Analyzed the relationship of different variables among three datasets via data visualization.
Loaded data by SQL language, constructed local server by UNIX, connected local server with HighChart by Node.js, and wrote JavaScript, HTML5, and CSS code to arrange the page.
CONCLUSION: plotted three charts, stacked bar chart (found that in 2010, Female is much keen on GED test than Male), multiple line chart (found that all five cities have peak PM2.5 value in winter), and area chart (the client could purchase a huge-power vehicle with low price if he chooses a vehicle between the range 50HP and 150HP).
Used various charts to reflect the relationship between PM2.5 and time. The dataset was collected from 2011 to 2016.
The stacked bar chart was selected to show the average monthly PM 2.5 value in the different quarter; the bar chart was selected to show the number of days among 3 different range, 35 ug/m3 or below, 35 ~ 75 ug/m3, and 75 ~ 150 ug/m3; the line chart was selected to show the PM 2.5 distribution among 3 time periods within a day, Hour 0 to 7, Hour 8 to 15, and Hour 16 to 23.
CONCLUSION: The concentration of December, 2013 is 122.2 ug/m3, which was the biggest one among the dataset; In shanghai from 2011 to 2016, the concentration of PM2.5 of most months was 35 to 75 ug/m3, which occupied 75.0%; The three areas had similar attribution, which it means that the PM2.5’s daily average number can be strong enough to depict the whole day’s concentration of PM2.5.