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Identifying the cargo types of road freight with semi-supervised trajectory semantic enhancement 基于半监督轨迹语义增强的道路货物类型识别
IF 5.7 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-29 DOI: 10.1080/13658816.2023.2288116
Yibo Zhao, Shifen Cheng, Beibei Zhang, Feng Lu
Identifying road freight cargo types is crucial for regional economic interaction and transportation optimization. Existing methods primarily rely on manual labeling and the rule, neither of which ...
道路货物类型的确定对区域经济互动和交通运输优化具有重要意义。现有的方法主要依靠人工标注和规则,这两者都不是……
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引用次数: 0
GeoAI in urban analytics 城市分析中的GeoAI
IF 5.7 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-28 DOI: 10.1080/13658816.2023.2279978
Stefano De Sabbata, Andrea Ballatore, Harvey J. Miller, Renée Sieber, Ivan Tyukin, Godwin Yeboah
Published in International Journal of Geographical Information Science (Vol. 37, No. 12, 2023)
发表于《国际地理信息科学杂志》2023年第37卷第12期
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引用次数: 0
Spatial cooperative simulation of land use-population-economy in the Greater Bay Area, China 大湾区土地利用-人口-经济空间协同模拟
IF 5.7 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-27 DOI: 10.1080/13658816.2023.2285459
Wei Tu, Wei Gao, Mingxiao Li, Yao Yao, Biao He, Zhengdong Huang, Jie Zhang, Renzhong Guo
Fast urbanization brings great challenges to sustainable development goals, such as excessive exploitation and population explosion. Classical cellular automata (CA) have been widely used to indepe...
快速城市化给可持续发展目标带来了巨大的挑战,如过度开发和人口爆炸。经典元胞自动机(CA)已被广泛用于独立…
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引用次数: 0
Stochastic gradient geographical weighted regression (sgGWR): scalable bandwidth optimization for geographically weighted regression 随机梯度地理加权回归(sgGWR):用于地理加权回归的可扩展带宽优化
IF 5.7 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-24 DOI: 10.1080/13658816.2023.2285471
Hayato Nishi, Yasushi Asami
GWR (Geographical Weighted Regression) is a widely accepted regression method under spatial dependency. Since the calibration of GWR is computationally intensive, some efficient methods for calibra...
地理加权回归(GWR)是一种被广泛接受的空间依赖回归方法。由于GWR的标定需要大量的计算量,因此有一些有效的标定方法。
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引用次数: 0
A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards 基于空间案例推理的元胞自动机局部滑坡灾害预测
IF 5.7 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-15 DOI: 10.1080/13658816.2023.2273877
Jianhua Chen, Kaihang Xu, Zheng Zhao, Xianxia Gan, Huawei Xie
Predicting landslide hazards benefits geological disaster prevention and control. A novel cellular automaton (CA) integrating spatial case-based reasoning (SCBR), namely SCBR-CA, is proposed in thi...
预测滑坡灾害有利于地质灾害防治。本文提出了一种基于空间案例推理(SCBR)的元胞自动机(CA),即SCBR-CA。
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引用次数: 0
Space–time prism and accessibility incorporating monetary budget and mobility-as-a-service 时空棱镜和可达性结合货币预算和流动性即服务
IF 5.7 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-15 DOI: 10.1080/13658816.2023.2280642
Jing Qin, Feixiong Liao
Recent years in time geography have witnessed a flourishment of space–time prism (STP) modeling extensions for enhancing realism. However, there is little research on the incorporation of monetary ...
近年来,时空棱镜(STP)建模扩展在时间地理学中蓬勃发展,以增强真实感。然而,很少有研究纳入货币…
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引用次数: 0
Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning 结合轨迹挖掘和强化学习的互联电动出租车深度在线推荐
IF 5.7 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-15 DOI: 10.1080/13658816.2023.2279969
Wei Tu, Haoyu Ye, Ke Mai, Meng Zhou, Jincheng Jiang, Tianhong Zhao, Shengao Yi, Qingquan Li
There is a growing interest in the optimization of vehicle fleets management in urban environments. However, limited attention has been paid to the integrated optimization of electric taxi fleets a...
