Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and situational awareness. The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping; (2) automated rapid impact assessment; (3) predictive infrastructure failure prediction and monitoring; and (4) smart situational awareness capabilities. We demonstrate the components of these core capabilities of the smart flood resilience framework in the context of the 2017 Hurricane Harvey in Harris County, Texas. First, we present the use of flood sensors for the prediction of floodwater overflow in channel networks and inundation of co-located road networks. Second, we discuss the use of social media and machine learning techniques for assessing the impacts of floods on communities and sensing emotion signals to examine societal impacts. Third, we describe the use of high-resolution traffic data in network-theoretic models for nowcasting of flood propagation on road networksmore »
This content will become publicly available on November 1, 2023
Unveiling spatial patterns of disaster impacts and recovery using credit card transaction fluctuations
The objective of this study is to examine spatial patterns of disaster impacts and recovery of communities based on fluctuations in credit card transactions (CCTs). Such fluctuations could capture the collective effects of household impacts, disrupted accesses, and business closures and thus provide an integrative measure for examining disaster impacts and community recovery. Existing studies depend mainly on survey and sociodemographic data for disaster impacts and recovery effort evaluations, although such data has limitations, including large data collection efforts and delayed timeliness results. Also, there are very few studies have concentrated on spatial patterns of disaster impacts and short-term recovery of communities, although such investigation can enhance situational awareness during disasters and support the identification of disparate spatial patterns of disaster impacts and recovery in the impacted regions. This study examines CCTs data Harris County (Texas, USA) during Hurricane Harvey in 2017 to explore spatial patterns of disaster impacts and recovery duration from the perspective of community residents and businesses at ZIP-code and county scales, respectively, and to further investigate their spatial disparities across ZIP codes. The results indicate that individuals in ZIP codes with populations of higher income experienced more severe disaster impact and recovered more quickly than those more »
- Award ID(s):
- 1846069
- Publication Date:
- NSF-PAR ID:
- 10394491
- Journal Name:
- Environment and Planning B: Urban Analytics and City Science
- Volume:
- 49
- Issue:
- 9
- Page Range or eLocation-ID:
- 2378 to 2391
- ISSN:
- 2399-8083
- Sponsoring Org:
- National Science Foundation
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