Group20 Report
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Contents
Introduction and Motivation
In the winter of 2014, Beijing suffered severe smog weather. The haze has lasted for half a year, and the number of heavy polluted days was more than one month. Air quality issues and environment issues were being discussed throughout the country, everyone was questioning himself about the consequences of developing economy by sacrificing environment. Our living conditions has become bad, and we are faced with the tracheitis, pneumoconiosis, asthma, to name just a few.
Chinese government has frequently proposed to abandon the outdated idea called “treatment after pollution” and to protect the environment while developing the economy. Therefore, after three years, has China’s air quality become better? In our project, we want to apply the visual analytics tools to visualize the changes of air quality of major cities of China. We will show the fluctuation of the historical AQI (Air Quality Index), and the pollution sources, like PM2.5, PM10, SO2, O3, CO2 and so on.
Review and critique on past work
As increasing people are attaching more importance to environment issues, there are a lot of researchers over the world studying how to improve our environment, including air quality problem, and they have contributed many substantial outcomes to this area. In China, researchers have studied air quality from various aspects based on the background of China. Some of them focused on impact of human factors on air quality, such as increasement of population and GDP, some of them focused on specific areas, like Beijing, Shanghai or eastern areas, to analyze that how the air quality is changing, and others are also interested in forecasting air quality in the future. As experts have already enriched this research area, we will focus on using visualization tools to help people better and more effectively understand the changing trend of air quality and its pollution sources. We hope that we can give some contribution raising awareness of protecting environment of people.
Data preparation
Data introduction
Our datasets were collected from various government departments containing AQI and its pollutants: AQI (Air Quality Index), PM2.5, PM10, SO2, NO2, CO, O3.We choose monthly data from 2014-01 to 2018-8 as our target dataset containing 6162 record rows. The columns are shown as below:
Variable name | Variable property |
---|---|
Date | Monthly data from 2014-01 to 2018-08 |
AQI | Air quality index, as it increases, air quality is getting poor |
Range | Air quality range |
Air quality level | Five levels based on range |
PM2.5 | One of pollutants affecting AQI, monthly data from 2014-01 to 2018-08 |
PM10 | One of pollutants affecting AQI, monthly data from 2014-01 to 2018-09 |
SO2 | One of pollutants affecting AQI, monthly data from 2014-01 to 2018-10 |
O3 | One of pollutants affecting AQI, monthly data from 2014-01 to 2018-11 |
CO2 | One of pollutants affecting AQI, monthly data from 2014-01 to 2018-12 |
NO2 | One of pollutants affecting AQI, monthly data from 2014-01 to 2018-13 |
City | 110 cities from China |
Log | Longitude of each city gotten by R coding |
Lat | Latitude of each city gotten by R coding |
Data pre-processing
1) Use R to get 110 cities’ longitude and latitude.
2) Reformat the data structure to do better time series analysis.
3) Combine all the cities’ air quality data file into one csv file.
The part of final data structure is shown as below:
Project Timeline
Task(s) | Week |
---|---|
Confirmation of topic, data-set and proposal | 7 |
Email consultation with Prof Kam for feedback on proposal | 8 (18 Jun) |
Sketch out visual analytics solution | 9 |
F2F consultation with Prof Kam for feedback on sketch | 10 |
Implementation of visual analytics solution using Tableau/R | 11 |
F2F consultation with Prof Kam for feedback on progress/obstacles | 12 |
Finalize the wiki report and poster | 13 |
Poster presentation cum conference | 14 (14 Aug) |
Visualization Approach
Data Source
All these first-tier city AQI and responding date and some index contribute to API(PM2.5,PM10,SO2,CO,NO2,O3)(2014-2017).
GDP in those first tier city from 2014-2017.
Forest cover rate in the province of those cities.
Car number in those cities(2016-2017).
Total factory numbers in those cities.
Respiratory disease.
http://data.stats.gov.cn/easyquery.htm?cn=E0103
https://www.aqistudy.cn/historydata/
http://www.sc.stats.gov.cn/tjcbw/tjnj/2017/zk/indexch.ht
http://www.stats-hb.gov.cn/
http://www.bjstats.gov.cn/tjsj/
http://www.gzstats.gov.cn/tjsj/hgjjsjk/
http://www.gdstats.gov.cn/tjsj/zh/
http://tjj.zj.gov.cn/tjsj/ydsj/gy/
http://www.njtj.gov.cn/
http://www.cqtj.gov.cn/tjsj/sjjd/
http://stats.tj.gov.cn/
http://tj.jiangsu.gov.cn/
http://www.shaanxitj.gov.cn/
http://www.hntj.gov.cn/
http://www.ln.stats.gov.cn/
http://www.stats-sd.gov.cn/
http://www.ha.stats.gov.cn