在城市环境中,车队管理的优化日益引起人们的兴趣。然而,对电动出租车车队的综合优化问题的研究却很少。
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引用次数: 0
Generating lane-level road networks from high-precision trajectory data with lane-changing behavior analysis 基于变道行为分析的高精度轨迹数据生成车道级道路网
1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-12 DOI: 10.1080/13658816.2023.2279977
Mengyue Yuan, Peng Yue, Can Yang, Jian Li, Kai Yan, Chuanwei Cai, Chongshan Wan
ABSTRACT–Recent advances in mobile mapping systems have facilitated the collection of high-precision trajectory data in centimeter positioning accuracy. It provides the potential to infer lane-level road networks, which are essential for autonomous driving navigation. This task is challenging due to the complicated lane merging and diverging structures as well as the lane-changing patterns in trajectory data. This paper presents a lane-level road network generation method from high-precision trajectory data with lane-changing behavior analysis. Trajectories are firstly partitioned by detecting road intersections and changes in lane structure. Subsequently, in regions with consistent lane structure, a principal curve fitting algorithm is developed to extract lane centerlines. Erroneous lanes generated by lane-changing behavior are pruned based on a constructed lane intersection graph. In regions with merging and diverging lanes, a lane-group fitting algorithm is designed. This algorithm estimates lane locations by incorporating a Gaussian mixture model with lane width prior knowledge and then infers lane-level topological structures using trajectory flow information. The proposed method is evaluated on a real-world high-precision trajectory dataset. Comprehensive experiments demonstrate that it outperforms state-of-the-art methods in four metrics. Under complex scenarios, the method is capable of generating lane-level road networks with higher completeness and fewer fragments.Keywords: Lane-level road networkhigh-precision trajectory datalane-changing behavior AcknowledgmentWe thank the editor and anonymous reviewers for their constructive comments.Data and codes availability statementThe data and codes that support the findings of this study are available at the link: https://doi.org/10.6084/m9.figshare.23529336. A subset of the data is shared for demonstration purposes.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 We use the term lane-changing to describe the driving behavior in trajectory data, while lane transition refers to special road network structure where lanes merge or diverge.Additional informationFundingThe work was supported by the Chongqing Technology Innovation and Application Development Project [Grant No. CSTB2022TIAD-DEX0013]; funding from the State Key Laboratory of Intelligent Vehicle Safty Technology and Chongqing Changan Automobile Co. Ltd; and the Fundamental Research Funds for the Central Universities [Grant No. 2042022dx0001].Notes on contributorsMengyue YuanMengyue Yuan is an M.S. student in the School of Remote Sensing and Information Engineering at Wuhan University. Her research interest is geospatial data mining and transportation geography. She contributed to the idea, study design, methodology, implementation, and manuscript writing of this paper.Peng YuePeng Yue is a professor at Wuhan University. He serves as the deputy dean at the School of Remote Sensing and Informati
