A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk

Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate‐area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge‐ and data‐based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability.     

Are People Willing to Buy Natural Disaster Insurance in China? Risk Awareness,Insurance Acceptance,and Willingness to Pay

After the Wenchuan earthquake (magnitude 7.9, May 12, 2008), intensive debates on how China should establish a natural disaster insurance system were initiated among researchers, policymakers, and insurance professionals. Our focus was the social aspects of disaster insurance, explored in China through a nationwide survey. Our questionnaires investigated people's risk awareness, insurance acceptance, their opinions on governmental measures for disaster management, and their willingness to pay for disaster house insurance. We analyzed the results at both regional and individual scales. We found that the integrated hazard index and respondents’ experience of insurance (considered objective factors), and their opinions on the importance of insurance and government responsibility (considered subjective factors) showed strong correlation with the regional overall acceptance of disaster insurance. An individual's decision to participate highly depended on his/her experience of both insurance and disaster and his/her opinion on the importance of insurance as a coping mechanism. Respondents from poverty‐stricken or less‐developed counties were not necessarily more reluctant to accept natural disaster insurance, though they exhibited relatively lower ability to afford insurance. In general, respondents had correct perceptions of natural disasters in their areas; however, people from regions with a greater multihazard threat showed less willingness to accept disaster insurance because they tended to expect the government to undertake to cover losses and considered insurance to be less important. People's willingness to pay for an assumed disaster house insurance was also investigated and analyzed. We consequently discuss the policy implications for developing a disaster insurance system in China.     

Spatially Representing Vulnerability to Extreme Rain Events Using Midwestern Farmers’ Objective and Perceived Attributes of Adaptive Capacity

Potential climate‐change‐related impacts to agriculture in the upper Midwest pose serious economic and ecological risks to the U.S. and the global economy. On a local level, farmers are at the forefront of responding to the impacts of climate change. Hence, it is important to understand how farmers and their farm operations may be more or less vulnerable to changes in the climate. A vulnerability index is a tool commonly used by researchers and practitioners to represent the geographical distribution of vulnerability in response to global change. Most vulnerability assessments measure objective adaptive capacity using secondary data collected by governmental agencies. However, other scholarship on human behavior has noted that sociocultural and cognitive factors, such as risk perceptions and perceived capacity, are consequential for modulating people's actual vulnerability. Thus, traditional assessments can potentially overlook people's subjective perceptions of changes in climate and extreme weather events and the extent to which people feel prepared to take necessary steps to cope with and respond to the negative effects of climate change. This article addresses this knowledge gap by: (1) incorporating perceived adaptive capacity into a vulnerability assessment; (2) using spatial smoothing to aggregate individual‐level vulnerabilities to the county level; and (3) evaluating the relationships among different dimensions of adaptive capacity to examine whether perceived capacity should be integrated into vulnerability assessments. The result suggests that vulnerability assessments that rely only on objective measures might miss important sociocognitive dimensions of capacity. Vulnerability indices and maps presented in this article can inform engagement strategies for improving environmental sustainability in the region.     

Elite Recruitment and Political Stability: The Impact of the Abolition of China's Civil Service Exam

This paper studies how the abolition of an elite recruitment system—China's civil exam system that lasted over 1,300 years—affects political stability. Employing a panel data set across 262 prefectures and exploring the variations in the quotas on the entry‐level exam candidates, we find that higher quotas per capita were associated with a higher probability of revolution participation after the abolition and a higher incidence of uprisings in 1911 that marked the end of the 2,000 years of imperial rule. This finding is robust to various checks including using the number of small rivers and short‐run exam performance before the quota system as instruments. The patterns in the data appear most consistent with the interpretation that in regions with higher quotas per capita under the exam system, more would‐be elites were negatively affected by the abolition. In addition, we document that modern human capital in the form of those studying in Japan also contributed to the revolution and that social capital strengthened the effect of quotas on revolution participation.     

