县级城镇空间拓展的规划对策

城市规划对于一个城市未来的发展至关重要,结合县级城镇特点,建设性的提出了城镇空间拓展的规划对策,可供相关人士参考。     

基于复杂系统的物流企业空间聚集研究

本文把物流行业作为一个复杂系统．从系统的自组织角度出定。通过构造吸引力函数．给出了各地区物流行业投资吸引力的计算方法；同时．引用吸引子的概念描述了物流企业的空间聚集演化动力学系统建模方法；最后，本文分析了北京、天津、上海三地的物流投资吸引力厦聚集特性。     

住房、交通与城市空间规划

首先归纳了城市空间结构理论框架下居住模式和交通体系空间互动关系的基本理论,之后对国际学术界相关的定量研究成果进行了综述,着重讨论了住房与交通相互作用所形成的社会效应,包括对居民住房支付能力、中低收入者福利和环境的影响,最后提出了住房和交通相协调的城市空间规划的三个目标,并就我国目前的实际情况提出了若干政策建议.     

城市空间郊区化演变趋势及问题——以上海市为例

在分析上海城市经济空间、居住空间和人口空间郊区化演变趋势特征以及郊区化演变的动力机制的基础上,进行了中外城市空间郊区化演变比较,提出了上海城市郊区化演变过程中需要警惕的一些问题,并针对这些问题进行了一定的思考.     

中西方居住空间分异动因比较研究

城市居住空间分异是城市化过程中难以避免的现象,中西方城市居住空间分异的形成动因不同.通过对国内外城市居住空间分异的动因的分析、比较,归纳出其发展过程中呈现的不同动因.     

大城市社会空间结构演变及其治理——以上海市为例

随着市场化改革的深入,当前中国大城市社会空间正呈现重构与分异的演变趋势,同时也带来许多城市社会问题.以上海为例,在分析当前中国大城市呈现的社会空间结构演变的特征基础上,分析了可能产生的负社会效应,并提出了一些城市空间治理的对策建议.     

Knowledge,diversity and entrepreneurship: a spatial analysis of new firm formation in Great Britain

This paper argues that the diversity of a region's knowledge stock and its balance between knowledge-based manufacturing and services are both important determinants of variations in the rate of new business formation across spatial areas. An empirical study of new firm formation across 408 local unitary authorities and districts in Great Britain over the period 2001–2007 is presented, taking explicit account of the interactions amongst spatial units through an econometric modelling procedure involving maximum likelihood spatial models and alternative spatial weighing matrices. The results indicate that both related and unrelated knowledge diversity have a positive impact on the rate of business formation, whilst aggregate local economy-wide industrial and ethnic diversity has no impact; the division of the knowledge stock between services and manufacturing is also significant, with services having a positive impact on new business formation in contrast to a negative impact for high-technology manufacturing. There is also some support for the view that low wages and economic inactivity constrain new business formation, whilst a competitive regional environment has a positive impact. The results imply that polices should be aimed at creating a diverse set of knowledge-based industries rather than specialization in a narrow range.     

The Spatial Durbin Model and the Common Factor Tests

Abstract

The spatial Durbin model occupies an interesting position in the field of spatial econometrics. It is the reduced form of a model with cross-sectional dependence in the errors and it may be used as the nesting equation in a more general approach of model selection. Specifically, in this equation we obtain the common factor tests (of which the likelihood ratio is the best known) whose objective is to discriminate between substantive and residual dependence in an apparently misspecified equation. Our paper tries to delve deeper into the role of the spatial Durbin model in the problem of specifying a spatial econometric model. We include a Monte Carlo study related to the performance of the common factor tests presented in the paper in small sample sizes.     

A Spatial Modelling Framework for Income Estimation

Abstract

The main aim of this paper is to provide a spatial modelling framework for income estimation through the application of a contemporary spatial analysis technique. The application refers to the modelling of mean recorded household income in the area covered by the postcodes of the municipality of Athens in 2001. The main findings suggest that there is a very strong relationship between the proportion of people with a postgraduate qualification (namely, a Master's degree or PhD) and mean household income. Furthermore, there is evidence that this relationship is not stationary across space. This finding allows a better understanding as well as modelling of the main determinants of income in Athens.     

Small-sample Properties of Panel Spatial Autoregressive Models: Comparison of the Bayesian and Maximum Likelihood Methods

Abstract

This article considers autoregressive (SAR) models. We method to estimate the parameters of likelihood (ML) method. Our Bayesian by the Monte Carlo studies. We found the efficient as the ML estimators.