Difference between revisions of "IS428 AY2018-19T1 Ye Min Oo"

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==Task 1: Spatio-temporal Analysis of Official Air Quality==
 
==Task 1: Spatio-temporal Analysis of Official Air Quality==
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The table below are the findings from the dataset.
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The following table consists of the findings from dataset.
 
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==Task 1: Tableau Public Link==
 
==Task 1: Tableau Public Link==
 
https://public.tableau.com/profile/ye.min.oo#!/vizhome/Task1-AirConcetrationLevelsDashboard/AirConcentrationLevelsfrom20132018?publish=yes
 
https://public.tableau.com/profile/ye.min.oo#!/vizhome/Task1-AirConcetrationLevelsDashboard/AirConcentrationLevelsfrom20132018?publish=yes
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==Task 2: Spatio-temporal Analysis of Citizen Science Air Quality Measurements ==
 
==Task 2: Spatio-temporal Analysis of Citizen Science Air Quality Measurements ==
  
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The table below are the findings for sensors.
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The following table consists of the findings for sensors.
 
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The table below are the findings for air pollution measurements.
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The following table consists of the findings for air pollution measurements.
 
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==Task 3==
 
==Task 3==
  
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Urban air pollution is a complex issue.  There are many factors affecting the air quality of a city.  Some of the possible causes are:
+
The following table consists of the findings from dataset.
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* Local energy sources.  For example, according to [http://unmaskmycity.org/project/sofia/ Unmask My City], a global initiative by doctors, nurses, public health practitioners, and allied health professionals dedicated to improving air quality and reducing emissions in our cities, Bulgaria’s main sources of PM10, and fine particle pollution PM2.5 (particles 2.5 microns or smaller) are household burning of fossil fuels or biomass, and transport. 
 
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* Local meteorology such as temperature, pressure, rainfall, humidity, wind etc
 
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* Local topography
 
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* Complex interactions between local topography and meteorological characteristics.
 
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* Transboundary pollution, for example, the haze that intruded into Singapore from our neighbors.
 
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In this third task, you are required to reveal the relationships between the factors mentioned above and the air quality measure detected in Task 1 and Task 2.  Limit your response to no more than 5 images and 600 words.
 
  
 
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! No || Description || Image
 
! No || Description || Image
 
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|-
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| #1 || Based on the finding, although for some of the regions, higher humidity level leads to higher level of measurements for P1 and P2. However, in some of the regions with lesser humidity level, there is also a relatively high level of measurements for P1 and P2. || [[Image:humidity.JPG|800px|center]] [[Image:line.JPG|800px|center]]
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| #1 || Based on the finding, although for some of the regions, higher humidity level leads to higher level of measurements for P1 and P2. However, in some of the regions with lesser humidity level, there is also a relatively high level of measurements for P1 and P2. || Based on the Days of the Month [[Image:humidity1.JPG|800px|center]] Based on the Months of the Year[[Image:line.JPG|800px|center]]
 
|-
 
|-
 
| #2 || Based on Topology Map, Sofia-Grad is located at a middle range of the elevation level and has relatively high air concentration levels. This shows that the higher the elevation level, the lower the air concentration. || Elevation Map [[Image:Topology.JPG|400px|center]] Sofia-Grad Map [[Image:sofia.JPG|1000px|center]]
 
| #2 || Based on Topology Map, Sofia-Grad is located at a middle range of the elevation level and has relatively high air concentration levels. This shows that the higher the elevation level, the lower the air concentration. || Elevation Map [[Image:Topology.JPG|400px|center]] Sofia-Grad Map [[Image:sofia.JPG|1000px|center]]
 +
|-
 +
| #3 || The air concentration levels in Sofia-Grad are high mainly due to the people's daily activities and the traffic which can be seen in the graph. || Sofia-Grad Map [[Image:sofia.JPG|1000px|center]]
 
|-
 
|-
 
|}
 
|}
  
 
==Task3: Tableau Public Link==
 
==Task3: Tableau Public Link==
 +
https://public.tableau.com/profile/ye.min.oo#!/vizhome/Task3-HumidityVsP1P2/Dashboard2?publish=yes
 +
 +
==Further Improvements==
 +
A more elaborate and insightful analysis could have been done with more timing and better knowledge of the dataset.
 +
Furthermore, a more interactive and insightful dashboards could also be made based on it.
 +
For example, finding out the realtionships between air concentration levels and other external factors could be useful and insightful for Sofia city.
  