Impact Factors and Risk Analysis of Tropical Cyclones on a Highway Network

Coastal areas typically have high social and economic development and are likely to suffer huge losses due to tropical cyclones. These cyclones have a great impact on the transportation network, but there have been a limited number of studies about tropical‐cyclone‐induced transportation network functional damages, especially in Asia. This study develops an innovative measurement and analytical tool for highway network functional damage and risk in the context of a tropical cyclone, with which we explored the critical spatial characteristics of tropical cyclones with regard to functional damage to a highway network by developing linear regression models to quantify their relationship. Furthermore, we assessed the network's functional risk and calculated the return periods under different damage levels. In our analyses, we consider the real‐world highway network of Hainan province, China. Our results illustrate that the most important spatial characteristics were location (in particular, the midlands), travel distance, landfalling status, and origin coordinates. However, the trajectory direction did not obviously affect the results. Our analyses indicate that the highway network of Hainan province may suffer from a 90% functional damage scenario every 4.28 years. These results have critical policy implications for the transport sector in reference to emergency planning and disaster reduction.     

Corporations and economic inequality around the world: The paradox of hierarchy

Using time-series data from the US since 1950 and from 53 countries around the world in 2006, this chapter documents a strong negative relation between an economy's employment concentration (that is, the proportion of the labor force employed by the largest 10, 25, or 50 firms) and its level of income inequality. Within the US, we find that trends in the relative size of the largest employers (up in the 1960s and 1970s, down in the 1980s and 1990s, up in the 2000s) are directly linked to changes in inequality, and that corporate size is a proximal cause of the extravagant increase in social inequality over the past generation. We conclude that organization theory can provide a distinctive contribution to understanding societal outcomes.     

Multidimensional Approach for Tsunami Vulnerability Assessment: Framing the Territorial Impacts in Two Municipalities in Portugal

This study presents a new multidimensional methodology for tsunami vulnerability assessment that combines the morphological, structural, social, and tax component of vulnerability. This new approach can be distinguished from previous methodologies that focused primarily on the evaluation of potentially affected buildings and did not use tsunami numerical modeling. The methodology was applied to the Figueira da Foz and Vila do Bispo municipalities in Portugal. For each area, the potential tsunami‐inundated areas were calculated considering the 1755 Lisbon tsunami, which is the greatest disaster caused by natural hazards that ever occurred in Portugal. Furthermore, the four components of the vulnerability were calculated to obtain a composite vulnerability index. This methodology enables us to differentiate the two areas in their vulnerability, highlighting the characteristics of the territory components. This methodology can be a starting point for the creation of a local assessment framework at the municipal scale related to tsunami risk. In addition, the methodology is an important support for the different local stakeholders.     

A Sensitivity Analysis of the Social Vulnerability Index

The Social Vulnerability Index (SoVI), created by Cutter et al. (2003) , examined the spatial patterns of social vulnerability to natural hazards at the county level in the United States in order to describe and understand the social burdens of risk. The purpose of this article is to examine the sensitivity of quantitative features underlying the SoVI approach to changes in its construction, the scale at which it is applied, the set of variables used, and to various geographic contexts. First, the SoVI was calculated for multiple aggregation levels in the State of South Carolina and with a subset of the original variables to determine the impact of scalar and variable changes on index construction. Second, to test the sensitivity of the algorithm to changes in construction, and to determine if that sensitivity was constant in various geographic contexts, census data were collected at a submetropolitan level for three study sites: Charleston, SC; Los Angeles, CA; and New Orleans, LA. Fifty-four unique variations of the SoVI were calculated for each study area and evaluated using factorial analysis. These results were then compared across study areas to evaluate the impact of changing geographic context. While decreases in the scale of aggregation were found to result in decreases in the variance explained by principal components analysis (PCA), and in increases in the variance of the resulting index values, the subjective interpretations yielded from the SoVI remained fairly stable. The algorithm's sensitivity to certain changes in index construction differed somewhat among the study areas. Understanding the impacts of changes in index construction and scale are crucial in increasing user confidence in metrics designed to represent the extremely complex phenomenon of social vulnerability.     

The importance of recognizing generational differences in HRD policy and practices: a study of workers in Qinhuangdao,China

Amid the success of becoming a market economy and a member of the World Trade Organization, the response to China's economic reform is not the same across generational cohorts. The Cultural Revolution (CR) cohort, a by-product of the history of contemporary China, has been left behind. Based on the results of 384 highly structured interviews, this study found that the CR cohort sampled was the least satisfied with China's recent economic reform. The study calls for future research and collaboration among HRD scholars and practitioners to address generational differences and specifically the needs of the CR cohort.     

