中国企业R&D国际化:时空格局与区位选择影响因素

基于中国企业海外R&D投资数据和零断尾负二项回归分析方法,探讨了1998—2015年中国企业R&D国际化的时空格局特征,并从东道国角度分析影响其区位选择的主要因素。结果表明：研究期内中国新建海外R&D机构数量快速扩张;海外R&D投资区域分布广泛,呈现分散和集聚并存的特征;以华为为例分析了典型中国企业海外R&D投资的空间布局特征,认为目前华为海外R&D投资呈现集中布局在欧洲,零散分布在亚洲、美洲、非洲的空间特征,主要是由其以全球技术升级和满足多样化的市场需求等R&D国际化的战略动机所决定。回归分析结果表明,中国企业对发达国家和发展中国家进行R&D投资的影响因素有共同点但也存在一定的差异：① 现阶段东道国的R&D需求不是影响中国企业对其进行R&D投资的主要因素;② 代表中国与东道国经济往来密切程度的关联要素是影响中国企业R&D投资的重要决策因素;③ 发达国家吸引中国企业对其进行R&D投资主要是因其创新能力强、R&D资源数量多,而政策及制度供给是影响中国企业对发展中国家进行R&D投资的主要因素;④ 地理距离对中国企业R&D“走出去”的阻碍作用主要体现在对发展中国家的R&D投资中。     

1980—2015年风蚀影响下中国北方土壤有机质与养分流失时空特征

中国北方干旱/半干旱区是全球主要的沙尘源区之一,风蚀造成的沙尘排放可导致大量的土壤有机质（SOM）与养分流失,并通过传输与沉降过程对其进行空间再分配,对空气质量、气候变化、植被生长及生物地球化学过程等具有重要影响。本文利用WRF/Chem（Weather Research Forecasting with Chemistry）v3.7.1大气化学传输模型,对1980—2015年间中国北方沙尘排放及其引起的SOM、全氮（TN）与全磷（TP）的时空变化过程进行了精细化模拟,探究了中国北方风蚀引起的SOM、TN与TP养分流失的时空变化特征。结果表明：① 1980—2015年来平均每年约有66.59 Tg的沙尘颗粒排放至大气;② 沙尘排放具有较大的时空差异,沙尘排放源区主要集中在新疆东部、内蒙古西部的巴丹吉林沙漠和腾格里沙漠等地区;③ 每年因沙尘排放引起的SOM、TN和TP流失量分别约为0.07 Tg、0.004 Tg和0.005 Tg;④ 1980—2015年间中国北方沙尘排放及其引起的SOM、TN与TP的流失量具有较强的年际波动,未呈现显著的趋势性变化特征。本研究对于认知风蚀在碳循环与养分循环过程中的作用,以及对深入了解中国北方的土地退化机理具有重要意义。     

东北三省城市土地利用效益评价及耦合协调关系研究

在阐述城市土地利用效益系统耦合协调性内涵及其交互作用机理的基础上,构建综合评价指标体系,运用均方差决策法、耦合协调度模型,对东北三省2003~2015年城市土地利用综合效益及其主成分间耦合协调关系进行测度,并分析其时空演化过程。研究认为：① 城市土地利用综合效益表现出随时间“先升后降”的特征,区内三省间差异明显,中心和门户城市长期处于较高水平。② 城市土地利用经济效益亦存在“先升后降”的时序变化特征,空间上表现为“南高北低”,低值区范围缩减明显;社会效益差异不显著,在南北方向上表现出“凸”字型分布特征;区域生态环境效益整体较高,高值区由集中趋向分散。③ 城市土地利用经济、社会、生态环境效益间耦合协调关系在各时间节点均表现为失调,且以中度失调为主,但中心城市和门户城市的三效益耦合协调关系相对较好,向协调方向发展,哈长与辽中南城市群地区协调性优于其他区域,吉林省与辽宁省东部的部分城市协调关系趋向恶化。④ 城市土地利用效益间关系失调主要受复合因素的作用,经济效益滞后是制约土地利用效益耦合协调关系提升的主因,生态环境效益滞后不是限制土地利用效益耦合协调性提升的主要因素。     

