CAREER: Spatial Network Database approach for Emergency Management Information Systems

职业：应急管理信息系统的空间网络数据库方法

• 批准号: 1844565
• 负责人: KwangSoo Yang
• 金额: $ 50万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844565&HistoricalAwards=false
• 关键词: CAREER; Spatial; Network; Database; approach

Emergency Management Information Systems (EMIS) are an increasingly important tool for understanding, managing, and governing transportation-related systems, as well as for testing the stability or vulnerability of these systems against interference. Recently, EMIS have benefitted from both volunteer geographic information (VGI) and crowdsourcing as powerful methods of collecting user-generated datasets. However, these data sources are challenging due to their very large size, variety, and update rates required to ensure the timely and accurate delivery of useful emergency information and response for disastrous events. Developing fundamental data processing components for advanced relevant queries which can clearly and succinctly deliver critical information in the case of an emergency is critically important and challenging. This research focuses on three interrelated domains: 1) evacuation route planning 2) resource assignment, and 3) transportation resilience. This research investigates innovative queries in these three domains in the context of emergency management. The outcome of this project has potential benefit to a wide range of societal applications, such as transportation management, logistics, public safety, resource assignment, and service delivery and thus aligns well with the NSF mission: to promote the progress of science; to advance the national health, prosperity and welfare . Educational objectives of this project include broadening participation of Hispanic women, increasing undergraduate research opportunities including research-intensive course development, and promotion of team science skills. The goals of this project are to identify promising solutions for addressing the challenge of EMIS and to develop an advanced spatial query processing platform that clearly and succinctly delivers critical information in emergencies. First, this project designs and develops the problem-solving framework that can integrate different technical components, including geometry, topology, graph theory, and optimization techniques. Second, this project investigates multiple inherent constraints for spatial networks and identifies main bottlenecks for query processing. Third, this project develops fast and scalable query processing mechanisms that overcome these bottlenecks and produce simple and concise information for emergency management. A key research challenge is to identify structural patterns or optimal substructures of the spatial network optimization problem that can enhance the scalability and efficiency of spatial network query processing. The components of the query processing framework include frequent suffix tree mining, graph simplification, bi-partite graph clustering, minimum polygon covering, graph partitioning, spectral method, random walk, and expander graph mining. These components are integrated to develop fast and scalable spatial network queries and to provide simple and concise information for EMIS. The outcomes of this project include data processing tools, spatial and spatial network optimization algorithms, queries, and visualization tools. This project validates the performance of new spatial network queries using historical and real-time datasets and provides a web-based educational system to enhance student learning.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.

应急管理信息系统（EMIS）是一个越来越重要的工具，用于理解，管理和管理运输相关系统，以及测试这些系统的稳定性或抗干扰的脆弱性。最近，EMIS受益于志愿者地理信息（VGI）和众包作为收集用户生成的数据集的强大方法。然而，这些数据源是具有挑战性的，因为它们的规模非常大，种类繁多，需要确保及时准确地提供有用的应急信息和应对灾难性事件的更新率。开发高级相关查询的基本数据处理组件，以便在紧急情况下清晰简洁地提供关键信息，这一点至关重要，也具有挑战性。本研究的重点是三个相互关联的领域：1）疏散路线规划，2）资源分配，3）运输弹性。本研究探讨在这三个领域的应急管理的背景下，创新的查询。该项目的成果对广泛的社会应用具有潜在的益处，例如运输管理，物流，公共安全，资源分配和服务提供，因此与NSF的使命保持一致：促进科学进步;促进国家健康，繁荣和福利。该项目的教育目标包括扩大西班牙裔妇女的参与，增加本科生的研究机会，包括研究密集型课程的开发，以及促进团队科学技能。该项目的目标是确定有前途的解决方案，以应对EMIS的挑战，并开发一个先进的空间查询处理平台，清晰，简洁地提供紧急情况下的关键信息。首先，本项目设计并开发了一个问题解决框架，该框架可以集成不同的技术组件，包括几何、拓扑、图论和优化技术。其次，本项目研究了空间网络的多种固有约束，并确定了查询处理的主要瓶颈。第三，本项目开发快速和可扩展的查询处理机制，克服这些瓶颈，并产生简单，简洁的信息，应急管理。一个关键的研究挑战是识别空间网络优化问题的结构模式或最优子结构，可以提高空间网络查询处理的可扩展性和效率。查询处理框架的组成部分包括频繁后缀树挖掘、图简化、二分图聚类、最小多边形覆盖、图划分、谱方法、随机游走和扩展图挖掘。这些组件集成开发快速和可扩展的空间网络查询，并提供简单和简洁的信息EMIS。该项目的成果包括数据处理工具，空间和空间网络优化算法，查询和可视化工具。该项目使用历史和实时数据集验证新的空间网络查询的性能，并提供一个基于网络的教育系统，以提高学生的学习。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

Size constrained k simple polygons

• DOI: 10.1007/s10707-020-00416-9
• 发表时间: 2020-07
• 期刊: GeoInformatica
• 影响因子: 2
• 作者: Kwangsoo Yang;Kwang Woo Nam;Ahmad Qutbuddin;Aaron Reich;Valmer Huhn

RealROI: Discovering Real Regions of Interest From Geotagged Photos

• DOI: 10.1109/access.2022.3197169
• 发表时间: 2022-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Nam, Kwang Woo;Yang, Kwangsoo
• 通讯作者: Yang, Kwangsoo

Abnormal Driving Detection Using GPS Data

使用 GPS 数据检测异常驾驶

• DOI: 10.1109/honet59747.2023.10374718
• 发表时间: 2023
• 期刊: Robotics and IoT (HONET
• 作者: Boateng, Charles;Yang, Kwangsoo;Ara Ghoreishi, Seyedeh Gol;Jang, Jinwoo;Jan, Muhammad Tanveer;Conniff, Joshua;Furht, Borko;Moshfeghi, Sonia;Newman, David;Tappen, Ruth
• 通讯作者: Tappen, Ruth

Turn Constrained Shortest Path

转弯约束最短路径

• DOI: 10.1109/honet59747.2023.10374669
• 发表时间: 2023
• 期刊: Robotics and IoT (HONET
• 作者: Allani, Amogh;Yang, KwangSoo
• 通讯作者: Yang, KwangSoo

Node-attributed Spatial Graph Partitioning

节点属性空间图划分

• DOI: 10.1145/3397536.3422198
• 发表时间: 2020
• 期刊: Proceedings of the 28th International Conference on Advances in Geographic Information Systems
• 作者: Bereznyi, Daniel;Qutbuddin, Ahmad;Her, YoungGu;Yang, KwangSoo
• 通讯作者: Yang, KwangSoo