skip to main content


Title: 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 located in lower income ZIP codes for most business sectors. Our findings not only enhance the understanding of spatial patterns and disparities in disaster impacts and recovery for better community resilience assessment but also could benefit emergency managers, city planners, and public officials in enhanced situational awareness and resource allocation.  more » « less
Award ID(s):
1846069
NSF-PAR ID:
10394491
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Environment and Planning B: Urban Analytics and City Science
Volume:
49
Issue:
9
ISSN:
2399-8083
Page Range / eLocation ID:
2378 to 2391
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The use of crowdsourced data has been finding practical use for enhancing situational awareness during disasters. While recent studies have shown promising results regarding the potential of crowdsourced data (such as user-generated flood reports) for flash flood mapping and situational awareness, little attention has been paid to data imbalance issues that could introduce biases in data and assessment. To address this gap, in this study, we examine biases present in crowdsourced reports to identify data imbalance with a goal of improving disaster situational awareness. Three biases are examined: sample bias, spatial bias, and demographic bias. To examine these biases, we analyzed reported flooding from 3-1-1 reports (which is a citizen hotline allowing the community to report problems such as flooding) and Waze reports (which is a GPS navigation app that allows drivers to report flooded roads) with respect to FEMA damage data collected in the aftermaths of Tropical Storm Imelda in Harris County, Texas, in 2019 and Hurricane Ida in New York City in 2021. First, sample bias is assessed by expanding the flood-related categories in 3-1-1 reports. Integrating other flooding related topics into the Global Moran's I and Local Indicator of Spatial Association (LISA) revealed more communities that were impacted by floods. To examine spatial bias, we perform the LISA and BI-LISA tests on the data sets—FEMA damage, 3-1-1 reports, and Waze reports—at the census tract level and census block group level. By looking at two geographical aggregations, we found that the larger spatial aggregations, census tracts, show less data imbalance in the results. Through a regression analysis, we found that 3-1-1 reports and Waze reports have data imbalance limitations in areas where minority populations and single parent households reside. The findings of this study advance understanding of data imbalance and biases in crowdsourced datasets that are growingly used for disaster situational awareness. Through addressing data imbalance issues, researchers and practitioners can proactively mitigate biases in crowdsourced data and prevent biased and inequitable decisions and actions. 
    more » « less
  2. Abstract

    Damages in critical infrastructure occur abruptly, and disruptions evolve with time dynamically. Understanding the situation of critical infrastructure disruptions is essential to effective disaster response and recovery of communities. Although the potential of social media data for situation awareness during disasters has been investigated in recent studies, the application of social sensing in detecting disruptions and analyzing evolutions of the situation about critical infrastructure is limited. To address this limitation, this study developed a graph‐based method for detecting credible situation information related to infrastructure disruptions in disasters. The proposed method was composed of data filtering, burst time‐frame detection, content similarity calculation, graph analysis, and situation evolution analysis. The application of the proposed method was demonstrated in a case study of Hurricane Harvey in 2017 in Houston. The findings highlighted the capability of the proposed method in detecting credible situational information and capturing the temporal and spatial patterns of critical infrastructure events that occurred in Harvey, including disruptive events and their adverse impacts on communities. The proposed methodology can improve the ability of community members, volunteer responders, and decision makers to detect and respond to infrastructure disruptions in disasters.

     
    more » « less
  3. Abstract

    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 networks and the disrupted access to critical facilities, such as hospitals. Fourth, we introduce how location-based and credit card transaction data were used in spatial analyses to proactively evaluate the recovery of communities and the impacts of floods on businesses. These analyses show that the significance of core capabilities of the smart flood resilience framework in helping emergency managers, city planners, public officials, responders, and volunteers to better cope with the impacts of catastrophic flooding events.

     
    more » « less
  4. Abstract Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents’ ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy , rapidity , and proximity . The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches. 
    more » « less
  5. This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process. 
    more » « less