Pub Date : 2023-11-15DOI: 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。
{"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}
Pub Date : 2023-11-12DOI: 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
{"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}
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
{"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}
Pub Date : 2023-11-07DOI: 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
{"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}
Pub Date : 2023-11-03DOI: 10.1080/13658816.2023.2274475
Jing Huang, Teng Fei, Yuhao Kang, Jun Li, Ziyu Liu, Guofeng Wu
AbstractEstimating road traffic noise is essential for examining the quality of sounding environment and mitigating such a non-negligible pollutant in urban areas. However, existing estimated models often have limited applicability to specific traffic conditions, while the required parameters may not be readily available for city-wide collection. This paper proposes a data-driven approach for measuring road-level acoustic information of traffic with street view imagery. Specifically, we utilize portable vehicle-equipped hardware for in-situ noise acquisition and employ a deep learning model ResNet to learn high-level visual features from street view images that are closely associated with road traffic noise. The ResNet captures meaningful patterns from the input data, and the output probability vectors are then fed into a Random-Forest regression algorithm to quantitatively estimate the noise in decibels for different road segments. The MAE and RMSE of the DCNN-RF model are 2.01 and 2.71, respectively. Additionally, we employ a gradient-weighted Class Active Mapping approach to visually interpret our deep learning model and explore the significant elements in streetscapes that contribute to the model's estimations. Our proposed framework facilitates low-cost and fine-scale road traffic noise estimations and sheds light on how auditory information could be inferred from street imagery, which may benefit practices in geography and urban planning.Keywords: Road traffic noisestreet view imagerydeep learningbuild environmenturban planning AcknowledgmentThe authors would like to thank Urli for the valuable advice provided during the initial stages of the experiment and Mr. Mengze Gao for designing and 3D-printing the enclosure for the data acquisition device. Thanks to the financial support from the National Natural Science Foundation of China [42271476]; the Wuhan University 351 Talent Program, 2020; the State Key Laboratory of Resources and Environmental Information System [2023OPEN007] and the Guangdong Science and Technology Strategic Innovation Fund [2020B1212030009].Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe original in-situ road traffic noise data with geographic coordinates collected by the experimental vehicle using our portable device, as well as the road traffic noise estimation model and some sample street view images used for demonstration are available at https://github.com/kellyhuang313/traffic-noise-estimation. Instructions for executing the code are provided in the README.txt.Notes1 https://www.openstreetmap.org/Additional informationFundingThis work was supported by the National Natural Science Foundation of China [42271476]; the Wuhan University 351 Talent Program, 2020; and the Guangdong Science and Technology Strategic Innovation Fund [2020B1212030009]. This work was also supported by State Key Laboratory of Resources and Environmental Information Sys
{"title":"Estimating urban noise along road network from street view imagery","authors":"Jing Huang, Teng Fei, Yuhao Kang, Jun Li, Ziyu Liu, Guofeng Wu","doi":"10.1080/13658816.2023.2274475","DOIUrl":"https://doi.org/10.1080/13658816.2023.2274475","url":null,"abstract":"AbstractEstimating road traffic noise is essential for examining the quality of sounding environment and mitigating such a non-negligible pollutant in urban areas. However, existing estimated models often have limited applicability to specific traffic conditions, while the required parameters may not be readily available for city-wide collection. This paper proposes a data-driven approach for measuring road-level acoustic information of traffic with street view imagery. Specifically, we utilize portable