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Abstract
Brazil presents the highest number of lightning-related deaths in the world. This study aimed to identify the key victims’ characteristics associated with such fatalities in Brazil and to develop a model that predicts the number of deaths as function of the victims’ data. The dataset provided by the Department of Informatics of the Unified Health System in Brazil (DATASUS) was analyzed and machine learning regression techniques were applied. The gradien-boosting regressor (GBR) model was found to be the most effective, achieving a prediction accuracy of 97%. Through the analysis of 34 initial variables, 10 variables were identified as having the greatest influence on the model’s outcomes. These variables included race, gender, age group, occupational accidents, education, and location of death. Understanding these characteristics is crucial for implementing targeted prevention and safety strategies in various regions, helping to mitigate the risk of lightning-related deaths worldwide. Additionally, the methodology used in this study can serve as a framework for similar research in different locations, allowing for the identification of important factors specific to each region. By adapting the machine learning regression techniques and incorporating local datasets, researchers can gain valuable insights into the determinants of lightning-related fatalities, enabling the development of effective prevention and safety measures tailored to specific geographical areas.
Significance Statement
This study presents a machine learning approach using the gradient-boosting regressor (GBR) method to estimate the weekly number of lightning-related deaths with an impressive 97% prediction accuracy. The research includes a comprehensive analysis of various factors, such as race, gender, age group, occupational accidents, education, and location of death, providing valuable insights for targeted preventive strategies and safety measures. The findings significantly contribute to understanding lightning-related fatalities in Brazil. The proposed machine learning model demonstrates a robust and accurate fit to the data, allowing for a comprehensive understanding of patterns and underlying trends in lightning fatalities.
Abstract
Brazil presents the highest number of lightning-related deaths in the world. This study aimed to identify the key victims’ characteristics associated with such fatalities in Brazil and to develop a model that predicts the number of deaths as function of the victims’ data. The dataset provided by the Department of Informatics of the Unified Health System in Brazil (DATASUS) was analyzed and machine learning regression techniques were applied. The gradien-boosting regressor (GBR) model was found to be the most effective, achieving a prediction accuracy of 97%. Through the analysis of 34 initial variables, 10 variables were identified as having the greatest influence on the model’s outcomes. These variables included race, gender, age group, occupational accidents, education, and location of death. Understanding these characteristics is crucial for implementing targeted prevention and safety strategies in various regions, helping to mitigate the risk of lightning-related deaths worldwide. Additionally, the methodology used in this study can serve as a framework for similar research in different locations, allowing for the identification of important factors specific to each region. By adapting the machine learning regression techniques and incorporating local datasets, researchers can gain valuable insights into the determinants of lightning-related fatalities, enabling the development of effective prevention and safety measures tailored to specific geographical areas.
Significance Statement
This study presents a machine learning approach using the gradient-boosting regressor (GBR) method to estimate the weekly number of lightning-related deaths with an impressive 97% prediction accuracy. The research includes a comprehensive analysis of various factors, such as race, gender, age group, occupational accidents, education, and location of death, providing valuable insights for targeted preventive strategies and safety measures. The findings significantly contribute to understanding lightning-related fatalities in Brazil. The proposed machine learning model demonstrates a robust and accurate fit to the data, allowing for a comprehensive understanding of patterns and underlying trends in lightning fatalities.