An Integrated Risk Assessment Model of Township‐Scaled Land Subsidence Based on an Evidential Reasoning Algorithm and Fuzzy Set Theory

Land subsidence risk assessment (LSRA) is a multi‐attribute decision analysis (MADA) problem and is often characterized by both quantitative and qualitative attributes with various types of uncertainty. Therefore, the problem needs to be modeled and analyzed using methods that can handle uncertainty. In this article, we propose an integrated assessment model based on the evidential reasoning (ER) algorithm and fuzzy set theory. The assessment model is structured as a hierarchical framework that regards land subsidence risk as a composite of two key factors: hazard and vulnerability. These factors can be described by a set of basic indicators defined by assessment grades with attributes for transforming both numerical data and subjective judgments into a belief structure. The factor‐level attributes of hazard and vulnerability are combined using the ER algorithm, which is based on the information from a belief structure calculated by the Dempster‐Shafer (D‐S) theory, and a distributed fuzzy belief structure calculated by fuzzy set theory. The results from the combined algorithms yield distributed assessment grade matrices. The application of the model to the Xixi‐Chengnan area, China, illustrates its usefulness and validity for LSRA. The model utilizes a combination of all types of evidence, including all assessment information—quantitative or qualitative, complete or incomplete, and precise or imprecise—to provide assessment grades that define risk assessment on the basis of hazard and vulnerability. The results will enable risk managers to apply different risk prevention measures and mitigation planning based on the calculated risk states.     

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top‐down (or downscaling) approach to disaggregate administrative‐unit level asset value to grid‐cell level. To do so, finding the highly correlated “surrogate” indicators is the key. A combination of three data sets—nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc‐second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time.     

社会保险与家庭金融风险资产投资

社会保险是否影响家庭在金融风险资产中的投资决策呢?本文使用两期家庭最优决策模型得到的理论分析结果表明,社会保险不仅能够提高家庭在风险资产中的投资广度和深度,而且对于不确定性更大、风险承受更强家庭的影响更为显著.基于省级层面得到的宏观实证检验结果和基于2011年家庭金融调查数据得到家庭层面的微观实证检验结果都支持了上述理论结论.本文的发现对于协调社会保险发展和建立多层级金融市场具有很强的政策意义.     

Assessing Major Accident Risks to Support Land‐Use Planning Using a Severity‐Vulnerability Combination Method: A Case Study in Dagushan Peninsula,China

Major accident risks posed by chemical hazards have raised major social concerns in today's China. Land‐use planning has been adopted by many countries as one of the essential elements for accident prevention. This article aims at proposing a method to assess major accident risks to support land‐use planning in the vicinity of chemical installations. This method is based on the definition of risk by the Accidental Risk Assessment Methodology for IndustrieS (ARAMIS) project and it is an expansion application of severity and vulnerability assessment tools. The severity and vulnerability indexes from the ARAMIS methodology are employed to assess both the severity and vulnerability levels, respectively. A risk matrix is devised to support risk ranking and compatibility checking. The method consists of four main steps and is presented in geographical information‐system‐based maps. As an illustration, the proposed method is applied in Dagushan Peninsula, China. The case study indicated that the method could not only aid risk regulations on existing land‐use planning, but also support future land‐use planning by offering alternatives or influencing the plans at the development stage, and thus further enhance the roles and influence of land‐use planning in the accident prevention activities in China.     

What Happens When Wal‐Mart Comes to Town: An Empirical Analysis of the Discount Retailing Industry

In the past few decades multistore retailers, especially those with 100 or more stores, have experienced substantial growth. At the same time, there is widely reported public outcry over the impact of these chain stores on other retailers and local communities. This paper develops an empirical model to assess the impact of chain stores on other discount retailers and to quantify the size of the scale economies within a chain. The model has two key features. First, it allows for flexible competition patterns among all players. Second, for chains, it incorporates the scale economies that arise from operating multiple stores in nearby regions. In doing so, the model relaxes the commonly used assumption that entry in different markets is independent. The lattice theory is exploited to solve this complicated entry game among chains and other discount retailers in a large number of markets. It is found that the negative impact of Kmart's presence on Wal‐Mart's profit was much stronger in 1988 than in 1997, while the opposite is true for the effect of Wal‐Mart's presence on Kmart's profit. Having a chain store in a market makes roughly 50% of the discount stores unprofitable. Wal‐Mart's expansion from the late 1980s to the late 1990s explains about 40–50% of the net change in the number of small discount stores and 30–40% for all other discount stores. Scale economies were important for Wal‐Mart, but less so for Kmart, and the magnitude did not grow proportionately with the chains' sizes.     

