基于同城化视角的人口空间动态与区域经济发展耦合关联机理研究

人口空间动态与区域经济发展的耦合关系是同城化区域间共同面对的重要课题。本文在解构人口空间动态维度主体的基础上,将人口空间动态的诉求机制与动因机理架构于更低交易费用的利益诉求之上,解析人口空间动态的模式选择,深入探讨人口空间动态与区域经济发展的耦合关联性。在此基础上,提出引导人口流动与合理分布的策略。研究成果将有助于更好地理解同城化态势下人口空间演变的动因机理,为同城区域的经济发展、城市规划、文化交融等方面的科学设计提供重要参考。     

Earthquakes occurrences estimation through a parametric semi-Markov approach

The estimation of earthquakes’ occurrences prediction in seismic areas is a challenging problem in seismology and earthquake engineering. Indeed, the prevention and the quantification of possible damage provoked by destructive earthquakes are directly linked to this kind of prevision. In our paper, we adopt a parametric semi-Markov approach. This model assumes that a sequence of earthquakes is seen as a Markov process and besides it permits to take into consideration the more realistic assumption of events’ dependence in space and time. The elapsed time between two consecutive events is modeled as a general Weibull distribution. We determine then the transition probabilities and the so-called crossing states probabilities. We conclude then with a Monte Carlo simulation and the model is validated through a large database containing real data.     

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。     

Optimal financing and dividend policy with Markovian switching regimes

This work investigates an optimal financing and dividend problem for an insurer whose surplus process is modulated by an observable continuous-time and finite-state Markov chain. We assume that the insurer should never go bankrupt by issuing new equity. The goal of the insurer is to maximize the expected present value of the dividends payout minus the discounted cost of equity issuance. We obtain the optimal policies and explicit expressions for the value functions when the risk reserve process is modeled by both upward jump model and its diffusion approximation. Numerical illustrations of the sensitivities of the model parameters are provided.     

Hierarchical likelihood approach to non-Gaussian factor analysis

Factor models, structural equation models (SEMs) and random-effect models share the common feature that they assume latent or unobserved random variables. Factor models and SEMs allow well developed procedures for a rich class of covariance models with many parameters, while random-effect models allow well developed procedures for non-normal models including heavy-tailed distributions for responses and random effects. In this paper, we show how these two developments can be combined to result in an extremely rich class of models, which can be beneficial to both areas. A new fitting procedures for binary factor models and a robust estimation approach for continuous factor models are proposed.     

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.     

Reframing Resilience: Equitable Access to Essential Services

We urgently need to put the concept of resilience into practice if we are to prepare our communities for climate change and exacerbated natural hazards. Yet, despite the extensive discussion surrounding community resilience, operationalizing the concept remains challenging. The dominant approaches for assessing resilience focus on either evaluating community characteristics or infrastructure functionality. While both remain useful, they have several limitations to their ability to provide actionable insight. More importantly, the current conceptualizations do not consider essential services or how access is impaired by hazards. We argue that people need access to services such as food, education, health care, and cultural amenities, in addition to water, power, sanitation, and communications, to get back some semblance of normal life. Providing equitable access to these types of services and quickly restoring that access following a disruption are paramount to community resilience. We propose a new conceptualization of community resilience that is based on access to essential services. This reframing of resilience facilitates a new measure of resilience that is spatially explicit and operational. Using two illustrative examples from the impacts of Hurricanes Florence and Michael, we demonstrate how decisionmakers and planners can use this framework to visualize the effect of a hazard and quantify resilience-enhancing interventions. This “equitable access to essentials” approach to community resilience integrates with spatial planning, and will enable communities not only to “bounce back” from a disruption, but to “bound forward” and improve the resilience and quality of life for all residents.     

Spatial Distribution of Flood Risk and Quality of Spatial Management: Case Study in Odra Valley,Poland

This article presents methodological solutions aimed at presenting the spatial distribution of flood risk and quality of spatial management (land use), indicating both those areas used reasonably and those requiring modification. The purpose was to identify key risk areas and risk‐free areas from the point of view of human security and activity on the floodplains, based on the examples of the vicinities of Wroclaw and Raciborz in the Odra Valley, Poland. Due to recent climate change, Poland has suffered the effects of severe flooding (e.g., 1997, 2001, 2010). The analyses conducted were motivated by the European Parliament and Council's recently implemented Directive 2007/60/WE, as well as by the demand for studies for local spatial planning. The analysis indicates that reasonably developed areas do not account for the majority of those studied, making up 36% of the Wroclaw area and 15% of the Raciborz area.     

Bootstrapping Periodic State-Space Models

This article is concerned with how the bootstrap can be applied to study conditional forecast error distributions and construct prediction regions for future observations in periodic time-varying state-space models. We derive, first, an algorithm for assessing the precision of quasi-maximum likelihood estimates of the parameters. As a result, the derived algorithm is exploited for numerically evaluating the conditional forecast accuracy of a periodic time series model expressed in state space form. We propose a method which requires the backward, or reverse-time, representation of the model for assessing conditional forecast errors. Finally, the small sample properties of the proposed procedures will be investigated by some simulation studies. Furthermore, we illustrate the results by applying the proposed method to a real time series.