viewing the green box.
3.	Here is a figure showing the average change of CPM, relative to the first day of the readings, 6th April 2020. Boxes that are coloured blue represent a decrease in CPM, and boxes that are brown represent an increase in CPM. From the red box, we are able to see that all neighbourhoods affected see a net increase in CPM on the last day of the readings, 10th April 2020.

Mobile

Step	Info
1.	The above figure shows mobile sensor readings that are >= 100 CPM in 20 different neighbourhoods, Null Palace Hills Northwest Old Town Safe Town Southwest Downtown Wilson Forest Scenic Vista Broadview Chapparal Terrapin Springs Pepper Mill Cheddarford Easton Weston Southton Oak Willow East Parton West Parton Similar to the static readings, the peak of readings seems to happen sometime after the 8th April 2020, as highlighted by the blue box. We can observe an increased number of readings as well, by observing the number of circles
2.	Looking at each day, we can observe an increasing number of >= CPM 100 as evidenced by the density of the left figure, highlighted by the brown box, as well as comparing the absolute values highlighted by the blue box. Unlike static sensors, mobile sensors have the ability to travel between neighbourhoods, which is why we do not really see a sharp increase/decrease of neighbourhoods affected by radiation, as highlighted by the green box.
3.	The above figure shows the average change of CPM for mobile sensors, relative to the first day of the readings, 6th April 2020. Boxes that are coloured blue represent a decrease in CPM, and boxes that are brown represent an increase in CPM. Referencing the red box, there are three neighbourhoods, namely Broadview, Chapparal and Terrapin Springs that see a decrease in CPM during the last day. This could be due to the radiation moving, or because the sensors are mobile, the total number of times being sense decrease for that particular neighbourhood. The rest of the neighbourhoods all have an increase in their CPM, with Wilson Forest being the obvious outlier. Taking a closer look, we can see a very dense plot of circles highlighted by the yellow box for Wilson Forest during 9th April-10th April ties in with our results in highlighted by the greenbox.
4.	Note on 'Null' neighbourhood For mobile sensors, there are a few data points that are denoted with 'Null', as they do not happen within St Hilmark. Denoted by the purple boxes, we can see that they come from Jade Bridge, Himark Bridge, and somewhere along the Wilson Forest Hwy.

Q2) Use visual analytics to represent and analyze uncertainty in the measurement of radiation across the city

Q2a) Compare uncertainty of the static sensors to the mobile sensors. What anomalies can you see? Are there sensors that are too uncertain to trust?

Static

Step

Info

1.

As highlighted by the green box in the picture, there seems to be a lack of readings from sometime during the night 9th April, to the night of 10th April. We can see that Sensor ID 15 is located in Safe Town, which is the region where the nuclear powerplant is located, thus it is really strange as it should be constantly pick up readings of radiation due to its close proximity. Another uncertain factor is that there are two readings that are negative CPM, which do not make sense. Thus, the readings from the sensor-id 4 and sensor-id 14 may be subjected to scrutiny.

Mobile

Step

Info

1.

Mobile sensors have more pockets of no readings available, but there are a few clusters that stand out. For example, highlighted by the green box, we can see that a considerable amount of sensors have no readings sometime in the afternoon of 9th April to the night of 9th April. The black box also highlights a cluster of missing readings from the afternoon of 10th April to the night of 10th April. The brown box highlights sensors 6,33,34,48 and 49 all having outages sometime during the late morning of 8th April, after which they do not pick up any readings at all. On top of that, the pink box highlights a highly unusual reading of CPM, being an all-time high of 57345 by sensor 12, located in Old Town. Based on the references that I have read, such a high dosage will lead to instant death, and since it happened only once, it might be due to a malfunction in reading by the sensor.

Q2b) Which regions of the city have greater uncertainty of radiation measurement? Use visual analytics to explain your rationale.

Static

Step	Info
1.	For static sensors, it seems like there are no discernible patterns of uncertainty for specific neighbourhoods. The only exceptions are the in Downtown and Cheddarford which have negative CPM readings, which are illogical.

Mobile

Step	Info
1.	For mobile sensors, excluding the Null neighbourhood, Chapparal, Oak Willow, Terrapin Springs and Wilson Forest all have high levels of missing readings as highlighted by the brown boxes, increasing their uncertainty. On top of that, Old Town picked up a very high reading of CPM 57345, which is extremely unusual, thus making the reading uncertain.

Q2c) What effects do you see in the sensor readings after the earthquake and other major events? What effect do these events have on uncertainty?

Note on Earthquake

I have highlighted in the visualizations by a pink area for each respective earthquake, 6th and 8th April.

