NYC Air Pollution Raster Dashboard

Project Title: NYC Air Pollution Raster Dashboard

Project Description: This was my final project in my Advanced GIS class. This online dashboard was created using ESRI’s ArcOnline GIS service, and the maps were created using ESRI’s ArcGIS Pro mapping application. The goal was to create an interactive online resource that the general public could access to be informed on historic NYC air pollution data, information on air pollutants, and how those air pollutants and trends have a direct effect on the viewer.

Methods: I originally framed my question around how air pollution had gotten worse in NYC as time has gone on, however, after going through NYCCAS Air Pollution Rasters I discovered that some of the air pollutants were getting better. This changed my original framing of the project from an analysis on how and why air pollutants were getting worse, to an informative dashboard on air pollutants and their effects along with responsible practices. I used 10 historical raster images (2009-2018) per air pollutant pulled from NYC OpenData for the 2 selected air pollutants: Nitric Oxide (NO), and Particulate Matter 2.5 (PM2.5). I represented each year as a layer on the map to allow for the user to interact and visualize the changes in air pollution over time. I conducted research on what exactly the consequences of each air pollutant presents and curated that information into language that was accessible to the general public.

My Role: I was the sole data curator, cartographer, and creator of the online dashboard.

Learning Outcome Achieved: Technology

Rationale: This dashboard sought out to directly apply GIS tools to improve information functions regarding environmental safety geared towards the general public. I used the digital GIS tools ArcGIS Pro and ArcOnline to construct this project. Both of these tools required me to exercise technical skills in data curation, data analysis, python scripting, and cartographic best practices. Because each raster image year-to-year contained a different range of air pollutant data, I needed to standardize the symbology range in order to accurately visualize and compare the raster imagery. I used python scripts to calculate statistics on each raster image that then created a histogram of the measured air pollutant. These calculations helped establish a standard symbology range. There was constant technological troubleshooting while creating the maps and final dashboard. Displaying the raster data online and finalizing the symbology range proved to be the most challenging and adaptive aspects of the project.