Before this class, I had a very limited knowledge of the world of sensor journalism. I thought sensors were strictly used for gathering data for scientific experiments and had no room in the world of journalism. However, after listening to Patrick Herron and Lily Bulli’s presentations and participating in our in-class water conductivity workshop, I’ve discovered just how useful sensors can be when it comes to reporting. There are huge benefits to using data to tell stories. Mainly, in comparison to interviewing people, data can be a much more reliable source of information. Asking someone about whether they think the Mystic River is polluted is not the same as actually testing a sample and gathering concrete, numeric evidence that proves it is. Data adds a level of authenticity to your story that simple word of mouth can’t match. The other benefit of using sensors specifically to tell stories is that it allows you to call attention to such important matters as the pollution of the Mystic River. While anyone can write a story about how the Mystic River is polluted and needs to be cleaned up, it’s hard to get people to gather around the project unless you have evidence. Sensors allow journalists to gather the data they need from water samples and to write multiple, well researched stories from their findings that call attention to the issue. Patrick Herron’s presentation about his work with the Mystic River was extremely eye opening to just how beneficial sensors are when it comes to reporting. When he showed us the picture of the sign that read “No Swimming” in front of beach full of people swimming, it made it clear how imperative the issue of pollution was. It was also enlightening to see the large sensor that he and his team were using to collect data and the see the work that we had done in our in-class workshop applied in the real world on a larger scale. Having said this, there are drawbacks to using sensors to tell data driven stories. Patrick told us that in his efforts to gather data from the river, he had handed our sensors to volunteers to and had found that most of the data collected was inconsistent. This showed that human error can play a huge part in data collection and create a margin of error. From my own experience testing a sample of water from the Charles, I also noticed a few inconsistencies that could negatively affect the data. Number one, everyone in the class who collected Charles River samples extracted the sample from different locations, the samples yielded different levels of conductivity. In order to get accurate data, we would have to come up with a way to get one single level of conductivity that someone represented most of the Charles. There’s also the question of how you gather numeric data from the frequency admitted from the sensor. While it’s easy to judge which sample is the most conductive given by the noise admitted by the sensor, it’s hard to translate those finding into words. Some sort of pitch reader would be needed to read the frequency and translate it into numbers, but this process could muddle the data slightly and give us slightly inaccurate readings. I think that for future journalists to be able to tell stories with sensors, the technology would have to be more readily available. The sensor that Patrick showed us seemed to be an extremely effective way to gather data, but he also mentioned that the sensor was $10,000 and that’s not something a lot of journalists can afford just to gather data for potentially a single story. If smaller, more affordable sensors were more accessible to journalists, we could gather data and write stories more easily and frequently. Even having access to the basic sensors we used in class would be effective and affordable for journalists. I think sensor journalism is an extremely effective way to tell stories and it’s something that should increase as the world of journalism shifts more into the world of technology.
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The tools of environmental science have been available to journalists for as long as there has been environmental science. Environmental scientists commonly use simple tools like tape measures, rulers, light meters, thermometers, shovels, field guides, color charts, cameras, and notebooks. Environmental scientists might walk around a wetland documenting the depth to ground water with a shovel, ruler, and notebook. The edge of a wetland might be delineated by identifying where certain species of plant grow. Photos taken throughout the year can document the highest and lowest water levels in a pond. But there has never been anything called shovel journalism, or ruler journalism.
Journalists could have purchased or borrowed the tools needed to answer many environmental questions, but instead they spent their time doing other things. Most journalists would not have known what to do with the tools of an environmental scientist. Had they tried, their lack of training and experience would have probably prevented them from using the tools correctly or interpreting the results properly.
Why do journalists suddenly want to do the job of environmental scientists? What has changed to make them think they have any idea how to carry out a field study or interpret the results?
Chris
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@cfastie I think it's not that journalists all want to be environmental scientists but that the advent of cheap sensors and the ability (of some) to store and process information is growing at a rapid rate. Journalism - as providers of public information - is positioned to be able to tell important, public stories from these new instruments and data streams, but as the students are detailing, there are a number of challenges to being able to do so, one of which is definitely expertise. It's highly unlikely journalists will start to be trained like scientists all of a sudden but I think what we can hope for is that they learn about some of the critical issues surrounding experiment design and data collection, so that they see these methods as a viable way to collect evidence, to think creatively about how to gather evidence for a story and that they might be well-positioned in the future to carry out collaborations.
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Hi Catherine,
Thanks for the level-headed summary. I was hoping to engage your students, but then they retreated behind the new “response” tag which is very clever. Anyway the students were apparently far too smart to take my bait.
Discussions of this topic always seem to lack very much context. For example, you could argue that sensor journalism has been around for a long time. In the 19th century, journalists could not publish warnings about where hurricanes would make landfall in four days, or where the temperature would be below zero in a week. For the last several decades the media has been bombarding us with severe weather warnings because many thousands of sensors send data quickly to central locations where scientists run weather models with the data and make predictions. The journalists have nothing to do with the sensors or the analysis, but their reporting depends on them. Many of the stories journalists report today depend on information collected by sensors and analyzed by scientists or technicians. Journalists are not clamoring to use their own resources to add more weather stations, or tide gauges, or seismographs, or radar stations, etc., etc., etc.
Weather and tides and earthquakes are mostly politically neutral, so everyone supported government expansion of the sensor networks to track these things. Pollution is usually politically charged, so governments (e.g., EPA) have been slower to expand the network of contaminant sensors in the air and water. Also, these sensors are more expensive and less reliable than weather stations.
As sensors become less expensive and more reliable, private citizens could build out the network of pollution sensors, and journalists could be part of that expansion. But in most cases, by the time sensors are cheap and easy enough for journalists to put them to good use, many other groups will have already done it. Maybe more often than not, the journalists should be looking for those groups instead of ordering things from Adafruit.
Chris
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