Static

Step	Info
1.	The only effect we can observe for static sensors is that for sensor 15 located in Safe Town, it might be possible that the earthquake affected the collection of readings as the lack of readings happened directly after the earthquake of 9th April, denoted by the brown box.

Mobile

Step

Info

1.

For mobile sensors, after the 6th April earthquake, we can see missing readings from Chapparal, Cheddarford, Oak Willow and Wilson Forest, as highlighted by the red boxs After the 8th April earthquake, we can see a huge amount of missing sensor readings, with constant downtime in neighbourhoods like Chapparal, Oak Willow, Pepper Mill, Terrapin Springs and Wilson Forest, as highlighted by the brown boxes. What is interesting to note are the neighbourhoods that are highlighted by the green boxes, Downtown, Easton, Scenic Vista, Southon, Southwest and Weston. These neighbourhoods had all along consistant readings before the 8th April earthquake, and this could be caused due to the earthquake.

Q3) Given the uncertainty you observed in question 2, are the radiation measurements reliable enough to locate areas of concern?

Q3a) Highlight potential locations of contamination, including the locations of contaminated cars. Should St. Himark officials be worried about contaminated cars moving around the city?

Step	Info
1.	If we take a look at readings above 100 CPM based on static sensors, we can see that there are 7 neighbourhoods that are potentially contaminated as they happen numerous times within a day. They are Broadview Cheddarford Downtown Old Town Palace Hills Safe Town South West
2.	Based on my findings in question 1, I am going to assume the coolant leaked sometime after April 8th 00 00. Firstly, we take a look at cars in the neighbourhood of Safe Town during that time period. We can see that there are 9 cars affected, as evidenced by the green box. They have the mobile id: 9 13 15 21 22 32 39 43 44
3.	In the above screenshot, I have filtered for the 9 cars affected by the coolant leaked. I am then looking at readings above 100 CPM, after April 8th 00 00. Based on the highlighted red and green box, I have identified 13 potential neighbourhoods that have been contaminated. They are: Cheddarford Downtown East Parton Easton Old Town Pepper Mill Safe Town Scenic Vista Southon Southwest West Parton Weston Wilson Forest
4.	When comparing the findings of both the mobile and static sensors, we can see that there are some neighbourhoods that overlap. These neighbourhoods are the ones that are the most likely to be contaminated, although this does not disqualify any other neighbourhood found by the mobile sensor as the static sensors are rooted in place. There are 5 neighbourhoods to take special note of, and they are: Cheddarford Downtown Old Town Safe Town South West
5.	Officials should be wary about contaminated cars! For example, highlighted in red is a contaminated car moving between Easton, Safe Town and Southon. Another example of this would be the area highlighted by the orange box, where a contaminated car moves between Easton, Safe Town and West Parton. This phenomena happens for a multitude of cars and is something that needs to be further investigated.

Q3b) Estimate how many cars may have been contaminated when coolant leaked from the Always Safe plant. Use visual analysis of radiation measurements to determine if any have left the area.

Step	Info
1.	Assuming the coolant leaked sometime after April 8th 00 00, we can assume that 9 cars have been contaminated, as highlighted by the green box They have the mobile id: 9 13 15 21 22 32 39 43 44
2.	In relation to Safe Town where the nuclear plant is located in, as well as tracking the movements of the 9 cars from 8th April 00 00, we can see that all 9 cars have travelled out of the Safe Town neighbourhood into other neighbourhoods from the above image. We can also see, highlighted in red, Cars with sensor-id's 9,22,21 leaving St Hilmark

Q3c) Indicated where you would deploy more sensors to improve radiation monitoring in the city. Would you recommend more static sensors or more mobile sensors or both? Use your visualization of radiation measurement uncertainty to justify your recommendation.

Step	Info
1.	I would recommend at least one sensor being placed around each neighbourhood, as well as a sensor for each entrance and exit to St Hilmark.
2.	Based on the results found by https://wiki.smu.edu.sg/1920t1is428g1/IS428_AY2019-20T1_Assign_Linus_Cheng_Xin_Wei#Q2.29_Use_visual_analytics_to_represent_and_analyze_uncertainty_in_the_measurement_of_radiation_across_the_city, we can clearly see that static sensors are more reliable and consistent when compared with mobile sensors. We also tackled the fact that there might be cross-contamination caused by mobile sensors. Based on these reasons, I would recommend putting up more static sensors over mobile sensors, to the point where mobile sensors are redundant.

Q4) Summarize the state of radiation measurements at the end of the available period. Use your novel visualizations and analysis approaches to suggest a course of action for the city. Use visual analytics to compare the static sensor network to the mobile sensor network. What are the strengths and weaknesses of each approach? How do they support each other?

