Exploring the raison d'etre behind metric selection in network analysis: a systematic review

Network analysis is a useful tool to analyse the interactions and structure of graphs that represent the relationships among entities, such as sectors within an urban system. Connecting entities in this way is vital in understanding the complexity of the modern world, and how to navigate these complexities during an event. However, the field of network analysis has grown rapidly since the 1970s to produce a vast array of available metrics that describe different graph properties. This diversity allows network analysis to be applied across myriad research domains and contexts, however widespread applications have produced polysemic metrics. Challenges arise in identifying which method of network analysis to adopt, which metrics to choose, and how many are suitable. This paper undertakes a structured review of literature to provide clarity on raison d'etre behind metric selection and suggests a way forward for applied network analysis. It is essential that future studies explicitly report the rationale behind metric choice and describe how the mathematics relates to target concepts and themes. An exploratory metric analysis is an important step in identifying the most important metrics and understanding redundant ones. Finally, where applicable, one should select an optimal number of metrics that describe the network both locally and globally, so as to understand the interactions and structure as holistically as possible.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-022-00476-w.

Agrivoltaics: A Climate-Smart Agriculture Approach for Indian Farmers

India is a leader when it comes to agriculture. A significant part of the country’s population depends on agriculture for livelihood. However, many of them face challenges due to using unreliable farming techniques. Sometimes the challenges increase to the extent that they commit suicide. Besides, India is highly populated, and its population is steadily increasing, requiring its government to grow its GDP and increase its energy supply proportionately. This paper reviews integrating solar farming with agriculture, known as Agrivoltaics, as a Climate-Smart Agriculture (CSA) option for Indian farmers. This study is further supported by the Strength, Weaknesses, Opportunities, and Threats (SWOT) analysis of agrivoltaics. Using the SWOT analysis, this article presents how agrivoltaics can make agriculture sustainable and reliable. This paper identifies rural electrification, water conservation, yield improvement, sustainable income generation, and reduction in the usage of pesticides as the strengths of agrivoltaics. Similarly, the paper presents weaknesses, opportunities, and threats to agrivoltaics in India. The research concludes with the findings that agrivoltaics have the potential of meeting multiple objectives such as meeting global commitments, offering employment, providing economic stability, increasing clean energy production capacity, conserving natural resources, and succeeding in several others. The paper also includes a discussion about the findings, suggestions, and implications of adopting agrivoltaics on a large scale in India.

CORONA High-Resolution Satellite and Aerial Imagery for Change Detection Assessment of Natural Hazard Risk and Urban Growth in El Alto/La Paz in Bolivia, Santiago de Chile, Yungay in Peru, Qazvin in Iran, and Mount St. Helens in the USA

Urban growth and natural hazard events are continuous trends and reliable monitoring is demanded by organisations such as the Intergovernmental Panel on Climate Change, the United Nations Office for Disaster Risk Reduction, or the United Nations Human Settlements Programme. CORONA is the program name of photoreconnaissance satellite imagery available from 1960 to 1984 provides an extension of monitoring ranges in comparison to later satellite data such as Landsat that are more widely used. Providing visual comparisons with aerial or high-resolution OrbView satellite imagery, this article demonstrates applications of CORONA images for change detection of urban growth and sprawl and natural hazard exposure. Cases from El Alto/ La Paz in Bolivia, Santiago de Chile, Yungay in Peru, Qazvin in Iran, and Mount St. Helens in the USA are analysed. After a preassessment of over 20 disaster events, the 1970 Yungay earthquake-triggered debris avalanche and the natural hazard processes of the 1980 Mt St. Helens volcanic eruption are further analysed. Usability and limitations of CORONA data are analysed, including the availability of data depending on flight missions, cloud cover, spatial and temporal resolution, but also rather scarce documentation of natural hazards in the 1960s and 70s. Results include the identification of urban borders expanding into hazard-prone areas such as mountains, riverbeds or erosion channels. These are important areas for future research, making more usage of this valuable but little-used data source. The article addresses geographers, spatial planners, political decision makers and other scientific areas dealing with remote sensing.

