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Abstract
Previous research indicates that forecast uncertainty can, in certain formats and decision contexts, provide actionable insights that help users in their decision-making. However, how to best disseminate forecast uncertainty, which factors affect successful uptake, and how forecast uncertainty transforms into better decision-making remains an ongoing topic for discussion in both academic and operational contexts. Interpreting and using visualizations of forecast uncertainty are not straightforward, and choosing how to represent uncertainty in forecast products should be dependent on the specific audience in mind. We present findings from an interactive stakeholder workshop that aimed to advance context-based insights on the usability of graphical representations of forecast uncertainty in the field of maritime operations. The workshop involved participants from various maritime sectors, including cruise tourism, fisheries, government, private forecast service providers, and research/academia. Geographically situated in Norway, the workshop employed sea spray icing as a use case for various decision scenario exercises, using both fixed probability and fixed threshold formats, supplemented with temporal ensemble diagrams. Accumulated operational expertise and characteristics of the forecast information itself, such as color coding and different forms of forecast uncertainty visualizations, were found to affect perceptions of decision-making quality. Findings can inform codesign processes of translating ensemble forecasts into usable and useful public and commercial forecast information services. The collaborative nature of the workshop facilitated knowledge sharing and coproduction between forecast providers and users. Overall, the study highlights the importance of incorporating methodological approaches that consider the complex and dynamic operational contexts of ensemble-based forecast provision, communication, and use.
Significance Statement
We wanted to understand how maps showing uncertainty in weather forecasts can help maritime users in their operational decisions. We organized a workshop with Norwegian maritime stakeholders and forecasters, who interpreted maps that combined layers of maritime operational activities and the likelihood of sea spray icing (an important hazard for ships operating on higher latitudes). The results show that contextual knowledge, and the use visual formats such as traffic light colors may help users to understand the maps. The results will help to better communicate weather forecasts to maritime users and gives suggestions about how to involve users in codesigning forecast products. Follow-up research could use our approach to investigate other hazardous conditions, such as wind, waves and sea ice.
Abstract
Previous research indicates that forecast uncertainty can, in certain formats and decision contexts, provide actionable insights that help users in their decision-making. However, how to best disseminate forecast uncertainty, which factors affect successful uptake, and how forecast uncertainty transforms into better decision-making remains an ongoing topic for discussion in both academic and operational contexts. Interpreting and using visualizations of forecast uncertainty are not straightforward, and choosing how to represent uncertainty in forecast products should be dependent on the specific audience in mind. We present findings from an interactive stakeholder workshop that aimed to advance context-based insights on the usability of graphical representations of forecast uncertainty in the field of maritime operations. The workshop involved participants from various maritime sectors, including cruise tourism, fisheries, government, private forecast service providers, and research/academia. Geographically situated in Norway, the workshop employed sea spray icing as a use case for various decision scenario exercises, using both fixed probability and fixed threshold formats, supplemented with temporal ensemble diagrams. Accumulated operational expertise and characteristics of the forecast information itself, such as color coding and different forms of forecast uncertainty visualizations, were found to affect perceptions of decision-making quality. Findings can inform codesign processes of translating ensemble forecasts into usable and useful public and commercial forecast information services. The collaborative nature of the workshop facilitated knowledge sharing and coproduction between forecast providers and users. Overall, the study highlights the importance of incorporating methodological approaches that consider the complex and dynamic operational contexts of ensemble-based forecast provision, communication, and use.
Significance Statement
We wanted to understand how maps showing uncertainty in weather forecasts can help maritime users in their operational decisions. We organized a workshop with Norwegian maritime stakeholders and forecasters, who interpreted maps that combined layers of maritime operational activities and the likelihood of sea spray icing (an important hazard for ships operating on higher latitudes). The results show that contextual knowledge, and the use visual formats such as traffic light colors may help users to understand the maps. The results will help to better communicate weather forecasts to maritime users and gives suggestions about how to involve users in codesigning forecast products. Follow-up research could use our approach to investigate other hazardous conditions, such as wind, waves and sea ice.