vehicle-equipped hardware for in-situ noise acquisition and employ a deep learning model ResNet to learn high-level visual features from street view images that are closely associated with road traffic noise. The ResNet captures meaningful patterns from the input data, and the output probability vectors are then fed into a Random-Forest regression algorithm to quantitatively estimate the noise in decibels for different road segments. The MAE and RMSE of the DCNN-RF model are 2.01 and 2.71, respectively. Additionally, we employ a gradient-weighted Class Active Mapping approach to visually interpret our deep learning model and explore the significant elements in streetscapes that contribute to the model's estimations. Our proposed framework facilitates low-cost and fine-scale road traffic noise estimations and sheds light on how auditory information could be inferred from street imagery, which may benefit practices in geography and urban planning.Keywords: Road traffic noisestreet view imagerydeep learningbuild environmenturban planning AcknowledgmentThe authors would like to thank Urli for the valuable advice provided during the initial stages of the experiment and Mr. Mengze Gao for designing and 3D-printing the enclosure for the data acquisition device. Thanks to the financial support from the National Natural Science Foundation of China [42271476]; the Wuhan University 351 Talent Program, 2020; the State Key Laboratory of Resources and Environmental Information System [2023OPEN007] and the Guangdong Science and Technology Strategic Innovation Fund [2020B1212030009].Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe original in-situ road traffic noise data with geographic coordinates collected by the experimental vehicle using our portable device, as well as the road traffic noise estimation model and some sample street view images used for demonstration are available at https://github.com/kellyhuang313/traffic-noise-estimation. Instructions for executing the code are provided in the README.txt.Notes1 https://www.openstreetmap.org/Additional informationFundingThis work was supported by the National Natural Science Foundation of China [42271476]; the Wuhan University 351 Talent Program, 2020; and the Guangdong Science and Technology Strategic Innovation Fund [2020B1212030009]. This work was also supported by State Key Laboratory of Resources and Environmental Information Sys","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"26 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819359","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}
Pub Date : 2023-10-27DOI: 10.1080/13658816.2023.2270285
Alexis Comber, Paul Harris, Chris Brunsdon
This paper proposes a novel spatially varying coefficient (SVC) regression through a Geographical Gaussian Process GAM (GGP-GAM): a Generalized Additive Model (GAM) with Gaussian Process (GP) splines parameterised at observation locations. A GGP-GAM was applied to multiple simulated coefficient datasets exhibiting varying degrees of spatial heterogeneity and out-performed the SVC brand-leader, Multiscale Geographically Weighted Regression (MGWR), under a range of fit metrics. Both were then applied to a Brexit case study and compared, with MGWR marginally out-performing GGP-GAM. The theoretical frameworks and implementation of both approaches are discussed: GWR models calibrate multiple models whereas GAMs provide a full single model; GAMs can automatically penalise local collinearity; GWR-based approaches are computationally more demanding; MGWR is still only for Gaussian responses; MGWR bandwidths are intuitive indicators of spatial heterogeneity. GGP-GAM calibration and tuning are also discussed and areas of future work are identified, including the creation of a user-friendly package to support model creation and coefficient mapping, and to facilitate ease of comparison with alternate SVC models. A final observation that GGP-GAMs have the potential to overcome some of the long-standing reservations about GWR-based regression methods and to elevate the perception of SVCs amongst the broader community.
