Difference between revisions of "IS428 AY2018-19T1 Gokarn Malika Nitin"
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==<div style="background: #581845; padding: 15px; line-height: 0.3em; text-indent: 15px; font-size:18px; font-family:Open Sans, Arial, sans-serif"><font color= #ffffff>Problem and Motivation</font></div>== | ==<div style="background: #581845; padding: 15px; line-height: 0.3em; text-indent: 15px; font-size:18px; font-family:Open Sans, Arial, sans-serif"><font color= #ffffff>Problem and Motivation</font></div>== | ||
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Therefore, the aim of this assignment is to reveal the spatiotemporal patterns of air quality and measurement techniques in Sofia City of Bulgaria, thereby identifying issues of concern. | Therefore, the aim of this assignment is to reveal the spatiotemporal patterns of air quality and measurement techniques in Sofia City of Bulgaria, thereby identifying issues of concern. | ||
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==<div style="background: #581845; padding: 15px; line-height: 0.3em; text-indent: 15px; font-size:18px; font-family:Open Sans, Arial, sans-serif"><font color= #ffffff>Dataset Analysis and Transformation Process</font></div>== | ==<div style="background: #581845; padding: 15px; line-height: 0.3em; text-indent: 15px; font-size:18px; font-family:Open Sans, Arial, sans-serif"><font color= #ffffff>Dataset Analysis and Transformation Process</font></div>== | ||
Revision as of 05:36, 11 November 2018
Contents
Problem and Motivation
Dataset Analysis and Transformation Process
Task 1: Spatio-temporal Analysis of Official Air Quality
Characterize the past and most recent situation with respect to air quality measures in Sofia City. What does a typical day look like for Sofia city? Do you see any trends of possible interest in this investigation? What anomalies do you find in the official air quality dataset? How do these affect your analysis of potential problems in the environment?
Your submission for this questions should contain no more than 10 images and 1000 words.
Task 2: Spatio-temporal Analysis of Citizen Science Air Quality Measurements
Using appropriate data visualisation, you are required will be asked to answer the following types of questions:
- Characterize the sensors’ coverage, performance and operation. Are they well distributed over the entire city? Are they all working properly at all times? Can you detect any unexpected behaviours of the sensors by analyzing the readings they capture? Limit your response to no more than 4 images and 600 words.
- Now turn your attention to the air pollution measurements themselves. Which part of the city shows relatively higher readings than others? Are these differences time-dependent? Limit your response to no more than 6 images and 800 words.
Task 3
Urban air pollution is a complex issue. There are many factors affecting the air quality of a city. Some of the possible causes are:
- Local energy sources. For example, according to 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.
- Local meteorology such as temperature, pressure, rainfall, humidity, wind etc
- Local topography
- Complex interactions between local topography and meteorological characteristics.
- Transboundary pollution, for example, the haze that intruded into Singapore from our neighbours.
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.
Software
- Tableau - for visualization of the various tasks
- Python - for geocoding
