Palm Distributions for Log Gaussian Cox Processes

This paper establishes a remarkable result regarding Palm distributions for a log Gaussian Cox process: the reduced Palm distribution for a log Gaussian Cox process is itself a log Gaussian Cox process that only differs from the original log Gaussian Cox process in the intensity function. This new result is used to study functional summaries for log Gaussian Cox processes.     

Residential mobility and the spatial dispersion of personal networks: Effects on social support

Using a representative national sample of personal networks, this article explores how the spatial dispersion of networks, residential mobility and social support are linked. Three issues will be addressed here. Firstly, how is the spatial dispersion of personal networks related to individuals’ social characteristics, network composition and residential mobility? Secondly, how do the spatial dispersion of networks, residential mobility and their combined effect influence the number and (thirdly) the structure of emotional support ties? Results showed that the extent of the support was affected neither by the geographical distribution of the networks nor by residential mobility. Living far from one's birthplace, however, exerted two distinct, and opposite effects on the support network structure. On the one hand, mobility led to high spatial dispersion of personal contacts, which in turn favored a sparsely knit network centered around the mobile individual. On the other hand, by controlling for the effect of distance between the contacts, we found that individuals that cited long-distance ties tended to be part of more transitive support networks than those that cited local ties. We interpreted the latter effect as evidence that transitive ties may survive greater spatial distances than intransitive ones. These findings are discussed in view of spatial mobility and social network research.     

城市空间关系问题研究——以黄冈与武汉城市圈内周边城市空间关系为例

城市化的快速发展使得城市空间关系不断变化,如何准确界定城市空间关系对城市战略选择至关重要。本文在总结前人对城市空间关系研究的基础上,将城市空间关系分为依附型、竞争型、共生型、异质型四种。进而在此基础上以黄冈为案例,研究黄冈与武汉城市圈内周边城市的关系,并提出黄冈的定位和发展战略。     

90年代以来甘肃省人力资本水平空间差异探讨

随着知识生产与创造的高素质科技劳动者逐步成为一个地区能否吸引现代经济活动的关键．人力资本在区域经济增长中越来越受到人们的关注。文章从时间和空间两方面入手．探讨了90年代以来甘肃省人力资本水平的空间差异及其变化状况。结果表明：甘肃省区域人力资本水平得到明显提高。人力资本水平的地理空间格局由1990年的中部高、西北和东南部低的“凸”字形分布渐渐转向2000年的中西北部高。而东南部低的倾斜式分布：区域人力资本水平差异仍然较大,其增长幅度的区域差异也十分显著。文章还就人力资本差异的形成机制进行了探讨,并提出了促使区域人力资本可持续发展的相关建议和对策。     

城市多元生鲜市场空间格局的演变及其相互关联——以昆明主城区为例

生鲜市场关系到民生、城乡经济联系和就业等问题,孤立地研究某一类型的生鲜市场具有片面性,不利于理解整个城市的生鲜市场功能和全局管理。通过对昆明市主城区各种生鲜市场实地调查和问卷,运用ArcGIS空间叠加分析、核密度分析和统计分析等多种方法描述了该城市多元生鲜市场的空间格局的形成和演变,归纳不同生鲜市场的特征及其多元生鲜市场共存格局的原因,并对未来演变趋势进行分析,提出此消彼长的互动关联性对各主体所隐含的机遇和挑战,由此说明从地域视角开展的研究,能综合把握全局特征,以及各类型之间的彼此关联性,可弥补以往单一类型研究的不足,并对政府的相关决策进行讨论。     

“空间、空间生产”五问——对张之沧教授几个观点的质疑

既然空间是物质的存在方式,它就不可能具有主观性、意识性,也不可能是精神的存在方式。先在的、外在于人类实践的自然空间不可能是人创造的。我们在改造自然的同时,也改变了自然事物的原有空间形式,新的空间形式是适合于人类生活的。但我们不能创造物质本身,只能改变物质的具体形态,因而,我们也不能创造空间本身,只能创造具体事物的空间形式。社会存在是物质的高级存在方式,社会存在的空间形式可以称之为社会空间。但"社会空间"不表示这种空间具有社会性。社会存在包含着自然存在,不是自然存在的存在是非存在。社会空间实际上是社会存在的自然物质基础具有的空间形式。空间是物质的自然属性而非社会属性,"社会空间"不具有社会性。空间的生产只是生产的一个方面,不能用"空间生产"的概念代替"物质生活资料的生产"和"生产方式"的概念,应当从生产方式来理解人类历史和帝国主义、霸权主义在空间上的扩张、争夺。     

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.     

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.     

Causes of Increasing Ages at Marriage: An International Regression Study

Abstract

The mean age at marriage is increasing in nearly all regions of the world, with the gender age difference at marriage tending to decrease. Five hypotheses for these trends (economic modernization, supply and demand, social/cultural/religious influences, healthcare quality, and longevity risk sharing) are tested through cross-country regression analyses of the timing and prevalence of marriage, using 40 explanatory variables from 156 countries. The dependent variables are female mean age at marriage, gender age difference, and proportion of females married by age 20-24. Ample evidence of the impact of economic modernization and education is found. The influence of cultural beliefs is evidenced by the presence of religious variables in several selected regression equations.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.