{"title":"Multiscale spatially varying coefficient modelling using a Geographical Gaussian Process GAM","authors":"Alexis Comber, Paul Harris, Chris Brunsdon","doi":"10.1080/13658816.2023.2270285","DOIUrl":"https://doi.org/10.1080/13658816.2023.2270285","url":null,"abstract":"This paper proposes a novel spatially varying coefficient (SVC) regression through a Geographical Gaussian Process GAM (GGP-GAM): a Generalized Additive Model (GAM) with Gaussian Process (GP) splines parameterised at observation locations. A GGP-GAM was applied to multiple simulated coefficient datasets exhibiting varying degrees of spatial heterogeneity and out-performed the SVC brand-leader, Multiscale Geographically Weighted Regression (MGWR), under a range of fit metrics. Both were then applied to a Brexit case study and compared, with MGWR marginally out-performing GGP-GAM. The theoretical frameworks and implementation of both approaches are discussed: GWR models calibrate multiple models whereas GAMs provide a full single model; GAMs can automatically penalise local collinearity; GWR-based approaches are computationally more demanding; MGWR is still only for Gaussian responses; MGWR bandwidths are intuitive indicators of spatial heterogeneity. GGP-GAM calibration and tuning are also discussed and areas of future work are identified, including the creation of a user-friendly package to support model creation and coefficient mapping, and to facilitate ease of comparison with alternate SVC models. A final observation that GGP-GAMs have the potential to overcome some of the long-standing reservations about GWR-based regression methods and to elevate the perception of SVCs amongst the broader community.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"156 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136261882","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}
Pub Date : 2023-10-13DOI: 10.1080/13658816.2023.2268665
Qiliang Liu, Yongchuan Zhu, Jie Yang, Xiancheng Mao, Min Deng
AbstractIn multivariate spatial interpolation, the accuracy of a variable of interest can be improved using ancillary variables. Although geostatistical methods are widely used for multivariate spatial interpolation, these methods usually require second-order stationary assumption of spatial processes, which is difficult to satisfy in practice. We developed a new multivariate spatial interpolation method based on Yang-Chizhong filtering (CoYangCZ) to overcome this limitation. CoYangCZ does not solve the multivariate spatial interpolation problem from a purely statistical point of view but integrates geometry and statistics-based strategies. First, we used a weighted moving average method based on binomial coefficients (i.e. Yang-Chizhong filtering) to fit the spatial autocorrelation structure of each spatial variable from a geometric perspective. We then quantified the spatial autocorrelation of each spatial variable and the correlations between different spatial variables by analyzing the variances of different spatial variables. Finally, we obtain the best linear unbiased estimators at the unsampled locations. Experiments on air pollution and meteorological datasets show that CoYangCZ has a higher interpolation accuracy than cokriging, regression kriging, gradient plus-inverse distance squared, sequential Gaussian co-simulation, and the kriging convolutional network. CoYangCZ can adapt to second-order non-stationary spatial processes; therefore, it has a wider scope of application than purely statistical methods.Keywords: Multivariate spatial processesspatial interpolationYang Chizhong filteringgeostatistics AcknowledgementsWe gratefully acknowledge the comments from the editor and the reviewers.Author contributionsQiliang Liu, Yongchuan Zhu, and Jie Yang conceived and designed the presented idea. Yongchuan Zhu and Jie Yang implemented the experiments and analysed the results. Qiliang Liu and Yongchuan Zhu wrote the manuscript. Xiancheng Mao and Min Deng reviewed the manuscript, and provided comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe findings of this study are backed by data and codes that can be found on ‘figshare.com’, with the identifier at the public link: https://doi.org/10.6084/m9.figshare.24230179.Additional informationFundingThis study was funded through support from National Natural Science Foundation of China (NSFC) [No. 42271484 and 41971353] and Natural Science Foundation of Hunan Province [No. 2021JJ20058].Notes on contributorsQiliang LiuQiliang Liu is currently a professor at Central South University, Hunan, China. His research interests focus on multi-scale spatio-temporal data mining and spatiotemporal statistics. He has published more than 30 peer-reviewed journal articles in these areas.Yongchuan ZhuYongchuan Zhu is currently a postgraduate student at Central South University and his research interests focus on spatial statistics.Jie Yan