Abstract
This study introduces the urban heat island vulnerability index (UHIVI) for Recife, Brazil, the center of the most populated metropolitan area in the Northeast region. The index, encompassing sensitivity, adaptive capacity, and exposure, integrates demographic data through factor analysis to derive a social vulnerability index (SVI). Urban heat island (UHI) intensity data addresses exposure, enabling a comprehensive analysis of both the physical and social dimensions of the city. Results reveal heightened UHI exposure in the city center and coastal areas, correlating with higher urbanization density. However, populations in most areas of these regions demonstrated higher adaptive capacities, translating to lower UHI vulnerability. Conversely, less-discussed areas in traditional UHI approaches, with limited adaptive capacity and heightened sensitivity, emerge, shedding light on previously overlooked urban vulnerabilities. Regions near the city center featuring irregular settlements prove most susceptible to UHI. Illiteracy, aging demographics, and local environmental conditions emerge as the three main factors contributing to UHIVI. The index’s application unveils spatial complexities and inequalities, offering urban planners a nuanced understanding of the city. This comprehensive insight aids in policy development and decision-making, empowering planners to address urban disparities effectively. The UHIVI thus emerges as a valuable tool for understanding the challenges of urban planning, fostering more resilient and equitable urban development.
Abstract
This study introduces the urban heat island vulnerability index (UHIVI) for Recife, Brazil, the center of the most populated metropolitan area in the Northeast region. The index, encompassing sensitivity, adaptive capacity, and exposure, integrates demographic data through factor analysis to derive a social vulnerability index (SVI). Urban heat island (UHI) intensity data addresses exposure, enabling a comprehensive analysis of both the physical and social dimensions of the city. Results reveal heightened UHI exposure in the city center and coastal areas, correlating with higher urbanization density. However, populations in most areas of these regions demonstrated higher adaptive capacities, translating to lower UHI vulnerability. Conversely, less-discussed areas in traditional UHI approaches, with limited adaptive capacity and heightened sensitivity, emerge, shedding light on previously overlooked urban vulnerabilities. Regions near the city center featuring irregular settlements prove most susceptible to UHI. Illiteracy, aging demographics, and local environmental conditions emerge as the three main factors contributing to UHIVI. The index’s application unveils spatial complexities and inequalities, offering urban planners a nuanced understanding of the city. This comprehensive insight aids in policy development and decision-making, empowering planners to address urban disparities effectively. The UHIVI thus emerges as a valuable tool for understanding the challenges of urban planning, fostering more resilient and equitable urban development.
Abstract
In recent decades, changes in precipitation, temperature, and air circulation patterns have led to increases in the occurrences of extreme weather events. These events can have devastating effects on communities causing destruction to property and croplands, as well as negative impacts on public health. As changes in the climate are projected to continue throughout the remainder of the twenty-first century, the ability for a community to plan for extreme weather events is essential to its survival. In this paper, we introduce a new index for examining the potential impacts of climate extremes on community resilience throughout the conterminous United States at the county level. We use an established disaster resilience index (baseline resilience indicators for communities) together with a revised version of the U.S. climate extremes index to create a combined measure of climate resilience—the climate extremes resilience index (CERI). To demonstrate the index, we test it on the 2021 Pacific Northwest heat wave, a 1000-yr weather event made 150 times as likely by climate change. To promote the use of the index, we also introduce a Google Earth Engine web app to calculate and map the CERI for the CONUS. By developing a web application for calculating the CERI, we expand the use of climate-resilience indices beyond theoretical applications. We anticipate that this tool and the CERI could be useful for policy makers to plan for climate-related disasters, as well as help the public with understanding and visualizing the impacts of extreme climatic events.
Abstract
In recent decades, changes in precipitation, temperature, and air circulation patterns have led to increases in the occurrences of extreme weather events. These events can have devastating effects on communities causing destruction to property and croplands, as well as negative impacts on public health. As changes in the climate are projected to continue throughout the remainder of the twenty-first century, the ability for a community to plan for extreme weather events is essential to its survival. In this paper, we introduce a new index for examining the potential impacts of climate extremes on community resilience throughout the conterminous United States at the county level. We use an established disaster resilience index (baseline resilience indicators for communities) together with a revised version of the U.S. climate extremes index to create a combined measure of climate resilience—the climate extremes resilience index (CERI). To demonstrate the index, we test it on the 2021 Pacific Northwest heat wave, a 1000-yr weather event made 150 times as likely by climate change. To promote the use of the index, we also introduce a Google Earth Engine web app to calculate and map the CERI for the CONUS. By developing a web application for calculating the CERI, we expand the use of climate-resilience indices beyond theoretical applications. We anticipate that this tool and the CERI could be useful for policy makers to plan for climate-related disasters, as well as help the public with understanding and visualizing the impacts of extreme climatic events.
