International Journal of Geographical Information Science最新文献

{"title":"Identifying the cargo types of road freight with semi-supervised trajectory semantic enhancement","authors":"Yibo Zhao, Shifen Cheng, Beibei Zhang, Feng Lu","doi":"10.1080/13658816.2023.2288116","DOIUrl":"https://doi.org/10.1080/13658816.2023.2288116","url":null,"abstract":"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 ...","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"26 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138537170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeoAI in urban analytics","authors":"Stefano De Sabbata, Andrea Ballatore, Harvey J. Miller, Renée Sieber, Ivan Tyukin, Godwin Yeboah","doi":"10.1080/13658816.2023.2279978","DOIUrl":"https://doi.org/10.1080/13658816.2023.2279978","url":null,"abstract":"Published in International Journal of Geographical Information Science (Vol. 37, No. 12, 2023)","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"15 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138537121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial cooperative simulation of land use-population-economy in the Greater Bay Area, China","authors":"Wei Tu, Wei Gao, Mingxiao Li, Yao Yao, Biao He, Zhengdong Huang, Jie Zhang, Renzhong Guo","doi":"10.1080/13658816.2023.2285459","DOIUrl":"https://doi.org/10.1080/13658816.2023.2285459","url":null,"abstract":"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...","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"85 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138537181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic gradient geographical weighted regression (sgGWR): scalable bandwidth optimization for geographically weighted regression","authors":"Hayato Nishi, Yasushi Asami","doi":"10.1080/13658816.2023.2285471","DOIUrl":"https://doi.org/10.1080/13658816.2023.2285471","url":null,"abstract":"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...","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"5 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138537171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards","authors":"Jianhua Chen, Kaihang Xu, Zheng Zhao, Xianxia Gan, Huawei Xie","doi":"10.1080/13658816.2023.2273877","DOIUrl":"https://doi.org/10.1080/13658816.2023.2273877","url":null,"abstract":"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...","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"4 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138537125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Space–time prism and accessibility incorporating monetary budget and mobility-as-a-service","authors":"Jing Qin, Feixiong Liao","doi":"10.1080/13658816.2023.2280642","DOIUrl":"https://doi.org/10.1080/13658816.2023.2280642","url":null,"abstract":"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 ...","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"16 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138537137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning","authors":"Wei Tu, Haoyu Ye, Ke Mai, Meng Zhou, Jincheng Jiang, Tianhong Zhao, Shengao Yi, Qingquan Li","doi":"10.1080/13658816.2023.2279969","DOIUrl":"https://doi.org/10.1080/13658816.2023.2279969","url":null,"abstract":"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...","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"4 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138537179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating lane-level road networks from high-precision trajectory data with lane-changing behavior analysis","authors":"Mengyue Yuan, Peng Yue, Can Yang, Jian Li, Kai Yan, Chuanwei Cai, Chongshan Wan","doi":"10.1080/13658816.2023.2279977","DOIUrl":"https://doi.org/10.1080/13658816.2023.2279977","url":null,"abstract":"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","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135037988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracting and evaluating typical characteristics of rural revitalization using web text mining","authors":"Kunkun Fan, Daichao Li, Haidong Wu, Yingjie Wang, Hu Yu, Zhan Zeng","doi":"10.1080/13658816.2023.2280990","DOIUrl":"https://doi.org/10.1080/13658816.2023.2280990","url":null,"abstract":"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","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"4 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135037677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adding attention to the neural ordinary differential equation for spatio-temporal prediction","authors":"Peixiao Wang, Tong Zhang, Hengcai Zhang, Shifen Cheng, Wangshu Wang","doi":"10.1080/13658816.2023.2275160","DOIUrl":"https://doi.org/10.1080/13658816.2023.2275160","url":null,"abstract":"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","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"27 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}