III: Small: DataWorld: Externalizing Hidden Data Flows for Situated Analytics

了解日常生活中的数据可以帮助专业人士，政策制定者和公民使我们的社会更美好，更公平，更高效。例如，想象一下，通过步行穿过你想要居住的社区，并从社交媒体，人口普查数据和附近的学校发现有关该地区的事实来寻找房子。或者，想象一下，一位建筑师向她的客户展示了一座新的办公楼，它位于城市的实际位置，背景是游荡的太阳，潮起潮落的人群和一波又一波的车流。然而，大多数收集到的信息无法基于地理空间进行访问和整合。该项目将研究如何通过添加可视化来连接数据和收集数据的世界，这些可视化将通过使用增强现实（AR）使底层数据在真实的世界中可见。这将帮助人们使用互联网上的大量数据，将其作为周围世界的一部分放在指尖。该项目将部署在大学校园内，以帮助学生，教师和游客调整其事件，警报和历史背景的“心跳”。最后，该项目还将让学生参与数据库的完善和贡献，帮助吸引代表性不足的学生从事计算职业，并建立一个对数据和地理位置相结合感兴趣的研究人员社区。为了实现这些目标，该项目将建立一个名为DataWorld的框架，用于创建位置数据流并使用AR技术将其外部化。实际上，这将把真实的世界与隐藏的数据世界融合在一起。这一框架将联合收割机结合来自社交媒体、公共数据库和流行网站等各种来源的现有数据，并使DataWorld用户能够提供基层贡献。这一框架将适用于三个不同的主题。(1)公共安全，其中有关犯罪，紧急情况和当前事件的信息可以帮助用户。(2)历史意识，其中定位的数据流将被用来揭示那些谁来到我们面前的脚步，如放置在他们的地理背景旧报纸的故事，突出斗争-大和小-民权运动，并显示随着时间的推移在原地的城市发展。(3)公民意识，其中所处的数据流可以传播有关当前事件的信息，促进可持续性和环保意识行为，并促进基层的众包数据收集，促进一种“虚拟”地理藏宝形式，数据可以隐藏在世界各地。这些应用程序不仅将提供新的技术和框架，有助于我们的知识，位于数据，数据可视化和增强现实，但将部署在实践中使用的马里兰州校园作为一个试验台。该项目将为手持设备和头戴式显示器生成一个或多个DataWorld应用程序，这将使人们能够向DataWorld平台提供基层数据，并在现场查看数据。在项目结束时，将公布DataWorld数据库的匿名版本。最后，教育材料、源代码和文档也将在项目期间作为开源发布。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding data from everyday life could help professionals, policymakers, and citizens make our society better, fairer, and more efficient. For example, imagine hunting for a house by walking through the neighborhood in which you want to live and discovering facts about the area from social media, census data, and nearby schools. Alternatively, imagine an architect showing her clients a new office building in its actual location in the city with the wandering sun, ebb and flow of crowds, and waves of car traffic as a backdrop. However, most of collected information cannot be accessed and integrated based on geographical space. This project will investigate how to bridge data and the world from where it was collected by adding visualizations that will make the underlying data visible in the real world by using Augmented Reality (AR). This will help people use the massive troves of data on the internet by literally putting it at their fingertips as part of the world around them. The project will be deployed on a university campus to help students, faculty, and visitors tune in to its "heartbeat" of events, alerts, and historical background. Finally, the project will also engage students in refining and contributing to the database, help attract underrepresented students to computing careers, and build a community of researchers interested in the combination of data and geographical place.To achieve these goals, this project will build a framework called DataWorld for creating situated data streams and externalizing them using AR technology. In effect, this will blend the real world with the hidden world of data. This framework will both combine existing data from a wide variety of sources, such as social media, public databases, and popular websites, as well as enable grassroot contributions from DataWorld users. This framework will then be applied to three separate themes. (1) Public safety, where information about crime, emergencies, and current events can help users. (2) History awareness, where the situated data streams will be used to reveal the footsteps of those who came before us, such as placing old newspaper stories in their geographical context, highlighting the struggles -- large and small -- of the civil rights movement, and showing urban development in situ over time. (3) Civic awareness, where the situated data streams can disseminate information about current events, promote sustainability and environmentally-conscious behavior, and facilitate crowdsourced data collection at a grassroots level, fostering a form of "virtual" geocaching where data can be hidden in the world. These applications will not only provide new techniques and frameworks that contribute to our knowledge of situated data, data visualization, and Augmented Reality, but will be deployed in practice using the University of Maryland campus as a testbed. The project will generate one or several DataWorld apps, both for handheld devices as well as head-mounted displays, that will enable both contributing grassroot data to the DataWorld platform as well as viewing the data in situ. At the end of the project, an anonymized version of the DataWorld database will be published. Finally, educational material, source code, and documentation will also be released as open source during the project.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.