这些线条也许看上去很像北美和欧洲之间的航线，或是夜间卫星图，城市像星座一样被点亮。

　　But look again.

　　但请再瞧一瞧。

　　These animations chart the movement of Western culture over the past 2,000 years, researchers report Friday in the journal Science.

　　根据一研究团队在周五《科学》杂志上的报告，这些动画其实纪录了过去2000年间西方文化的迁移。

　　To make these movies, art historian Maximilian Schich and his colleagues mapped the births and deaths of more than 150,000 notable artists and cultural leaders, such as famous painters, actors, architects, politicians, priests and even antiquarians (people who collect antiques).

　　为了制作出这些影像，艺术史学家马克西米兰·舒尺和他的团队在地图上标出了15多万位艺术名人和文化领袖的出生和死亡地点，其中不乏著名的画家、演员、建筑师、政治家、牧师，甚至是古文物收藏家。

　　A shimmering blue dot lights up each new birth, while red dots represent each death.

　　地图上闪闪发亮的蓝点代表出生地点，红点则代表死亡地点。

　　We can watch as artists flock from rural areas to urban centers like London, Paris, Rome and Berlin after the Renaissance. Then in the late 17th century, people start to catapult from Europe into the eastern U.S. and then eventually leapfrog over to the West Coast.

　　我们可以看到，艺术家在文艺复兴之后纷纷从乡村迁往伦敦、巴黎、罗马和柏林等中心城市。而到了17世纪后叶，他们很快从欧洲迁往美国东部，最后争相迁往西海岸。

　　"We're interested in the shape of the coral reef of culture," says Schich, of the University of Texas. "We are taking a systems biology approach to art history."

　　舒尺来自德克萨斯大学，他表示，“我们对文化所呈现的外形很感兴趣。我们采用了一种系统生物学的方法来研究艺术史。”

　　After mapping the births and deaths, Schich and his team analyzed demographic data to build a model for how people and their cultural achievements ebb and flow across continents.

　　标出出生和死亡地点后，舒尺和他的团队对人口数据进行了分析建模，借此反映艺术家及他们的文化成就是如何在大陆间变迁。

　　Right now the team has only maps for the U.S. and Europe. But Schich hopes to extend these visualizations beyond the Western world.

　　目前团队仅绘制了美国和欧洲地区的地图。但舒尺希望继续利用这种可视化方式呈现西方世界以外的地区。

　　And the model isn't just fun to look at. The data also reveal trends and patterns in human migration over the past two millennia.

　　该模型不只是看上去有意思而已，其数据反映了人类在过去2000年间的迁移趋势和模式。

　　The models are the latest application of a rapidly growing field, called network science — which uses visualizations to find the underlying patterns and trends in complex data sets.

　　该模型是网络科学的最新应用成果。网络科学利用可视化技术反映复杂数据集的潜在模式和趋势，目前该领域的发展速度很快。