Abstract
Domestic climate migration is likely to increase in the future, but we know little about public perceptions and attitudes about climate migrants and migration. Understanding how perceptions and attitudes are formed is a critical task in assessing public support for assistance policies and developing effective messaging campaigns. In this paper, we aim to better understand how the U.S. public perceives domestic climate migrants. We use novel survey data to identify the relationship between climate change risk perceptions and awareness of “climate migrants,” belief that domestic climate migration is currently happening in the United States, perceived voluntariness of domestic climate migrant relocation, and support for the development of assistance programs for domestic climate migrants. We utilize a large, nationally representative panel of U.S. adults (N = 4074) collected over three waves in 2022. We find that climate change risk perceptions and perceptions of whether migration is voluntary are key drivers of perceptions and attitudes toward domestic climate migrants. We provide key suggestions to policy makers and decision-makers to improve outcomes for host and migrant communities.
Significance Statement
This study illuminates factors that influence the how the public forms perceptions and attitudes about domestic climate migrants in the United States. For the first time, we offer insight into the drivers of public opinion toward domestic climate migrants and migration. Our results indicate that the various perceptions of climate migrants are largely driven by preexisting climate change risk perceptions and respondent characteristics. Our findings create a new connection with the existing literature on climate change risk perceptions and offer an opportunity for decision-makers and policy makers to create effective messaging campaigns on topics related to domestic climate migration in the United States.
Abstract
Domestic climate migration is likely to increase in the future, but we know little about public perceptions and attitudes about climate migrants and migration. Understanding how perceptions and attitudes are formed is a critical task in assessing public support for assistance policies and developing effective messaging campaigns. In this paper, we aim to better understand how the U.S. public perceives domestic climate migrants. We use novel survey data to identify the relationship between climate change risk perceptions and awareness of “climate migrants,” belief that domestic climate migration is currently happening in the United States, perceived voluntariness of domestic climate migrant relocation, and support for the development of assistance programs for domestic climate migrants. We utilize a large, nationally representative panel of U.S. adults (N = 4074) collected over three waves in 2022. We find that climate change risk perceptions and perceptions of whether migration is voluntary are key drivers of perceptions and attitudes toward domestic climate migrants. We provide key suggestions to policy makers and decision-makers to improve outcomes for host and migrant communities.
Significance Statement
This study illuminates factors that influence the how the public forms perceptions and attitudes about domestic climate migrants in the United States. For the first time, we offer insight into the drivers of public opinion toward domestic climate migrants and migration. Our results indicate that the various perceptions of climate migrants are largely driven by preexisting climate change risk perceptions and respondent characteristics. Our findings create a new connection with the existing literature on climate change risk perceptions and offer an opportunity for decision-makers and policy makers to create effective messaging campaigns on topics related to domestic climate migration in the United States.
Abstract
During peak disease transmission in 2021, the compounding threat posed by the pandemic and hurricane season required coastal states to understand evacuation behaviors during a major hurricane to inform the planning process. While research relating to hurricane evacuation behavior and perceptions of risk has increased since the start of the pandemic, there is minimal understanding of how perceptions have changed now the COVID-19 vaccine is available. A total of 1075 individuals across seven U.S. coastal states participated in a study on evacuation intentions postvaccine availability. Findings revealed that most survey participants (50.9%) preferred to stay home if a major hurricane threatened their area, and only 3.9% would evacuate to a public shelter. Approximately half (56.2%) of individuals viewed the risk of being in a shelter as more dangerous than enduring hurricane hazards. When considering shelter use, nearly half of respondents (49.4%) stated they would evacuate to a shelter before the pandemic; now, only one-third (34.3%) would consider evacuating to a shelter during the pandemic. Statistically significant findings include the relationship between those who lived in evacuation zones A or B (25.5%) and the choice to shelter in place at home (40.5%) or evacuate to a hotel (36.9%). There was a statistically significant relationship between the level of education and choosing to evacuate to a hotel. Additionally, the influence of pet ownership on evacuation decision-making was found to be statistically significant. Officials can use the results of this study to strengthen community preparedness and planning strategies across diverse populations.
