Geographic Polarization,Partisan Voting,And The Battle Over Same‐Sex Marriage Within The Culture War

Geographic polarization arises when partisan, or like‐minded, voters live in enclaves separate from voters of differing partisan behavior. Research studies at multiple scales of analysis suggest that geographic polarization most typically occurs regarding partisan voting behavior; however, few studies have analyzed polarization with a focus on policy preferences. This research examines same‐sex marriage policy through two statewide issues that shared the presidential election ballot in 2004. The results of that presidential election and these two ballot initiatives are examined in Cincinnati, Ohio, and counties in its surrounding Metropolitan Statistical Area (MSA) in both Ohio and Kentucky. A geographically polarized relationship was found between partisan results and same‐sex marriage results. In addition, the findings suggest that despite a close link between partisan voting and issue voting on same‐sex marriage, spatial clustering (geographic polarization) was identified of those who voted differently from their fellow partisans.     

Identifying American Beer Geographies: A Multiscale Core-Cluster Analysis of U.S. Breweries

The recent growth in the U.S. brewing industry is remarkable, and the prevailing number of breweries has not been seen since the late nineteenth century. Several studies have shown that beer-producing facilities are spatially uneven across the United States. These previous studies used spatial units, however, such as metropolitan statistical areas, that might bias conclusions. Using a multiscale core-cluster approach, we explicitly identify where significant agglomerations of brewers are located. Our approach offers two refinements to standard cluster detection methods. First, instead of using fixed spatial boundaries, our method allows us to measure the concentration of brewery point locations across a spectrum of spatial scales. Additionally, our approach enables us to account for important underlying factors that influence the location of beer production. We use point data for all U.S. breweries in 2014. Our results show that the localization of beer production is significant and strongest at small spatial scales and diminishes rapidly with increasing distance, after controlling for population. We map the results to show the spatial variation in brewery agglomeration across the United States.     

基于特征的时间序列聚类方法研究进展

时间序列聚类可以根据相似性将对象集分为不同的组, 从而反映出同组对象的相似性特征和不同组对象之间的差异特征。当序列维度较高时, 传统的时间序列聚类方法容易受噪声影响, 难以定义合适的相似性度量, 聚类结果往往意义不明确。当数据有缺失或不等长时, 聚类方法也难以实施。基于上述问题, 一些学者提出了基于特征的时间序列聚类方法, 不仅可以解决上述问题, 还可以发现序列本质特征的相似性。本文根据时间序列的不同特征, 综述了基于特征的时间序列聚类方法的研究进展, 并进行了分析和评述;最后对未来研究进行了展望。     

鲁棒多特征谱聚类的高光谱影像波段选择

传统谱聚类的高光谱影像波段选择模型中,采用的波段相似矩阵受到噪声或异常值的影响且仅能表征波段的单一相似特征,导致波段子集的选取结果受到限制.本文从波段选择的目的 出发,提出鲁棒多特征谱聚类方法,整合多个特征的波段相似矩阵来形成综合相似矩阵以解决上述问题.该方法假设4种相似性度量包括光谱信息散度、光谱角度距离、波段相关性...     

长江三角洲第一硬质黏土层粒度多元统计分析及沉积环境判别

长江三角洲第一硬质黏土层成因是长期争论的问题。对上海市青浦区第一硬质黏土层全样粒度多元统计分析,并与南京周家山下蜀黄土、长江悬浮颗粒物对比,探索长江三角洲第一硬质黏土层成因。第一硬质黏土层机械颗粒组成均一,频率曲线呈正偏态、中等或尖锐峰形的双峰分布,频率累积曲线呈"S"型分布,具有风成沉积特性。粒度特征分析为沉积环境判别提供沉积学证据时,综合运用多元统计分析可以使分析结果更准确。聚类分析将硬黏土与典型风成沉积物下蜀黄土粒度分布归为一类,长江悬浮颗粒物单独为一类,表明硬黏土与下蜀黄土成因一致。下蜀黄土与长江悬浮颗粒物粒度参数判别函数,将绝大部分硬黏土样品判定为风成沉积。粒级—标准偏差曲线结合现代风场,显示硬黏土粒度特征存在高空西风和低空西北季风两个主控因子。表明长江三角洲第一硬质黏土层是由西风和西北季风控制的风成成因堆积物。     

虚拟化技术在地震信息网络业务中的应用

根据VMware公司的虚拟机组件ESX3．5及管理组件Virtual Center2．5,结合现有硬件技术平台,在山东省地震局实现虚拟化技术的实际部署和应用。结果发现,将虚拟化技术应用到地震行业信息网络业务,可以提高工作效率,具有重要的实际意义。     

基于聚类分析的多尺度相似地震快速识别方法及其在汶川地震东北端余震序列分析中的应用

相似地震是具有相似波形记录的一组地震,往往以地震丛集的方式发生.而重复地震是一种特殊的相似地震,一般具有相近的震源机制解和几乎重合的破裂面积.对相似地震特别是重复地震的研究是我们认识断层的结构和变化的重要手段.本文提出了一种基于相似度距离概念和聚类分析技术的相似地震识别方法,可以利用单个台站对其记录到的地震事件进行快速的相似地震和重复地震识别.我们将此方法应用于汶川地震东北端的余震序列,获得了该地区相似地震的分布图像,并对其中存在的重复地震的发震机制进行了讨论分析.     

青藏高原数字照片植被覆盖度自动算法与应用

覆盖度是植被评价的重要指标,也是遥感反演的关键参数。估算植被覆盖度的常用方法是目测法,但这种方法受观测人员的主观影响。近年来有研究人员利用冬小麦垂直数字照片的HLS颜色空间的色度特征,设计了自动提取覆盖度算法,具有较高的计算精度。但青藏高原植被颜色丰富多样,下垫面背景色彩差异很大,这种常规覆盖度自动提取算法存在困难。论文分析了青藏高原地表垂直数字照片的图像颜色特征,发现超绿色算法对绿色植被比较敏感,能够增强植被和背景的灰度差异,有效抑制土壤背景干扰。并采用K均值聚类算法,设计了青藏高原植被覆盖度的自动提取软件。通过将覆盖度自动提取结果和人工监督分类进行比较,两者误差在5%以内。此外,通过分析分类后的结果图像,提出了进一步改进的方法。     

Spatio-temporal epidemic type aftershock sequence model for Tangshan aftershock sequence

Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akaike Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.     

基于自适应聚类算法的GPS三频载波相位组合观测值优化选取

推导了GPS三频载波相位组合观测值模型,并且对组合观测值进行了误差分析。根据模糊聚类分析理论,对由长波长标准得到的三频载波相位组合观测值进行分析,运用基于相异度矩阵的自适应聚类算法对其进行分类。对各类组合观测值适用条件进行分析,利用实测L1、L2数据和模拟的L5数据通过矩阵变换求解模糊度法对其进行验证。从而得出结论：通过模糊聚类分析不仅可以选取满足不同长短基线的较优组合,而且大大减少了运用枚举所有组合来筛选较好组合的工作量。