{"title":"CoYangCZ: a new spatial interpolation method for nonstationary multivariate spatial processes","authors":"Qiliang Liu, Yongchuan Zhu, Jie Yang, Xiancheng Mao, Min Deng","doi":"10.1080/13658816.2023.2268665","DOIUrl":"https://doi.org/10.1080/13658816.2023.2268665","url":null,"abstract":"AbstractIn multivariate spatial interpolation, the accuracy of a variable of interest can be improved using ancillary variables. Although geostatistical methods are widely used for multivariate spatial interpolation, these methods usually require second-order stationary assumption of spatial processes, which is difficult to satisfy in practice. We developed a new multivariate spatial interpolation method based on Yang-Chizhong filtering (CoYangCZ) to overcome this limitation. CoYangCZ does not solve the multivariate spatial interpolation problem from a purely statistical point of view but integrates geometry and statistics-based strategies. First, we used a weighted moving average method based on binomial coefficients (i.e. Yang-Chizhong filtering) to fit the spatial autocorrelation structure of each spatial variable from a geometric perspective. We then quantified the spatial autocorrelation of each spatial variable and the correlations between different spatial variables by analyzing the variances of different spatial variables. Finally, we obtain the best linear unbiased estimators at the unsampled locations. Experiments on air pollution and meteorological datasets show that CoYangCZ has a higher interpolation accuracy than cokriging, regression kriging, gradient plus-inverse distance squared, sequential Gaussian co-simulation, and the kriging convolutional network. CoYangCZ can adapt to second-order non-stationary spatial processes; therefore, it has a wider scope of application than purely statistical methods.Keywords: Multivariate spatial processesspatial interpolationYang Chizhong filteringgeostatistics AcknowledgementsWe gratefully acknowledge the comments from the editor and the reviewers.Author contributionsQiliang Liu, Yongchuan Zhu, and Jie Yang conceived and designed the presented idea. Yongchuan Zhu and Jie Yang implemented the experiments and analysed the results. Qiliang Liu and Yongchuan Zhu wrote the manuscript. Xiancheng Mao and Min Deng reviewed the manuscript, and provided comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe findings of this study are backed by data and codes that can be found on ‘figshare.com’, with the identifier at the public link: https://doi.org/10.6084/m9.figshare.24230179.Additional informationFundingThis study was funded through support from National Natural Science Foundation of China (NSFC) [No. 42271484 and 41971353] and Natural Science Foundation of Hunan Province [No. 2021JJ20058].Notes on contributorsQiliang LiuQiliang Liu is currently a professor at Central South University, Hunan, China. His research interests focus on multi-scale spatio-temporal data mining and spatiotemporal statistics. He has published more than 30 peer-reviewed journal articles in these areas.Yongchuan ZhuYongchuan Zhu is currently a postgraduate student at Central South University and his research interests focus on spatial statistics.Jie Yan","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858084","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}
Pub Date : 2023-10-13DOI: 10.1080/13658816.2023.2266493
Hao Guo, Andre Python, Yu Liu
