Detecting regional dominant movement patterns in trajectory data with a convolutional neural network     

Can Yang  Győző Gidófalvi 《International journal of geographical information science》2020,34(5):996-1021

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MARC: a robust method for multiple-aspect trajectory classification via space,time, and semantic embeddings     

Lucas May Petry  Camila Leite Da Silva  Andrea Esuli  Chiara Renso  Vania Bogorny 《International journal of geographical information science》2020,34(7):1428-1450

ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.  相似文献   

Pedestrian network generation based on crowdsourced tracking data     

Xue Yang  Chang Ren  Yang Chen  Zhong Xie  Qingquan Li 《International journal of geographical information science》2020,34(5):1051-1074

ABSTRACT

Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness.  相似文献   

Dynamic,discontinuous stream networks: hydrologically driven variations in active drainage density,flowing channels and stream order          下载免费PDF全文

S. E. Godsey  J. W. Kirchner 《水文研究》2014,28(23):5791-5803

Despite decades of research on the ecological consequences of stream network expansion, contraction and fragmentation, surprisingly little is known about the hydrological mechanisms that shape these processes. Here, we present field surveys of the active drainage networks of four California headwater streams (4–27 km²) spanning diverse topographic, geologic and climatic settings. We show that these stream networks dynamically expand, contract, disconnect and reconnect across all the sites we studied. Stream networks at all four sites contract and disconnect during seasonal flow recessions, with their total active network length, and thus their active drainage densities, decreasing by factors of two to three across the range of flows captured in our field surveys. The total flowing lengths of the active stream networks are approximate power‐law functions of unit discharge, with scaling exponents averaging 0.27 ± 0.04 (range: 0.18–0.40). The number of points where surface flow originates obey similar power‐law relationships, as do the lengths and origination points of flowing networks that are continuously connected to the outlet, with scaling exponents averaging 0.36–0.48. Even stream order shifts seasonally by up to two Strahler orders in our study catchments. Broadly, similar stream length scaling has been observed in catchments spanning widely varying geologic, topographic and climatic settings and spanning more than two orders of magnitude in size, suggesting that network extension/contraction is a general phenomenon that may have a general explanation. Points of emergence or disappearance of surface flow represent the balance between subsurface transmissivity in the hyporheic zone and the delivery of water from upstream. Thus the dynamics of stream network expansion and contraction, and connection and disconnection, may offer important clues to the spatial structure of the hyporheic zone, and to patterns and processes of runoff generation. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

历史名人轨迹的空间可视化与分析     

王妮满  秦昆  罗俊  陈昆  胡圣 《地球信息科学学报》2020,22(5):978-988

历史名人的行为轨迹反映了当时的历史文化背景,通过历史名人行为轨迹的空间化和可视化,可以对历史社会状态进行探索和分析。对历史名人的社交关系网络进行可视化研究,有利于剖析当时的政治背景与人物关系。目前,基于GIS的空间人文社会科学深层次分析方法和工具还很少,根据地理位置对历史人物的社交网络进行分时段的研究也很少。本文以玄奘和欧阳修为例,探索了基于WebGIS的历史人物轨迹空间可视化分析方法,基于核密度估计与标准差椭圆的空间分析方法,分析历史名人轨迹点的空间分布特征,统计迁徙指数、首都距、家乡距、成长地距以分析基于距离的轨迹点移动特点;分时段构建了历史名人的空间社交网络,并结合历史背景、名人事迹、名人作品和空间化结果进行了综合分析。分析结果表明： ① 历史名人的迁移轨迹与当时的历史人口迁移趋势基本是一致的,受社会变动影响较大;② 历史名人在事业上升期有更大的社交网络圈,而在人生没落阶段社交网络圈减小。本文对历史名人轨迹的空间可视化与分析方法进行了探索,可以为空间人文社会科学相关领域的分析研究提供参考。  相似文献   

Inference of in situ stress from thermoporoelastic borehole breakouts based on artificial neural network     

Hua Zhang  Shunde Yin 《国际地质力学数值与分析法杂志》2019,43(16):2493-2511

The determination of in situ stresses is very important in petroleum engineering. Hydraulic fracturing is a widely accepted technique for the determination of in situ stresses nowadays. Unfortunately, the hydraulic fracturing test is time-consuming and expensive. Taking advantage of the shape of borehole breakouts measured from widely available caliper and image logs to determine in situ stress in petroleum engineering is highly attractive. By finite element modeling of borehole breakouts considering thermoporoelasticity, the authors simulate the process of borehole breakouts in terms of initiation, development, and stabilization under Mogi-Coulomb criterion and end up with the shape of borehole breakouts. Artificial neural network provides such a tool to establish the relationship between in situ stress and shape of borehole breakouts, which can be used to determine in situ stress based on different shape of borehole breakouts by inverse analysis. In this paper, two steps are taken to determine in situ stress by inverse analysis. First, sets of finite element modeling provide sets of data on in situ stress and borehole breakout measures considering the influence of drilling fluid temperature and pore pressure, which will be used to train an artificial neural network that can eventually represent the relationship between the in situ stress and borehole breakout measures. Second, for a given measure of borehole breakouts in a certain drilling fluid temperature, the trained artificial neural network will be used to predict the corresponding in situ stress. Results of numerical experiments show that the inverse analysis based on finite element modeling of borehole breakouts and artificial neural network is a promising method to determine in situ stress.  相似文献   

Impacts of small cascaded hydropower plants on river discharge in a basin in Southern China     

Xu Xiao  Xiaohong Chen  Lilan Zhang  Rouyi Lai  Jie Liu 《水文研究》2019,33(10):1420-1433

The run‐off volume altered by the construction of hydropower plants affects ecohydrological processes in catchments. Although the impacts of large hydropower plants have been well documented in the literature, few studies have been conducted on the impacts of small cascaded hydropower plants (SCHPs). To evaluate the impacts of SCHPs on river flow, we chose a representative basin affected by hydropower projects and, to a lesser degree, by other human activities, that is, the Qiuxiang River basin in Southern China. The observed river discharge and climate data during the period of 1958–2016 were investigated. The datasets were divided into a low‐impact period and a high‐impact period based on the number of SCHPs and the capacities of the reservoirs. The daily river discharge alteration was assessed by applying the Indicators of Hydrologic Alteration. To separate the impact of the SCHPs on the local river discharge from that of climate‐related precipitation, the back‐propagation neural network was used to simulate the monthly average river discharge process. An abnormal result was found: Unlike large reservoirs in large watersheds, the SCHPs regulated the flows during the flood season but were not able to mitigate the droughts during the dry season due to their limited storage and the commonly occurring inappropriate interregulations of the SCHPs. The SCHPs also reduced the annual average river discharge in the research basin. The contribution of the SCHPs to the river discharge changes was 85.37%, much higher than the contributions of climate change (13.43%) and other human activities (1.20%). The results demonstrated that the impacts of the SCHPs were different from those of large dams and reservoirs that regulate floods and relieve droughts. It is necessary to raise the awareness of the impacts of these river barriers.  相似文献   

Extracting drainage networks and their connectivity using LiDAR data          下载免费PDF全文

Jennifer Roelens  Ine Rosier  Stefaan Dondeyne  Jos Van Orshoven  Jan Diels 《水文研究》2018,32(8):1026-1037

Policies, measures, and models geared towards flood prevention and managing surface waters benefit from high quality data on the presence and characteristics of drainage ditches. As a cost and labour effective alternative for acquiring such data through field surveys, we propose a method (a) to extract vector data representing ditch drainage networks based on local morphologic features derived from high resolution digital elevation models (DEM) and (b) to identify possible connections in the ditch network by calculating a probability of the connectivity using a logistic regression where the predictor variables are characteristics of the ditch centre lines or derived from the DEM. Using Light Detection and Ranging (LiDAR) derived DEMs with a 1 m resolution, the method was developed and tested for a mixed agricultural residential area in north‐eastern Belgium. The derived ditch segments had an error of omission of 8% and an error of commission of 5%. The original positional accuracy of the centre lines of the extracted ditches was 0.6 m and could be improved to 0.4 m by shifting each vertex to the position of the lowest LiDAR point located within a radius equal to the spatial resolution of the used DEM. About 69% of the false disconnections in the network were identified and corrected leading to a reduction of the unconnected parts of the ditch network by 71%. The extracted and connected network approximated the reference ditch network fairly well.  相似文献   

中国"互联网+"发展的空间关联网络及其影响因素——基于社会网络分析视角的实证研究     

蔡海亚  赵永亮  南永清 《地理科学》2021,41(6):933-940

借助社会网络分析方法研究“互联网+”发展的空间关联网络特征,并利用QAP方法探究中国“互联网+”空间关联网络的影响因素。研究发现,“互联网+”发展的空间关联呈现显著网络特征,可划分为“净溢出”“经纪人”“主受益”以及“双向溢出”四大类型板块,并且板块内部具有较明显的“等级”属性。技术创新、基础设施、人力资本、市场发展、对外开放对“互联网+”发展的空间关联网络存在正向影响。地理距离对“互联网+”空间关联网络存在抑制作用,随着地理距离的不断增大,“互联网+”的知识溢出和流动效应逐步衰减。  相似文献   

镇域传统产业集群创新网络演进特征及形成机理——以安徽高沟镇电缆产业为例     

李俊峰  柏晶菁  王淑婧 《地理科学》2021,41(6):1039-1049

基于专利合作数据和实地调研数据,借助UCINET对安徽高沟镇电缆产业集群创新网络进行量化分析,揭示镇域传统产业集群创新网络的构成与演化特征,并运用多维邻近性理论探究其演化机理。研究表明：① 产业集群创新网络是由网络主体、网络联结和网络结构三大要素构成,要素之间相互联系、协同发展;② 镇域传统产业集群创新网络形成时间久,经历了初生期、结成期和发展期3个阶段,创新主体由单一转向多元、联结方式由非正式转向正式、联结强度由弱变强、联结结构由星型转向网状;③ 受地理位置、产业性质及经济基础的限制,地理邻近在镇域传统产业集群创新网络形成中具有重要作用;认知邻近存在滞后性,且其作用发挥一定程度上受到地理邻近制约;由于镇域传统产业集群自我创新能力较弱,创新网络对政府及合作组织的依赖性更强,制度邻近及组织邻近作用有待进一步凸显。  相似文献