Mobility of ‘the defeated’: internal migration and social advancement in a post-civil war society

The article examines the relationship between the movement across space and social mobility by analyzing the conditions for internal migration in Spain during the years of the Franco regime. Specifically it reflects on the ways in which migration from the countryside into self-constructed shantytowns in the greater Madrid area was perceived by migrants themselves, and on the strategies that enabled migration to be carried through. By focusing on the challenges that internal migration posed to the spatial practices and mobility regimes of the dictatorship, the article also explores the relationship between spatial movement, social mobility and political repression within the context of a nationalist dictatorship.     

1990—2010年长三角地区城市体系的演进——基于人口普查数据的研究

集聚经济和专业化是主导区域城市体系演进的两股主要力量,但二者对区域城市体系中不同城市的增长速度有着不同的影响.前者促进中心城市的发展,而后者主要是促进外围城市的发展.文章使用“四普”、“五普”和“六普”数据资料研究发现,尽管长三角各城市的规模都有了很大的提高,但城市体系内部总体上呈现出扁平化趋势,中小城市发展速度相对更快.这表明,专业化分工是促进长三角城市体系的主要动力.     

Spatially Balanced Sampling of Continuous Populations

When sampling from a continuous population (or distribution), we often want a rather small sample due to some cost attached to processing the sample or to collecting information in the field. Moreover, a probability sample that allows for design‐based statistical inference is often desired. Given these requirements, we want to reduce the sampling variance of the Horvitz–Thompson estimator as much as possible. To achieve this, we introduce different approaches to using the local pivotal method for selecting well‐spread samples from multidimensional continuous populations. The results of a simulation study clearly indicate that we succeed in selecting spatially balanced samples and improve the efficiency of the Horvitz–Thompson estimator.     

Bayesian spatial quantile regression for areal count data,with application on substitute care placements in Texas

Quantile regression (QR) allows one to model the effect of covariates across the entire response distribution, rather than only at the mean, but QR methods have been almost exclusively applied to continuous response variables and without considering spatial effects. Of the few studies that have performed QR on count data, none have included random spatial effects, which is an integral facet of the Bayesian spatial QR model for areal counts that we propose. Additionally, we introduce a simplifying alternative to the response variable transformation currently employed in the QR for counts literature. The efficacy of the proposed model is demonstrated via simulation study and on a real data application from the Texas Department of Family and Protective Services (TDFPS). Our model outperforms a comparable non-spatial model in both instances, as evidenced by the deviance information criterion (DIC) and coverage probabilities. With the TDFPS data, we identify one of four covariates, along with the intercept, as having a nonconstant effect across the response distribution.     

Transect-spacing design of ice cores on the Antarctic continent

Chemical analyses of ice cores, drilled deep into an ice sheet, provide a historical record of the earth's atmosphere that dates back as far as 400,000–500,000 years. Although the atmosphere mixes quite well, it is recognized that spatial variability associated with ice-core locations should be allowed for. In this article, spatial statistical methodology is applied to the design question of finding the best spacing of ice-core locations on a partial transect of Antarctica.     

Simultaneous confidence intervals for one-way layout based on generalized pivotal quantities

In this paper, we consider simultaneous confidence intervals for all-pairwise comparisons of treatment means in a one-way layout under heteroscedasticity. Two kinds of simultaneous intervals are provided based on the fiducial generalized pivotal quantities of the interest parameters. We prove that they both have asymptotically correct coverage. Simulation results and an example are also reported. It is concluded from calculational evidence that the second kind of simultaneous confidence intervals, which we provide, performs better than existing methods.     

Subsampling Variance Estimation for Non-stationary Spatial Lattice Data

ABSTRACT.  Most proposed subsampling and resampling methods in the literature assume stationary data. In many empirical applications, however, the hypothesis of stationarity can easily be rejected. In this paper, we demonstrate that moment and variance estimators based on the subsampling methodology can also be employed for different types of non-stationarity data. Consistency of estimators are demonstrated under mild moment and mixing conditions. Rates of convergence are provided, giving guidance for the appropriate choice of subshape size. Results from a small simulation study on finite-sample properties are also reported.     

空间相互作用与城市经济增长--来自长三角的证据

基于以往研究对空间相互作用方向性考虑不足的事实,文章以长三角133个县市为例,构建了融合新经济地理学与中心地理论的理论框架,将表征空间需求关联的总体市场潜能细分为来自同层级、高等级、低等级三个方向,利用空间计量模型估计了总体及不同方向的空间相互作用对长三角地区人均GDP增长与人口增长的效应。结果显示：在控制空间依赖导致的间接溢出效应等因素后,并未发现存在要素价格绝对调整或要素数量绝对调整的证据,总体市场潜能的提高同时促进了城市人均GDP增长与人口增长;就不同方向的空间相互作用而言,大城市的自身发展有利于中等城市的人均GDP增长,大城市之间、大城市对下级城市、小城市对上级城市形成了人口增长的良性互动格局,而中等城市之间以及中等城市对大城市的人口增长则表现为回流效应。     

长三角创新资源的空间错配与调整政策——基于次级和县级区域的实证研究

依据对长江三角洲次级和县级区域资源配置现状的实证研究发现：一是长江三角洲区域在经历高速重化工业化的同时,开始跨入了内生增长模式的门槛,人力资本的贡献日益显现;二是越来越多的次级区域工业生产能力和技术创新能力正在超越北京、上海以及省会城市,由此国家的科技资源和高等教育资源也应当流向这些区域,使产业、科技、教育资源能够融合在同一个空间,以大大提高整个国家技术创新投入的收益;三是转变经济发展方式和实施创新型国家建设战略的真正落脚点是支持和推动更多的次级和县级区域建立高等教育体系,创造更多的人力资本,开展更多的技术创新活动,使更多的区域采取基于技术进步的增长战略。充足的证据表明,现在到了需要采取一个重视创新资源效率的区域增长战略的时候了。     

The local geographies of welfare in Tunisia: Does neighbourhood matter?*

Amara M, Ayadi M. The local geographies of welfare in Tunisia: Does neighbourhood matter? The aim of this article is to show that spatial analysis techniques outperform non‐spatial statistical counterparts for understanding the geographic determinants of welfare and poverty in Tunisia. First, an Exploratory Spatial Data Analysis, based on a Geographical Information System, was conducted to visualise the local spatial structure of welfare. Second, a spatial autoregressive (SAR) model and a geographically weighted regression (GWR) model, respectively, were used to deal with both spatial autocorrelations and unobserved spatial heterogeneity of households' behaviours. Spatial and non‐spatial models were compared according to their predictive performances. Results of this case study confirm that SAR and GWR spatial models are preferable to the traditional non‐spatial regression model and that they give a better approximation of the Tunisian poverty map.