Abstract
During peak disease transmission in 2021, the compounding threat posed by the pandemic and hurricane season required coastal states to understand evacuation behaviors during a major hurricane to inform the planning process. While research relating to hurricane evacuation behavior and perceptions of risk has increased since the start of the pandemic, there is minimal understanding of how perceptions have changed now the COVID-19 vaccine is available. A total of 1075 individuals across seven U.S. coastal states participated in a study on evacuation intentions postvaccine availability. Findings revealed that most survey participants (50.9%) preferred to stay home if a major hurricane threatened their area, and only 3.9% would evacuate to a public shelter. Approximately half (56.2%) of individuals viewed the risk of being in a shelter as more dangerous than enduring hurricane hazards. When considering shelter use, nearly half of respondents (49.4%) stated they would evacuate to a shelter before the pandemic; now, only one-third (34.3%) would consider evacuating to a shelter during the pandemic. Statistically significant findings include the relationship between those who lived in evacuation zones A or B (25.5%) and the choice to shelter in place at home (40.5%) or evacuate to a hotel (36.9%). There was a statistically significant relationship between the level of education and choosing to evacuate to a hotel. Additionally, the influence of pet ownership on evacuation decision-making was found to be statistically significant. Officials can use the results of this study to strengthen community preparedness and planning strategies across diverse populations.
Abstract
Climate change has negatively affected agricultural productivity in Indonesia. This study conducted a bibliometric analysis of the literature on soil salinity caused by climate change, discussed the impact of soil salinity on Indonesian agriculture, examined various strategies for adaptation to salinity, and delivered some ideas for future research. An analysis of 39 identified Scopus articles related to farmers’ vulnerability, adaptation, and practices was carried out. This study was performed in November 2022 and employed Bibliometrix R package and VOSviewer software. Findings show that salinity has left Indonesia’s agriculture vulnerable to reduced food production, especially for small-scale farmers losing crop yields and land. Various adaptation measures have been initiated, such as restoring soil fertility and using saline-resistant varieties. Irrigation facilities improvements have also been carried out to reduce the risks of soil salinity expansion. Farmers also try social action measures, such as selling assets, borrowing money for daily needs, and even changing jobs. However, for farmers to survive and sustain their businesses, any such measures need to produce satisfactory results. A review of the existing literature reveals a lack of soil salinity studies in Indonesia, which simultaneously points to research gaps not only on the issue of the impact of salinity on income and the vulnerability of small farmers but also on the development of adaptation strategies to address salinity due to climate change.
Significance Statement
Soil salinization caused by climate change is a disastrous problem in Indonesia’s coastal areas that presents a major challenge to the productivity of rice agriculture and difficulties in addressing sustainable food security. To provide researchers with a clear understanding of the current emphasis and future trends in climate change–induced salinity research, systematically analyzing the relevant literature in the existing research area is necessary. The bibliometric analysis in this study shows that research on salinity due to climate change in Indonesia still needs to be completed. Further comprehensive studies to find a focus for managing coastal soil salinity are urgently required to reduce vulnerability and increase adaptation to salinity due to climate change.