AbstractIn spatial regression models, spatial heterogeneity may be considered with either continuous or discrete specifications. The latter is related to delineation of spatially connected regions with homogeneous relationships between variables (spatial regimes). Although various regionalization algorithms have been proposed and studied in the field of spatial analytics, methods to optimize spatial regimes have been largely unexplored. In this paper, we propose two new algorithms for spatial regime delineation, two-stage K-Models and Regional-K-Models. We also extend the classic Automatic Zoning Procedure to a spatial regression context. The proposed algorithms are applied to a series of synthetic datasets and two real-world datasets. Results indicate that all three algorithms achieve superior or comparable performance to existing approaches, while the two-stage K-Models algorithm largely outperforms existing approaches on model fitting, region reconstruction and coefficient estimation. Our work enriches the spatial analytics toolbox to explore spatial heterogeneous processes.Keywords: Regionalizationspatial heterogeneityspatial regimespatial regression NotesAcknowledgmentsThe authors thank members of Spatial Analysis Group, Spatio-temporal Social Sensing Lab for helpful discussion. The constructive comments from anonymous reviewers are gratefully acknowledged.Data and codes availability statementThe data and codes that support the findings of this study are available at https://github.com/Nithouson/regreg.Disclosure statementThe authors declare that they have no conflict of interest.Notes1 For example, the population in each region is required to be as similar as possible or above a predefined value (see Duque et al. (Citation2012), Folch and Spielman (Citation2014), Wei et al. (Citation2021)).2 Note that the optimization of spatial regimes differs from Openshaw (Citation1978), where spatial units are aggregated into areas, and each area is treated as an observation in a global regression model.3 Note that in EquationEquation 1(1) L(R)=∑j=1p∑1≤i1
在空间回归模型中，空间异质性可以考虑连续或离散规格。后者与空间连接区域的描述有关，这些区域具有变量之间的均匀关系(空间制度)。虽然在空间分析领域已经提出和研究了各种区划算法，但优化空间制度的方法在很大程度上尚未得到探索。本文提出了两种新的空间状态描述算法:两阶段k -模型和区域k -模型。我们还将经典的自动分区程序扩展到空间回归环境。提出的算法应用于一系列合成数据集和两个真实数据集。结果表明，这三种算法的性能都优于或与现有方法相当，而两阶段K-Models算法在模型拟合、区域重建和系数估计方面大大优于现有方法。我们的工作丰富了空间分析工具箱，以探索空间异构过程。关键词:区域化;空间异质性;空间制度;感谢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https://github.com/Nithouson/regreg.Disclosure获取声明作者声明无利益冲突。注1例如，要求每个地区的人口尽可能接近或高于预定义值(参见Duque et al. (Citation2012)， Folch and Spielman (Citation2014)， Wei et al. (Citation2021))请注意，空间制度的优化不同于Openshaw (Citation1978)，在Openshaw中，空间单元被聚合成区域，每个区域被视为全局回归模型中的一个观测值注意，在等式1(1)L(R)=∑j=1p∑1≤i1<i2≤nI[ui1,ui2∈Rj]||xi1−xi2||2，(1)中，求和中考虑的单位对个数为∑j=1M(|Rj|2)，当|Rj|(j=1，…，M)彼此接近时，考虑的单位对个数越小。因此，目标函数可能倾向于具有相似单元数的区域的解在整篇论文中，我们描述了点阵数据(面单位上的空间数据)的情况。我们的方法也适用于建立邻接关系后的点观测数据(例如，与k近邻(KNN)或Delaunay三角剖分)这通常发生在min_obs接近n/p时，其中p是区域的数量。鉴于min_obs≪n/p，正如我们在实验中观察到的那样，这个问题不会造成问题如果单元不足的区域有两个或两个以上的相邻区域，我们选择合并后总SSR最小的相邻区域当min_obs太大或K太小(接近p)时，可能会出现区域数量小于p的例外情况，因此算法无法通过合并'微集群'来产生所需的区域数量。这个问题可以通过调整min_obs和K.8来解决。系数向量的OLS估计为β=(XTX) - 1XTy，其中X为自变量的nrx (m+1)矩阵，y为因变量的n维向量。在这里，通过添加一个常数为1的自变量，将截距包含在β中。通过应用Sherman-Morrison公式(Bartlett Citation1951)来更新(XTX)−1项，可以将时间复杂度从O(m2(nr+m))降低到O(m(nr+m))设βi,j表示系数βi在区域Rj中的值。在每次模拟中，列表(−2，−1,0,1,2)被随机洗牌两次，分别用作(β1,1，…，β1,5)和(β2,1，…，β2,5)Helbich等人(Citation2013)也对GWR系数进行了主成分分析。跳过这一步，因为在我们的实验中不需要降维在K-Models算法的两个阶段可以使用不同的min_obs值。这里min_obs=10用于合并阶段，而分区阶段的min_obs是本文中自变量的数量加1即使考虑平均SSR而不是最低SSR，两阶段k模型和AZP的表现也始终优于GWR-Skater和Skater-reg;Regional-K-Models与skater - regg相当，优于gwr - skater在我们的机器上，GWR估计没有在30分钟内完成在一台CPU为Intel酷睿i5-1135G7 (2.40 GHz)、内存为16GB的计算机上对King County房价数据集进行了实验。 基金资助:国家自然科学基金项目(41830645,42271426,41971331,82273731)、智慧广州时空信息云平台建设项目(GZIT2016-A5-147)和国家重点研发计划项目(2021YFC2701905)。郭浩，北京大学遥感与地理信息系统研究所博士研究生。他于2020年获得北京大学地理信息科学学士学位和数学双学士学位。主要研究方向为空间分析、地理空间人工智能和空间优化。Andre Python，浙江大学数据科学中心ZJU100青年统计学教授。他在瑞士弗里堡大学获得学士学位和硕士学位，在英国圣安德鲁斯大学获得博士学位。他开发并应用空间模型和可解释的机器学习算法，以更好地理解观察到的空间现象模式背后的机制。刘宇，现任北京大学遥感与地理信息系统研究所gisscience博雅教授。他分别于1994年、1997年和2003年获得北京大学学士学位、硕士学位和博士学位。主要研究方向为基于大地理数据的人文社会科学。
{"title":"Extending regionalization algorithms to explore spatial process heterogeneity","authors":"Hao Guo, Andre Python, Yu Liu","doi":"10.1080/13658816.2023.2266493","DOIUrl":"https://doi.org/10.1080/13658816.2023.2266493","url":null,"abstract":"AbstractIn spatial regression models, spatial heterogeneity may be considered with either continuous or discrete specifications. The latter is related to delineation of spatially connected regions with homogeneous relationships between variables (spatial regimes). Although various regionalization algorithms have been proposed and studied in the field of spatial analytics, methods to optimize spatial regimes have been largely unexplored. In this paper, we propose two new algorithms for spatial regime delineation, two-stage K-Models and Regional-K-Models. We also extend the