Course of action

Step	Info
1.	Perform maintenance on static sensors 4,14 and mobile sensor 12 and ensure they are working as intended, as they have captured faulty readings.
2.	For static sensors, I would recommend doubling the number of sensors in each location. This is to ensure that if one sensor dies, the other one is still able to get readings. We can also look at the average of both sensors to see if there are any anomalies in the readings.
3.	Track down these 9 potentially contaminated cars (9,13,15,21,22,32,39,43,44) and perform radiation decontamination on both the drivers and the cars.
4.	High priority to find cars with mobile id's 9,21,22 (highlighted in a red box by step 3) as soon as possible to perform decontamination. They might have travelled in and out of St Hilmark, leading to contamination that is even further out.
5.	Referencing Step 4 of https://wiki.smu.edu.sg/1920t1is428g1/IS428_AY2019-20T1_Assign_Linus_Cheng_Xin_Wei#Q3a.29_Highlight_potential_locations_of_contamination.2C_including_the_locations_of_contaminated_cars._Should_St._Himark_officials_be_worried_about_contaminated_cars_moving_around_the_city.3F, officials should look into potential contamination in these areas (Cheddarford, Downtown, Old Town, Safe Town, South West) first as dangerous readings appear in both mobile and static sensors. After wards, take a look at the other low priority areas that have been picked up by the two types of sensors
6.	Standardize routes taken by mobile sensors, especially around the borders for efficient and effective readings.

Strengths & Weakness

Static

Step	Info
Strengths	Extremely reliable due to high availability, as evidenced by the area highlighted in the red box Removal of human error except for maintenance (misplacing, breaking the sensor) Safer as compared to mobile sensors, if there is a high CPM reading, only the sensor will be affected. Compared to mobile sensor, where there is a chance the human and car will be contaminated by the radiation and spread.
Weakness	Only covers 9 neighbourhoods in St Hilmark as highlighted by the green box. Fixed location leads to a limit of the area being monitored, as compared to a mobile sensor

Mobile

Step	Info
Strengths	Covers almost the whole St Hilmark, as evidenced by the green box As mobile sensors are mobile, it is not restricted to being in one area, allowing one sensor to pickup a wider range of data
Weakness	Not as reliable as static sensors, random periods of no availability as highlighted by the orange boxes May be limited to only roads for cars, static sensors are able to be placed almost anywhere Dangerous for human if there is radiation, as well as potential radiation being spread by contaminated cars Might not be able to pick up consistent radiation readings if moving too fast or the driver does not stay in the same area for too long

Support

Step	Info
1.	Mobile Sensors are able to support Static sensors by covering a wider area and reach in the same neighbourhood. Highlighted in green is an example of the Broadview neighbourhood, where we can see a wider area of readings as compared to the static sensor. The static sensor is able to be placed in a strategic location that a mobile sensor carried by car may not reach due to road limitations, like the top of the hospital for example.
2.	Both sensors are able to make up for any downtime each sensor has. An example of this would be the downtime of static sensor readings during the 9th April highlighted by the red and green boxes. We can see that the mobile sensor are able to give us readings during the static sensors downtime, as highlighted by the brown and blue boxes.

Q5) The data for this challenge can be analyzed either as a static collection or as a dynamic stream of data, as it would occur in a real emergency. Describe how you analyzed the data - as a static collection or a stream. How do you think this choice affected your analysis?

Step	Info
1.	Referencing https://www.techopedia.com/definition/31590/static-data, all my analysis has been done statically (i.e the data was prepared and stored nicely before visualization, and the data never changes). The static collection allowed me to easily see patterns, as there is a definitive end and start. For example, referencing the red box, I am able to see an outage of readings from sensor 15, at April 9th. Another reason is because I am not dealing with real time data, everything has already been prepared.
2.	In the case of static data, all I had to really do was to prepare it for analysis (e.g. cleaning missing values, data entry errors, manupilation of rows). Although it was really slow at times, I knew that so long my computer could handle the operations I gave it I would not have any trouble in the long run. This might change where if I were to use a dynamic data, I have to decide with the constant stream of growing data, if my computer is able to perform operators and how big/small batches I should process my data.

Interactive Visualization

Visualization can be found here:

https://public.tableau.com/profile/linus.cheng.xin.wei#!/vizhome/is428AY2019-20T1AssignLinusChengXinWei/Story1

Dashboard 1: Dangerous Radiation Readings by Neighbourhoods

Component

Component	Description/Purpose
1. Scatterplot, highlighted by green box	Shows >= 100 CPM readings over time, scaling with colour, and in what neighbourhood Allows for identification of neighbourhoods that might be dangerous due to radiation
2. Table, highlighted by orange box	Shows the numeric count of >= 100 CPM readings over time, and in what neighbourhood Ability to see the concentration of the total number of dangerous readings happening overtime.
3. Thematic map, highlighted by blue box	Shows the location of >= 100 CPM readings over time, scaling with colour. Supplements scatterplot as a visualization of where the sensors are.
4. Table, highlighted by red box	Shows the difference in aggregated radiation change over time, in relation to the first entry. Allows viewer to see net change in radiation, to determine whether a neighbourhood has been constantly increasing or decreasing its net radiation.