Abstract
Crowdsourced meteorological data may provide a useful supplement to operational observations. However, the willingness of various parties to share their data remains unclear. Here, a survey on data applications was carried out to investigate the willingness to participate in crowdsourcing observations. Of the 21 responses, 71% expressed difficulty in meeting the requirement of data services using only their own observations and revealed that they would be willing to exchange data with other parties under some framework; moreover, 90% expressed a willingness to participate in crowdsourcing observations. The findings suggest that in a way the social foundation of crowdsourcing has been established in China. Additionally, a case study on precipitation monitoring was performed in Guangzhou, the capital city of Guangdong Province, South China. Three sources of hourly measurements were combined after data quality control and calibration and interpolated over Guangzhou (gridded precipitation was based on combined data, and it is referred to as the COM grid). Subsequently, the COM grid was compared with the grid data based only on observations from the China Meteorological Administration using three indices, namely, cumulative precipitation, precipitation intensity, and heavy rain hours. The results indicate that requirement for more observations could benefit from crowdsourced data, especially on uneven terrain and in regions covered by sparse surface stations.
Abstract
Crowdsourced meteorological data may provide a useful supplement to operational observations. However, the willingness of various parties to share their data remains unclear. Here, a survey on data applications was carried out to investigate the willingness to participate in crowdsourcing observations. Of the 21 responses, 71% expressed difficulty in meeting the requirement of data services using only their own observations and revealed that they would be willing to exchange data with other parties under some framework; moreover, 90% expressed a willingness to participate in crowdsourcing observations. The findings suggest that in a way the social foundation of crowdsourcing has been established in China. Additionally, a case study on precipitation monitoring was performed in Guangzhou, the capital city of Guangdong Province, South China. Three sources of hourly measurements were combined after data quality control and calibration and interpolated over Guangzhou (gridded precipitation was based on combined data, and it is referred to as the COM grid). Subsequently, the COM grid was compared with the grid data based only on observations from the China Meteorological Administration using three indices, namely, cumulative precipitation, precipitation intensity, and heavy rain hours. The results indicate that requirement for more observations could benefit from crowdsourced data, especially on uneven terrain and in regions covered by sparse surface stations.
Abstract
An extreme Saharan dust storm (named Godzilla) arrived at the Caribbean region in June 2020, deteriorating the air quality to hazardous levels and unhealthy conditions for sensitive groups of people. Our main objective was to characterize populations at risk for Saharan dust by analyzing distribution and levels of dust events in Puerto Rico, and by conducting an online survey to assess community perceptions on Saharan dust health effects. Three daily satellite aerosols products from 2013 to 2020 were retrieved from the Visible Infrared Imaging Radiometer Suite over Puerto Rico to better understand the patterns, frequency, and seasonality of aerosols. The atmospheric results indicated that extreme values (>99th) of big size aerosols (e.g., Sahara dust) were observed over Puerto Rico on 22 June 2020. A total of 1504 qualified people participated in the survey during the summer of 2020, and it was analyzed with descriptive statistics, frequency analysis, and chi-square tests. 51% of the survey participants were on the age group of 25–44 years old, and 65% of the participants had at least one preexisting health condition (respiratory diseases 27%; cardiovascular diseases 28%). Nearly 90% of the participants indicated that Saharan dust affected the health status of both the respondents and their family members. Irritation of eyes (22%), nose (24%), and throat (23%), as well as breathing difficulties (10%), were the most common symptoms reported. Understanding patients’ health profiles associated with Saharan dust is essential before developing public health strategies to minimize exacerbation of health conditions in Puerto Rico.