classic Automatic Zoning Procedure to a spatial regression context. The proposed algorithms are applied to a series of synthetic datasets and two real-world datasets. Results indicate that all three algorithms achieve superior or comparable performance to existing approaches, while the two-stage K-Models algorithm largely outperforms existing approaches on model fitting, region reconstruction and coefficient estimation. Our work enriches the spatial analytics toolbox to explore spatial heterogeneous processes.Keywords: Regionalizationspatial heterogeneityspatial regimespatial regression NotesAcknowledgmentsThe authors thank members of Spatial Analysis Group, Spatio-temporal Social Sensing Lab for helpful discussion. The constructive comments from anonymous reviewers are gratefully acknowledged.Data and codes availability statementThe data and codes that support the findings of this study are available at https://github.com/Nithouson/regreg.Disclosure statementThe authors declare that they have no conflict of interest.Notes1 For example, the population in each region is required to be as similar as possible or above a predefined value (see Duque et al. (Citation2012), Folch and Spielman (Citation2014), Wei et al. (Citation2021)).2 Note that the optimization of spatial regimes differs from Openshaw (Citation1978), where spatial units are aggregated into areas, and each area is treated as an observation in a global regression model.3 Note that in EquationEquation 1(1) L(R)=∑j=1p∑1≤i1<i2≤nI[ui1,ui2∈Rj]||xi1−xi2||2,(1) , the number of considered unit pairs in the sum is ∑j=1M(|Rj|2), which is smaller if |Rj|(j=1,…,M) are close to each other. Hence the objective function might favor solutions whose regions have similar numbers of units.4 Throughout the paper, we describe the case of lattice data (spatial data on areal units). Our approach is also applicable to point observation data after building adjacency (with k-nearest neighbors (KNN) or Delaunay triangulation, for example).5 This usually happens when min_obs is close to n/p, where p is the number of regions. Given min_obs≪n/p, this issue does not cause problems, as observed in our experiments.6 If a region with inadequate units has two or more neighboring regions, we select the neighbor which minimizes the total SSR after the merge.7 When min_obs is too large or K is too small (close to p), exceptions may occur that the number of","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135859064","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}
Pub Date : 2023-10-11DOI: 10.1080/13658816.2023.2268668
Wenhao Yu, Guanwen Wang
AbstractAs an important task in spatial data mining, trajectory transportation mode recognition can reflect various individual behaviors and traveling patterns in urban space. As trajectory is essentially a sequence, many scholars use the sequence inference models to mine the information in trajectory data. However, such methods often ignored the spatial correlation between trajectory points and implemented the evaluation based only on representative feature statistics selected in the trajectory data preprocessing stage, thus have difficulties in acquiring high-order traveling pattern features. In this study, we propose a novel ensemble recognition method for representing trajectory data with the graph structure based on sequence and dependency relations. This method integrates the sequence of trajectory points and the correlation between characteristic points of a travel path into a fused graph convolutional network to obtain semantic feature information at multiple levels. We validate our proposed method with experiments on the trajectory benchmark dataset from the Microsoft GeoLife project. The results demonstrated that our proposed graph network outperforms other baseline methods in the transportation mode recognition task of trajectories. This method can help to discover the movement patterns of urban residents, and further provide effective assistance for the management of cities.Keywords: Trajectory datagraph convolution networktransportation mode recognitionfeature extractionfeature fusion AcknowledgmentsThe authors are grateful to the associate editor, Urska Demsar, and the anonymous referees for their valuable comments and suggestions. The project was supported by the National Natural Science Foundation of China (42371446 and 42071442) and by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No.CUG170640). This research was also supported by Meituan.Author contributionsWenhao Yu: Conceptualization, methodology, formal analysis, validation, writing—original draft preparation, writing—review and editing, supervision, project administration, funding acquisition; Guanwen Wang: Methodology, validation, formal analysis, investigation, writing—original draft preparation, writing—review and editing, visualization. All authors have read and agreed to the published version of the manuscript.