Interactive Technique

Technique	Info
1. Select: Pointer, Orange boxes	User can click on navigation buttons to access different sheets User can also click on specific points or objects
2. Select: Hover, Brown Box	User can hover over specific points or objects to bring up a tooltip with information displayed
3. Select: Brush, Red boxes	User can select multiple points or objects
4. Filter, Blue box	User can filter by date, sensor type and neighbourhood for different views
5. Connection: Linked Multiple Views, Pink Box	All four components are linked up together. By selecting a data point, on any component, the other components will automatically update as well

Dashboard 2: Sensor Availability and Location

Component

Component	Description/Purpose
1. Gantt chart, highlighted by green box	Shows all readings over time by sensor ID Allows for identification of any downtime or missing readings the sensors might have
2. Scatterplot, highlighted by orange box	Shows the unreliable reading of static sensors
3. Scatterplot, highlighted by blue box	Shows the unreliable reading of mobile sensors
4. Reference Map, highlighted by red box	Shows the location/path taken by the sensors Allows the viewer to see the location of any sensor, as well as the path that was taken throughout the designated time period

Interactive Technique

Technique	Info
1. Select: Pointer, Orange boxes	User can click on navigation buttons to access different sheets User can also click on specific points or objects
2. Select: Hover, Brown Box	User can hover over specific points or objects to bring up a tooltip with information displayed
3. Select: Brush, Red boxes	User can select multiple points or objects
4. Filter, Blue box	User can filter by sensor type, neighbourhood, and sensor id for different views
5. Connection: Linked Multiple Views, Pink Boxes	The gnatt chart and reference map are linked together. By highlighting parts of the gnatt chart, the location of each entry will appear in the reference map
6. Select: Slicer, Black boxes	User can select start and end for CPM readings, as well as start and end of Date

Dashboard 3: Potential Contanimated Path and Sensor movement through Neighbourhoods

Component

Component	Description/Purpose
1. Reference Map, highlighted by green box	Shows path taken by contaminated cars, as well as CPM reading scaling with size. Allows the viewer to spot any potential contamination spread, as well as whether or not cars have left the area
2. Reference Map, highlighted by orange box	I split the contaminated cars into 2, so as to properly visualize the paths with ease Shows path taken by contaminated cars, as well as CPM reading scaling with size. Allows the viewer to spot any potential contamination spread, as well as whether or not cars have left the area
3. Gantt chart, highlighted by blue box	Shows reading of different sensors over time, as they travel through neighbourhoods
4. Reference Map, highlighted by red box	Shows the location/path taken by the sensors Allows the viewer to see the location of any sensor, as well as the path that was taken throughout the designated time period

Interactive Technique

Technique	Info
1. Select: Pointer, Orange boxes	User can click on navigation buttons to access different sheets User can also click on specific points or objects
2. Select: Hover, Brown Box	User can hover over specific points or objects to bring up a tooltip with information displayed
3. Select: Brush, Red boxes	User can select multiple points or objects
4. Filter, Blue box	User can filter by sensor type, neighbourhood, and sensor id for different views
5. Connection: Linked Multiple Views, Pink Boxes	The Gantt chart and reference map are linked together. By highlighting parts of the gnatt chart, the location of each entry will appear in the reference map. In this case, it produces a path for example.
6. Select: Slicer, Black boxes	User can select start and end for CPM readings, as well as start and end of Date. In this case, I have set the Date to the 8th of April as a start, as thats where my findings conclude that the coolant leaked happened.

References

Tableau Online tips: Extracts, live connections, & cloud data - https://www.tableau.com/about/blog/2016/4/tableau-online-tips-extracts-live-connections-cloud-data-53351

Information about understanding Counts Per Minute (cpm) - https://wattsupwiththat.com/2011/03/17/live-real-time-monitoring-map-of-radiation-counts-in-the-usa/

More Information about understanding Counts Per Minute (cpm)- https://www.gqelectronicsllc.com/forum/topic.asp?TOPIC_ID=4348

Information about radiation spread - https://radiationanswers.org/radiation-questions-answers/nuclear-power.html

Static vs Dynamic Data - https://www.techopedia.com/definition/31590/static-data

Comments

Please comment I will note with thanks