Human-centric infrastructure resilience: Uncovering well-being risk disparity due to infrastructure disruptions in disasters

Inequality in infrastructure access and its association with health disparities

Economic, social and environmental infrastructure forms a fundamental pillar of societal development. Ensuring equitable access to infrastructure for all residents is crucial for achieving the Sustainable Development Goals, yet knowledge gaps remain in infrastructure accessibility and inequality and their associations with human health. Here we generate gridded maps of economic, social and environmental infrastructure distribution and apply population-weighted exposure models and mixed-effects regressions to investigate differences in population access to infrastructure and their health implications across 166 countries. The results reveal contrasting inequalities in infrastructure access across regions and infrastructure types. Global South countries experience only 50-80% of the infrastructure access of Global North countries, whereas their associated inequality levels are 9-44% higher. Both infrastructure access and inequality are linked to health outcomes, with this relationship being especially pronounced in economic infrastructure. These findings underscore the necessity of informed decision-making to rectify infrastructure disparities for promoting human well-being.

Disaster waste and debris clean-up decisions of government actors in the United States: social process and socio-material systems

In the United States, debris removal is one of the costliest and most time-consuming elements of disaster response and recovery. It is essential to reducing secondary environmental and health risks, and to community recovery and rebuilding. Analysis of debris removal and waste management, though, primarily treats it as a series of operational steps and technical decisions. In contrast, this article analyses disaster debris removal decision-making as a social process. We present the findings of an ethnographic study that engaged over 70 government actors from federal, state, local, and Tribal agencies in focus groups and interviews. By examining the experiences of these actors, who are central to debris removal decisions, this article identifies decision points that send waste down particular pathways from collection to final disposal. Three operational areas of concern that emerge from the analysis are: local control and capacity, cost and reimbursement, and balance between urgency and sustainability. This article shows how social processes in particular socio-material systems shape these decisions, such as the interplay of waste and disaster institutional arrangements. Finally, it shares practical implications for social process workarounds to operational challenges, such as interagency and interlevel relationships, that can support on-the-ground decision-making.

Understanding the spatial disparity in socio-economic recovery of coastal communities following typhoon disasters

The increasing risk of climate change in the Anthropocene underscores the importance and urgency of enhancing resilience to climate-related disasters. However, the assessment of resilience to disasters with traditional statistical data is spatially inexplicit and timeliness inadequate, and the determinants of resilience remain unclear. In this study, we employed spatially detailed daily nighttime light images to assess socio-economic disturbance and track near real-time recovery of coastal communities in Southeast China following super typhoon Meranti. Furthermore, we constructed a "exposure-sensitivity-adaptive capacity" framework to explore the role of key factors in shaping spatiotemporal patterns of recovery. Our case study showed a significant spatial disparity in socio-economic recovery in the post-typhoon period. Low-urbanized areas recovered relatively rapidly with the weakest socio-economic disturbance they suffered, and middle-urbanized areas experienced the slowest recovery despite the disruption being moderate. Remarkably, high-urbanized areas were the most severely impacted by the typhoon but recovered fast. The exposure to hazard, socio-economic sensitivity, and adaptive capacity in communities explained well the spatial disparity of resilience to the typhoon. Maximum wind speed, percentage of the elderly, and percentage of low-income population significantly negatively correlated with resilience, whereas commercial activity intensity, spatial accessibility of hospitals, drainage capacity, and percentage of green open space showed significantly positive relationships with resilience. Notably, the effects of key factors on resilience were spatially heterogeneous. For instance, maximum wind speed exhibited the strongest influence on resilience in middle-urbanized areas, while the effect of commercial activity intensity was most pronounced in low-urbanized areas. Conversely, spatial accessibility of hospitals and drainage capacity showed the strongest influence in high-urbanized areas. Our study highlights the necessity of linking post-disaster recovery with intensity of hazard, socio-economic sensitivity, and adaptive capacity to understand community resilience for better disaster risk reduction.

