Quartz takes a look at the Missing Maps project, which I suppose can best be described as a way to jumpstart mapping the unmapped developing regions of the world with OpenStreetMap. What’s interesting about Missing Maps is how it systematically deals out tasks to people best able to do them: remote volunteers trace imagery, community volunteers do the tagging and labelling. There’s even an app, MapSwipe, that gives its users “the ability to swipe through satellite images and indicate if they contain features like houses, roads or paths. These are then forwarded onto Missing Maps for precise marking of these features.” [WMS]
In Smithsonian, Erin Blakemore explores the on-the-ground, amateur efforts to get disadvantaged communities—slums, shanty towns, whatever they may be called—on the map, like the Map Kibera and Mapillary projects, and the implications of such projects.
Sterling Quinn, who is earning his Ph.D. in geography at Penn State, notes that there are downsides to user-generated maps. Just because an underserved community makes its way onto the map doesn’t mean it becomes less vulnerable, says Sterling. “Putting yourself on the map may make you more vulnerable to people who want to exploit the area,” he tells Smithsonian.com.
Point Google Maps or OpenStreetMap at a city like Dar es Salaam, Tanzania and you’ll get a reasonably good map. What you won’t get is Street View or street-level imagery—or, necessarily, the data that comes from a street-level understanding of the territory. NPR’s Nadia Whitehead looks at a joint project of the World Bank and Mapillary, a company that crowdsources street-level photos, to produce those images. “Volunteers are mounting camera rigs to their tuk tuks—three-wheeled motor-powered vehicles—to snap pictures as they cruise Dar es Salaam’s dirt roads. Others download the Mapillary app on their smartphones and capture images as they walk or hitch rides on motorbikes. In all, more than 260 people have volunteered.” [via]
Researchers are mapping the shift in Swiss German dialect usage via an iOS app. The app asks users to take a 16-question survey based on maps from a language atlas that mapped Swiss German usage circa 1950. The app predicts the user’s actual home dialect location based on those maps; differences between that prediction and the user’s actual home dialect location reveal how Swiss German has changed over time. They ended up getting responses from 60,000 speakers. PLOS ONE article. [via]