Lei Zou
Biographic Data
| ID | 3635458 |
|---|---|
| NAME | Lei Zou |
| GIVEN NAMES | Lei |
| FAMILY NAME | Zou |
| SIGNATURE | ZOU L |
| AFFILIATIONS | Texas A&M University |
| ORCID | 0000-0001-6206-3558 |
| VERIFIED | Yes |
| TOTAL WORKS | 27 |
| TOTAL CITATIONS | 46 |
| WORKS AS AUTHOR | 27 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 4 |
Spatio-Temporal Analysis of Regional Fire Service Accessibility for Underground Parking Garages
Underground parking garages in high-density megacities are high-risk environments where strong confinement and large fire loads pose severe safety threats. In this study, an evaluation model is proposed based on the entropy weight method combined with dynamic traffic conditions to determine the regional fire service accessibility index Cj. Taking Shenzhen, a megacity in China, as the study area, POI data were used to identify 510 fire stations as…
FlowsDT
Communities worldwide increasingly confront flood hazards intensified by climate change, urban expansion, and environmental degradation. Addressing these challenges requires real-time flood analysis, precise flood forecasting, and robust risk communications with stakeholders to implement efficient mitigation strategies. Recent advances in hydrodynamic modeling and digital twins afford new opportunities for high-resolution flood simulation and vis…
Rapid Disaster Response and Damage Estimation with Social Media and Pretrained Large Language Models
Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models
Street-view images offer unique advantages for disaster damage estimation as they capture impacts from a visual perspective and provide detailed, on-the-ground insights. Despite several investigations attempting to analyze street-view images for damage estimation, they mainly focus on using post-disaster images. The potential of time-series street-view images remains underexplored. Pre-disaster images provide valuable benchmarks for accurate dama…
Understanding the disparate impacts of the 2021 Texas winter storm and power outages through mobile phone location data and nighttime light images
Prime
Deciphering spatial-temporal dynamics of flood exposure in the United States
Economic impacts of floods in China and adaptation strategies under climate change
Climate change has intensified the frequency and severity of heavy rainfall and flooding in China, posing serious threats to economic and social stability. Over the past 40 years, the increasing trend of these events has led to significant economic losses, highlighting the urgent need to develop effective adaptation strategies in both research and policy. This study used the Adaptive Regional Input–Output model to comprehensively assess the econo…
Overlooked voices under strict lockdown
The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase w…
Sensing the pulse of the pandemic
Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users' reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without proper adjustment will lead to overlooking the voices of digitally marginalized communities …
Social media for emergency rescue
Predicting Flash Flood Economic Damage at the Community Scale
Rainfall-induced flash floods are characterized by their rapid onset and small spatial scale. With little lead time for warning, floodwater can accumulate rapidly and its force can damage roads, swamp houses, destroy bridges, and scour out channels. Having data-driven estimates of potential economic losses from flash floods (before they occur) helps authorities make informed decisions about planning and prioritizing mitigation projects. This arti…
Algorithmic uncertainties in geolocating social media data for disaster management
The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. How…
A Machine Learning Approach for Detecting Rescue Requests from Social Media
Hurricane Harvey in 2017 marked an important transition where many disaster victims used social media rather than the overloaded 911 system to seek rescue. This article presents a machine-learning-based detector of rescue requests from Harvey-related Twitter messages, which differentiates itself from existing ones by accounting for the potential impacts of ZIP codes on both the preparation of training samples and the performance of different mach…
VictimFinder
Impact assessment of climate change and urbanization on the nonstationarity of extreme precipitation
Impact of the Covid-19 pandemic on travel behavior
Perioperative Exercise Intention and Influencing Factors
Objectives: This study aimed to evaluate the level and factors affecting the perioperative exercise intention in China. Design: This study was a cross-sectional survey in Southwest China. Methods: Four hundred and ninety nine participants were randomly sampled in eight medical centers from November 23, 2020 to November 27, 2020. The survey included sociodemographic information and a 24-item modified questionnaire, which aimed to evaluate the atti…
Twitter Use in Hurricane Isaac and Its Implications for Disaster Resilience
Disaster resilience is the capacity of a community to “bounce back” from disastrous events. Most studies rely on traditional data such as census data to study community resilience. With increasing use of social media, new data sources such as Twitter could be utilized to monitor human response during different phases of disasters to better understand resilience. An important research question is: Does Twitter use correlate with disaster resilienc…
Economics over Risk
We analyzed the migration consideration factors of residents from the Mississippi River Delta in southern Louisiana, which is under the influence of rising sea levels, subsiding land, and increasing flood risks. Through the use of a telephone survey of 1,125 adult individuals in twenty-four parishes, we gathered the demographic data and flood risk experiences and perceptions of each respondent. The data were analyzed through descriptive statistic…
Beyond big data
A synthesis of disaster resilience measurement methods and indices
Community Resilience to Drought Hazard in the South-Central United States
