Secular geographical polarization in the American South: The case of Texas, 1996–2010

This article uses fine-grained data to demonstrate that, since 1996, the State of Texas has undergone a process of ‘secular geographical polarization’ – a continuous divergence in the geographical bases of its political parties. It is suggested that this process exemplifies a new era of partisan politics in the American South. Analyses of spatial regression models show that the geographical polarization can be partially explained by a tighter link between demographic characteristics and aggregate voting patterns, but that growth in spatial clustering cannot be attributed entirely to demographics. The possibility that spatially-bounded social contexts are affecting partisan change is thus explored. Finally, the article's findings are considered in light of the growing debate about geographical polarization in the American electorate.     

产业集群研究的最新进展及理论前沿

产业集群是一种世界性的经济现象,近年来日益引起学术界的广泛注意,各 国学者从不同角度提出了许多有价值的观点。本文首先回顾了产业集群理论研究的发展 历程,然后从五个方面重点探讨了产业集群研究的最新进展及理论前沿问题。     

Clustering digital forensic string search output

This research comparatively evaluates four competing clustering algorithms for thematically clustering digital forensic text string search output. It does so in a more realistic context, respecting data size and heterogeneity, than has been researched in the past. In this study, we used physical-level text string search output, consisting of over two million search hits found in nearly 50,000 allocated files and unallocated blocks. Holding the data set constant, we comparatively evaluated k-Means, Kohonen SOM, Latent Dirichlet Allocation (LDA) followed by k-Means, and LDA followed by SOM. This enables true cross-algorithm evaluation, whereas past studies evaluated singular algorithms using unique, non-reproducible datasets. Our research shows an LDA + k-Means using a linear, centroid-based user navigation procedure produces optimal results. The winning approach increased information retrieval effectiveness, from the baseline random walk absolute precision rate of 0.04, to an average precision rate of 0.67. We also explored a variety of algorithms for user navigation of search hit results, finding that the performance of k-means clustering can be greatly improved with a non-linear, non-centroid-based cluster and document navigation procedure, which has potential implications for digital forensic tools and use thereof, particularly given the popularity and speed of k-means clustering.     

基于文本挖掘的网络赌博取证系统的设计与研究

网络犯罪作为一种犯罪新形式呈增长趋势。网络赌博是近年来严打的网络犯罪行为之一,由于网络、地域等原因,对于如何有效提取网络赌博的web文档证据成为公安、司法机关需要解决的问题。基于web文档非结构化等特点,结合文本挖掘的聚类算法和关联规则算法,设计的一种网络赌博取证系统,可对网络赌博的web文档进行取证。实验表明,该系统可以有效地对关联web文档作出推荐,为案情提供线索。