OGDEN, Utah – In my work life, I often deal with geospatial data. This data not only carries the customary sorts of attributes we see every day but also geographic attributes, like points, lines, enclosed areas, polygons, and surfaces. This data is typically projected from latitude, longitude, and sea-level-elevation data to other coordinate systems to facilitate analysis and viewing.
One of the things I find odd about dealing with geospatial data is how much it is monetized and bound up in restrictive license agreements. If you search for “geospatial data” using your favorite search engine, you’ll probably see several pages of links to organizations that sell data or create and sell geospatial analysis and visualization software, all under restrictive licensing. But if you dig deeper, you’ll find some wonderful open data and open source software.
Most people interested in open data, open source, and the world around them have heard of OpenStreetMap. OpenStreetMap incorporates data from “citizen mappers” and provides it to all under the Open Data Commons Open Database License (ODbL).
When I deal with geospatial data at work, I am primarily interested in analyzing the interactions between different data items. For example, I might want to see where a proposed electrical transmission line crosses or passes nearby small rural settlements. I mainly use two open tools: QGIS, an open source geographic information system (GIS), and PostGIS, a spatial extension to the PostgreSQL object-relational database. A few weeks ago, I spotted a post on the QGIS users’ mailing list from Seán Lynch, who runs a new-to-me open database that tracks global litter.
Seán was soliciting interest from anyone who thinks litter could be a problem that citizen science could tackle. I interviewed Seán to learn more about his work.
Chris Hermansen: In a nutshell, what is OpenLitterMap?
Seán Lynch: OpenLitterMap is an open source, interactive, and accessible database of the world’s litter and plastic pollution. We are developing a data-collection experience to harness the unprecedented potential of data collectors (citizen scientists) around the world.
Nearly everyone is equipped with a smartphone, a powerful device that can collect data, but we are not yet harnessing this unprecedented human potential. Litter mapping is an important catalyst for the development of crowd-powered science, as not only is plastic pollution globally ubiquitous, but litter is easily identifiable.
Due to its abundance and notoriety, litter has become a topic that people increasingly recognize as an environmental problem. These factors give litter mapping a remarkably low barrier to entry that can bring many people into data collection and the scientific process for the first time. OpenLitterMap is not just a map or open database of the world’s plastic pollution. It’s an important catalyst to help build up society’s capacity to collect data.
Inspired by OpenStreetMap, we apply the same principles of crowdsourcing and open data to plastic pollution. We want everyone, everywhere and anywhere, to be able to share data on litter and plastic pollution on the streets, beaches, and anywhere else where plastic can be found. These maps tell powerful stories about plastic pollution in a very local and global context, and anyone can use our open data to help improve government policy and extend producer responsibility. Once this is developed, we want to integrate chemical pollution mapping and develop an increased capacity for biodiversity monitoring. But to achieve this, the world needs an introduction to citizen science, which we aspire to deliver.
CH: In what ways is OpenLitterMap “open”?
SL: Since we launched in 2017, our data has been openly available via the Open Database License. This includes GPS, timestamp, 120 predefined types of litter, 60+ corporate brands, the litter’s presence (picked up, still there), and the full OpenStreetMap address at every location a photo was taken. Anyone can download our data for free and use it for any purpose, without restriction.
More recently, we launched the web app (Laravel + Vue) and mobile app (React Native) as open source under GPLv3. There was a delay in launching the code open source, as this was my first coding project, and I needed some time to develop the skills to manage an open source project. Since then, I have worked a few jobs as a web or full-stack developer, launched v2 of the mobile app, and scratched the surface of object detection. Soon, we will release all image file paths and the OpenLitterAI under the same GPL license. We are currently building a tool in the browser to label our images with bounding boxes, all of which will be released open source.
We also have an open Slack channel, and we try to run a weekly Zoom call for an hour, where we discuss a different aspect of the app that is topical that week. We also just launched GitHub discussions, where we will discuss all aspects of the platform. We would love to hear your thoughts and ideas about it!
CH: When was OpenLitterMap first available?
SL: The research for OpenLitterMap began in 2008 when I was introduced to GIS while studying geography at university. I wanted to use GIS to map, communicate, and fix problems of illegal dumping in my community. In 2013, during a Master’s in GIS, I was introduced to OpenStreetMap and decided to apply the same principles of crowdsourcing and open data to plastic pollution. After doing a second Master’s in coastal and marine environments, I developed the OpenLitterMap FOSS4G methodology and then began teaching myself how to code.
OpenLitterMap.com finally launched as a web app on the 15th of April 2017, and the mobile apps followed in 2019 (v1.0) and 2020 (v2.0). However, although we are in production, we have a lot of work to do.
CH: Who are OpenLitterMap’s users?
SL: Although I have not profiled our users, I have spoken with a broad spectrum of different types of people using our app. Primarily, these include university students, people volunteering after work, older retirees, young educators, and people who are just concerned about the precarious state of the environment.
More recently, our first corporate sponsor joined the movement by giving their global workforce a half day with their families to pick up litter and record their positive environmental impact. We had a team of engineers meticulously scrape through hedgerows and bushes and carefully tag each photo with 100% accuracy.
We have recently launched our first campaign video that introduces OpenLitterMap, which will hopefully open the door to many people who are new to data collection, our shared open values, and citizen science. Next year, we would love to introduce OpenLitterMap to schools and start growing a global army of open data collectors, but citizen science is not currently anywhere near ready to achieve that.
CH: How did you get started in mapping?
SL: My interest in mapping became official the day I was introduced to GIS. Simulating and being able to control real-world data in a computer was a powerful technology and a career I wanted to become proficient in. It looked like a video game using real-world data, and I had a lot of experience with games. “Maybe I could somehow combine my interest in gaming with GIS? I might have a competitive advantage here,” I thought. Although the day I was introduced to GIS, my interest shifted from digital games to digital science, my experience with digital maps started long before. In fact, the first non-TV screen I ever saw was a map of Super Mario Bros World-1 when I was four, and I have been hooked since!
After doing a Master of Science in GIS and remote sensing at University College Cork, here in my native Cork City in Ireland, I expressed my interest to join a training school in citizen science with the Vespucci Institute (COST ENERGIC Action IC1203), where I met leading practitioners and facilitators in citizen science and software developers for the first time. This was a remarkable experience, as my interest in citizen science was nurtured, and I got the inspiration to make an app. In my second Master’s, I reviewed all available litter-mapping frameworks and found them to be completely inadequate. So, I turned on the GPS of my Android device and started taking geotagged photos of litter to collect the pre-marine data that I was interested in. I made my first litter maps with a plugin called Photo2Shape on QGIS, which extracted GPS coordinates from the geotagged images and converted them into a shapefile, which I was comfortable manipulating before I learned how to write PHP and JavaScript.