Abstract
An extreme Saharan dust storm (named Godzilla) arrived at the Caribbean region in June 2020, deteriorating the air quality to hazardous levels and unhealthy conditions for sensitive groups of people. Our main objective was to characterize populations at risk for Saharan dust by analyzing distribution and levels of dust events in Puerto Rico, and by conducting an online survey to assess community perceptions on Saharan dust health effects. Three daily satellite aerosols products from 2013 to 2020 were retrieved from the Visible Infrared Imaging Radiometer Suite over Puerto Rico to better understand the patterns, frequency, and seasonality of aerosols. The atmospheric results indicated that extreme values (>99th) of big size aerosols (e.g., Sahara dust) were observed over Puerto Rico on 22 June 2020. A total of 1504 qualified people participated in the survey during the summer of 2020, and it was analyzed with descriptive statistics, frequency analysis, and chi-square tests. 51% of the survey participants were on the age group of 25–44 years old, and 65% of the participants had at least one preexisting health condition (respiratory diseases 27%; cardiovascular diseases 28%). Nearly 90% of the participants indicated that Saharan dust affected the health status of both the respondents and their family members. Irritation of eyes (22%), nose (24%), and throat (23%), as well as breathing difficulties (10%), were the most common symptoms reported. Understanding patients’ health profiles associated with Saharan dust is essential before developing public health strategies to minimize exacerbation of health conditions in Puerto Rico.
Abstract
To support human flourishing in a climate-changed world, individuals and communities will have to take costly and challenging adaptation actions. Although there is evidence of increasing public concern over climate change, current levels of engagement and adaptation action remain insufficient. There is a need for innovative ways to bring individuals and communities into the climate movement. Public art installations that creatively communicate relevant aspects of the problem may represent one largely untapped pathway to greater levels of engagement. Here, we examined how virtual exposure to a public art installation, FutureSHORELINE, impacted climate change risk perceptions, attitudes, behaviors, and emotions. The installation depicted sea level rise impacts and solutions for a shoreline area in Boston, Massachusetts. In study 1 (N = 474), participants were randomly assigned to view the art in different formats: video, stills, or 360° viewers. Exposure to this installation, in any format, was associated with greater perceived risk of climate change, feelings of personal responsibility to address climate change, and likelihood of engaging in community-led initiatives related to climate change as compared with pre-art-exposure levels. In study 2 (N = 294), the video was compared, with and without text, with a no-information control. This study revealed that the video impacted emotional reactions to climate change. Public art installations may present a model by which to make information about the local impacts of climate change and proposed adaptation solutions visible to diverse audiences, providing a novel way to increase public concern and engagement.
Significance Statement
While much climate change art has been created, efforts to systematically evaluate its impacts are sparse. The purpose of this work was to examine how viewing a landscape installation impacted climate change and sea level rise perceptions. Across two studies, we evaluated the impacts of viewing a Boston (Massachusetts)-based landscape installation depicting the impacts of, and a solution to, sea level rise and flooding. Our results highlight the potential usefulness of art as a means of communicating about climate change.
Abstract
To support human flourishing in a climate-changed world, individuals and communities will have to take costly and challenging adaptation actions. Although there is evidence of increasing public concern over climate change, current levels of engagement and adaptation action remain insufficient. There is a need for innovative ways to bring individuals and communities into the climate movement. Public art installations that creatively communicate relevant aspects of the problem may represent one largely untapped pathway to greater levels of engagement. Here, we examined how virtual exposure to a public art installation, FutureSHORELINE, impacted climate change risk perceptions, attitudes, behaviors, and emotions. The installation depicted sea level rise impacts and solutions for a shoreline area in Boston, Massachusetts. In study 1 (N = 474), participants were randomly assigned to view the art in different formats: video, stills, or 360° viewers. Exposure to this installation, in any format, was associated with greater perceived risk of climate change, feelings of personal responsibility to address climate change, and likelihood of engaging in community-led initiatives related to climate change as compared with pre-art-exposure levels. In study 2 (N = 294), the video was compared, with and without text, with a no-information control. This study revealed that the video impacted emotional reactions to climate change. Public art installations may present a model by which to make information about the local impacts of climate change and proposed adaptation solutions visible to diverse audiences, providing a novel way to increase public concern and engagement.
Significance Statement
While much climate change art has been created, efforts to systematically evaluate its impacts are sparse. The purpose of this work was to examine how viewing a landscape installation impacted climate change and sea level rise perceptions. Across two studies, we evaluated the impacts of viewing a Boston (Massachusetts)-based landscape installation depicting the impacts of, and a solution to, sea level rise and flooding. Our results highlight the potential usefulness of art as a means of communicating about climate change.