Social Capital and Disaster Resilience Nexus: A Study of Flash Flood Recovery in Jeddah City

Governments, researchers, and humanitarian agencies have increasingly focused on reducing disaster impacts and enhancing the resilience of individuals, households, and communities, as the human and economic costs of natural disaster events have dramatically increased over the past century. Achieving resilience in a disaster context means the ability to survive future natural disasters with minimum loss of life and property as well as the ability to create a greater sense of place among residents, a stronger, more diverse economy, and a more economically integrated and diverse population. However, less attention has been paid to the significance of social capital in a post-disaster context and its contribution in building community resilience. It is very obvious that the contribution of social capital to post-disaster resilience in a Middle Eastern/Saudi Arabian context is virtually unknown. With a focus on the Saudi Arabian context, this research paper develops a social capital framework centered on resilience and post-disaster recovery. To conduct this study, a holistic approach to data collection is followed through questionnaire surveys, structured and non-structured interviews with citizens, and informal discussions with government and major stakeholders related to flash flood disaster management in the City of Jeddah. It is interesting to note that several religious institutions have played important roles in evacuating people and providing help for a quick recovery. In addition, government organizations are taking the recovery process seriously by providing necessary help in the flood-stricken areas. Within the scope of the given framework, the research explores and evaluates the role of social capital in post-disaster recovery efforts through a case study of the 2009 and 2011 Jeddah flash floods.

History of Disaster Epidemiology

Disaster epidemiology is not a new field, and the methods utilized to conduct disaster epidemiology studies are no different than the methods used in everyday applied public health research and investigations. The only difference is the circumstances under which the methods are employed. The challenges of conducting epidemiologic studies during a disaster are many—limited access to study sites and populations; access to reliable electricity, connectivity, and communication systems; and typically a short time frame in which to gather, analyze, and report data to decision-makers so that it can be utilized to prevent morbidity and mortality. However, many innovations have been developed by disaster epidemiologists to meet these challenges. Rapid needs assessments, innovative surveillance and tracking systems, and adapted epidemiologic study designs are some of the innovations that will be discussed in this chapter.

A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio-temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio-temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio-temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin-wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.

Increasing risk over time of weather-related hazards to the European population: a data-driven prognostic study

BACKGROUND

The observed increase in the effects on human beings of weather-related disasters has been largely attributed to the rise in population exposed, with a possible influence of global warming. Yet, future risks of weather-related hazards on human lives in view of climate and demographic changes have not been comprehensively investigated.

METHODS

We assessed the risk of weather-related hazards to the European population in terms of annual numbers of deaths in 30 year intervals relative to the reference period (1981-2010) up to the year 2100 (2011-40, 2041-70, and 2071-100) by combining disaster records with high-resolution hazard and demographic projections in a prognostic modelling framework. We focused on the hazards with the greatest impacts-heatwaves and cold waves, wildfires, droughts, river and coastal floods, and windstorms-and evaluated their spatial and temporal variations in intensity and frequency under a business-as-usual scenario of greenhouse gas emissions. We modelled long-term demographic dynamics through a territorial modelling platform to represent the evolution of human exposure under a corresponding middle-of-the-road socioeconomic scenario. We appraised human vulnerability to weather extremes on the basis of more than 2300 records collected from disaster databases during the reference period and assumed it to be static under a scenario of no adaptation.

FINDINGS

We found that weather-related disasters could affect about two-thirds of the European population annually by the year 2100 (351 million people exposed per year [uncertainty range 126 million to 523 million] during the period 2071-100) compared with 5% during the reference period (1981-2010; 25 million people exposed per year). About 50 times the number of fatalities occurring annually during the reference period (3000 deaths) could occur by the year 2100 (152 000 deaths [80 500-239 800]). Future effects show a prominent latitudinal gradient, increasing towards southern Europe, where the premature mortality rate due to weather extremes (about 700 annual fatalities per million inhabitants [482-957] during the period 2071-100 vs 11 during the reference period) could become the greatest environmental risk factor. The projected changes are dominated by global warming (accounting for more than 90% of the rise in risk to human beings), mainly through a rise in the frequency of heatwaves (about 2700 heat-related fatalities per year during the reference period vs 151 500 [80 100-239 000] during the period 2071-100).

INTERPRETATION

Global warming could result in rapidly rising costs of weather-related hazards to human beings in Europe unless adequate adaptation measures are taken. Our results could aid in prioritisation of regional investments to address the unequal burden of effects on human beings of weather-related hazards and differences in adaptation capacities.

FUNDING

European Commission.

Predictability of state-level flood damage in the conterminous United States: the role of hazard, exposure and vulnerability

Understanding historical changes in flood damage and the underlying mechanisms is critical for predicting future changes for better adaptations. In this study, a detailed assessment of flood damage for 1950-1999 is conducted at the state level in the conterminous United States (CONUS). Geospatial datasets on possible influencing factors are then developed by synthesizing natural hazards, population, wealth, cropland and urban area to explore the relations with flood damage. A considerable increase in flood damage in CONUS is recorded for the study period which is well correlated with hazards. Comparably, runoff indexed hazards simulated by the Variable Infiltration Capacity (VIC) model can explain a larger portion of flood damage variations than precipitation in 84% of the states. Cropland is identified as an important factor contributing to increased flood damage in central US while urbanland exhibits positive and negative relations with total flood damage and damage per unit wealth in 20 and 16 states, respectively. Overall, flood damage in 34 out of 48 investigated states can be predicted at the 90% confidence level. In extreme cases, ~76% of flood damage variations can be explained in some states, highlighting the potential of future flood damage prediction based on climate change and socioeconomic scenarios.