Disparities in access and association between access to critical facilities during day-to-day and disrupted access as a result of storm extreme weather events

This study examines the relationship between households' access to critical facilities day-to-day and during weather-related extreme events. Despite a robust understanding of both day-to-day access and access during disasters, the interplay between the two remains unclear. To bridge this knowledge gap, we propose a novel empirical approach, using a Texas statewide household survey (N = 810). The survey evaluates day-to-day and past events access, exploring the experiences of respondents during multiple recent disasters, rather than focusing on a specific hazard. Using correlation analysis, we examined various access-related factors such as day-to-day trip duration, alternative trip duration, and loss of access during past events. Additionally, we evaluated the association between access-related factors and sociodemographic characteristics such as income, ethnicity, and urban status. The results indicate: (1) daily trip duration to critical facilities is associated with disrupted access during storm events, and (2) disparities persist during both day-to-day times and during extreme events. These results bring new insights to the existing body of knowledge on day-to-day access and access during disasters. The findings provide scientifically grounded evidence to city managers and planners, emphasizing the need for equitable distribution of facilities to enhance access to essential facilities both in daily life and during extreme weather-related events.

Revealing hazard-exposure heterophily as a latent characteristic of community resilience in social-spatial networks

We present a latent characteristic in socio-spatial networks, hazard-exposure heterophily, to capture the extent to which populations with dissimilar hazard exposure could assist each other through social ties. Heterophily is the tendency of unlike individuals to form social ties. Conversely, populations in hazard-prone spatial areas with significant hazard-exposure similarity, homophily, would lack sufficient resourcefulness to aid each other to lessen the impact of hazards. In the context of the Houston metropolitan area, we use Meta's Social Connectedness data to construct a socio-spatial network in juxtaposition with flood exposure data from National Flood Hazard Layer to analyze flood hazard exposure of spatial areas. The results reveal the extent and spatial variation of hazard-exposure heterophily in the study area. Notably, the results show that lower-income areas have lower hazard-exposure heterophily possibly caused by income segregation and the tendency of affordable housing development to be located in flood zones. Less resourceful social ties in hazard-prone areas due to their high-hazard-exposure homophily may inhibit low-income areas from better coping with hazard impacts and could contribute to their slower recovery. Overall, the results underscore the significance of characterizing hazard-exposure heterophily in socio-spatial networks to reveal community vulnerability and resilience to hazards.

Impact of public transport disruption on access to healthcare facility and well-being during the COVID-19 pandemic: A qualitative case study in Metro Manila, Philippines

The COVID-19 pandemic has forced many governments to halt public transport operations. A consequence of such disruption is the reduction in access to critical facilities by individuals who rely on public transport for their daily mobility. We investigate the impact disparities caused by the restriction of public transportation on the access of healthcare workers and patients to healthcare facilities during the COVID-19 pandemic. Metro Manila is an appropriate case study site because the duration of suspension of public transport in the mega-city is one of the longest in the world. The prolonged duration of the lockdown could have devastating impacts on the well-being of individuals who are reliant on public transport to access essential services. Guided by the Yin-Eisenhardt approach to qualitative research, we examined the data from 55 individuals using within-case and cross-case analyses iteratively for the purpose of building a model on the impact of change in access due to public transport disruption on well-being. We mobilized constructs and concepts known in the literature, such as well-being, access, disruption, resistance, resilience, and vulnerability, in developing our two-step conceptual model. Given the profound impact of the prolonged and system-wide suspension of public transport on the well-being of individuals, it is necessary to provide sufficient public transport and active transport infrastructure and services that can cover their mobility needs. The two-step conceptual model from this study can provide guidance on specific policy interventions.

The impact of Hurricane Maria on individuals living with non-communicable disease in Puerto Rico: the experience of 10 communities

Background

Coinciding with the rising non-communicable disease (NCD) prevalence worldwide is the increasing frequency and severity of natural hazards. Protecting populations with NCDs against natural hazards is ever more pressing given their increased risk of morbidity and mortality in disaster contexts. This investigation examined Hurricane Maria’s impacts across ten lower SES municipalities in Puerto Rico with varying community characteristics and hurricane impacts to understand experiences of supporting individuals with NCD management in the six-month period following the hurricane.