Abstract
Climate change has negatively affected agricultural productivity in Indonesia. This study conducted a bibliometric analysis of the literature on soil salinity caused by climate change, discussed the impact of soil salinity on Indonesian agriculture, examined various strategies for adaptation to salinity, and delivered some ideas for future research. An analysis of 39 identified Scopus articles related to farmers’ vulnerability, adaptation, and practices was carried out. This study was performed in November 2022 and employed Bibliometrix R package and VOSviewer software. Findings show that salinity has left Indonesia’s agriculture vulnerable to reduced food production, especially for small-scale farmers losing crop yields and land. Various adaptation measures have been initiated, such as restoring soil fertility and using saline-resistant varieties. Irrigation facilities improvements have also been carried out to reduce the risks of soil salinity expansion. Farmers also try social action measures, such as selling assets, borrowing money for daily needs, and even changing jobs. However, for farmers to survive and sustain their businesses, any such measures need to produce satisfactory results. A review of the existing literature reveals a lack of soil salinity studies in Indonesia, which simultaneously points to research gaps not only on the issue of the impact of salinity on income and the vulnerability of small farmers but also on the development of adaptation strategies to address salinity due to climate change.
Significance Statement
Soil salinization caused by climate change is a disastrous problem in Indonesia’s coastal areas that presents a major challenge to the productivity of rice agriculture and difficulties in addressing sustainable food security. To provide researchers with a clear understanding of the current emphasis and future trends in climate change–induced salinity research, systematically analyzing the relevant literature in the existing research area is necessary. The bibliometric analysis in this study shows that research on salinity due to climate change in Indonesia still needs to be completed. Further comprehensive studies to find a focus for managing coastal soil salinity are urgently required to reduce vulnerability and increase adaptation to salinity due to climate change.
Abstract
Weather index insurance (WII) has long been advertised as a viable alternative to crop yield insurance. WII products were first developed to assist climate-vulnerable farmers from developing countries where establishing a well-structured crop insurance market is expressively difficult due to the poor transport infrastructure and the prevalence of sparsely distributed small-scale farms. In Brazil, the semiarid region stands out as the one that concentrates the ideal conditions for the implementation of a WII product since it houses thousands of climate-vulnerable farmers. With this in mind, we designed and priced a WII product for farmers from the semiarid region of Brazil and posteriorly investigated its risk efficiency. To do so, we first investigated crop yield responses to aridity, enabling the selection of locations for which the WII product was posteriorly assessed. Second, we grouped selected locations into specific contracts according to geographical proximity and evaluated each of these contracts to attest the risk efficiency of the proposed WII product using the method of stochastic efficiency with respect to a function (SERF), which identifies utility efficient alternatives for a range of risk attitudes. Our results show that the WII product may be effective in protecting farmers from adverse variations in production revenue, possibly being attractive for utility-maximizer farmers that are sufficiently risk averse.
Abstract
Weather index insurance (WII) has long been advertised as a viable alternative to crop yield insurance. WII products were first developed to assist climate-vulnerable farmers from developing countries where establishing a well-structured crop insurance market is expressively difficult due to the poor transport infrastructure and the prevalence of sparsely distributed small-scale farms. In Brazil, the semiarid region stands out as the one that concentrates the ideal conditions for the implementation of a WII product since it houses thousands of climate-vulnerable farmers. With this in mind, we designed and priced a WII product for farmers from the semiarid region of Brazil and posteriorly investigated its risk efficiency. To do so, we first investigated crop yield responses to aridity, enabling the selection of locations for which the WII product was posteriorly assessed. Second, we grouped selected locations into specific contracts according to geographical proximity and evaluated each of these contracts to attest the risk efficiency of the proposed WII product using the method of stochastic efficiency with respect to a function (SERF), which identifies utility efficient alternatives for a range of risk attitudes. Our results show that the WII product may be effective in protecting farmers from adverse variations in production revenue, possibly being attractive for utility-maximizer farmers that are sufficiently risk averse.
