Geospatial Artificial Intelligence Approaches for Understanding Location Descriptions in Natural Disasters and Their Spatial Biases

这个项目的目的是了解人们在自然灾害期间如何在社交媒体上描述地点。这些来自社交媒体的数据对灾难应对工作可能是有益的，为了进一步实现这一目标，人们正在开发计算算法，从社交媒体发布的信息中提取位置信息。然而，不同人群对社交媒体的使用不均衡，以及描述地点的不同方式，可能会使识别地点的过程复杂化。该项目通过加强对人们在自然灾害期间描述地理位置的方式、不同位置提取算法方法的有效性以及所描述位置的潜在空间偏差的理解，推进了知识的发展。这些知识为未来的救灾实践提供信息，有助于拯救生命，减少救灾工作中的不平等现象，从而造福社会。该项目为本科生和研究生提供跨学科的研究经验，并将加强学术界和工业界的合作伙伴关系。该项目产生的数据集和算法工具将公开共享。受到自然灾害影响的人们越来越多地使用Twitter等社交媒体平台。在这些平台上发布的求助信息中，往往包含对受害者和事故地点的描述。然而，人们对自然灾害期间社交媒体上如何描述位置的理解有限，这阻碍了通过计算工具自动提取位置信息。本项目涉及三个研究问题：(1)在自然灾害期间，人们在社交媒体上使用的典型位置描述形式是什么？(2)不同地理空间人工智能（GeoAI）方法提取这些位置描述并在地理空间中表示它们的效果如何？(3)在自然灾害期间，社交媒体上的位置描述存在哪些空间偏差？研究小组正在与应急管理专家合作，了解社交媒体上的位置描述，研究多种基于地理知识的人工智能位置提取方法，并调查提取位置的空间偏差及其与脆弱社区的关系。所获得的关于位置描述的知识和开发的方法可以应用于未来不同情况下的灾害。该奖项反映了美国国家科学基金会的法定使命，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。

The objective of this project is to understand how people describe locations on social media during natural disasters. These data from social media are potentially beneficial in disaster response efforts, and to further this goal, computational algorithms are being developed to extract location information from social media postings. However, uneven use of social media by different populations and varying ways of describing places can complicate the process of identifying locations. This project advances knowledge by enhancing the understanding of the ways in which people describe geographic locations during natural disasters, the effectiveness of different algorithmic approaches for location extraction, and the potential spatial biases in the described locations. Such knowledge benefits society by informing future disaster response practices to help save lives and reduce inequality in response efforts. This project provides interdisciplinary research experience for undergraduates and graduates and will enhance academia and industry partnership. The datasets and algorithmic tools produced from this project will be publicly shared. Social media platforms, such as Twitter, are increasingly being used by people impacted by natural disasters. Descriptions about the locations of victims and accidents are often contained in help-seeking messages posted on these platforms. However, a limited understanding exists of how locations are described on social media during natural disasters, which hinders their automatic extraction via computational tools. This project addresses three research questions: (1) What are the typical forms of location descriptions used by people on social media during natural disasters? (2) How effective are different geospatial artificial intelligence (GeoAI) approaches for extracting these location descriptions and representing them in geographic space? And (3) What spatial biases have characterized location descriptions on social media during natural disasters? The research team is collaborating with emergency management specialists to understand location descriptions on social media, examine multiple geo-knowledge-informed AI approaches for location extraction, and investigate the spatial biases of the extracted locations and their relation to vulnerable communities. The obtained knowledge about location descriptions and the developed methods can be applied to future disasters in diverse settings.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.