Methods

We conducted 40 qualitative interviews with mayors, first responders, faith leaders, community leaders, and municipal employees from 10 municipalities in Puerto Rico. Using QSR NVivo software, we deductively and inductively coded interview transcripts and undertook thematic analysis to characterize community-level hurricane impact and consequences for NCD management, and to identify convergent and divergent themes.

Results

Damages to infrastructure, including healthcare facilities and roadways, complicated the provision of timely health care for NCDs, patient transport, and pharmaceutical/medical supply chain continuity. Lengthy power outages at both healthcare facilities and private residences were barriers to healthcare service delivery, use of medical equipment, and storage of prescription medications with refrigeration, and led to a widespread mental health crisis. Cascading failures such as fuel shortages further compounded these challenges. The consequences of these impacts included the reported exacerbation of health conditions and loss of life among NCD patients.

Conclusions

Study findings identify contributors to morbidity and mortality among individuals with NCDs following Hurricane Maria. With the growing frequency of catastrophic disasters from natural hazards, the experiences of communities that endured these impacts offer important lessons regarding policies and practices to better support community disaster resilience and address the evolving preparedness needs of NCD patients.

Human activity and mobility data reveal disparities in exposure risk reduction indicators among socially vulnerable populations during COVID-19 for five U.S. metropolitan cities

Non-pharmacologic interventions (NPIs) promote protective actions to lessen exposure risk to COVID-19 by reducing mobility patterns. However, there is a limited understanding of the underlying mechanisms associated with reducing mobility patterns especially for socially vulnerable populations. The research examines two datasets at a granular scale for five urban locations. Through exploratory analysis of networks, statistics, and spatial clustering, the research extensively investigates the exposure risk reduction after the implementation of NPIs to socially vulnerable populations, specifically lower income and non-white populations. The mobility dataset tracks population movement across ZIP codes for an origin-destination (O-D) network analysis. The population activity dataset uses the visits from census block groups (cbg) to points-of-interest (POIs) for network analysis of population-facilities interactions. The mobility dataset originates from a collaboration with StreetLight Data, a company focusing on transportation analytics, whereas the population activity dataset originates from a collaboration with SafeGraph, a company focusing on POI data. Both datasets indicated that low-income and non-white populations faced higher exposure risk. These findings can assist emergency planners and public health officials in comprehending how different populations are able to implement protective actions and it can inform more equitable and data-driven NPI policies for future epidemics.

Toward human-centric urban infrastructure: Text mining for social media data to identify the public perception of COVID-19 policy in transportation hubs

The COVID-19 pandemic has made transportation hubs vulnerable to public health risks. In response, policies using nonpharmaceutical interventions have been implemented, changing the way individuals interact within these facilities. However, the impact of building design and operation on policy efficacy is not fully discovered, making it critical to investigate how these policies are perceived and complied in different building spaces. Therefore, we investigate the spatial drivers of user perceptions and policy compliance in airports. Using text mining, we analyze 103,428 Google Maps reviews of 64 major hub airports in the US to identify representative topics of passenger concerns in airports (i.e., Staff, Shop, Space, and Service). Our results show that passengers express having positive experiences with Staff and Shop, but neutral or negative experiences with Service and Space, which indicates how building design has impacted policy compliance and the vulnerability of health crises. Furthermore, we discuss the actual review comments with respect to 1) spatial design and planning, 2) gate assignment and operation, 3) airport service policy, and 4) building maintenance, which will construct the foundational knowledge to improve the resilience of transportation hubs to future health crises.

Natural Disasters Related Traumatic Injuries/Fatalities in the United States and Their Impact on Emergency Preparedness Operations

INTRODUCTION

From 2015 to 2019, the United States experienced a 17% increase in weather-related disasters.

OBJECTIVES

We aimed to study the patterns of natural disaster-related traumatic injuries and fatalities across the United States from 2014 to 2019 and to provide recommendations that can serve to mitigate the impact these natural disasters have on trauma patient morbidity and mortality.

METHODS

A retrospective analysis of the National Safety Council (2014-2019) of natural disaster-related injuries and fatalities was conducted. Descriptive statistics and independent-samples t tests were performed, with significance defined as p < .05.