移动测绘系统的最新进展促进了高精度轨迹数据的收集,定位精度达到厘米级。它提供了推断车道级道路网络的潜力,这对自动驾驶导航至关重要。由于轨迹数据中存在复杂的车道合并和发散结构以及变道模式,这一任务具有一定的挑战性。提出了一种基于变道行为分析的高精度轨迹数据的车道级道路网生成方法。首先通过检测道路交叉口和车道结构的变化对轨迹进行分割。然后,在车道结构一致的区域,提出一种主曲线拟合算法提取车道中心线。基于构建的车道交叉图,对变道行为产生的错误车道进行修剪。在车道合并和发散区域,设计了一种车道群拟合算法。该算法通过引入具有车道宽度先验知识的高斯混合模型来估计车道位置,然后利用轨迹流信息推断车道级拓扑结构。在实际高精度轨迹数据集上对该方法进行了验证。综合实验表明,它优于最先进的方法在四个指标。在复杂场景下,该方法生成的车道级道路网络具有更高的完整性和更少的碎片。关键词:车道级路网高精度轨迹数据改变行为感谢编辑和匿名审稿人提出的建设性意见。数据和代码可用性声明支持本研究结果的数据和代码可从以下链接获得:https://doi.org/10.6084/m9.figshare.23529336。为了演示目的,共享数据的一个子集。披露声明作者未报告潜在的利益冲突。注1我们用变道来描述轨迹数据中的驾驶行为,而车道过渡是指车道合并或分流的特殊路网结构。额外informationFundingThe工作得到了重庆技术创新和应用程序开发项目(批准号CSTB2022TIAD-DEX0013];智能汽车安全技术国家重点实验室和重庆长安汽车股份有限公司资助;中央高校基本科研业务费[批准号:2042022dx0001]。袁孟岳,武汉大学遥感与信息工程学院硕士研究生。主要研究方向为地理空间数据挖掘和交通地理。她对本文的构思、研究设计、方法、实施和稿件撰写都做出了贡献。彭岳是武汉大学的一名教授。现任遥感与信息工程学院副院长、湖北省智能地理信息处理工程中心主任、地理空间信息与定位服务研究所所长。他监督了这项研究,并对研究的设计、方法和手稿的撰写做出了贡献。杨灿,武汉大学遥感与信息工程学院博士后研究员。主要研究方向为轨迹模式挖掘与识别。他对这篇论文的思想、研究设计、方法和手稿的撰写都做出了贡献。李健,智能汽车安全技术国家重点实验室、重庆长安汽车有限公司工程师。他对本文的思想、研究设计和方法都做出了贡献。严凯,智能汽车安全技术国家重点实验室、重庆长安汽车有限公司工程师。他对本文的思想、研究设计和方法都做出了贡献。蔡传伟,武汉大学遥感与信息工程学院博士生。他对本文的研究设计和方法做出了贡献。万崇山,武汉大学遥感与信息工程学院硕士研究生。他对本文的实现做出了贡献。
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引用次数: 0
Extracting and evaluating typical characteristics of rural revitalization using web text mining 基于网络文本挖掘的乡村振兴典型特征提取与评价
1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-12 DOI: 10.1080/13658816.2023.2280990
Kunkun Fan, Daichao Li, Haidong Wu, Yingjie Wang, Hu Yu, Zhan Zeng
AbstractEvaluating typical rural characteristics reveals certain advantages of rural revitalization and is crucial for understanding rural disparities and promoting development. Field research and statistical data can reflect the spatial distribution of local resources and development models. However, due to cost limitations and statistical constraints, it is impossible to effectively compare and evaluate the characteristics of rural development at the long time series, large scale and fine granularity required for sustainable regeneration. This study proposes a web-based method for the extraction and evaluation of rural revitalization characteristics (WERRC). The BERT-BiLSTM-Attention model categorizes rural web texts according to five themes: industrial prosperity, ecological livability, rural civilization, effective governance, and prosperous life. The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm extracts rural characteristics, and the relative advantages of these features are compared among 100 Chinese villages. WERRC extracts the typical characteristics, obtains the spatial distribution and relative advantage, and then ranks them according to the five themes. The relationship between national policy guidance and rural development is explored. The results support further exploration of differentiated, high-quality development modes that incorporate rural advantages into policy, adjust industrial structure, and optimise revitalization strategies at the rural scale.Keywords: Rural revitalizationtypical village characteristicsweb text miningcharacteristic extractionregional sustainable development Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data, codes, and instructions that support the findings of this study are available with the identifier(s) at the private