Cross-scale analysis of cluster correspondence using different operational neighborhoods

Cluster correspondence analysis examines the spatial autocorrelation of multi-location events at the local scale. This paper argues that patterns of cluster correspondence are highly sensitive to the definition of operational neighborhoods that form the spatial units of analysis. A subset of multi-location events is examined for cluster correspondence if they are associated with the same operational neighborhood. This paper discusses the construction of operational neighborhoods for cluster correspondence analysis based on the spatial properties of the underlying zoning system and the scales at which the zones are aggregated into neighborhoods. Impacts of this construction on the degree of cluster correspondence are also analyzed. Empirical analyses of cluster correspondence between paired vehicle theft and recovery locations are conducted on different zoning methods and across a series of geographic scales and the dynamics of cluster correspondence patterns are discussed.      

贝加尔裂谷系及邻区断裂现今活动变化及成因(英文)

已有的地质和地球物理方法无法对年、月、旬等现今时间尺度上的断层活动变化进行有效预测。运用基于地震活动定量指数运算程序的地理信息技术可以解决这个问题。这种方法被应用于贝加尔裂谷系(BRS)及邻区的研究当中。研究发现,断层活动变化发生在以几年为周期的时间尺度上,这无法用区域应力场的变化进行合理解释。沿着穿越贝加尔裂谷系的剖面编制了活动断层图以及地震活动定量指数曲线。提出的这种方法可以根据地震活动定量指数对活动断层进行仔细的分类,从而为解决地震中期预报相关问题开辟了重要途径。这种方法还被用来研究断层现今活动的时空变化和形成机制。研究发现,在实时尺度上断层活动具有高频发生的特征,这种现象可能是由于脆性岩石圈板内和块体内部运动产生的慢变形波的干扰导致的。利用变形波的通过速度可以对活动断层进行分组,各组具有不同的地质和地球物理特征参数,同时还可以对变形波波前的方向以及某一区域内主要断层在实时间隔内(地质上的瞬间)的活动情况进行预测。     

Income distribution dynamics and cross-region convergence in Europe

This paper presents a continuous version of the model of distribution dynamics to analyse the transition dynamics and implied long-run behaviour of the EU-27 NUTS-2 regions over the period 1995–2003. It departs from previous research in two respects: first, by introducing kernel estimation and three-dimensional stacked conditional density plots as well as highest density regions plots for the visualisation of the transition function, based on Hyndman et al. (J Comput Graph Stat 5(4):315–336, 1996), and second, by combining Getis’ spatial filtering view with kernel estimation to explicitly account for the spatial dimension of the growth process. The results of the analysis indicate a very slow catching-up of the poorest regions with the richer ones, a process of shifting away of a small group of very rich regions, and highlight the importance of geography in understanding regional income distribution dynamics.