Drought is a hazard that inflicts costly damage to agricultural, hydrologic, and ecological systems and affects human health and prosperity. A comprehensive assessment of resilience to the drought hazard in various communities and an identification of the main variables that affect resilience is crucial to coping with the hazard and promoting resilience. This study assessed the community resilience to drought hazards of all 503 counties of Arkans…
Modeling the Dynamics of Community Resilience to Coastal Hazards Using a Bayesian Network
Studies on how variables of community resilience to natural hazards interact as a system that affects the final resilience (i.e., their dynamical linkages) have rarely been conducted. Bayesian network (BN), which represents the interdependencies among variables in a graph while expressing the uncertainty in the form of probability distributions, offers an effective way to investigate the interactions among different resilience components and addr…
Mining Twitter Data for Improved Understanding of Disaster Resilience
Coastal communities faced with multiple hazards have shown uneven responses and behaviors. These responses and behaviors could be better understood by analyzing real-time social media data through categorizing them into the three phases of the emergency management: preparedness, response, and recovery. This study analyzes the spatial–temporal patterns of Twitter activities during Hurricane Sandy, which struck the U.S. Northeast on 29 October 2012…
Mining Twitter Data for Improved Understanding of Disaster Resilience
Coastal communities faced with multiple hazards have shown uneven responses and behaviors. These responses and behaviors could be better understood by analyzing real-time social media data through categorizing them into the three phases of the emergency management: preparedness, response, and recovery. This study analyzes the spatial–temporal patterns of Twitter activities during Hurricane Sandy, which struck the U.S. Northeast on 29 October 2012…
Impact of the Covid-19 pandemic on travel behavior
Beyond big data
Changes in Exposure to Flood Hazards in the United States
This article conducts a national, county-based assessment of the changes in population and urban areas in high-risk flood zones from 2001 to 2011 in the contiguous United States. The U.S. Federal Emergency Management Agency's (FEMA) 100-year flood maps, land cover data, and census data were used to extract the proportion of developed (urban) land in flood zones by county at the two time points, and indexes of difference were calculated. Local Mor…
Modeling the Dynamics of Community Resilience to Coastal Hazards Using a Bayesian Network
Studies on how variables of community resilience to natural hazards interact as a system that affects the final resilience (i.e., their dynamical linkages) have rarely been conducted. Bayesian network (BN), which represents the interdependencies among variables in a graph while expressing the uncertainty in the form of probability distributions, offers an effective way to investigate the interactions among different resilience components and addr…
Community Resilience to Drought Hazard in the South-Central United States
Drought is a hazard that inflicts costly damage to agricultural, hydrologic, and ecological systems and affects human health and prosperity. A comprehensive assessment of resilience to the drought hazard in various communities and an identification of the main variables that affect resilience is crucial to coping with the hazard and promoting resilience. This study assessed the community resilience to drought hazards of all 503 counties of Arkans…
Overlooked voices under strict lockdown
The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase w…
A cyberinfrastructure for community resilience assessment and visualization
Disaster resilience is a major societal challenge. Cartography and GIS can contribute substantially to this research area. This paper describes a cyberinfrastructure for disaster resilience assessment and visualization for all counties in the United States. Aided by the Application Programming Interface-enabled web mapping and component-oriented web tools, the cyberinfrastructure is designed to better serve the US communities with comprehensive r…
Algorithmic uncertainties in geolocating social media data for disaster management
The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. How…
A cyberinfrastructure for community resilience assessment and visualization
Disaster resilience is a major societal challenge. Cartography and GIS can contribute substantially to this research area. This paper describes a cyberinfrastructure for disaster resilience assessment and visualization for all counties in the United States. Aided by the Application Programming Interface-enabled web mapping and component-oriented web tools, the cyberinfrastructure is designed to better serve the US communities with comprehensive r…
Changes in Exposure to Flood Hazards in the United States
This article conducts a national, county-based assessment of the changes in population and urban areas in high-risk flood zones from 2001 to 2011 in the contiguous United States. The U.S. Federal Emergency Management Agency's (FEMA) 100-year flood maps, land cover data, and census data were used to extract the proportion of developed (urban) land in flood zones by county at the two time points, and indexes of difference were calculated. Local Mor…
A synthesis of disaster resilience measurement methods and indices
Community Resilience to Drought Hazard in the South-Central United States
Drought is a hazard that inflicts costly damage to agricultural, hydrologic, and ecological systems and affects human health and prosperity. A comprehensive assessment of resilience to the drought hazard in various communities and an identification of the main variables that affect resilience is crucial to coping with the hazard and promoting resilience. This study assessed the community resilience to drought hazards of all 503 counties of Arkans…
Modeling the Dynamics of Community Resilience to Coastal Hazards Using a Bayesian Network