The 2011 flood event in the Mekong Delta: preparedness, response, damage and recovery of private households and small businesses

Floods frequently cause substantial economic and human losses, particularly in developing countries. For the development of sound flood risk management schemes that reduce flood consequences, detailed insights into the different components of the flood risk management cycle, such as preparedness, response, flood impact analyses and recovery, are needed. However, such detailed insights are often lacking: commonly, only (aggregated) data on direct flood damage are available. Other damage categories such as losses owing to the disruption of production processes are usually not considered, resulting in incomplete risk assessments and possibly inappropriate recommendations for risk management. In this paper, data from 858 face-to-face interviews among flood-prone households and small businesses in Can Tho city in the Vietnamese Mekong Delta are presented to gain better insights into the damage caused by the 2011 flood event and its management by households and businesses.

REVIEW OF FINANCE AND INVESTMENT IN DISASTER RESILIENCE IN THE BUILT ENVIRONMENT

In stark comparison to reductions achieved in the number of human casualties from disasters it is now evident that economic losses are increasing at a much higher rate. While several events contribute to economic loss from disasters, loss from built environment (property) damages and its consequential effects are significant. The overarching purpose of this paper is to systematically summarise and synthesise literature, critically discuss issues, and identify methodological problems as well as research, practice gaps and potential solutions to matters relating to financing and investment in Disaster resilience in the Built Environment. Literature materials used for this the paper were gathered from reputable sources. As part of the findings of the study, a growing shift towards investment and enhancement of disaster resilience through the principles of corporate social responsibility and public private partnership was observed. However, many of the documented investments were made after disaster struck once or nearby.

Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data

An accurate understanding of flood risk and its drivers is crucial for effective risk management. Detailed risk projections, including uncertainties, are however rarely available, particularly in developing countries. This paper presents a method that integrates recent advances in global-scale modeling of flood hazard and land change, which enables the probabilistic analysis of future trends in national-scale flood risk. We demonstrate its application to Indonesia. We develop 1000 spatially-explicit projections of urban expansion from 2000 to 2030 that account for uncertainty associated with population and economic growth projections, as well as uncertainty in where urban land change may occur. The projections show that the urban extent increases by 215%-357% (5th and 95th percentiles). Urban expansion is particularly rapid on Java, which accounts for 79% of the national increase. From 2000 to 2030, increases in exposure will elevate flood risk by, on average, 76% and 120% for river and coastal floods. While sea level rise will further increase the exposure-induced trend by 19%-37%, the response of river floods to climate change is highly uncertain. However, as urban expansion is the main driver of future risk, the implementation of adaptation measures is increasingly urgent, regardless of the wide uncertainty in climate projections. Using probabilistic urban projections, we show that spatial planning can be a very effective adaptation strategy. Our study emphasizes that global data can be used successfully for probabilistic risk assessment in data-scarce countries.

Structured Coupling of Probability Loss Distributions: Assessing Joint Flood Risk in Multiple River Basins

Losses due to natural hazard events can be extraordinarily high and difficult to cope with. Therefore, there is considerable interest to estimate the potential impact of current and future extreme events at all scales in as much detail as possible. As hazards typically spread over wider areas, risk assessment must take into account interrelations between regions. Neglecting such interdependencies can lead to a severe underestimation of potential losses, especially for extreme events. This underestimation of extreme risk can lead to the failure of riskmanagement strategies when they are most needed, namely, in times of unprecedented events. In this article, we suggest a methodology to incorporate such interdependencies in risk via the use of copulas. We demonstrate that by coupling losses, dependencies can be incorporated in risk analysis, avoiding the underestimation of risk. Based on maximum discharge data of river basins and stream networks, we present and discuss different ways to couple loss distributions of basins while explicitly incorporating tail dependencies. We distinguish between coupling methods that require river structure data for the analysis and those that do not. For the later approach we propose a minimax algorithm to choose coupled basin pairs so that the underestimation of risk is avoided and the use of river structure data is not needed. The proposed methodology is especially useful for large-scale analysis and we motivate and apply our method using the case of Romania. The approach can be easily extended to other countries and natural hazards.