Abstract
A cool environment is critical for protecting vulnerable populations from the adverse health effects associated with exposure to extreme heat. Although cooling centers are commonly established to provide temporary heat relief to the public, there is limited research exploring the spatial distributions and accessibility of cooling centers across cities in Texas. The intent of this study was to examine the spatial characteristics of cooling center locations throughout the Texas Triangle megaregion and evaluate the proximity of cooling centers to vulnerable populations. Specifically, spatial clustering analysis was used to quantitatively characterize the spatial distributions of cooling centers in San Antonio, Houston, and Dallas, while spatial lag regression was conducted to evaluate the relationships between indicators of socioeconomic vulnerability and proximity to cooling centers. The findings indicated that cooling centers exhibited clustering at short distances, which suggested there were potential spatial redundancies. The distributions of the cooling centers also illustrated possible accessibility issues due to the concentration of the locations in urban cores. The spatial lag regression models highlighted several problematic relationships, as elderly and disabled populations were located at significantly greater distances from cooling centers in San Antonio and Dallas, respectively. However, numerous insignificant relationships were also observed, which suggested that cooling center locations did not consistently marginalize or favor vulnerable populations. Therefore, a higher degree of intentionality that explicitly considers cooling center proximity to the vulnerable populations they aim to serve might be beneficial as planners and emergency managers determine cooling center locations in response to extreme heat.
Abstract
A cool environment is critical for protecting vulnerable populations from the adverse health effects associated with exposure to extreme heat. Although cooling centers are commonly established to provide temporary heat relief to the public, there is limited research exploring the spatial distributions and accessibility of cooling centers across cities in Texas. The intent of this study was to examine the spatial characteristics of cooling center locations throughout the Texas Triangle megaregion and evaluate the proximity of cooling centers to vulnerable populations. Specifically, spatial clustering analysis was used to quantitatively characterize the spatial distributions of cooling centers in San Antonio, Houston, and Dallas, while spatial lag regression was conducted to evaluate the relationships between indicators of socioeconomic vulnerability and proximity to cooling centers. The findings indicated that cooling centers exhibited clustering at short distances, which suggested there were potential spatial redundancies. The distributions of the cooling centers also illustrated possible accessibility issues due to the concentration of the locations in urban cores. The spatial lag regression models highlighted several problematic relationships, as elderly and disabled populations were located at significantly greater distances from cooling centers in San Antonio and Dallas, respectively. However, numerous insignificant relationships were also observed, which suggested that cooling center locations did not consistently marginalize or favor vulnerable populations. Therefore, a higher degree of intentionality that explicitly considers cooling center proximity to the vulnerable populations they aim to serve might be beneficial as planners and emergency managers determine cooling center locations in response to extreme heat.
Abstract
This study used a model to calculate the proportional drop for every vehicle class based on 266 climate patterns consisting of seven temperature groups and varied snowfalls. The winter traffic models use weigh-in-motion (WIM) traffic collected on the commuter roadway for 5 years. The marginal impact and combined effect of meteorological conditions on the proportional decrease in winter traffic volume are evaluated. The predicted percentage decrease in traffic for all three vehicle classes increases as temperature decreases and snowfall increases. Mathematical functions are fitted for the decreased patterns for the considered vehicle type. Roadway authorities may utilize traffic percentage decrease to identify weather-related traffic changes when planning winter highway operation and maintenance.
Abstract
This study used a model to calculate the proportional drop for every vehicle class based on 266 climate patterns consisting of seven temperature groups and varied snowfalls. The winter traffic models use weigh-in-motion (WIM) traffic collected on the commuter roadway for 5 years. The marginal impact and combined effect of meteorological conditions on the proportional decrease in winter traffic volume are evaluated. The predicted percentage decrease in traffic for all three vehicle classes increases as temperature decreases and snowfall increases. Mathematical functions are fitted for the decreased patterns for the considered vehicle type. Roadway authorities may utilize traffic percentage decrease to identify weather-related traffic changes when planning winter highway operation and maintenance.
