When I started contributing edits to OpenStreetMap in earnest, I couldn’t help notice certain idiosyncrasies in its tagging: for example, there was a tag for brothels, which I didn’t need to use, but there wasn’t one for daycares, which in Quebec there are rather a lot of. That seemed odd. And it was indicative of a project whose contributors were overwhelmingly male. On CityLab, Sarah Holder examines OSM’s abysmally low female participation rate (only two to five percent of contributors are women), makes the case for better representation, and looks at where women are making a difference to the map. Because a map built overwhelmingly by men can have some massive blind spots.
When it comes to increasing access to health services, safety, and education—things women in many developing countries disproportionately lack—equitable cartographic representation matters. It’s the people who make the map who shape what shows up. On OSM, buildings aren’t just identified as buildings; they’re “tagged” with specifics according to mappers’ and editors’ preferences. “If two to five percent of our mappers are women, that means only a subset of that get[s] to decide what tags are important, and what tags get our attention,” said Levine.
Sports arenas? Lots of those. Strip clubs? Cities contain multitudes. Bars? More than one could possibly comprehend.
Meanwhile, childcare centers, health clinics, abortion clinics, and specialty clinics that deal with women’s health are vastly underrepresented. In 2011, the OSM community rejected an appeal to add the “childcare” tag at all. It was finally approved in 2013, and in the time since, it’s been used more than 12,000 times.
Interestingly, when you look at crisis mappers, the female participation rate jumps: to 27 percent, based on a survey of the Humanitarian OpenStreetMap Team community.
There are many circumstances where the amount of data vastly exceeds the ability to process and analyze it—and computers can only do so much. Enter crowdsourcing. Steve Coast points to Digital Globe’s Tomnod project, which basically crowdsources satellite image analysis. In the case of the current project to map the presence of Weddell seals on the Antarctic Peninsula and the ice floes of the Weddell Sea, users are given an image tile and asked to indicate whether there are seals in the image. It’s harder than it looks, but it’s the kind of routine task that most people can do—many hands, light work and all that—and it helps researchers focus their attention where it needs focusing. (A similar campaign for the Ross Sea took place in 2016.)
Another ongoing campaign asks users to identify flooded and damaged infrastructure and trash heaps in post-Hurricane Maria Puerto Rico.
Today Esri is proud to announce that we are making our own global collection of satellite imagery available to the OSM community directly through our existing World Imagery Service. This regularly updated resource provides one meter or better satellite and aerial photography in many parts of the world, 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k), 2.5m SPOT Imagery (~1:288k to ~1:72k), 1 meter or better NAIP in the US and many other curated sources, so we know it will make a welcome addition to OSM’s growing catalog of reference layers.
OSM editors have been able to trace maps from satellite imagery for years; other sources of such imagery have included Bing and Yahoo (back when Yahoo Maps was a thing). Different sources have different strengths, so this can only help the project. (Esri’s imagery makes no difference where I am, but that’s not a surprise.)
Google Map Maker, Google’s tool to allow users to edit its maps, has been shut down, Ars Technica reports. “A support page went up over the weekend declaring that Map Maker is closed but that ‘many of its features are being integrated into Google Maps.’” You may recall that Map Maker was temporarily suspended in 2015 after a series of embarrassing edits came to light; its editing tools have been increasingly limited to a smaller circle of editors.
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]
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]
To be fair to Google, crowdsourcing map data does have its pitfalls: OpenStreetMap has to deal with this sort of thing all the time. You have to have something in place to deal with bad-faith edits. None of the edits I’ve made to Google Maps went through without someone reviewing them, so I’m surprised that this could happen. That said, when you need your map updated fast (such as during natural disasters like yesterday’s earthquake in Nepal), it’s hard to beat crowdsourcing.