RESULTS

Floods produced significantly more mean fatalities per year than tornadoes (118 vs. 33; 95% CI [32.0, 139.0]), wildfires (118 vs. 43, 95% CI [24.8, 155.6]), hurricanes (118 vs. 13, 95% CI [51.5, 159.2]), and tropical storms (118 vs. 15, 95% CI [48.8, 158.2]). Tornadoes produced significantly more mean injuries per year than floods (528 vs. 43, 95% CI [255.9, 715.8]), wildfires (528 vs. 69, 95% CI [227.1, 691.2]), hurricanes (528 vs. 26, 95% CI [270.1, 734.2]), and tropical storms (528 vs. 4, 95% CI [295.9, 753.5]). Southern states experienced greater disaster-related morbidity and mortality over the 6-year study period than other regions with 2,752 injuries and 771 fatalities.

CONCLUSIONS

The incidence of traumatic injuries and fatalities related to certain natural disasters in the United States has significantly increased from 2014 to 2019. Hospital leaders, public health, emergency preparedness personnel, and policy makers must collaborate to implement protocols and guidelines that ensure adequate training, supplies, and personnel to maintain trauma surge capacity, improve emergency preparedness response, and reduce associated morbidity and mortality.

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

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.

Detecting Early-Warning Signals in Time Series of Visits to Points of Interest to Examine Population Response to COVID-19 Pandemic

The objective of this paper is to examine population response to COVID-19 and associated policy interventions through detecting early-warning signals in time series of visits to points of interest (POIs). Complex systems, such as cities, would demonstrate early-warning signals (e.g., increased autocorrelation and standard deviation) when they approach phase transitions responding to external perturbation, such as crises, policy changes, and human behavior changes. In urban systems, population visits to POIs, such as restaurants, museums, and hospitals, represent a state of cities as complex systems. These states may undergo phase transitions due to population response to pandemic risks and intervention policies (e.g., social distancing and shelter-in-place orders). In this study, we conducted early-warning signal detection on population visits to POIs to examine population response to pandemic risks, and we evaluated time lags between detected early-warning dates and dates of first cases and policy interventions. We examined two early-warning signals, the increase of autocorrelation at-lag-1 and standard deviation, in time series of population visits to POIs in 17 metropolitan cities in the United States of America. We examined visits to grouped POIs according to two categories of essential services and non-essential services. The results show that: (1) early-warning signals for population response to COVID-19 were detected between February 14 and March 11, 2020 in 17 cities; (2) detected population response had started prior to shelter-in-place orders in 17 cities; (3) early-warning signals detected from the essential POIs visits appeared earlier than those from non-essential POIs; and 4) longer time lags between detected population response and shelter-in-place orders led to a less decrease in POI visits. The results show the importance of detecting early-warning signals during crises in cities as complex systems. Early-warning signals could provide important insights regarding the timing and extent of population response to crises to inform policymakers.

Disparate patterns of movements and visits to points of interest located in urban hotspots across US metropolitan cities during COVID-19

We examined the effect of social distancing on changes in visits to urban hotspot points of interest. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped census-block-group to point-of-interest (POI) movement networks in 16 cities in the United States. We adopted a modified coarse-grain approach to examine patterns of visits to POIs among hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in hotspot POI visitors. Sixteen cities were divided into two categories using a time series clustering method. In one category, which includes the cities of San Francisco, Seattle and Chicago, we observed a considerable decrease in hotspot POI visitors, while in another category, including the cities of Austin, Houston and San Diego, the visitors to hotspots did not greatly decrease. While all the cities exhibited overall decreased visitors to POIs, one category maintained the proportion of visitors to hotspot POIs. The proportion of visitors to some POIs (e.g. restaurants) remained stable during the social distancing period, while some POIs had an increased proportion of visitors (e.g. grocery stores). We also identified POIs with significant flux-in changes, indicating that related businesses were greatly affected by social distancing. The study was limited to 16 metropolitan cities in the United States. The proposed methodology could be applied to digital trace data in other cities and countries to study the patterns of movements to POIs during the COVID-19 pandemic.