link https://github.com/afxltsbl/Regional-Feature-Extraction.Additional informationFundingThis research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant number XDA23100502 ant the National Natural Science Foundation of China, Grant number 42301523.Notes on contributorsKunkun FanKunkun Fan is a master’s student at the Academy of Digital China (Fujian), Fuzhou University. His primary research interests include web text mining and traffic trajectory data mining. He contributed to the concept, review and analysis of this paper.Daichao LiDaichao Li is currently an associate researcher at the Academy of Digital China (Fujian), Fuzhou University. Her research interests include spatiotemporal data mining, spatiotemporal knowledge graphs, and spatiotemporal data visualization and visual analysis. She contributed to the conception, editing, and review of this paper.Haidong WuHaidong Wu is a lecturer at the School of Economics and Management, Fuzhou University. His research interests include data management and Internet economy and big data analysis. He co
乡村典型特色评价揭示了乡村振兴的一定优势,对于认识乡村差异、促进乡村发展具有重要意义。实地调研和统计数据可以反映当地资源的空间分布和发展模式。然而,由于成本限制和统计约束,无法对可持续再生所需的长时间序列、大规模、细粒度的乡村发展特征进行有效的比较和评价。本研究提出一种基于网络的乡村振兴特征提取与评价方法。BERT-BiLSTM-Attention模型根据产业繁荣、生态宜居、乡村文明、有效治理和繁荣生活五个主题对乡村网络文本进行分类。利用词频-逆文档频率(TF-IDF)算法提取乡村特征,并在中国100个乡村中比较这些特征的相对优势。WERRC提取典型特征,得到空间分布和相对优势,并根据五大主题进行排序。探讨了国家政策引导与农村发展的关系。研究结果为进一步探索将农村优势纳入政策、调整产业结构、优化乡村振兴战略的差异化、高质量发展模式提供了依据。关键词:乡村振兴典型村落特征网络文本挖掘特征提取区域可持续发展披露声明作者未发现潜在利益冲突数据和代码可用性声明支持本研究结果的数据、代码和说明可通过私有链接https://github.com/afxltsbl/Regional-Feature-Extraction.Additional获取。本研究得到了中国科学院战略重点研究项目(资助号:XDA23100502)和中国国家自然科学基金(资助号:42301523)的支持。范坤坤,福州大学数字中国研究院(福建)硕士研究生。他的主要研究兴趣包括网络文本挖掘和交通轨迹数据挖掘。他参与了本文的构思、综述和分析。李岱超,福州大学数字中国研究院(福建)副研究员。主要研究方向为时空数据挖掘、时空知识图谱、时空数据可视化与可视化分析。她参与了这篇论文的构思、编辑和审稿。吴海东,福州大学经济管理学院讲师。主要研究方向为数据管理与互联网经济、大数据分析。他对本文的讨论和分析做出了贡献。王英杰,中国科学院地理科学与资源研究所副教授。主要研究方向为旅游地理信息系统、地图制图与地理信息系统、旅游资源开发与规划。他对本文的分析和讨论做出了贡献。胡雨,博士,毕业于中国科学院大学。现任中国科学院地理科学与资源研究所副研究员。主要研究方向为旅游地理学、生态旅游。他对本文的审查和讨论做出了贡献。詹增,湖南地图学出版社专家。她为本文的分析和结论做出了贡献。
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引用次数: 0
Adding attention to the neural ordinary differential equation for spatio-temporal prediction 增加对时空预测神经常微分方程的关注
1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-11-07 DOI: 10.1080/13658816.2023.2275160
Peixiao Wang, Tong Zhang, Hengcai Zhang, Shifen Cheng, Wangshu Wang
AbstractExplainable spatio-temporal prediction gains attraction in the development of geospatial artificial intelligence. The neural ordinal differential equation (NODE) emerges as a new solution for explainable spatio-temporal prediction. However, challenges still need to be solved in most existing NODE-based prediction models, such as difficulty modeling spatial data and mining long-term temporal dependencies in data. In this study, we propose a spatio-temporal attentional NODE (STA-ODE) to address the two challenges above. First, we define a spatio-temporal ordinary differential equation to predict a value at each time iteratively by a novel spatio-temporal derivative network. Second, we develop an attention mechanism to fuse multiple prediction values for capturing long-term temporal dependencies in data. To train the STA-ODE model, we design a loss function that aligns the prediction results in spatial dimension with prediction results