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.     

Modelling daily multivariate pollutant data at multiple sites

Summary. This paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4-year period. Such multiple-pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6-day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling.     

The use of GIS in informal settlement upgrading: its role and impact on the community and on local government

The Urban Geographical Information Systems (GIS) Group within the Department of Civil Engineering at the University of Cape Town has been coordinating a pilot informal settlement upgrading in Cape Town since 1998. The project objective has been the evolution of a model-based approach to informal settlement upgrading that is both structured and replicable. It was felt that the only way this could be achieved was through the use of a spatial data management system operated through a GIS system. The spatial database has been used for all facets of data collection and data process and forms the basis for all decision-making. Thus it covers all physical data pertaining to the site, cadastral and shack data, demographic and socio-economic data (with an in-depth review of every household) economic opportunities and physical planning and design data. The result is a comprehensive, integrated, settlement upgrading methodology that is built upon a GIS-based spatial data management framework. Such a framework is seen as the basic building block for large-scale informal settlement upgrading.     

改善全息圆锥准直线及提高准直精度的一种方法

用一张计算全息图可产生锥面波,锥面波衍射图样的中心在空间是连续的,可作为直线基准。本文提出了提高锥面波直线准直精度的一种方法即空间滤波法。实验结果与理论分析一致。     

论历史街区保护与再生的原则和手段

文章主要论述了历史街区在现代化城市建设中的重要价值;对其进行保护与再生的基本原则,即文化认同原则、保用结合原则和可持续性原则;历史街区保护与再生手段,包括对原有建筑物的保护与更新、对原有街区空间肌理的维护与扩展、对原有街区人文精神的继承与延续。     

金融集聚与江淮城市群城镇化空间计量分析

基于江淮城市群2002—2012年10个城市面板数据,构建金融集聚和人口城镇化相关的指标体系,通过计算金融集聚Moran's I得出江淮城市群具有空间正相关关系,并以此建立了金融集聚和人口城镇化空间滞后模型和空间误差模型。结果显示,江淮城市群金融集聚对人口城镇化发展有促进作用,且处于上升发展趋势。控制变量人口密度、二三产业从业人员比率对人口城镇化的发展有一定影响。     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Binary probability maps using a hidden conditional autoregressive Gaussian process with an application to Finnish common toad data

The Finnish common toad data of Heikkinen and Hogmander are reanalysed using an alternative fully Bayesian model that does not require a pseudolikelihood approximation and an alternative prior distribution for the true presence or absence status of toads in each 10 km×10 km square. Markov chain Monte Carlo methods are used to obtain posterior probability estimates of the square-specific presences of the common toad and these are presented as a map. The results are different from those of Heikkinen and Hogmander and we offer an explanation in terms of the prior used for square-specific presence of the toads. We suggest that our approach is more faithful to the data and avoids unnecessary confounding of effects. We demonstrate how to extend our model efficiently with square-specific covariates and illustrate this by introducing deterministic spatial changes.     

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。