 
=The Data Sets=
 
=The Data Sets=
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* Topography data (TOPO-DATA)
 
* Topography data (TOPO-DATA)
  
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They can be download by click on this [https://storage.cloud.google.com/global-datathon-2018/sofia-air/air-sofia.zip link].
+
They can be downloaded by clicking on this [https://storage.cloud.google.com/global-datathon-2018/sofia-air/air-sofia.zip link].
  
−
=Visualisation Software Used=
+
==Softwares Used==
  
 
*Tableau
 
*Tableau
−
*JMP Pro
+
*Excel
−
*Qlik Sense
+
*R Studio
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*Microsoft Power BI
 
  
 
=Reference=
 
=Reference=
−
 
+
https://community.tableau.com/thread/129970 </br>
 +
http://www.tomnash.eu/how-to-combine-multiple-csv-files-into-one-using-cmd/
  
 
=Suggestions=
 
=Suggestions=
  
 
Kindly help comment below for further improvements.
 
Kindly help comment below for further improvements.

Latest revision as of 23:37, 11 November 2018

IS428_2018-19_T1_YeMinOo Assignment Submission Page


To be a Visual Detective

The assignments require you to put the concepts, methods, and techniques you had learned in class to solve a real-world problem using visual analytics techniques. Students should also use the assignments to gain hands-on experience in using the data visualization toolkits I had shared with you to complete the assignment.

Overview

Air pollution is an important risk factor for health in Europe and worldwide. A recent review of the global burden of disease showed that it is one of the top ten risk factors for health globally. Worldwide an estimated 7 million people died prematurely because of pollution; in the European Union (EU) 400,000 people suffer a premature death. The Organisation for Economic Cooperation and Development (OECD) predicts that in 2050 outdoor air pollution will be the top cause of environmentally related deaths worldwide. In addition, air pollution has also been classified as a leading environmental cause of cancer.

Air quality in Bulgaria is a big concern: measurements show that citizens all over the country breathe in air that is considered harmful to health. For example, concentrations of PM2.5 and PM10 are much higher than what the EU and the World Health Organization (WHO) have set to protect health.

Bulgaria had the highest PM2.5 concentrations of all EU-28 member states in urban areas over a three-year average. For PM10, Bulgaria is also leading on the top polluted countries with 77 μg/m3on the daily mean concentration (EU limit value is 50 μg/m3).

According to the WHO, 60 percent of the urban population in Bulgaria is exposed to dangerous (unhealthy) levels of particulate matter (PM10).

Data Preparation

No Issues Solution
#1 Air Tube data and EEA data show date and time as one data. So, it is hard to visualize the data based on year, month, or date. Using “LEFT” and “RIGHT” formulas from tableau, separated the date and time data into three different categories (“By Date”, “By Hour”, and “By Year”) for better visualization.

For combined.csv

LEFT(RIGHT([Datetime End],15),8) - Hour, LEFT([Datetime End],7) - Month, LEFT([Datetime End],10) - Date, LEFT([Datetime End],4) - Year

For AirTube.csv

LEft(RIGHT([Time],9),8) - Hour, LEFT([Time],4) - Year

#2 EEA data.zip has timeseries data from 2013 to 2018 for 5 stations. However, the files are not consolidated. Each file represents a time series data of a particular year for a station.
Timeseries.PNG
 Using R Studio, combined all the csv files into “combine.csv” which now contains 5 years of data for 5 stations. Moreover, the 5 stations data for each year is also grouped by year for better understanding of data. (Example: 2013 data will be grouped into “combined2013.csv”).
Timeseries combined.PNG
#3 Air Tube data has two files and each of them has a geohash data column. It was difficult to plot the location on Tableau. Using R Studio, “geohash” data is geocoded and converted to latitude and longitude data.
Geohash.PNG
#4 All given data files are in csv format and each column value is separated by comma. Although the data are organized and prepared, it will be difficult when plotting certain charts or graphs.
BeforeComma.PNG
 Using Excel, all commas were delimited by comma for better analysis and understanding of data.
Comma.PNG

Task 1: Spatio-temporal Analysis of Official Air Quality

The following table consists of the findings from dataset.