Declining vulnerability to river floods and the global benefits of adaptation

The global impacts of river floods are substantial and rising. Effective adaptation to the increasing risks requires an in-depth understanding of the physical and socioeconomic drivers of risk. Whereas the modeling of flood hazard and exposure has improved greatly, compelling evidence on spatiotemporal patterns in vulnerability of societies around the world is still lacking. Due to this knowledge gap, the effects of vulnerability on global flood risk are not fully understood, and future projections of fatalities and losses available today are based on simplistic assumptions or do not include vulnerability. We show for the first time (to our knowledge) that trends and fluctuations in vulnerability to river floods around the world can be estimated by dynamic high-resolution modeling of flood hazard and exposure. We find that rising per-capita income coincided with a global decline in vulnerability between 1980 and 2010, which is reflected in decreasing mortality and losses as a share of the people and gross domestic product exposed to inundation. The results also demonstrate that vulnerability levels in low- and high-income countries have been converging, due to a relatively strong trend of vulnerability reduction in developing countries. Finally, we present projections of flood losses and fatalities under 100 individual scenario and model combinations, and three possible global vulnerability scenarios. The projections emphasize that materialized flood risk largely results from human behavior and that future risk increases can be largely contained using effective disaster risk reduction strategies.

Local spatial and temporal factors influencing population and societal vulnerability to natural disasters

The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county-level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county-level social vulnerability. The results demonstrate that the dynamic characteristics of China's county-level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering-scattering-gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.

Measuring psychological resilience to disasters: are evidence-based indicators an achievable goal?

Despite rising interest on the concept of societal resilience and its measurement, little has been done to provide operational indicators. Importantly, an evidence-based approach to assess the suitability of indicators remains unexplored. Furthermore few approaches that exist do not investigate indicators of psychological resilience, which is emerging as an important component of societal resilience to disasters. Disasters are events which overwhelm local capacities, often producing human losses, injury and damage to the affected communities. As climate hazards and disasters are likely to increase in the coming decades, strengthening the capacity of societies to withstand these shocks and recover quickly is vital. In this review, we search the Web of Knowledge to summarize the evidence on indicators of psychological resilience to disasters and provided a qualitative assessment of six selected studies. We find that an evidence-based approach using features from systematic reviews is useful to compile, select and assess the evidence and elucidate robust indicators. We conclude that strong social support received after a disaster is associated with an increased psychological resilience whereas a female gender is connected with a decrease in the likelihood of a resilient outcome. These results are consistent across disaster settings and cultures and are representative of approximately 13 million disaster-exposed civilians of adult age. An approach such as this that collects and evaluates evidence will allow indicators of resilience to be much more revealing and useful in the future. They will provide a robust basis to prioritize indicators to act upon through intersectoral policies and post-disaster public health interventions.

Projections of future extreme weather losses under changes in climate and exposure

Many attempts are made to assess future changes in extreme weather events due to anthropogenic climate change, but few studies have estimated the potential change in economic losses from such events. Projecting losses is more complex as it requires insight into the change in the weather hazard but also into exposure and vulnerability of assets. This article discusses the issues involved as well as a framework for projecting future losses, and provides an overview of some state-of-the-art projections. Estimates of changes in losses from cyclones and floods are given, and particular attention is paid to the different approaches and assumptions. All projections show increases in extreme weather losses due to climate change. Flood losses are generally projected to increase more rapidly than losses from tropical and extra-tropical cyclones. However, for the period until the year 2040, the contribution from increasing exposure and value of capital at risk to future losses is likely to be equal or larger than the contribution from anthropogenic climate change. Given the fact that the occurrence of loss events also varies over time due to natural climate variability, the signal from anthropogenic climate change is likely to be lost among the other causes for changes in risk, at least during the period until 2040. More efforts are needed to arrive at a comprehensive approach that includes quantification of changes in hazard, exposure, and vulnerability, as well as adaptation effects.

Post Hoc Evaluation of Hazardous Weather: Snowstorms in the Montréal, Québec, Area in March 2008

The post hoc evaluation of meteorological events that have severe consequences for individuals, property, economic activities, and the environment is a powerful analytical tool. The paper provides a factual and evidence-based analytical framework for the study of hazardous weather events that have major social, economic, and environmental effects. It is a process that seeks to improve and to determine the relevance of services. The analysis of the winter storms that hit the Montréal area in early March 2008 examines the relationship between the storms, the collapse of 18 roofs, the death of 3 persons, and the closure of more than 200 schools. It highlights the limited perception of the risks associated with unusual and severe winter weather. It reveals the lack of attention given in meteorology to the important paradigmatic relationship that exists between the weather elements, the associated risk factors, and the ensuing impacts. This study reveals the need for improvements in meteorological services related to risk communication and knowledge management.