Abstract
Famine poses a significant threat to human food security and sustainable development. This study investigates the prevalence and magnitude of famine and its connection to climatic change/disasters at different spatiotemporal scales. The famine index was reconstructed using 13 828 famine-related literature records in China during the Qing Dynasty (CE 1644–1911). The study found that extreme drought/flood events instantaneously triggered famine at the seasonal to interannual scale, leading to intermittent occurrences of great famines. Drought-induced famine was the most prominent. Famine was positively correlated with drought in both short-term variations and long-term trends across different regions. The effect of floods on famines was double-edged and varied between the north and south of China. The severe famines that occurred between 1811 and 1878 were related to both climatic cooling and an increase in drought/flood events under a situation of growing population pressure on resources. The greatest famine of 1876–78 was probably the result of long-term interactions among intensifying human–land contradictions since the early nineteenth century, periodic droughts in north China, and a weakening of regional buffering mechanisms due to flood-induced declines in south China.
Abstract
Famine poses a significant threat to human food security and sustainable development. This study investigates the prevalence and magnitude of famine and its connection to climatic change/disasters at different spatiotemporal scales. The famine index was reconstructed using 13 828 famine-related literature records in China during the Qing Dynasty (CE 1644–1911). The study found that extreme drought/flood events instantaneously triggered famine at the seasonal to interannual scale, leading to intermittent occurrences of great famines. Drought-induced famine was the most prominent. Famine was positively correlated with drought in both short-term variations and long-term trends across different regions. The effect of floods on famines was double-edged and varied between the north and south of China. The severe famines that occurred between 1811 and 1878 were related to both climatic cooling and an increase in drought/flood events under a situation of growing population pressure on resources. The greatest famine of 1876–78 was probably the result of long-term interactions among intensifying human–land contradictions since the early nineteenth century, periodic droughts in north China, and a weakening of regional buffering mechanisms due to flood-induced declines in south China.
Abstract
In-depth analysis of the content of broadcast tornado warning coverage is limited. Such analysis is important due to local television’s role as a key source for tornado warning information. This study attempts to fill gaps in our knowledge regarding broadcast coverage of tornado warnings by demonstrating how local television news stations’ coverage of tornadic events can be systematically analyzed to better understand this element of warning communication. We reviewed both visual and verbal content for information such as the prominence of specific radar products, the geographic scale of warning communication, and common themes in verbal communication. A combination of deductive and inductive coding approaches was used to summarize the verbal content of the broadcasts. We found that the stations heavily used radar products with reflectivity and velocity surpassing correlation coefficient. The geographic scale of mapped products (street, city/county, and state level) appeared to be related to the rural or urban nature of the area warned, which may have implications for how readily rural residents would be able to personalize tornado threats. Verbal content was very similar between the two stations. The theme of monitoring and updating conditions, which included processes such as zooming in and out, making adjustments, reinforcing conditions, and providing damage reports was the most frequent communication type, likely because weathercasters use these processes to both communicate the warning and also to help themselves understand the situation. The results can inform future studies examining the influence of specific elements of broadcast warning coverage on risk perception and protective actions.
Significance Statement
Television is a key source for receiving or confirming tornado warnings, but few studies have examined the content of broadcast warnings in depth. This study examined the visual and verbal content of broadcast tornado warnings on two local television stations. Radar products were used heavily, and street-level coverage was more common when a tornado affected a metropolitan area. Coverage was most common at the city/county level. Verbal content included many elements of effective warning communication but at times included jargon that may not be understood by viewers. The results can serve as a springboard for future research on the impacts of these elements on risk perception and response. It can also serve future research by distinguishing what viewers of severe weather broadcasts are exposed to that nonviewers are not.