Abstract
Common disaster-phase models provide a useful heuristic for understanding how disasters evolve, but they do not adequately characterize the transitions between phases, such as the forecast and warning phase of predictable disasters. In this study, we use tweets posted by professional sources of meteorological information in Florida during Hurricane Irma (2017) to understand how visual risk communication evolves during this transition. We identify four subphases of the forecast and warning phase: the hypothetical threat, actualized threat, looming threat, and impact subphases. Each subphase is denoted by changes in the kinds of visual risk information disseminated by professional sources and retransmitted by the public, which are often driven by new information provided by the U.S. National Weather Service. In addition, we use regression analysis to understand the impact of tweet timing, content, risk visualization and other factors on tweet retransmission across Irma’s forecast and warning phase. We find that cone, satellite, and spaghetti-plot image types are retweeted more, while watch/warning imagery is retweeted less. In addition, manually generated tweets are retweeted more than automated tweets. These results highlight several information needs to incorporate into the current NWS hurricane forecast visualization suite, such as uncertainty and hazard-specific information at longer lead times, and the importance of investigating the effectiveness of different social media posting strategies. Our results also demonstrate the roles and responsibilities that professional sources engage in during these subphases, which builds understanding of disasters by contextualizing the subphases along the transition from long-term preparedness to postevent response and recovery.
Significance Statement
Visual information is an important tool for communicating about evolving tropical cyclone threats. In this study, we investigate the kinds of visualizations posted by professional weather communicators on Twitter during Hurricane Irma (2017) to understand how visual information shifts over time and whether different visuals are more retweeted. We find that visual information shifts substantially in the days before Irma’s impacts, and these shifts are often driven by changes in Irma’s strength or forecast track. Our results show that cone, satellite, and spaghetti-plot visualizations are retweeted more frequently, while watch/warning imagery is retweeted less. These results help us to understand how visual information evolves during predictable disasters, and they suggest ways that visual communication can be improved.
Abstract
Common disaster-phase models provide a useful heuristic for understanding how disasters evolve, but they do not adequately characterize the transitions between phases, such as the forecast and warning phase of predictable disasters. In this study, we use tweets posted by professional sources of meteorological information in Florida during Hurricane Irma (2017) to understand how visual risk communication evolves during this transition. We identify four subphases of the forecast and warning phase: the hypothetical threat, actualized threat, looming threat, and impact subphases. Each subphase is denoted by changes in the kinds of visual risk information disseminated by professional sources and retransmitted by the public, which are often driven by new information provided by the U.S. National Weather Service. In addition, we use regression analysis to understand the impact of tweet timing, content, risk visualization and other factors on tweet retransmission across Irma’s forecast and warning phase. We find that cone, satellite, and spaghetti-plot image types are retweeted more, while watch/warning imagery is retweeted less. In addition, manually generated tweets are retweeted more than automated tweets. These results highlight several information needs to incorporate into the current NWS hurricane forecast visualization suite, such as uncertainty and hazard-specific information at longer lead times, and the importance of investigating the effectiveness of different social media posting strategies. Our results also demonstrate the roles and responsibilities that professional sources engage in during these subphases, which builds understanding of disasters by contextualizing the subphases along the transition from long-term preparedness to postevent response and recovery.
Significance Statement
Visual information is an important tool for communicating about evolving tropical cyclone threats. In this study, we investigate the kinds of visualizations posted by professional weather communicators on Twitter during Hurricane Irma (2017) to understand how visual information shifts over time and whether different visuals are more retweeted. We find that visual information shifts substantially in the days before Irma’s impacts, and these shifts are often driven by changes in Irma’s strength or forecast track. Our results show that cone, satellite, and spaghetti-plot visualizations are retweeted more frequently, while watch/warning imagery is retweeted less. These results help us to understand how visual information evolves during predictable disasters, and they suggest ways that visual communication can be improved.