Studies on how variables of community resilience to natural hazards interact as a system that affects the final resilience (i.e., their dynamical linkages) have rarely been conducted. Bayesian network (BN), which represents the interdependencies among variables in a graph while expressing the uncertainty in the form of probability distributions, offers an effective way to investigate the interactions among different resilience components and addr…
Mining Twitter Data for Improved Understanding of Disaster Resilience
Coastal communities faced with multiple hazards have shown uneven responses and behaviors. These responses and behaviors could be better understood by analyzing real-time social media data through categorizing them into the three phases of the emergency management: preparedness, response, and recovery. This study analyzes the spatial–temporal patterns of Twitter activities during Hurricane Sandy, which struck the U.S. Northeast on 29 October 2012…
Beyond big data
Perioperative Exercise Intention and Influencing Factors
Objectives: This study aimed to evaluate the level and factors affecting the perioperative exercise intention in China. Design: This study was a cross-sectional survey in Southwest China. Methods: Four hundred and ninety nine participants were randomly sampled in eight medical centers from November 23, 2020 to November 27, 2020. The survey included sociodemographic information and a 24-item modified questionnaire, which aimed to evaluate the atti…
Twitter Use in Hurricane Isaac and Its Implications for Disaster Resilience
Disaster resilience is the capacity of a community to “bounce back” from disastrous events. Most studies rely on traditional data such as census data to study community resilience. With increasing use of social media, new data sources such as Twitter could be utilized to monitor human response during different phases of disasters to better understand resilience. An important research question is: Does Twitter use correlate with disaster resilienc…
Economics over Risk
We analyzed the migration consideration factors of residents from the Mississippi River Delta in southern Louisiana, which is under the influence of rising sea levels, subsiding land, and increasing flood risks. Through the use of a telephone survey of 1,125 adult individuals in twenty-four parishes, we gathered the demographic data and flood risk experiences and perceptions of each respondent. The data were analyzed through descriptive statistic…
A Machine Learning Approach for Detecting Rescue Requests from Social Media
Hurricane Harvey in 2017 marked an important transition where many disaster victims used social media rather than the overloaded 911 system to seek rescue. This article presents a machine-learning-based detector of rescue requests from Harvey-related Twitter messages, which differentiates itself from existing ones by accounting for the potential impacts of ZIP codes on both the preparation of training samples and the performance of different mach…
VictimFinder
Impact assessment of climate change and urbanization on the nonstationarity of extreme precipitation
Impact of the Covid-19 pandemic on travel behavior
Social media for emergency rescue
Predicting Flash Flood Economic Damage at the Community Scale
Rainfall-induced flash floods are characterized by their rapid onset and small spatial scale. With little lead time for warning, floodwater can accumulate rapidly and its force can damage roads, swamp houses, destroy bridges, and scour out channels. Having data-driven estimates of potential economic losses from flash floods (before they occur) helps authorities make informed decisions about planning and prioritizing mitigation projects. This arti…
Algorithmic uncertainties in geolocating social media data for disaster management
The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. How…
Understanding the disparate impacts of the 2021 Texas winter storm and power outages through mobile phone location data and nighttime light images
Prime
Deciphering spatial-temporal dynamics of flood exposure in the United States
Economic impacts of floods in China and adaptation strategies under climate change
Climate change has intensified the frequency and severity of heavy rainfall and flooding in China, posing serious threats to economic and social stability. Over the past 40 years, the increasing trend of these events has led to significant economic losses, highlighting the urgent need to develop effective adaptation strategies in both research and policy. This study used the Adaptive Regional Input–Output model to comprehensively assess the econo…
Overlooked voices under strict lockdown
The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase w…
Sensing the pulse of the pandemic
Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users' reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without proper adjustment will lead to overlooking the voices of digitally marginalized communities …
Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models
Street-view images offer unique advantages for disaster damage estimation as they capture impacts from a visual perspective and provide detailed, on-the-ground insights. Despite several investigations attempting to analyze street-view images for damage estimation, they mainly focus on using post-disaster images. The potential of time-series street-view images remains underexplored. Pre-disaster images provide valuable benchmarks for accurate dama…
Spatio-Temporal Analysis of Regional Fire Service Accessibility for Underground Parking Garages
Underground parking garages in high-density megacities are high-risk environments where strong confinement and large fire loads pose severe safety threats. In this study, an evaluation model is proposed based on the entropy weight method combined with dynamic traffic conditions to determine the regional fire service accessibility index Cj. Taking Shenzhen, a megacity in China, as the study area, POI data were used to identify 510 fire stations as…
Geography (18 works) · Computer Science (16 works) · Disaster Management and Resilience (14 works) · Flood Risk Assessment and Management (11 works) · Environmental Science (9 works) · Cartography (8 works) · Political science (8 works) · Social media (8 works) · Flood myth (7 works) · World Wide Web (7 works)