in temporal dimension to calibrate the parameters of the model. The proposed model was validated with three real-world spatio-temporal datasets (traffic flow dataset, PM2.5 monitoring dataset, and temperature monitoring dataset). Experimental results showed that STA-ODE outperformed seven existing baselines regarding prediction accuracy. In addition, we used visualization to demonstrate the sound interpretability and prediction accuracy of the STA-ODE model.Keywords: Geospatial artificial intelligencespatio-temporal predictionspatio-temporal attentionneural ordinary differential equation AcknowledgementsThe numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data and codes that support the findings of this study are available in ‘figshare.com’ with the identifier https://doi.org/10.6084/m9.figshare.22678153.Additional informationFundingThis project was supported by National Key Research and Development Program of China [Grant No. 2021YFB3900803], National Postdoctoral Innovation Talents Support Program [Grant No. BX20230360], Open funds of the Wuhan University-Huawei Geoinformatics Innovation Laboratory [Grant No. TC20210901025-2023-04], National Natural Science Foundation of China [Grant Nos. 42101423 and 42371470], Special Research Assistant Program of Chinese Academy of Sciences, Innovation Project of LREIS [Grant No. 08R8A092YA].Notes on contributorsPeixiao WangPeixiao Wang is a Postdoctoral Fellow from State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Nature Resources Research, Chinese Academy of Sciences. He received Ph.D. degree under from State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, and received the M.S. degree from The Academy of Digital China, Fuzhou University. His research topics
摘要可解释时空预测是地理空间人工智能发展的热点。神经有序微分方程(NODE)作为一种新的可解释时空预测方法而出现。然而,大多数现有的基于节点的预测模型仍然需要解决一些挑战,例如空间数据建模困难和挖掘数据中的长期时间依赖关系。在这项研究中,我们提出了一个时空注意节点(STA-ODE)来解决上述两个挑战。首先,我们定义了一个时空常微分方程,通过一个新的时空导数网络来迭代预测每个时间点的值。其次,我们开发了一种注意力机制来融合多个预测值,以捕获数据中的长期时间依赖性。为了训练STA-ODE模型,我们设计了一个损失函数,将空间维度的预测结果与时间维度的预测结果对齐,以校准模型的参数。利用3个真实时空数据集(交通流量数据集、PM2.5监测数据集和温度监测数据集)对模型进行了验证。实验结果表明,STA-ODE在预测精度方面优于现有的7条基线。此外,我们还利用可视化技术证明了STA-ODE模型具有良好的可解释性和预测精度。关键词:地理空间人工智能时空预测时空注意力神经常微分方程致谢本文的数值计算在武汉大学超级计算中心的超级计算系统上完成。披露声明作者未报告潜在的利益冲突。支持本研究结果的数据和代码可在“figshare.com”上获得,识别码为https://doi.org/10.6084/m9.figshare.22678153.Additional。基金资助:国家重点研发计划项目[批准号:2021YFB3900803];项目编号:BX20230360],武汉大学-华为地理信息创新实验室开放基金[批准号:20230360];国家自然科学基金项目[批准号:42101423和42371470],中国科学院特约科研助理计划,中国科学院科技创新工程项目[批准号:08R8A092YA]。作者简介王培晓,中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室博士后。武汉大学测绘与遥感信息工程国家重点实验室博士学位,福州大学数字中国研究院硕士学位。他的研究方向包括时空数据挖掘和时空预测,尤其关注交通运输系统的时空预测。张彤,武汉大学测绘与遥感信息工程国家重点实验室研究员。他获得了硕士学位。2003年获武汉大学地图学与地理信息系统(GIS)学士学位,2007年获美国圣地亚哥州立大学和加州大学圣巴巴拉分校地理学博士学位。他的研究课题包括城市计算和机器学习。张恒才,中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室副教授。毕业于中国科学院地理科学与资源研究所,获博士学位。中国地理信息系统学会理论与方法专业委员会委员、美国计算机学会SIGSPATIAL中国分会委员。他的兴趣集中在时空数据挖掘和3d计算。程世芬,中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室副教授。毕业于中国科学院地理科学与资源研究所,获博士学位。主要研究方向为时空数据挖掘、城市计算和智能交通。Wangshu Wangshu Wang是维也纳科技大学制图研究中心的博士后研究员。她于2023年获得维也纳理工大学博士学位。她的研究重点是时空数据挖掘和室内行人导航。
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引用次数: 0
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International Journal of Geographical Information Science
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