What？卫星照片能测肥胖度？！
Some public health problems are so large you can see them from space.
有些公众健康问题已经如此严重以至于你从太空中都可以看到它们。
Artificial intelligence can use satellite images to estimate a region’s level of obesity—even without spotting the overweight people, a new study reveals.
一项新研究显示，人工智能可以利用卫星照片来评估某个区域的肥胖度——甚至不用找出超重的人是谁。
Instead, it relies on cues such as the distribution of buildings and trees.
相反，它靠的是比如建筑和树分布的线索。
Knowing a neighborhood’s rate of overweight adults can help target interventions such as healthy eating campaigns.
了解一个社区中超重成年人的比例可以帮助制定干预措施，比如健康饮食活动。
But gathering such statistics tends to require expensive surveys or on-the-ground investigation.
但收集这些数据往往需要花费不小的调研或实地调查。
To find a better way, researchers downloaded nearly 150,000 Google Maps satellite images of 1695 census tracts (basically neighborhoods) in four cities: Los Angeles, California; Memphis, Tennessee; San Antonio, Texas; and the Seattle, Washington, area.
为了找到更好的办法，研究员们下载了来自四个大城市的1695个普查分区（基本上是周围的）的将近150,000张谷歌卫星照片：加州的洛杉矶市；田纳西州的孟菲斯市；德州的圣安东尼奥市；还有华盛顿州的西雅图市。
Then they fed the images into a neural network, an algorithm that finds patters in large amounts of data.
然后他们把这些照片输入到神经网络中，该算法可以从大量数据中找出规律。
The network helped the researchers focus on the most important features of the images, such as the amount of green area (the green blobs in the images below—in the middle and on the right—roughly corresponding to trees and grass in the images blow on the left), gray strips (the gray blobs in the middle, corresponding to roads on the left), or white rectangles (the red blobs on the right, corresponding to buildings on the left).
该网络可以帮助研究员们聚焦照片的最重要的特征，例如绿色区域（下图中的绿点—在中部和右侧—大概对应的是下图中左侧的照片树和草地）的量，灰色条带（中间的灰点，大概对应的是左侧的道路），或者白色的矩形（右侧的红点，大概对应的是左侧的建筑物）。
The team then used another program to find connections between these blobby visual features and obesity rates.
研究团队接下来用另一个程序来找到这些点的视角特征和肥胖度之间的关联。
In the end, the researchers could estimate an area’s obesity rate even better than they could with separately available stats such as the number of gyms and restaurants, they report today in JAMA Network Open.
最后，研究员们可以估算一个地区的肥胖度，其准确度甚至比单独的可用数据例如健身房和餐馆的数量更准确，他们在今天的《美国医学会杂志》网络开放版中进行了报告。
Neighborhood features also correlated with per capita income, suggesting they may be useful for estimating obesity in part because wealth affects both one’s weight and where one lives.
社区特征也与人均收入相关，这表明它们在估计肥胖方面可能有用，部分原因是财富既会影响一个人的体重，也会影响一个人住在哪里。
Assessing an area’s obesity rates may help city planners decide whom to encourage to be more physically active or where to make healthy food outlets more prevalent, the paper suggests.
在论文中建议到，评估一个地区的肥胖度也许可以帮助城市规划者们决定该鼓励谁更多地进行体育运动或者在哪里设立更多的健康食品商店。
Satellite data cannot fully substitute for more traditional measures of public health such as surveys, but as a supplement it’s cheap and fast.
卫星数据并不能完全取代更传统的公众健康的方法，例如调查，不过作为一种补充，它既便宜又快捷。
Like a supersize meal.
就好像一顿超级大餐一般。
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