The ‘Internet of Things’ requires critical assessment before it is fully realized. This crucial step will carry potentially profound questions: what will happen as more and more of the content we create online is automatically tagged with locational data? What can we learn from this profusion of geographic information? This data now provides a means for new insights such as developing epidemiological models to predict the spread of diseases better than current CDC models through examining social and spatial interactions with technology. Users throughout the world contribute geospatial information to better coordinate crisis response. Social relationships are similarly evolving as men use Grindr to find sex partners, and a simple Google maps search may perhaps reveal the directions from your house to a dissident safe house. Serious questions are mounting - this panel aims to raise several of them, and explore the transformative power this shift may bring.
Additional Supporting Materials
- At what point should a user’s geography be private? Google has the ability to remember the searches you’ve made for directions. What happens if Google matches your home address, your place of business, and the sketchy house on the edge of town to the existing details it knows? Beyond that, what happens when data from all the other virtual activities you engage in - the e-mails you write, the people you connect with on Facebook and Twitter - are similarly matched?
- Few can argue against the spectacular failure that was the location-based startup Color, after raising $40 million dollars on the idea alone, the application is all but dead just a year later. Yet, at the same time, another location-based startup, Grindr, has succeeded. Even more confoundingly, GirlsAroundMe, a very similar application, has failed to pick up much traction. What differences led to these divergent stories?
- One of the unintended results of Wikipedia is the creation of a vast dataset of who is editing what articles, where those editors live, and the places those articles are about. While it is the world’s encyclopedia, the digital divide still skews content online distinctly northward - as a result, articles may not necessarily represent local sentiments. Using this data, can anything be said of the post-colonial tradition living on online?
- Already, research has shown that the geographic spread of the common flu can be predicted faster, cheaper, and better than CDC models by simply geolocating tweets mentioning the flu. What does the future of Internet-aided epidemiology look like, where do the limits of these methods lie, and what are the dangers of crowd-sourcing data this critical to human health?
- In a similar vein, the open source Ushahidi software project has argued that crowd-sourcing of various data, when coupled with geographic metadata, can be more helpful in an emergency than an army of aid workers. Not all crises are created equally, though. In a situation like the Arab Spring, what are the potentials and pitfalls of these services, and how should they be handled, if they even can?
- Erhardt Graeff, Graduate Student and Researcher, MIT Center for Civic Media
- Devin Gaffney, Researcher, Oxford Internet Institute
- Mark Graham, Research Fellow, Oxford University
- Monica Stephens, Visiting Assistant Professor/Director of the Institute of Cartographic Design, Department of Geography, Humboldt State University
Erhardt Graeff, Graduate Student and Researcher, MIT Center for Civic Media
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