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

RAPID：检查 COVID-19 爆发期间的公共空间行为

• 批准号: 2027652
• 负责人: Paul Torrens
• 金额: $ 20万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027652&HistoricalAwards=false
• 关键词: RAPID; Examining; public; spatial; behavior

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.

COVID-19 大流行改变了我们日常生活的每时每刻的活动。一些社区已开始限制大部分人口的行动、活动、身体互动和社交。这些行动是必要的，旨在减少人际接触，作为减轻病毒潜在传播的广泛努力的一部分。人口密集的大都市地区采取了社交距离措施，这些措施具有紧迫感，这些地区是大部分 COVID-19 病例的聚集地。该项目将迅速努力获取有关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产生快速响应的洞察力。这将通过捕捉和编码沉浸式、第一人称、地理定位的大都市居民的日常街景活动视频日记来完成，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，该项目还将资助四名 STEM 相关领域的研究生。这项研究捕捉了新形式的空间行为是如何出现的，同时测试了现有的空间行为理论在特殊情况下如何成立。核心创新是关注日常街景场景中的个体体现，正如公共场所的肢体语言所揭示的潜在和明显的空间行为。这将通过一组招募的志愿者在大流行期间进行日常活动时的日常街景场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，从而表明个体、建筑环境和社会行为现象之间的关系。事后将使用一系列基于工作室的实验来微调新兴关系，在利用收集到的视频数据作为基本事实的受控环境中，部署动作捕捉，以系统地和经验性地追踪手势和习惯等非语言交流与空间行为的高分辨率时空细节之间的路径。为了促进更广泛的数据使用并促进更多研究，将使用高分辨率时空地理信息系统（GIS）和虚拟地理环境来整理来自现场和工作室实验的数据。这些资源将公开提供并与当地合作伙伴共享，对维护公众健康和福祉具有重要意义。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Smart and Sentient Retail High Streets

• DOI: 10.3390/smartcities5040085
• 发表时间: 2022-12-01
• 期刊: SMART CITIES
• 影响因子: 6.4
• 作者: Torrens, Paul M.

Inverse augmentation: Transposing real people into pedestrian models

逆向增强：将真人转变为行人模型

• DOI: 10.1016/j.compenvurbsys.2022.101923
• 发表时间: 2023
• 期刊: Environment and Urban Systems
• 作者: Torrens, Paul M.;Gu, Simin
• 通讯作者: Gu, Simin

Real-time experiential geosimulation in virtual reality with immersion-emission

浸入式发射虚拟现实中的实时体验式地理模拟

• DOI: 10.1145/3486184.3491079
• 发表时间: 2021
• 作者: Torrens, Paul M.;Gu, Simin
• 通讯作者: Gu, Simin

Force-Aware Interface via Electromyography for Natural VR/AR Interaction

• DOI: 10.1145/3550454.3555461
• 发表时间: 2022-10
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Yunxiang Zhang;Benjamin Liang;Boyuan Chen-;P. Torrens;S. F. Atashzar;Dahua Lin;Qinghong Sun