Our deep dive into air quality data is coming to a close.
We collected resources on visualizing and communicating air quality data, tested out some data viz tools, and talked to community scientists collecting air quality data. Here’s where you’ll find the new and updated resources we collectively created:
- Comparing Data Visualization Tools: a research note by @fongvania to help in selecting a data viz tool. It has an editable table describing features of different tools.
- How to visualize your location-based data in QGIS: a detailed how-to guide by @laurel_mire on a specific data viz tool, the open source qGIS.
- Simple Data Visualizations in R Studio: another note by @laurel_mire on getting started with data viz in the open source program R, using ggplot2.
- Create a “data story” to communicate environmental data: an activity with @renee on storytelling with data.
- Data Cleaning with OpenRefine: an activity by @fongvania on the all-important step of cleaning data before analysis and visualizations.
- Visualize data from a Simple Air Sensor using onboard serial hardware: an activity on reading and visualizing data from Public Lab’s DIY particulate matter sensor.
- Cleaning and Organizing Environmental Data: a wiki page to collect resources on clean and tidy data.
- Air Quality Data wiki page:more background on understanding and communicating air quality data, questions and activities from the Public Lab community, and information on taking action with air quality data.
Thanks so much to everyone who shared their knowledge, experiences, and questions during the review! 🎉
👋🏼 If you participated in the review and would like to share any feedback or receive updates, please fill out this brief form!
Summary of events
📊Presentation + discussion on communicating air quality data
On this call, we presented some basic steps of communicating with air quality data, from understanding data to cleaning data to data stories and visualizations. We also highlighted new activity posts and wikis on the Public Lab website covering these topics. Check out slides from the presentation below, and a recording of the call and all the links shared in the presentation and chat here
After the presentation, @fongvania facilitated an open discussion on air quality monitoring and data. People on the call talked about the local concerns they're working on, including a lack of air monitoring data available for Baltimore City, and silica dust pollution from frac sand mining in Wisconsin.
📣Guest speaker panel: Community scientists working with air quality data
Two guest speakers involved with community-led air monitoring projects joined us for this session.
Mary Jo Burke of LES Breathe (@LESBreathe) talked about her group's volunteer-led air monitoring near East River Park in the Lower East Side of Manhattan, New York City. A major construction project that's intended to mitigate storm damage has started in the park, and it involves tearing out hundreds of trees and potentially impacting air quality in the adjacent environmental justice community. LES Breathe, working within an organization called East River Park Action, is currently collecting baseline data on neighborhood air quality using low-cost air sensors.
Christian Torres is Special Projects Manager at Comite Civico del Valle, in the Imperial Valley of California. He spoke about his organization's incredible work developing and running an extensive community-led air monitoring program as part of their Identifying Violations Affecting Neighborhoods (IVAN) network. A hazard mapping activity provided community input on air sensor placement, and now their network of low-cost sensors give real-time information on air quality for the region. You can find a guidebook detailing their knowledge and experiences here: Guidebook for Developing a Community Air Monitoring Network.
⚙️Live build of the Simple Air Sensor and demo of data visualization
Air Research Curation Fellow @fongvania gave a step-by-step tutorial on how to build Public Lab's Simple Air Sensor, a compact particulate matter sensor with an LED that changes color depending on the air quality. @bhamster demonstrated the sensor in action and also showed us how to visualize and extract real-time data from the sensor. @fongvania then walked through the steps involved to convert raw data from sensors into a more usable format, conduct data quality checks, and create a simple graph using an open-source online visualization tool.
You can follow these instructions to build the Simple Air Sensor, and you can find instructions for the real-time data extraction here. We used RawGraphs in the data visualization demo, however, feel free to use other software, including but not limited to those listed in this comparison of data visualization tools.
Our next quarterly research area review will be on a topic within land and soil. More details to come in the new year, please follow the research-area-review tag if you'd like to keep updated! And please comment below if you have any questions, ideas, or feedback!