Public Lab Research note


Reflection on Sensor Journalism

by Jenn_Zarate | October 09, 2014 15:29 | 44 views | 0 comments | #11255 | 44 views | 0 comments | #11255 09 Oct 15:29

Read more: i.publiclab.org/n/11255


The popularity of data journalism is rising and sensors have become a vital device for collecting, sifting through and interpreting data that journalists (and their audiences) have never seen before. So, the concept of sensor journalism is generating or collecting data from sensors, and using that data to tell a story. Such sensors used and demanded by reporters, journalists, and technologists can produce quality data that can therefore be useful and informative to the public. However, due to technological, ethical and legal issues, there are many pitfalls that come with employing sensor journalism. Certain limitations may include, the law and ethics of reporting with sensors or even how accurate “said” data was gathered.

Sensors are ubiquitous. In other words, they are present all around us and react to the nature around you. According to Lily Bui, certain forms of sensor journalism can be seen as surveillance, but also as a way of tracking things in order to improve how we do them. Looking further into how certain forms can be seen as surveillance, a few examples of sensors that are not visible, such as radar trackers, satellite imagery, biochips or drones, create a potential pitfall where privacy is then questioned. It can be difficult as sensing becomes more prevalent to pin down a simple definition for this type of journalism (specifically) for broadcast or even multimedia journalism, said Fergus Pitts who authored Sensors and Journalism that was released in a report from the Tow Center for Digital Journalism. He said we should note that while sensor journalism may take on a different identity than other reporting, it should be approached the same way – ethically and skillfully (across all platforms).

Sensor journalism is a single tool that journalists can employ alongside conventional research and interviewing strategies. Three points that were central in Pitt’s work is first, how sensor journalism involves the surrounding community; second, journalists using sensors to gather data were, and should be, prepared for trial-and-error processes; and third, the reporters working with sensors combined traditional reporting and writing measures with sensor journalism to add color to the data.

Through our water conductivity workshop, we were able to exercise Pitts’ points to see what challenges and benefits came with sensor journalism. After building our own coqui’s, we detected the conductivity levels of three different water samples, testing to see whether they were high or low. Unfortunately, our coqui’s weren’t able to detect if that conductivity was harmful or not (quality of water). According to Jeff Walker in Water Quality Primer, “there are a lot of different types of pollutants. Conservative pollutants are something like salt, which doesn’t react with other things. It doesn’t settle out or get degraded. It just travels. Another word for that is “tracer” -- we can use it to see where the water came from.” With that said, a benefit with our workshop was using conductivity as a “tracer.” Conductivity is a direct measurement of the number of ions, including sodium, chloride, nitrite and calcium. So among our three samples in my group (Sample 1: Christian Science Center Reflection Pool, Sample 2: Copley Square Fountain, and Sample 3: Jamaica Pond) we were able to hear the different pitches of whether the sample had high conductivity or low conductivity.

A challenge with the workshop was considering how the two prongs used to measure the flow of electricity might not have been measured the same distance apart to detect whether it had high or low conductivity among all the coqui’s. Consequently, some samples could have been tampered with.

An opportunity that the workshop offered was that if the experiment were conducted over a period of time one would be able to notice a change in pitch (an inconsistency in the data), which could mean that something could have altered the quality of water.

The promise of story telling using data collected with sensors is that it’s informative for the sake of our audiences. For example, with all the samples we gathered as a class, we could map them out (as we did) and if were we able to detect the quality of the water, we could then infer what potential causes there may be (if the quality were bad hypothetically speaking). As journalists we could then develop stories that can create awareness/initiatives in certain neighborhoods to keep their water systems clean.


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