NYC Air Pollution 2008 – 2018 – Information Visualization

Introduction

As a former environmental scientist, who brings environmental concerns into all aspects of my current work, I chose to focus on air quality data throughout NYC for lab 2. Air Quality is an important topic across the board, and as a student pursuing a masters in city and regional planning at Pratt Institute, I am particularly interested in learning how to tell a story with important data such as this. Specifically, how has air quality in NYC changed over the past several years? Are there specific boroughs with higher particulate matter in the air? Through this module we learned about a broad variety of visualization and mapping tools which help us tell a story with our data. From visual hierarchy to color, there are many ways convey a message through charts and figures. For this lab I enjoyed playing around in Tableau and ultimately chose to use a combination of color scales and labels along with choosing different types of graphs (line vs bar) for different charts. Tableau is an incredible tool and I am happy to have had this opportunity to learn how to use it. I see myself using Tableau in my coming semesters and onward into my professional career, as I continue to make visual data graphics to convey strong stories.

Discussion

A vital part of an urban planner’s job is to convey important information to a client, boss, or the public. This information can be complex and dry in many cases, but is important non the less. As a planner with a background in GIS, it is always astonishing to me just how many urban planning departments lack proper training and skills in data visualization, let alone, a working knowledge of GIS. Data is the future, and a solid ability to communicate such data is essential, especially in the field of urban planning. Despite a short learning curve, Tableau makes displaying data easy and I will likely use this tool many times throughout my career.

When starting any type of new project, it is important to get to know your data. After familiarizing yourself with the different columns, rows, groups, and overall information you can begin loading different elements into your table and playing around with how to display and categorize all your data into graphs. Tableau also has a dashboard feature to arrange and display several graphs in one place. This makes it easy to put together cohesive graphics for presentations and public documents.

Materials

The primary tool used in lab two is Tableau Public. The website for this tool can be found here: https://public.tableau.com/s/ The tool is designed to be open to the public and tries to be user friendly. The tool is set up similarly to excel and is fairly intuitive once you understand the layout. Additionally, Tableau provides a variety of tutorial video.

Methods/process

Learning about Tableau:

To learn about Tableau I watched the demo video that Professor Sula posted on our canvas class page. In addition, I watched some of the tutorial videos posted by Tableau on their tutorial page of their website, found here: https://public.tableau.com/s/resources#currentTraining. These resources were very useful for understanding how to use the software and provided step by step instructions for every step of the way from uploading data, turning it into graphs, and designing the graphs to be visually appealing.

Obtaining Data:

To create my Tableau dashboard for lab 2 I searched for NYC pollution data on Data.Gov. I found a dataset that contains information on New York City’s air quality measured in particulate matter from 2009 to 2018 and downloaded the comma separated values file. The website where I downloaded this data from is here: https://catalog.data.gov/dataset/air-quality

Putting it all Together:

After learning how to use Tableau and choosing a dataset to use, I loaded the dataset into Tableau and begin playing around with different charts and graphs. Some of my early attempts looked like this:

Seasonal averages of air pollutants in NYC

I wanted to see a difference between summer and winter averages to better understand season air quality trends.

Early stages of using the dashboard view in Tableau

Then I started displaying the average air quality compared to the percent change from year to year.

Neither of these initial attempts told a clear story. I had a very helpful peer review meeting with my “peer” for this lab, Professor Sula, and got some helpful feedback about using instinctive colors and making sure data spikes align on the dashboard so as not to tell competing stories. I also used the online grid paper tool found here: http://gridzzly.com to sketch out my data trends for a new perspective. I spent more time on my data display and dashboard and ended up with these results:

Results

The Tableau dashboard I created can also be found here: https://public.tableau.com/profile/sophia3812#!/vizhome/NYCAirPollution/NYCAirQuality?publish=yes

This Tableau dashboard conveys the main message that air pollution has decreased throughout NYC over the past several years. The top chart shows that in just ten years the average particulate matter fell by about 5 pm2.5. The bottom chart shows borough specific line charts. This indicates Manhattan has significantly more air particulate pollutants and Staten Island experiences the least pollutants. This is likely a reflection of the number of cars in each borough. A follow up chart to include in the next version of this dashboard would be to incorporate a pollutant causing factor like number of cars on the road. I also wanted to break out the annual summer averages and compare them with the average winter averages in a shaded in line graph format but I was not able to do this given the format of the season data.

Reflection

I definitely had a learning curve at the beginning, but the more I worked with Tableau the easier it was to trouble shoot issues. I want to spend even more time on my dashboard making it look even better. I would play around with the labels even more to draw the eye to important parts of the chart. But given the time I had; I am proud of what I created. I really enjoyed learning and working with Tableau and plan to use this software in the future.