No Description Image
#1 Comparison of air concentration levels from 2013 to 2018.
YearlyAvgConcentrationLevels.PNG
#2 Typical Day air concentration level in the city. The concentration levels are withing the healthy range.
HourlyAvgConcentration.PNG
#3 The air concentration levels are higher in the mornings and evenings and tends to go lower in the afternoons.
Anomaly.JPG
#4 The air concentration levels are highest during Janurary, November and December.
2013Min1.JPG
2014Min1.JPG
2015Min1.JPG
2016Min1.JPG
2017Min1.JPG
2018Min1.JPG

Task 1: Tableau Public Link

https://public.tableau.com/profile/ye.min.oo#!/vizhome/Task1-AirConcetrationLevelsDashboard/AirConcentrationLevelsfrom20132018?publish=yes

Task 2: Spatio-temporal Analysis of Citizen Science Air Quality Measurements

The following table consists of the findings for sensors.

No Description Image
#1 The sensors are spreaded out across Bulgaria, but most of the sensors are located in Sofia-Grad.
SensorDistribution.JPG
#2 Not all sensors were working properly. BG0079A stopped working on 14/09/2018. BG0054A was either removed or stopped working from 2016 onwards. BG0079A
BG0079A.JPG
BG0054A
StoppedWorking.JPG

The following table consists of the findings for air pollution measurements.

No Description Image
#1 In terms of P1 and P2 measurements, geohash location (sx2wmbyty1u - Blagoevgrad) has the highest measurement. P1 Measurement
P1.JPG
P2 Measurement
P2.JPG
#2 In terms of time difference, the values are relatively higher in the mornings and evenings especially around 6 pm and tends to go lower during the afternoons.
TimePyramid.JPG

Task 2 - Tableau Public Link

https://public.tableau.com/profile/ye.min.oo#!/vizhome/Task2-SensorsandP1VsP2Data/SensorsDistributionandP1P2Values?publish=yes

Task 3

The following table consists of the findings from dataset.

No Description Image
#1 Based on the finding, although for some of the regions, higher humidity level leads to higher level of measurements for P1 and P2. However, in some of the regions with lesser humidity level, there is also a relatively high level of measurements for P1 and P2. Based on the Days of the Month
Humidity1.JPG
Based on the Months of the Year
Line.JPG
#2 Based on Topology Map, Sofia-Grad is located at a middle range of the elevation level and has relatively high air concentration levels. This shows that the higher the elevation level, the lower the air concentration. Elevation Map
Topology.JPG
Sofia-Grad Map
Sofia.JPG
#3 The air concentration levels in Sofia-Grad are high mainly due to the people's daily activities and the traffic which can be seen in the graph. Sofia-Grad Map
Sofia.JPG

Task3: Tableau Public Link

https://public.tableau.com/profile/ye.min.oo#!/vizhome/Task3-HumidityVsP1P2/Dashboard2?publish=yes

Further Improvements

A more elaborate and insightful analysis could have been done with more timing and better knowledge of the dataset. Furthermore, a more interactive and insightful dashboards could also be made based on it. For example, finding out the realtionships between air concentration levels and other external factors could be useful and insightful for Sofia city.

The Data Sets

Four major data sets in zipped file format are provided for this assignment, they are:

  • Official air quality measurements (5 stations in the city)(EEA Data.zip) – as per EU guidelines on air quality monitoring see the data description HERE…
  • Citizen science air quality measurements (Air Tube.zip), incl. temperature, humidity, and pressure (many stations) and topography (gridded data).
  • Meteorological measurements (1 station)(METEO-data.zip): Temperature; Humidity; Wind speed; Pressure; Rainfall; Visibility
  • Topography data (TOPO-DATA)

They can be downloaded by clicking on this link.

Softwares Used

  • Tableau
  • Excel
  • R Studio

Reference

https://community.tableau.com/thread/129970
http://www.tomnash.eu/how-to-combine-multiple-csv-files-into-one-using-cmd/

Suggestions

Kindly help comment below for further improvements.

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