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 with a DOI at (https://doi.org/10.6084/m9.figshare.21608310).Additional informationNotes on contributorsWenhao YuWenhao Yu received the B.S. and Ph.D. degrees in Geoinformatics from the Wuhan University, Wuhan, China, in 2010 and 2015, respectively. He is a professor at China University of Geosciences, Wuhan, China (CUG). His research interests include spatial data mining, map generalization, and deep learning.Guanwen
{"title":"Graph based embedding learning of trajectory data for transportation mode recognition by fusing sequence and dependency relations","authors":"Wenhao Yu, Guanwen Wang","doi":"10.1080/13658816.2023.2268668","DOIUrl":"https://doi.org/10.1080/13658816.2023.2268668","url":null,"abstract":"AbstractAs an important task in spatial data mining, trajectory transportation mode recognition can reflect various individual behaviors and traveling patterns in urban space. As trajectory is essentially a sequence, many scholars use the sequence inference models to mine the information in trajectory data. However, such methods often ignored the spatial correlation between trajectory points and implemented the evaluation based only on representative feature statistics selected in the trajectory data preprocessing stage, thus have difficulties in acquiring high-order traveling pattern features. In this study, we propose a novel ensemble recognition method for representing trajectory data with the graph structure based on sequence and dependency relations. This method integrates the sequence of trajectory points and the correlation between characteristic points of a travel path into a fused graph convolutional network to obtain semantic feature information at multiple levels. We validate our proposed method with experiments on the trajectory benchmark dataset from the Microsoft GeoLife project. The results demonstrated that our proposed graph network outperforms other baseline methods in the transportation mode recognition task of trajectories. This method can help to discover the movement patterns of urban residents, and further provide effective assistance for the management of cities.Keywords: Trajectory datagraph convolution networktransportation mode recognitionfeature extractionfeature fusion AcknowledgmentsThe authors are grateful to the associate editor, Urska Demsar, and the anonymous referees for their valuable comments and suggestions. The project was supported by the National Natural Science Foundation of China (42371446 and 42071442) and by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No.CUG170640). This research was also supported by Meituan.Author contributionsWenhao Yu: Conceptualization, methodology, formal analysis, validation, writing—original draft preparation, writing—review and editing, supervision, project administration, funding acquisition; Guanwen Wang: Methodology, validation, formal analysis, investigation, writing—original draft preparation, writing—review and editing, visualization. All authors have read and agreed to the published version of the manuscript.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 with a DOI at (https://doi.org/10.6084/m9.figshare.21608310).Additional informationNotes on contributorsWenhao YuWenhao Yu received the B.S. and Ph.D. degrees in Geoinformatics from the Wuhan University, Wuhan, China, in 2010 and 2015, respectively. He is a professor at China University of Geosciences, Wuhan, China (CUG). His research interests include spatial data mining, map generalization, and deep learning.Guanwen ","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136211026","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}
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