通过人工智能探索城市空间的社会生活
Arianna Salazar-Miranda1, Zhuangyuan Fan2, Michael Baick3
1School of the Environment, Yale University, New Haven, CT 06511.
概括
在过去的30年里,城市行人行为发生了重大变化. 人们走得更快,停留时间更少,这表明公共空间越来越多地被用作交通路线,而不是社交中心.
科学领域:
- 城市规划是城市规划.
- 社会学 社会学 社会学
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 威廉·怀特的观察研究强调了城市公共空间的社会重要性.
- 之前的研究依赖于手动分析行人行为.
研究的目的:
- 测量30年来城市公共空间行人行为的变化.
- 为了比较1979-1980年和2008-2010年之间的行人运动和社会互动模式.
主要方法:
- 利用计算机视觉和深度学习技术进行自动化视频分析.
- 分析了来自纽约,波士顿和费城的四个城市公共空间的视频.
- 测量步行速度,停留时间,群体大小和群体形成.
主要成果:
- 在30年的时间里,平均步行速度增加了15%.
- 在公共空间逗留的时间减少了50%.
- 独自行走的行人比例保持稳定,但群体相遇减少,这表明社会互动减少.
结论:
- 城市公共空间越来越多地被用作通道,而不是社交聚集区.
- 这种行为转变对城市社会参与和社区建设有重大影响.
- 计算机视觉技术的技术进步使得城市动态的大规模分析成为可能.
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