Throughout my first month in my data visualization class at Emerson, there are a few concepts I’ve found particularly salient in our readings, lectures, and hands-on experience with building a water sensor. Data journalism has many aspects I’ve noticed so far — the intent behind collecting it; as a type of journalism, there are inevitably ethical issues; and the practical application of sensors and the data. I’ll apply both my learned knowledge as well as my practical knowledge from building a Coqui sensor to each of these issues.

In the Tow Report on Sensor Journalism, a few different theories behind why sensor journalism is relevant are explained. The first is fairly obvious: many data journalists “[feel] there was a defect in the data being provided by official sources” (p. 20). This relates to many discussions we have had in class. For instance, during the Beijing Olympics, journalists brought sensors to test if the Chinese government had gone through on their promise to clean up air quality, because the government wouldn’t provide them with this information. Additionally, through collecting data yourself, your data set is as large as you want it to be, and you have immediate access to it. You can also be more confident in the way your data was collected.

The second idea presented by the Tow Report is that collecting data allows journalists to quantify observations that they might not have been able to quantify before. Sensors “can record an aspect of the world so that it can be specified and transparently communicated” (p. 21). This too relates to something we discussed in class — Boston’s pothole app to track the amount of potholes using residents’ smartphones. Though there are issues with this app as a whole — which I will touch upon later — sensors allow people to quantify something that may have been more difficult to quantify before. This is something I appreciated when I was building my own sensor. My group took water samples from Chinatown and Beacon Hill. The Beacon Hill water samples were noticeably “dirtier” than the Chinatown ones, so being able to actually quantify this “dirtiness” was a neat application of what I’d learned in class.

From what I’ve understood and seen so far from sensor journalism, I can see why it’s so important, especially now. As more news moves online to a more interactive interface, data visualizations with information from sensors take advantage of this new medium. They help stories be told that may not have been told before, and I think they are helping move journalism into the “Internet age.” It also unites journalists with scientists and hackers, which can prove difficult according to The Data Journalism Handbook because it’s two opposite groups of people working together, but it does form new teams, which allows for more brainstorming. In the handbook, multiple different case studies are shown, and every group said one of the biggest issues was working with these new groups of people. However, I think both hackers and journalists have important ideas to share with each other, and through working together, more effective procedures will develop eventually.

Next I’m going to address what I’ve seen so far as the ethical issues with sensor journalism from class discussions, readings, and my practical application of this concept. As I mentioned previously, the city of Boston (as well as many other municipalities) have begun using apps for smartphones to track potholes in the metropolitan area. This is a good idea — it uses citizens on their normal routine trips to track which streets are in need of repair. However, upon closer look, one can see the issues with this app. For instance, not every person has a smartphone, and smartphone ownership is fairly limited to a certain demographic. So, what this app actually does is discriminate against those who do not have access to smartphones. It does track where people with smartphones encounter potholes, but what does it do for the several citizens who do not have access to smartphones? Just in its very concept it automatically disqualifies several people from its practical use. This app could be seen as part of the solution, but it’s definitely not the entire solution, which leads me to my next point.

I think there’s something about charts, graphs, and data sets that make readers (as well as even journalists) immediately trust the provided information — if there are numbers, it has to be right, right? I think blindly trusting data is incredibly dangerous, because there are many factors we just might not know about. With my water sensor project, I can see many things that might’ve affected the water quality, but if someone is just grabbing data off a website, they wouldn’t know about those factors. Additionally, with the aforementioned pothole app, sometimes the full picture isn’t revealed through numbers. I think that’s why data and journalism work together to complement each other. Data can tell a lot, but it leaves a lot out, and writing also can tell a lot, but it’s brought to life with visualizations. Something I look forward to learning about is combining these two components to create really strong journalistic pieces that are comprehensive and informative.

Another potential issue with sensor journalism is something many scientists encounter. In my experience with the water quality project, though I really wanted to be unbiased with the data, I also wanted to discover something cool. I think many people examine data expecting to see something, so they are more willing to look at things that might not necessarily be there in order to fulfill their hypotheses. It’s understandable, but I think it’s something to be aware of — if you can’t replicate your results, then I think there’s an inherent issue there.

Despite these potential ethical issues with sensor journalism, it does present a lot of different opportunities. In Lily Bui’s presentation, she touched upon data donation, or outsourcing data collection to citizens. Though this is similar to the pothole app I’ve talked about before, because only a certain demographic has access to such sensors, open source sensors like the water sensor we built give experience to people who might not have had that experience otherwise. And, because it’s open source, it allows a wider variety of people access to the technology. This allows for a bigger, more diverse pool of data.

The last concept I am going to discuss is what I see the future for sensor journalism is. I mentioned earlier I wanted to learn how to incorporate data into my stories, and I also discussed some of the case studies we’ve read have shown how difficult it is for hackers and journalists to work together. In order for sensor journalism to be successful, I think it would be important to educate both journalists as well as hackers as to what the other does. Something I think my school Emerson is doing well is preparing students for 21st century journalism, especially by offering classes like Data Visualization. Though I’m not going to school to study data sets or computer coding, I think giving students a basic knowledge of sensor journalism is important. If more schools begin doing this, in the future, more and more journalists will be able to produce data pieces in the future.