Maternal Hurricane Exposure and Fetal Distress Risk

Logistic regression and spatial analytic techniques are used to model fetal distress risk as a function of maternal exposure to Hurricane Andrew. First, monthly time series compare the proportion of infants born distressed in hurricane affected and unaffected areas. Second, resident births are analyzed in Miami‐Dade and Broward counties, before, during, and after Hurricane Andrew. Third, resident births are analyzed in all Florida locales with 100,000 or more persons, comparing exposed and unexposed gravid females. Fourth, resident births are analyzed along Hurricane Andrew's path from southern Florida to northeast Mississippi. Results show that fetal distress risk increases significantly with maternal exposure to Hurricane Andrew in second and third trimesters, adjusting for known risk factors. Distress risk also correlates with the destructive path of Hurricane Andrew, with higher incidences of fetal distress found in areas of highest exposure intensity. Hurricane exposed African‐American mothers were more likely to birth distressed infants. The policy implications of in utero costs of natural disaster exposure are discussed.     

Comparing the Economic Impact of Natural Disasters Generated by Different Input–Output Models: An Application to the 2007 Chehalis River Flood (WA)

Due to the concentration of assets in disaster‐prone zones, changes in risk landscape and in the intensity of natural events, property losses have increased considerably in recent decades. While measuring these stock damages is common practice in the literature, the assessment of economic ripple effects due to business interruption is still limited and available estimates tend to vary significantly across models. This article focuses on the most popular single‐region input–output models for disaster impact evaluation. It starts with the traditional Leontief model and then compares its assumptions and results with more complex methodologies (rebalancing algorithms, the sequential interindustry model, the dynamic inoperability input–output model, and its inventory counterpart). While the estimated losses vary across models, all the figures are based on the same event, the 2007 Chehalis River flood that impacted three rural counties in Washington State. Given that the large majority of floods take place in rural areas, this article gives the practitioner a thorough review of how future events can be assessed and guidance on model selection.     

Have Industrial Relations in the UK Really Improved?

Abstract. The number of strikes reported in British industry has been on a downward trend over the past two decades, falling in 1998 to its lowest level since records began. This may indicate that relations within British industry have improved; however, the same period has also witnessed a sharp increase in the number of individual ACAS and employment tribunal cases. We discuss possible reasons for the changes in the patterns of industrial unrest over time and use individual micro‐data to examine whether the observed decline in strike activity has actually been associated with an improvement in perceptions of workplace industrial relations.     

Environmental Taxes and the Choice of Green Technology

We study several important aspects of using environmental taxes to motivate the choice of innovative and “green" emissions‐reducing technologies as well as the role of fixed cost subsidies and consumer rebates in this process. In our model, a profit‐maximizing monopolistic firm facing price‐dependent demand selects emissions control technology, production quantity, and price in response to the tax, subsidy, and rebate levels set by the regulator. The available technologies vary in environmental efficiency as well as in the fixed and variable costs. Both the optimal policy for the firm and the social‐welfare maximizing policy for the regulator are analyzed. We find that the firm's reaction to an increase in taxes may be non‐monotone: while an initial increase in taxes may motivate a switch to a greener technology, further tax increases may motivate a reverse switch. For the regulator, we compare the social welfare achievable in the centralized system (which serves as an upper bound) to the highest level achievable under different classes of environmental policies. If the regulator is limited to a tax‐only policy, then when the regulator is moderately concerned with environmental impacts, the tax level that maximizes social welfare simultaneously motivates the choice of clean technology and closes the gap to the upper bound; however, both low and high levels of societal environmental concerns may lead to the choice of dirty technology and significant welfare losses as compared to the centralized case. Supplementing the environmental taxation with fixed cost subsidies and consumer rebates can eliminate this effect, expanding the range of parameters over which the green technology is chosen and often closing the welfare gap to the centralized solution.     

Unequal Regionalism: Regional Planning in China and England

This paper responds to the revival of regionalism in the past two decades by showing that such a re-emergence is unequal. Comparing the practice of regional planning in China and England, this paper has demonstrated that the party-state in China and the liberal-democratic state in England have responded differently to this revival and developed different framework for regional administration and planning. These variations have not only elaborated planners' concern about planning practice in a ‘context of difference’ from social dimension to geographical variations, but also echoed geographers' affirmation that global trends take on different characters in local setting.