利用轨迹大数据进行城市道路交叉口识别及结构提取

交叉口是城市交通路网生成、更新的重要组成部分。本文基于车辆时空轨迹大数据,提出了一种城市交叉口自动识别方法。该方法首先通过轨迹跟踪识别轨迹数据中包含的车辆转向点对;然后基于距离和角度的生长聚类方法进行转向点对的空间聚类,并采用基于局部点连通性的聚类方法识别交叉口;最后利用交叉口范围圆和转向点对提取城市各级别路网下的交叉口结构。以武汉市出租车轨迹大数据为例,对武汉市城区内189个交叉口进行了探测。试验结果表明,本文所提方法可以准确地从轨迹大数据中识别出城市交叉口及其结构。     

基于滚球法的面要素聚合

在自动制图综合中,面要素聚合较为常用的方法是利用聚类方法将面要素聚类为若干个类簇,进而将每个类簇合并为更大的多边形图斑。针对地图制图领域的前沿研究课题——地图自动综合,该文提出了一种基于滚球法的面状要素聚合的新方法,通过实验阐述了该方法在居民地等面要素综合中的应用,并与传统的聚合算法进行了效率上的对比分析。该方法较传统的聚合方法效率上有很大提高,在大数据量聚合中有一定的应用前景,当选择合适的滚球半径进行综合时,有较理想的结果。     

基于FMM算法的夏秋季登陆中国热带气旋路径分类及特征分析

利用有限混合模型FMM聚类算法,将1951—2012年夏秋季(6—11月)登陆我国的热带气旋(Tropical Cyclone,TC)路径数据集分为三类,并对三类不同路径TC的季节变化、发生频数、环流形势等特征进行对比分析。研究表明,每类TC存在明显的特征差异:1)在夏季,第一、二类TC出现频数高于第三类,但在秋季第三类TC发生频数最高。2)第一类TC生成位置偏北,强度较强,生命史较长,路径略有向北发展的趋势,影响区域最广;第二类TC生命史最短,主要影响我国两广、福建一带;第三类TC生命史最长,路径略向西北方向发展。3)第一类TC在生成和消亡时的辐合程度最强,且副高脊线西伸脊点位置偏北;第二类TC在消亡时低层辐合最弱,且副高脊线西伸脊点位置偏西;第三类TC在生成时纬向风垂直切变最强,且副高脊线西伸脊点位置偏东南。     

中国住宅出让地价发育特征及其影响因素分析

基于中国土地市场网2009~2013年招、拍、挂出让的住宅用地数据,以县级行政区域作为研究单元,建立区域住宅地价综合模型,选取地价水平、地价增长率和交易宗数作为评价指标,采用Ward系统聚类法,综合分析了中国住宅出让地价的发育特征,利用多分类Logistic回归模型探索了各类地价发育形态的潜在影响因素。结果表明：① 研究期内中国住宅出让地价总体呈现东高西低、沿海高于内陆、城市群带动周边区域发展、城市群内部围绕中心城市增长等空间格局特征;中西部地价高增长的单元多于东部,西北、东北中南部、四川盆地、河西走廊以及长江中下游地区是地价高增长的聚集区;住宅市场活跃度呈阶梯状变化,活跃度较高的地区主要分布在山东半岛、长三角、长江中游、辽宁中部、哈尔滨、成渝、滇中、呼包鄂等城市群。② 根据综合特征,中国住宅出让地价空间发育可分为成熟稳健型、发展完善型、成长发展型、萌芽起步型和成长受阻型5类,相邻发育形态在空间关系上表现出互为邻里的特征。③ 各类发育区的主要影响因素差异显著。区位条件、居民收入和财政收入是地价发育成熟度的主要影响因素;人均GDP、国土开发度提升将促进地价形态发育程度;而人口吸引力不足、基础设施建设相对滞后等将导致地价形态发育受阻。     

Geographic Patterns of Craft Breweries at the Intraurban Scale

This article examines the intraurban geography of craft breweries in ten cities across the United States. First, through an exhaustive literature review, we outline both supply- and demand-side factors that might cause craft breweries to cluster. Second, we empirically test whether these establishments tend to cluster within cities using spatial statistical techniques. Many communities are attempting to support the establishment of more craft breweries as a way to boost tourism and economic development. The findings from this article aid in this discussion by providing insights into how craft brewers locate and the factors that could influence their location decision behavior. Our findings suggest that craft brewers do in fact cluster. There are both supply and demand factors responsible. On the supply side, the collaborative environment within the industry and the artisan nature of the industry's products allows for benefits of clustering to outweigh the costs associated with this behavior. On the demand side, the emergence of “brewery districts” allows individual brewers to enjoy the reputation benefits associated with the district in terms of increased foot traffic from locals and visitors looking to sample a variety of beers.     

Generating urban road intersection models from low-frequency GPS trajectory data

Detailed real-time road data are an important prerequisite for navigation and intelligent transportation systems. As accident-prone areas, road intersections play a critical role in route guidance and traffic management. Ubiquitous trajectory data have led to a recent surge in road map reconstruction. However, it is still challenging to automatically generate detailed structural models for road intersections, especially from low-frequency trajectory data. We propose a novel three-step approach to extract the structural and semantic information of road intersections from low-frequency trajectories. The spatial coverage of road intersections is first detected based on hotspot analysis and triangulation-based point clustering. Next, an improved hierarchical trajectory clustering algorithm is designed to adaptively extract the turning modes and traffic rules of road intersections. Finally, structural models are generated via K-segment fitting and common subsequence merging. Experimental results demonstrate that the proposed method can efficiently handle low-frequency, unstable trajectory data and accurately extract the structural and semantic features of road intersections. Therefore, the proposed method provides a promising solution for enriching and updating routable road data.     

低空急流识别及急流轴自动绘制方法研究

低空急流对强对流天气的预报有重要意义。针对目前低空急流主要由手工绘制,存在效率低、易受主观因素影响的问题,本文基于MICAPS高空全要素填图数据中的探空站风场信息,提出了一种低空急流自动识别及急流轴自动绘制算法。算法从急流轴定义出发,依次从风速、风向、探空站分布、中轴线位置等多个角度对低空急流轴进行检测。经过传递闭包聚类、急流轴关键点提取、不同风向急流轴关键点归并、急流轴平滑等步骤,实现了低空急流自动识别及急流轴的自动绘制。测试表明,在识别的基础上自动绘制的急流轴具有位置准确、形态自然、能完整反映急流水汽输送路径、适应复杂环境低空急流等特点。在291组测试数据中未发现空报,准确击中率达到94.96%。     

Predicting the visualization intensity for interactive spatio-temporal visual analytics: a data-driven view-dependent approach

The continually increasing size of geospatial data sets poses a computational challenge when conducting interactive visual analytics using conventional desktop-based visualization tools. In recent decades, improvements in parallel visualization using state-of-the-art computing techniques have significantly enhanced our capacity to analyse massive geospatial data sets. However, only a few strategies have been developed to maximize the utilization of parallel computing resources to support interactive visualization. In particular, an efficient visualization intensity prediction component is lacking from most existing parallel visualization frameworks. In this study, we propose a data-driven view-dependent visualization intensity prediction method, which can dynamically predict the visualization intensity based on the distribution patterns of spatio-temporal data. The predicted results are used to schedule the allocation of visualization tasks. We integrated this strategy with a parallel visualization system deployed in a compute unified device architecture (CUDA)-enabled graphical processing units (GPUs) cloud. To evaluate the flexibility of this strategy, we performed experiments using dust storm data sets produced from a regional climate model. The results of the experiments showed that the proposed method yields stable and accurate prediction results with acceptable computational overheads under different types of interactive visualization operations. The results also showed that our strategy improves the overall visualization efficiency by incorporating intensity-based scheduling.     

Tower-Based Greenhouse Gas Measurement Network Design——The National Institute of Standards and Technology North East Corridor Testbed

The North–East Corridor(NEC) Testbed project is the 3rd of three NIST(National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions(footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and,therefore, biasing the retrieved fluxes.     

优化K means算法在中国近海气候区划中的应用

利用我国近海多年的风、浪及温湿资料,采用DB指标和最大距离法相结合的优化K-means聚类算法,对冬季我国近海进行气候区划,避免了传统K-means算法中确定聚类数目和初始聚类中心的主观性。结果表明,冬季我国近海可被划分为3个区域:1区主要包括28°N以北的海域以及我国东南沿海的一条狭长区域,2区主要包括台湾周边海域、台湾海峡、巴士海峡以及自越南东南部起由西南部向东北部的南海大部分海域,3区则主要包括台湾岛西南部海域、北部湾、广东沿海以及南海东南部。根据给出的区划指标,分析各分区的气候特征及其对海上舰船航行和出海人员的影响,判定1区为基本适宜区,2区为不适宜区,3区为适宜区。