Abstract
In-depth analysis of the content of broadcast tornado warning coverage is limited. Such analysis is important due to local television’s role as a key source for tornado warning information. This study attempts to fill gaps in our knowledge regarding broadcast coverage of tornado warnings by demonstrating how local television news stations’ coverage of tornadic events can be systematically analyzed to better understand this element of warning communication. We reviewed both visual and verbal content for information such as the prominence of specific radar products, the geographic scale of warning communication, and common themes in verbal communication. A combination of deductive and inductive coding approaches was used to summarize the verbal content of the broadcasts. We found that the stations heavily used radar products with reflectivity and velocity surpassing correlation coefficient. The geographic scale of mapped products (street, city/county, and state level) appeared to be related to the rural or urban nature of the area warned, which may have implications for how readily rural residents would be able to personalize tornado threats. Verbal content was very similar between the two stations. The theme of monitoring and updating conditions, which included processes such as zooming in and out, making adjustments, reinforcing conditions, and providing damage reports was the most frequent communication type, likely because weathercasters use these processes to both communicate the warning and also to help themselves understand the situation. The results can inform future studies examining the influence of specific elements of broadcast warning coverage on risk perception and protective actions.
Significance Statement
Television is a key source for receiving or confirming tornado warnings, but few studies have examined the content of broadcast warnings in depth. This study examined the visual and verbal content of broadcast tornado warnings on two local television stations. Radar products were used heavily, and street-level coverage was more common when a tornado affected a metropolitan area. Coverage was most common at the city/county level. Verbal content included many elements of effective warning communication but at times included jargon that may not be understood by viewers. The results can serve as a springboard for future research on the impacts of these elements on risk perception and response. It can also serve future research by distinguishing what viewers of severe weather broadcasts are exposed to that nonviewers are not.
Abstract
Climate change has posed inequitable risks to different communities. Among communities of color in the United States, Native Americans stand out because 1) they desire resources to sustain resilient nations and 2) they have developed nature-based solutions through experiences with local climate-related challenges, which can provide deep insight for the whole society. Projection of climate risks for Native Americans is essential to assess future risks and support their climate-ready nations, yet there has been lack of useable information. In this study, we projected three climate hazards—heavy rainfall, 2-yr floods, and flash floods—for tribal nations in Oklahoma. To break down into tribal jurisdictions, we utilize a coupled regional climate model at 4 km and flash-flood forecast model at 1 km. A hazard–exposure–vulnerability risk framework is applied to integrate both climate and demographic changes in a high-emissions scenario. It is found that 1) Indigenous people are the most vulnerable community in Oklahoma; 2) heavy rainfall and 2-yr floods have marked increases in risks at 501.1% and 632.6%, respectively, while flash floods have a moderate increase (296.4%); 3) Native Americans bear 68.0%, 64.3%, and 64.0% higher risks in heavy rainfall, 2-yr flooding, and flash flooding, respectively, than the general population in Oklahoma; 4) in comparing climate and demographic changes, it is seen that population growth leads to greater climate hazard risks than does climate change; and 5) emerging tribal nations are projected to have 10 times as much population, resulting in great exposures to climate extremes. This study can raise awareness of the impact of climate changes and draw attention to address climate injustice issues for minoritized communities.
Significance Statement
This study examines the impact of climate change on a marginalized community—Native Americans in Oklahoma, home to 39 federally recognized tribal nations. We utilized the high-resolution climate simulation at 4-km resolution and hydrologic simulation at 1-km resolution to aggregate three climate extremes to tribal jurisdictions. We find that climate and demographic changes disproportionately put many Native Americans at risk. The heavy rainfall, 2-yr floods, and flash floods are all projected to have increased risks by 501.1%, 632.6%, and 296.4%, respectively. Those risks are 68.0%, 64.3%, and 64.0% higher than the state average for the general population, respectively. We urge proper attention to tribal nations to address climate injustice issues as a whole with the acknowledgment of their distinct relationships to their homelands as sovereign peoples.
