IS428 2018-19 Term1 Assign Yeo Qi Xun

To be a Visual Detective: Revealing spatio-temporal patterns

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 the 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).

The Task

In this assignment, you are required to use visual analytics approach to reveal spatio-temporal patterns of air quality in Sofia City and to identify issues of concern.

Using appropriate data visualisation, you are required will be asked to answer the following types of questions:

Task 1: Spatio-temporal Analysis of Official Air Quality

1. What are the typical patterns in the official air quality data? What does a typical day look like for Sofia city?
2. Describe up to ten of the most interesting patterns that appear in the official air quality data. Describe what is notable about the pattern and explain its possible significance.

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

1. What are the typical patterns in the citizen science air quality measurements data? What does a typical day look like for Sofia city?
2. Describe up to ten of the most interesting patterns that appear in the citizen science air quality measurements data. Describe what is notable about the pattern and explain its possible significance.

Task 3: Analyse Unmask My City's Claim

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. Production of electricity by burning of coal in thermal power plants and other industrial processes are a major contributor to unhealthy air. Coal plants are responsible for almost all of the country’s sulfur-dioxide and the majority of nitrogen-oxides emissions, causing smog and acid rain.

Motivation

These are the main motivations for the development of the visualization tool:

1. Understand the difference between official and unofficial data about air quality
2. Monitor emissions of each Coal Plant
3. Investigate anomalies with weather patterns
4. Tracking of air quality in different areas

The tool can be used by citizens and the government alike as it provides useful functions for them to understand more about air quality and how it affects all walks of life in Bulgaria.

Background Information

Official air quality measurements (5 stations in the city)

Data

You will have the following data and supporting information at your disposal:

• Official air quality measurements (5 stations in the city)
• Citizen science air quality measurements
• Meteorological measurements
• Topography data

The datasets above can be generally grouped into 3 different categories:

1. Air Quality Data
2. Meteorological Data
3. Topography Data

The data will then be visualized using Tableau. However, some data cleaning and preprocessing steps are required before the data is suitable for use in Tableau. I will be using python to execute the following data cleaning tasks

Findings - Task #1

What are the typical patterns in the official air quality data? What does a typical day look like for Sofia city?

Describe up to ten of the most interesting patterns that appear in the official air quality data. Describe what is notable about the pattern and explain its possible significance.

Findings - Task #2

What are the typical patterns in the citizen science air quality measurements data? What does a typical day look like for Sofia city?

Describe up to ten of the most interesting patterns that appear in the citizen science air quality measurements data. Describe what is notable about the pattern and explain its possible significance.

Conclusion

Link

Improvement

To perform the visual analysis, this is a list of the software which I used.

• Tableau
• Excel
• Chrome
• Python

Assignment Q&A

Need more clarification, please feel free to pen down your questions.

Comments

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