Approximate composite marginal likelihood inference in spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.     

Does geographical proximity matter in determining the profitability of banks?

Despite the existence of a burgeoning literature on bank profitability, yet, none of them gave due consideration to geographical proximity. I fulfill such a gap by analyzing the effects of COVID-19 on the profitability of top-rated banks. Findings confirm the prevalence of spatial dependence at both the global and sub-global with feedback effects being systematically higher than spillover effects. My study uncovers evidence of a COVID-19 induced decline in asset utilization. Findings advocate sharing economy as a potential tool to banks in combating any future pandemic risk with regionalized approach to supervision being deemed better than its globalized counterpart.     

The New Spatial Planning Act in Taiwan: A Messy Shift from Economic Development-Oriented Planning to Environmental Conservation-Oriented Planning?

On 18 December 2015, the spatial planning bill was finally passed by the Legislative Yuan in Taiwan. That was a special moment for planning community and authority since they had anticipated this moment for more than two decades. However, this is not the end of the story. On the contrary, the battle between the economic development promoters and environmental conservation supporters just starts. The Act introduces several new changes to secure its goal of nationwide sustainable development, but the introduction also raises some tensions. This article reviews the tensions and their socio-political context, and gives some suggestions to the Taiwanese government.     

高等教育布局结构优化的理论前提

作为经济社会发展的重要助推力量,高等教育布局结构优化的影响因素是多维的,主要包括国家战略、区域经济、社会文化、人口结构以及地理空间等等。科学预设高等教育布局结构优化的应然目标,需要正视布局结构优化的目标限度,并重点从“社会满足度”和“个体满足度”两个方面予以考量。实施高等教育布局结构的优化,需要甄选多向度的优化理路,实现多元布局手段的优势集合。要坚持“去趋同化与优势集中相统整”“域内均衡与域间协调相统整”“突显公平与保障效益相统整”“积极适应与适当超越相统整”“内循规律与外在干预相统整”等原则。     

New Spatial Concepts Between Innovation and Lock-in: The Case of the Dutch Deltametropolis

It is often proclaimed that planning concepts serve an innovative function in the political process. They are praised as powerful tools that carry new insights and understandings into policy arenas. By examining the emergence and development of a new planning concept in the Dutch national spatial policy-making process, the deltametropolis concept, this paper questions this position. It argues that new spatial concepts tend to lose much of their innovative capacities when they enter the political arena and become encased in the traditional urban–rural planning discourse, thus reaffirming established planning orientations. The Dutch case of the deltametropolis illustrates that new spatial concepts may entail potential for innovation as much as for lock-in.     

Integrated nested Laplace approximation for the analysis of count data via the combined model: A simulation study

The combined model accounts for different forms of extra-variability and has traditionally been applied in the likelihood framework, or in the Bayesian setting via Markov chain Monte Carlo. In this article, integrated nested Laplace approximation is investigated as an alternative estimation method for the combined model for count data, and compared with the former estimation techniques. Longitudinal, spatial, and multi-hierarchical data scenarios are investigated in three case studies as well as a simulation study. As a conclusion, integrated nested Laplace approximation provides fast and precise estimation, while avoiding convergence problems often seen when using Markov chain Monte Carlo.     

适应性网络空间抽样的设计及估计研究

随着大数据背景下抽样环境复杂化，特别是3S技术（遥感技术、地理信息系统和全球定位系统）日趋成熟，越来越多社会经济问题涉及空间抽样，其样本呈现出规模相对稀少、分布不均匀、局部聚集的特征，使得传统抽样调查面临着严重挑战。本文介绍了适应性抽样技术应用于空间网络环境的基本原理、主要操作步骤和马尔可夫链蒙特卡罗估计推断，并以广州市天河区的商户抽样为例讨论实际操作中应注意的问题，以期为流动人口、环境污染、区域经济研究等方面调查提供理论支撑和实证方法参考。     

Spatial Data Usage,Spatial Thinking and Spatial Knowledge Generation: The Case of Planning Practitioners in Arequipa,Peru

Spatial thinking and spatial knowledge generation in decision-making are still not mature fields of study in planning research, despite these being crucial elements in addressing the issues of the twenty-first-century city. This article contributes to their understanding by exploring their interrelationships with spatial data usage. Through storylines, it analyzes the arguments that planning practitioners offer in support of infrastructure-led decisions in Arequipa (Peru), before and after spatial data usage. The article concludes that spatial data usage improve spatial thinking to different extents, yet suggests aligning spatial data generation and the inclusion of GIS-based spatial analyses with the spatial knowledge needed by routine planning practice.