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.     

金融集聚、生产率增长与城乡收入差距的实证分析——基于动态空间面板模型

本文利用中国2003-2013年285个地级及以上城市的统计数据,采用动态空间面板模型实证分析了金融集聚对城市总体生产率增长及其内部城乡收入差距的影响,研究结论表明：金融集聚是促进城市总体生产率增长的重要推动力,同时也是导致城市内部城乡收入差距扩大的关键因素,这主要是因为金融集聚显著推动了城市居民人均收入水平的提高,而对农村居民人均收入水平的提升作用不显著。本文的政策含义在于,在城市金融集聚的扩散阶段还没有到来之前,政府旨在缓解城乡收入差距的金融政策可能会抑制城市总体生产率增长。     

感知风险对在线旅游产品购买意愿的影响分析

旅游电子商务发展迅猛,互联网购物平台的虚拟性和远程性强化了消费者感知风险,它也成为影响旅游电子商务发展的重要因素之一.文章聚焦消费者感知风险,运用SPSS 19.0及AMOS 21.0软件,构建以5个维度的感知风险为自变量、购买意愿为因变量、信任为调节变量的结构方程模型,探讨变量间相关关系.结果发现:感知风险对信任的影响差异较大,仅经济风险感知与信任呈显著负相关;感知风险对购买意愿总效应皆呈负相关;信任作为中介变量存在明显的调节作用,有效地降低了感知风险对购买意愿的负向影响.     

Spatial approaches to measure subnational inequality: Implications for Sustainable Development Goals

The United Nations expressed an interest in reducing subnational (i.e., province and state level) inequality. We propose using a spatial decomposition of the Gini coefficient (SDGC) to track changes in subnational inequality. Typically, agencies do not track summary measures of subnational clustering of development indicators. Tracking changes in the SDGC can help measure and reduce regional inequality. To illustrate the use of the SDGC, we first present data for 93 nations to obtain cross‐sectional variation. Next, to illustrate how the SDGC trends over time, changes in the Human Development Index in Mongolia are compared to Russia and China. The SDGC can show improvement, decline and persistent clustering of subnational level inequality. The SDGC is a useful measure for the United Nations' Sustainable Development Goals.     

Doing gender in the ‘new office’

This paper investigates how gender is performed in the context of an office setting designed to promote intensive, fluid networking. We draw on an ethnographically oriented study of the move of staff into a new office building constructed primarily from glass, and incorporating open plan offices, diverse collective areas and walking routes. Although the designers aimed to invoke changes in the behaviour of all staff, they conceptualized these changes in masculine terms. We therefore analyse the gender norms materialized by the workspaces of the ‘new office’ and how women responded to these. We suggest that the new office encourages an image of the ideal worker which brings together ways of acting and interacting that have been characterized as both masculine and feminine — active movement and spontaneous encounters, but also intensive face‐to‐face interaction and deep relationship‐building. Women are driven into this mode of working in an uncompromising, almost aggressive way, but a straightforward gender‐based dynamic does not emerge in their responses, with conventional gender characteristics being reshuffled and recombined.     

On pseudo-values for regression analysis in competing risks models

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15–27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223–229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496–509, 1999) and also time-dependent solutions of pseudo-value regression equations.     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

Testing spatial randomness based on empirical distribution function: A study on lattice data

In this article, we extended the empirical distribution function based test statistic _Ik$I_k$ of Skaug and Tjostheim [1993. Nonparametric test of serial independence based on the empirical distribution function. Biometrika 80, 591–602] in the time series setting to _Dn$D_n$ for spatial lattice data and derived the asymptotic distribution of the proposed test statistic _Dn$D_n$ under the null hypothesis of spatial independence. The size and power of the proposed test statistic under conditional autoregressive model (CAR) were simulated. We applied _Dn$D_n$, Moran's I and Geary's c   to the transformed and well-studied sudden infant death syndrome data from North Carolina and found that _Dn$D_n$ produced a much smaller p$p$-value in testing spatial independence.     

Multidimensional poverty: An alternative measurement approach for the United States?

International poverty research has increasingly underscored the need to use multidimensional approaches to measure poverty. Largely embraced in Europe and elsewhere, this has not had much impact on the way poverty is measured in the United States. In this paper, I use a comprehensive multidimensional framework including economic well-being, capability, and social inclusion to examine poverty in the US. Data from the 2004 General Social Survey support the interconnectedness among these poverty dimensions, indicating that the multidimensional framework utilizing a comprehensive set of information provides a compelling value added to poverty measurement. The suggested demographic characteristics of the various categories of the poor are somewhat similar between this approach and other traditional approaches. But the more comprehensive and accurate measurement outcomes from this approach help policymakers target resources at the specific groups.     

A flexible semiparametric transformation model for survival data

I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested.