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   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics     

HuiRan Gao  WanChang Zhang 《寒旱区科学》2020,12(6):404-417

The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions. With advancement of remote sensing and better understanding of frozen soil dynamics, discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change. However, as an important data source of frozen soil processes, remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes. Although great progress has been made in remote sensing and frozen soil physics, yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies. In the present study, a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed. In order to reduce the uncertainty of the simulation, the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation. The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau. The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%. These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study. The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory. The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil. The average accuracy increased by about 5% after integrating remotely sensed information on the surface soil. The simulation accuracy was significantly improved, especially in transition periods between freezing and thawing of the surface soil.  相似文献   

基于NDVI的新疆荒漠地区植被覆盖度遥感估算经验模型研究          下载免费PDF全文

岳健  穆桂金  唐自华  杨雪峰  林永崇  徐立帅 《干旱区地理》2020,43(1):153-160

新疆荒漠地区植被覆盖度遥感估算模型十分缺乏，给荒漠化监测等相关工作带来很大不便，开展植被覆盖度遥感估算经验模型研究，对于促进和完善相关地区的生态监测及研究工作具有积极的现实意义。通过对阜康市北部沙漠南缘和克拉玛依市中部平原荒漠进行无人机航拍，利用无人机遥感提取（光合）植被信息，并将无人机航拍影像的植被覆盖度统计单元与高分辨率卫星影像像元在空间上直接相对应，获取在高分辨率卫星影像像元尺度上的植被盖度，然后通过植被覆盖度和空间上与其相对应的源自高分辨率卫星影像的NDVI数据的拟合关系，建立基于源自高分二号影像的NDVI的阜康北部沙漠植被覆盖度遥感估算线性模型以及基于源自ZY1-02C影像的NDVI的克拉玛依平原荒漠植被覆盖度遥感估算二次多项式模型。研究中所采用的无人机遥感与卫星遥感相结合、植被覆盖度统计单元与卫星像元在空间上直接对应的方法，可避免以往相关工作中常以点位测量数据代表卫星像元数据所带来的不确定性。由于所用卫星影像的NDVI数据稳定性相对不足等原因，所建立的遥感估算模型的估算精度尚相对偏低，有待于今后进一步的工作加以改进。  相似文献   

无定河下游全新世古洪水研究          下载免费PDF全文

李晓刚  黄春长  庞奖励 《干旱区地理》2020,43(2):380-387

对无定河流域野外考察，在其下游苏家圪坨（SJGT）地点发现夹有古洪水滞流沉积物（SWD）的全新世剖面。古洪水SWD厚度30 cm，具有平行微薄层理特征，且直接覆盖在东汉文化层之上。室内分析结果表明古洪水SWD有着与2012年洪水SWD相似的沉积学特征，以粗粉沙为主，分选良好，磁化率和烧失量较小，说明它记录了一次无定河下游的大洪水事件。文化层光释光（OSL）测年结果显示该次古洪水事件发生在1 900~1 700 a BP。古洪水洪峰水位和洪峰流量分别为765.9 m和10 530 m³·s^-1。同时利用2012年洪水洪痕水位验证，在同一断面用相同参数恢复洪峰流量为1 030 m³·s^-1，实测洪峰流量为1 000 m³·s^-1，误差为3.09%，说明古洪水洪峰流量恢复结果合理可靠。研究结果延长了无定河下游洪水数据序列，对水资源、水能源的开发利用提供了科学依据。  相似文献   

西部省会城市新型城镇化水平综合测度研究          下载免费PDF全文

邹亚锋  李亚静  袁志鸿 《干旱区地理》2020,43(6):1612-1621

新型城镇化是中国经济社会健康、稳定、可持续发展的根本途径，也是消除中国城乡社会经 济“二元结构”的根本出路。根据新型城镇化的内涵，从人口、经济发展、生活质量、基础设施、资源 环境、城乡统筹 6 个方面，构建了包含 24 项指标的新型城镇化水平综合测度指标体系，运用熵值 法，对西部 11 个省会城市 2005—2015 年的城镇化发展水平进行综合测度。研究表明：西部省会城 市的新型城镇化发展水平存在显著差异，总体上可分为“高等水平”、“中等水平”、“低等水平”3 个 级别，基础设施建设是造成差异的主要驱动力。各城市不同年间的城镇化单项水平影响因素呈现 多元化，在今后的发展中应有所侧重，同时兼顾城镇化质量，推动城乡协调发展。  相似文献   

对欧亚非大陆自由主义民主格局演变的批判性分析     

麻洪川  郗笃刚  刘建忠  张晶  丁梓越 《世界地理研究》2020,29(6):1113-1124

自由主义民主作为西方谋求世界霸权而肆意输出的意识形态，以批判性话语剖析欧亚非大陆自由主义民主状况，对揭露西方民主输出的真实目的意义重大。探究欧亚非大陆“自由”等级格局演变特征，搭建“自由”等级格局演变影响因子体系，并揭示其演变影响机制。研究发现：①“自由”国家数量总体显著增长，从欧洲南部、西部、北部，亚洲南部、东北部扩散至欧洲东部、中部和非洲南部地区；“部分自由”国家数量总体明显增加，基本分布在亚洲南部、东南部，非洲东部、西部和南部；“不自由”国家数量总体略有减少，从欧洲东部、亚洲和非洲大部分地区压缩至亚非大部分地区。②“自由”等级标准差指数表现出明显上升趋势，而其变异系数表现出波动下降趋势。③总体上，欧亚非大陆具有相似“自由”等级的国家集聚程度在不断增强，其中，高值集聚出现波动增强；局部上，冷点、次冷点区域呈现“联成一片”的态势，热点、次热点区域则有“东进”的趋势。④社会经济、外部扩散、文化宗教、政治制度、西方行为等因素影响显著，且在整体和局部影响机制上呈现明显差异。⑤“自由之家”为突出和强调西方在国际政治领域的标准制定权和话语权，其“自由”等级指数具有极强的意识形态属性和战略意图。  相似文献   

珠三角基塘系统研究回顾及展望   总被引：2，自引：0，他引：2  

龚建周  蒋超  胡月明  杨颢 《地理科学进展》2020,39(7):1236-1246

基塘系统是中国劳动人民独创的低洼地利用方式和生态循环农业模式,珠三角基塘系统在历史上曾起到重要的作用,但目前其面积萎缩、生态功能退化。为了推动珠三角传统农业文化遗产的保护、促进区域人地关系协调发展,论文结合文献计量学方法对珠三角基塘系统研究文献进行梳理和概括,发现在基(田)塘生产力、区域水陆生态农业循环模式规律、社会经济发展与传统农业文化遗产保护的关系等方面已经展开研究;但是珠三角基塘系统演变机理研究比较薄弱,区域特色的农业文化遗产保护途径探索还鲜见。建议未来加强珠三角基塘系统演变机理、系统重构技术、农业文化遗产保护价值与途径的研究,以此深化基塘系统理论及实践研究,为协同区域农业生产格局与农业生态系统耦合关系提供支撑。  相似文献   

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes     

Yibin Ren  Huanfa Chen  Tao Cheng  Yang Zhang  Ge Chen 《International journal of geographical information science》2020,34(4):802-823

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.  相似文献   

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

  相似文献