4. Data visualization and analysis

4.1 Plotly 

Plotly creates leading open source tools for composing, editing, and sharing interactive data visualization via the Web. A number of online graphing tools are available, in addition to libraries for software such as R, Python and Matlab. Plotly has a graphical user interface for importing and analyzing data and the graphs can be downloaded for future use or embedded (e.g., on a website).Plotly-logo-01-square.png

With a free community account, you can build public charts & dashboards and export charts to PNG or JPG versions. Using the Chart Studio, you can create, design and share plots using your data without any coding!


# This is a great way to create data dashboards without the need for advanced coding skills. 

There are other, more intensive methods which are open-source and free, and provide the user considerable freedom in analysis and visualization of data.

4.2 R


R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

Learn more about R here.

There are a number of packages that cater to analysis of air quality data with openair being the most common.

openairproject.jpgOpenair is an R package primarily developed for the analysis of air pollution measurement data (learn more about the package here and here). Plots developed using openair have been used in a large number of research studies as well as reports and presentations. The openair manual (download here) provides detailed instructions on the use of different functions within the package. A sample exercise set and answer key is available here for practice.  Here are a few examples of outputs from openair:


Image credit: RPubs


4.3 Python 


Python is an open-source programming language and is free to use and distribute. Similar to R, Python includes a large number of libraries, several of which are relevant for air quality applications.

The python-aqi 0.5.1 allows for conversion between AQI value and pollutant concentration (µg/m³ or ppm).

4.4 GIS Mapping 

Geographic Information System (GIS) provides a framework to gather, manage, analyze, visualize, and store spatial data. GIS allows us to delve further into spatial patterns, trends, and relations present to solve real world problems and deliver effective solutions. Today almost every field makes use of GIS to make maps to analyze, communicate and share information. GIS can be used to identify problems, monitor changes, manage and respond to events, perform forecasting, set priorities, and understand trends (more here). GIS provides a powerful medium to tell stories and communicate effectively to public (examples). Simply, GIS provides a way to connect data with geography.

View Dr. Anobha Gurung’s slides.

4.5 Examples 

  1. UrbanEmissions.info uses Python and Plotly to visualize data from ambient air quality monitoring stations across India (box plots and time series)
  2. In Wales (UK), the government website uses openair for plotting air quality monitoring data (check out the website)
  3. Gareth Kennedy used Python to create a movie on air quality in Beijing using a series of images (link to page).


Additional Resources

A number of free resources are available for learning R/Python etc. There are also a number of online communities where you can seek answers (and ideas). A select number of courses and resources are listed below:

  • Plotly
    • Tutorial on Plotly, link
  • R
    • Introduction to R (DataCamp), link
    • R Programming (Coursera), link
    • R Basics – R Programming Language Introduction (Udemy), link
    • Tutorial on openair, link
    • Introduction to openair, link
    • R for Data Science, link
  • Python
    • Introduction to Python for Data Science (DataCamp), link
  • GIS Mapping

Tip: Stack Exchange is one of the best places to look for answers if you are stuck. Chances are, someone had the same error/issue before you, and an idea/solution/tip might already exist.