GDELT数据网络化挖掘与国际关系分析

21世纪以来的国际关系错综复杂、瞬息万变,给世界的经济、安全、外交等带来了深刻变化。这些变化对中国的内外政策产生了重大影响。全面及时地分析国际关系及其变化特征,对于中国的经济和外交发展规划具有重要参考价值。国际关系研究具有复杂性、及时性、时空性等特点,迫切需要时空大数据分析技术为其提供新的思路和技术手段。大众媒体如报纸、广播等记录着世界上发生的各种各样的事件,蕴含着丰富的信息,相对于记录个人活动的社交媒体数据,其更加适合于对人类社会进行大规模和长时间的分析。GDELT是一个免费开放的新闻数据库,它实时监测世界上印刷、广播、网络媒体中的新闻,对其进行文本分析并提取出人物、地点、组织和事件等关键信息。本文利用复杂网络的理论和方法对GDELT进行网络化挖掘并进一步分析国家关系。首先利用该数据构建国家交互网络,然后通过网络特征统计分析国家之间的交互关系,最后探测国家冲突事件交互网络的时序变化。研究发现：① 国家交互网络具有无标度特性,网络连接在整体和局部上都呈现出不均匀性,少数国家与其他国家有大量交互,大多数国家与其他国家的交互很少;一个国家与少数国家有大量交互,而与大多数国家的交互很少。② 国家冲突事件交互网络的突然变化往往对应一些重大事件。本文的研究可以为大数据时代的国际关系探索提供一个新的视角,同时也为新闻媒体数据的分析提供参考。     

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.     

A density-based approach for detecting network-constrained clusters in spatial point events

Existing spatial clustering methods primarily focus on points distributed in planar space. However, occurrence locations and background processes of most human mobility events within cities are constrained by the road network space. Here we describe a density-based clustering approach for objectively detecting clusters in network-constrained point events. First, the network-constrained Delaunay triangulation is constructed to facilitate the measurement of network distances between points. Then, a combination of network kernel density estimation and potential entropy is executed to determine the optimal neighbourhood size. Furthermore, all network-constrained events are tested under a null hypothesis to statistically identify core points with significantly high densities. Finally, spatial clusters can be formed by expanding from the identified core points. Experimental comparisons performed on the origin and destination points of taxis in Beijing demonstrate that the proposed method can ascertain network-constrained clusters precisely and significantly. The resulting time-dependent patterns of clusters will be informative for taxi route selections in the future.     

Two-stage permutation tests for determining homogeneity within a spatial cluster

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.     

模糊聚类定权法对SLR定轨精度的影响

针对卫星激光测距(satellite laser ranging,SLR)精密定轨过程中存在的测站观测数据合理定权问题,将一种改进的模糊聚类算法引入到SLR观测数据定权中。基于国际激光测距服务(International Laser Ranging Service,ILRS)提供的全球SLR测站性能报告,对测站进行近实时滑动分类定权,改变SLR数据处理中权重的经验或者随意性选取模式。经过LAGEOS1卫星2014年1月至2016年12月3年全球SLR实测数据处理的测试。结果表明,当考虑LAGEOS标准点总数、LAGEOS标准点RMS值以及LAGEOS标准点合格率这3项测站质量控制因素确定的测站权值能最大限度地提高卫星定轨精度和观测数据的使用效率,对参与计算的365个3d弧段数据,91.46%弧段精度得到提高,平均提高约3.7mm,且每个测站的定轨残差RMS也得到了降低。这对于正在迈向毫米级测量精度的SLR技术至关重要。     

一种高光谱遥感图像快速谱聚类算法

为获得分类效果更优良的遥感图像分类方式并解决高光谱遥感图像分类运算速度缓慢的问题,集成Lanczos算法与谱聚类算法,探讨了高光谱遥感图像谱聚类算法应用于遥感图像分类的可行性,提出了一种面向高光谱遥感图像的快速谱聚类算法;通过对比美国圣地亚哥机场高光谱遥感图像K-均值算法与谱聚类算法的分类结果,发现面向高光谱遥感图像的谱聚类算法易于识别线性地物,且分类的速度能得到较大提升。     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.