Abstract
Climate change has posed inequitable risks to different communities. Among communities of color in the United States, Native Americans stand out because 1) they desire resources to sustain resilient nations and 2) they have developed nature-based solutions through experiences with local climate-related challenges, which can provide deep insight for the whole society. Projection of climate risks for Native Americans is essential to assess future risks and support their climate-ready nations, yet there has been lack of useable information. In this study, we projected three climate hazards—heavy rainfall, 2-yr floods, and flash floods—for tribal nations in Oklahoma. To break down into tribal jurisdictions, we utilize a coupled regional climate model at 4 km and flash-flood forecast model at 1 km. A hazard–exposure–vulnerability risk framework is applied to integrate both climate and demographic changes in a high-emissions scenario. It is found that 1) Indigenous people are the most vulnerable community in Oklahoma; 2) heavy rainfall and 2-yr floods have marked increases in risks at 501.1% and 632.6%, respectively, while flash floods have a moderate increase (296.4%); 3) Native Americans bear 68.0%, 64.3%, and 64.0% higher risks in heavy rainfall, 2-yr flooding, and flash flooding, respectively, than the general population in Oklahoma; 4) in comparing climate and demographic changes, it is seen that population growth leads to greater climate hazard risks than does climate change; and 5) emerging tribal nations are projected to have 10 times as much population, resulting in great exposures to climate extremes. This study can raise awareness of the impact of climate changes and draw attention to address climate injustice issues for minoritized communities.
Significance Statement
This study examines the impact of climate change on a marginalized community—Native Americans in Oklahoma, home to 39 federally recognized tribal nations. We utilized the high-resolution climate simulation at 4-km resolution and hydrologic simulation at 1-km resolution to aggregate three climate extremes to tribal jurisdictions. We find that climate and demographic changes disproportionately put many Native Americans at risk. The heavy rainfall, 2-yr floods, and flash floods are all projected to have increased risks by 501.1%, 632.6%, and 296.4%, respectively. Those risks are 68.0%, 64.3%, and 64.0% higher than the state average for the general population, respectively. We urge proper attention to tribal nations to address climate injustice issues as a whole with the acknowledgment of their distinct relationships to their homelands as sovereign peoples.
Abstract
One of the most pressing and immediate climate concerns globally is inadequate and unsafe household water. The livelihoods of smallholder crop and livestock farmers are especially vulnerable to these challenges. Past research suggests that water insecurity is highly gendered, and women are theorized to be more aware of and impacted by water insecurity than men. Our study reengages this literature through a livelihood lens, comparing gendered perception of household water insecurity across crop and livestock subsistence modalities in a semiarid region of Burkina Faso in the Sahel region of West Africa, where water insecurity is closely intertwined with both seasonality and rainfall unpredictability. Our mixed-methods ethnographic study sampled matched men and women in households with water insecurity data collected from 158 coresident spousal pairs who engaged primarily in pastoralism or agriculture. Contrary to predictions from the existing literature, men engaged in livestock husbandry perceived greater water insecurity than matched women in the same household. We suggest this reflects men’s responsibility for securing water for the animals—which consume most of the household’s water among livestock farmers. In contrast, men engaged in cropping perceive less water insecurity than women in the same household, aligning with predictions from past research. Our findings suggest that the relationship between gender and water insecurity is more highly nuanced and related to livelihood strategies than previously recognized, with significant implications for how water insecurity is conceptualized theoretically and methodologically in the contexts of people’s everyday management and experience of the most immediate and proximate climate-related challenges.
Abstract
One of the most pressing and immediate climate concerns globally is inadequate and unsafe household water. The livelihoods of smallholder crop and livestock farmers are especially vulnerable to these challenges. Past research suggests that water insecurity is highly gendered, and women are theorized to be more aware of and impacted by water insecurity than men. Our study reengages this literature through a livelihood lens, comparing gendered perception of household water insecurity across crop and livestock subsistence modalities in a semiarid region of Burkina Faso in the Sahel region of West Africa, where water insecurity is closely intertwined with both seasonality and rainfall unpredictability. Our mixed-methods ethnographic study sampled matched men and women in households with water insecurity data collected from 158 coresident spousal pairs who engaged primarily in pastoralism or agriculture. Contrary to predictions from the existing literature, men engaged in livestock husbandry perceived greater water insecurity than matched women in the same household. We suggest this reflects men’s responsibility for securing water for the animals—which consume most of the household’s water among livestock farmers. In contrast, men engaged in cropping perceive less water insecurity than women in the same household, aligning with predictions from past research. Our findings suggest that the relationship between gender and water insecurity is more highly nuanced and related to livelihood strategies than previously recognized, with significant implications for how water insecurity is conceptualized theoretically and methodologically in the contexts of people’s everyday management and experience of the most immediate and proximate climate-related challenges.
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.
