RAPID: Examining public spatial behavior during the COVID-19 outbreak

新冠肺炎疫情改变了我们日常公共生活中的日常活动。一些社区开始限制大部分人口的行动、活动、身体互动和社会化。采取这些行动是必要的，目的是减少人类接触，作为减轻病毒潜在传播的广泛努力的一部分。在人口密集的大都市地区，社会疏远措施已经呈现出一种紧迫感，很大一部分新冠肺炎病例都在这些地区发生。该项目将启动一项快速努力，以获取关于大流行期间街景生活的高分辨率数据，目标是随着公共空间行为的变化而产生快速反应的洞察。这将通过捕捉和编码身临其境的第一人称地理位置视频日记来实现，这些日记记录了大都市居民的日常街景活动，因为生活发生了变化，以适应新的社交距离和宵禁命令。这些数据将通过当地社区伙伴关系广泛传播。此外，该项目将资助不同STEM相关领域的四名研究生。这项研究捕捉到了新的空间行为形式是如何出现的，同时测试了现有的空间行为理论在特殊情况下是如何成立的。中心创新是关注日常街景场景中的个人体现，通过公共场所的肢体语言揭示潜在和公开的空间行为。这将通过一组招募的志愿者在大流行期间从事日常活动的日常街景的第一人称视频片段来实现。这些数据将被手工和机器编码，以探索空间行为模式，这些模式可以指示个人、建筑环境和社会行为现象之间的关系。将通过在事后进行一系列基于工作室的实验来微调紧急关系，在利用收集的视频数据作为地面事实的受控环境中，系统地和经验地将运动捕捉部署到非语言交流(如手势和举止)与空间行为的高分辨率时空细节之间的追踪路径。为了促进更广泛地使用这些数据并促进更多的研究，将使用高分辨率时空地理信息系统和虚拟地理环境对来自实地和工作室实验的数据进行整理。这些资源将公开提供并与当地合作伙伴共享，这将对保障公共健康和福祉产生影响。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The COVID-19 pandemic has altered the moment-to-moment activities of our daily public lives. Some communities have initiated restrictions on the movement, activities, physical interaction, and socialization of large sections of the population. These actions have been borne of necessity, in a bid to reduce human contact as part of widespread efforts to mitigate the potential spread of the virus. Social distancing measures have taken on a sense of urgency in population-dense metropolitan areas, which host a large portion of the COVID-19 cases. This project will launch a rapid effort to acquire high-resolution data regarding life on streetscapes during the pandemic, with the goal of producing quick-response insight as changes in public spatial behavior unfold. This will be done by capturing and coding immersive, first-person, geolocated video- diaries of metropolitan residents going about their daily streetscape activity, as life shifts to adapt to new social distancing and curfew orders. The data will be disseminated broadly through local community partnerships. Additionally, the project will fund four graduate students in diverse STEM related fields.This research captures how new forms of spatial behavior emerge, while testing how existing theories of spatial behavior hold under extraordinary circumstances. The central innovation is to focus on individual embodiment in day-to-day streetscape scenes, as revealed in latent and overt spatial behavior through body language in public places. This will be accomplished through first-person video footage of everyday streetscape scenes from a group of recruited volunteers as they go about daily activities during a pandemic. The data will be hand and machine coded to explore patterns of spatial behavior that can indicate relationships between individuals, the built environment, and socio-behavioral phenomena. Emergent relationships will be fine-tuned, using a series of studio-based experiments after the fact, deploying motion capture to methodically and empirically trace-out pathways between non-verbal communication such as gestures and mannerisms, and high-resolution space-time details of spatial behavior, in a controlled setting that utilizes the collected video data as ground truth. To promote broader use of the data and to foster additional research, data will be collated—from both the field and from the studio experiments—using high-resolution space-time Geographic Information Systems (GIS) and virtual geographical environments. These resources will be made publicly available and shared with local partners, with implications for safeguarding public health and wellbeing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.