新型城镇化背景下中国流动人口研究:议题与展望

研究通过4个议题（户籍与人口流动,循环流动、定居意愿及家庭策略,社会网络和移民社区,迁移、流动性和移民的日常生活）,回顾了近年来乡城人口迁移的研究。指出：户籍政策对移民在城市中的生计带来的影响将减弱,移民的住房及在城市的居住模式和居住空间、社会交往、生活方式、移民身份建构及生活空间的重构等将会成为新的关注点。新文化地理学中关于移民“微观化”“生活化”的研究应受到更多的重视。换言之,如何理解移民在个体层面的诉求和移民的日常生活,将会是未来流动性研究的重点。如何立足于国内移民地理研究的基础与发展机遇,批判性地与西方移民研究搭建对话空间和平台,结合当前中国崛起过程中所呈现的前所未有的全球化与城镇化契机,为世界移民研究积累和贡献“中国知识”,将成为未来这一领域学者研究的方向。     

企业创新网络与多维邻近性关系研究述评

创新是引领经济发展的第一动力,企业创新网络相关研究近来受到经济地理学者的高度关注,纵观当前国内外企业创新网络相关研究文献,企业创新网络研究经历了从定性研究向定量研究转变的过程,尤其是基于专利或论文数据刻画创新网络结构特征成为当前研究的热点,然而企业创新网络结构形成的内在机制仍不够深入,不少学者引入多维邻近性框架用于解析企业创新网络形成的重要视角。本文正是基于这一背景,从企业创新网络的影响因素、演化动力及机理、典型案例企业创新网络三个方面梳理了当前经济地理学者的研究热点,并重点基于多维邻近性视角评述了企业创新网络运行机理。研究发现,众多邻近性中,地理、认知和组织邻近对企业创新合作具有较为重要的促进作用,在未来研究中还需加强最佳邻近尺度、不同邻近性替代或互补作用和重要性排序等问题的相关研究。     

Foliar Particulate Matter Distribution in Urban Road System of Beijing,China

Particulate matter(PM), one of the most important pollutants of traffic emission, threatens the health of urban ecosystems and citizens. Roadside trees play an important role in trapping PM, and the foliar PM load is a useful indicator for air PM pollution in road systems. To detect the relationships of foliar PM load with road structures, urbanization, and meteorology in road systems, we studied a widely-planted tree Sophora japonica L. in 100 roads and 10 yards of Beijing, China, and found that the foliar PM loads increased with the distances from the urban centre(DUC) linearly, while decreased with the road density. All-subsets regression analysis indicated that DUC, average monthly relative humidity, average monthly wind speed, and mean annual wind speed were the most important factors in predicting foliar PM load, rather than general situation of buildings and road cleanliness. The monthly relative humidity and monthly wind speed had a negative correlation to foliar PM, while the annual relative humidity and annual wind speed had a positive correlation to foliar PM. Suburban areas and these effective factors should be highlighted in PM control in Beijing.     

Modeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter

Interpersonal communication on online social networks has a significant impact on the society by not only diffusing information, but also forming social ties, norms, and behaviors. Knowing how the conversational discourse semantically and geographically vary over time can help uncover the changing dynamics of interpersonal ties and the digital traces of social events. This article introduces a framework for modeling and visualizing the semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. The framework consists of (1) a topic modeling workflow for modeling topics and extracting the evolution of conversational discourse; (2) a geo-social network modeling and smoothing approach to projecting connection characteristics and semantics of communication onto geographic space and time; (3) a web-based geovisual analytics environment for exploring semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. To demonstrate, geo-located and reciprocal user mention and reply tweets over the course of the 2016 primary and presidential elections in the United States from 1 August 2015 to 15 November 2016 were analyzed. The large portion of the topics extracted from mention tweets were related to daily life routines, human activities, and interests such as school, work, sports, dating, wearing, birthday celebration, music, food, and live-tweeting. Specific focus on the analysis of political conversations revealed that the content of conversational discourse was split between civil rights and election-related discussions of the political campaigns and candidates. These political topics exhibited major shifts in terms of content and the popularity in reaction to primaries, debates, and events throughout the study period. While civil rights discussions were more dominant and in higher intensity across the nation and throughout the whole time period, election-specific conversations resulted in temporally varying local hotspots that correlated with locations of primaries and events.     

Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN

ABSTRACT

The density-based spatial clustering of applications with noise (DBSCAN) method is often used to identify individual activity clusters (i.e., zones) using digital footprints captured from social networks. However, DBSCAN is sensitive to the two parameters, eps and minpts. This paper introduces an improved density-based clustering algorithm, Multi-Scaled DBSCAN (M-DBSCAN), to mitigate the detection uncertainty of clusters produced by DBSCAN at different scales of density and cluster size. M-DBSCAN iteratively calibrates suitable local eps and minpts values instead of using one global parameter setting as DBSCAN for detecting clusters of varying densities, and proves to be effective for detecting potential activity zones. Besides, M-DBSCAN can significantly reduce the noise ratio by identifying all points capturing the activities performed in each zone. Using the historic geo-tagged tweets of users in Washington, D.C. and in Madison, Wisconsin, the results reveal that: 1) M-DBSCAN can capture dispersed clusters with low density of points, and therefore detecting more activity zones for each user; 2) A value of 40 m or higher should be used for eps to reduce the possibility of collapsing distinctive activity zones; and 3) A value between 200 and 300 m is recommended for eps while using DBSCAN for detecting activity zones.     

Simulating the spatial diffusion of memes on social media networks

This article reports the findings from simulating the spatial diffusion processes of memes over social media networks by using the approach of agent-based modeling. Different from other studies, this article examines how space and distance affect the diffusion of memes. Simulations were carried out to emulate and to allow assessment of the different levels of efficiency that memes spread spatially and temporally. Analyzed network structures include random networks and preferential attachment networks. Simulated spatial processes for meme diffusion include independent cascade models and linear threshold models. Both simulated and real-world social networks were used in the analysis. Findings indicate that the numbers of information sources and opinion leaders affect the processes of meme diffusion. In addition, geography is still important in the processes of spatial diffusion of memes over social media networks.     

基于强化学习的锚泊辅助动力定位系统智能决策研究

针对半潜平台锚泊辅助动力定位系统的最优定位点问题,设计了基于强化学习中深度神经网络的Q学习(DQN)控制策略的锚泊辅助动力定位的智能决策系统。该决策系统中DQN方法与比例—积分—微分(PID)控制方法相结合使用,实现系统优化。在基于机器人操作系统(ROS)平台的动力定位时域模拟程序中进行数值仿真,仿真结果验证了该系统在定位点决策问题上的可靠性和有效性,从而使半潜平台在面对未知海况时,均能寻找到最优定位点,在保证锚泊辅助动力定位系统可靠性的同时降低功率消耗,提高经济性。     

一种新型的城市道路地图匹配方法

提出了一种充分利用道路交叉口约束的地图匹配方法,并采用低成本的IMU/里程计组合导航系统采集无人驾驶汽车的定位数据,利用开源的OpenStreetMap地图对其进行地图匹配.试验表明,本文算法适用于城市交通环境,并能满足实时解算要求,单点平均耗时0.11 ms,在查全率89%的情况下,匹配准确率可达100%.     

基于混合像元分解理论的路域植被等量水厚度遥感反演

为削弱混合像元对植被参数反演的影响,提出了基于混合像元分解理论反演路域植被等量水厚度的方法。利用PRO4SAIL模型正演获得的高光谱窄波段数据,模拟Landsat 8遥感影像宽波段植被冠层光谱数据,并进行等量水厚度的敏感植被指数的筛选;对覆盖研究区域的Landsat 8遥感影像进行线性混合像元分解,获取更加精确的植被冠层光谱反射率;同时,利用支持向量机构建等量水厚度估测模型,实现对路域植被等量水厚度的遥感反演。研究结果表明,利用混合像元分解后得到的植被冠层光谱参与模型反演得到的路域植被等量水厚度更加符合实际情况,为遥感影像反演植被参数提供了有效数据。     

基于自发地理信息的“一带一路”区域陆路交通状况分析

路网密度的研究是实施“一带一路”愿景之互联互通倡议的基础工作。“一带一路”顺应了世界经济全球化、一体化、多极化、信息化的趋势,牵连着蓬勃发展的亚太和发达的欧洲经济圈,带动中间国家的经济发展。在新形势下,如何通过交通基础设施建设推进丝绸之路经济带沿线区域间的互联互通、促进区域合作和经济发展,具有重要的现实意义。本文利用自发地理信息数据——OpenStreetMap（OSM）道路网数据,运用地理信息系统（GIS）空间分析技术,探究了“一带一路”陆路经济带辐射区域亚欧大陆的路网络密度和通行能力的空间分布格局,并分析了六大经济走廊节点城市的公路可达性空间格局。结果表明：“一带一路”陆路经济带的交通基础设施的发展存在着不平衡现象;路网密度和通行能力均呈现沿海高内陆低的空间格局;中亚、西亚地区和中南半岛地区成为道路水平落后带,限制了丝绸之路经济带资源整合与贸易畅通,建议在建设新跨国公路铁路同时,对原有老旧道路进行加宽加固等升级改造;全区域道路等级级配不合理;节点城市可达性具有明显的分异状况,六大经济走廊中83%的区域可达性在10 h以内。研究产出亚欧大陆1 km分辨率路网密度和通行能力数据集,发现了“一带一路”陆路经济带道路水平的薄弱区域,为“一带一路”陆路经济带的交通基础设施建设以及进一步的道路网研究提供了基础数据。