Pub Date : 2023-10-09DOI: 10.1080/13658816.2023.2266824
Rui Ma, Jiayu Chen, Chendi Yang, Xin Li
AbstractSemantic 3D city models have been widely used in computer graphics, geomatics, planning, construction, and urban simulation. While traditional geometric models are used only for visualization purposes, semantic 3D city models contain abundant detailed information, such as location, classification, and functional aspects. Such semantics can facilitate a better interpretation of the built environment by computers. However, the current semantic 3D city models are mostly specific to particular city object types and features, with unclear spatial semantics, which limits their broader applications. This study, therefore, proposes a novel framework called OSMsc, where OSM refers to OpenStreetMap and sc refers to semantic city. The OSMsc framework considers OSM as the primary data source to construct city objects within the specified study area, construct semantic connectors, enrich spatial semantics, and generate the CityJSON-formatted model. The case studies demonstrate that semantic 3D city models constructed by OSMsc are free from geometric and semantic errors, applicable to any city worldwide, and have potential for urban studies, such as urban morphology and urban microclimate analysis.Keywords: Semantic 3D city modelspatial semanticsCityJSONOpenStreetMap Authors’ contributionsRui Ma: conceptualization, data collection, coding design, analysis, manuscript writing and subsequent revisions. Jiayu Chen: conceptualization, manuscript review and subsequent revisions. Chendi Yang: data acquisition and visualization. Xin Li: project administration, conceptualization, manuscript writing, reviewing, and revisions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe source code for OSMsc is available at GitHub (https://github.com/ruirzma/osmsc) and the Semantic 3D City Models (S3CMs) of 25 cities in the US and Europe are available at Figshare (https://doi.org/10.6084/m9.figshare.21779507.v2).Additional informationNotes on contributorsRui MaRui Ma is a PhD candidate in the Department of Architecture and Civil Engineering, City University of Hong Kong. His research interests include urban energy modeling, GIS spatial analysis and semantic city modeling.Jiayu ChenJiayu Chen is an Associate Professor in the Department of Construction Management at Tsinghua University. His research focuses on human-centric intelligent construction systems, human-machine collaboration, and urban building digital modeling.Chendi YangChendi Yang is a PhD candidate in the Department of Architecture and Civil Engineering, City University of Hong Kong. Her main research interests include the built environment, spatial analysis, human behavior and urban analytics.Xin LiXin Li is an Associate Professor of Urban Planning at the Department of Architecture and Civil Engineering, City University of Hong Kong. Her research uses economic theories and statistical and GIS tools to study a wide range of urban issues,
{"title":"OSMsc: a framework for semantic 3D city modeling using OpenStreetMap","authors":"Rui Ma, Jiayu Chen, Chendi Yang, Xin Li","doi":"10.1080/13658816.2023.2266824","DOIUrl":"https://doi.org/10.1080/13658816.2023.2266824","url":null,"abstract":"AbstractSemantic 3D city models have been widely used in computer graphics, geomatics, planning, construction, and urban simulation. While traditional geometric models are used only for visualization purposes, semantic 3D city models contain abundant detailed information, such as location, classification, and functional aspects. Such semantics can facilitate a better interpretation of the built environment by computers. However, the current semantic 3D city models are mostly specific to particular city object types and features, with unclear spatial semantics, which limits their broader applications. This study, therefore, proposes a novel framework called OSMsc, where OSM refers to OpenStreetMap and sc refers to semantic city. The OSMsc framework considers OSM as the primary data source to construct city objects within the specified study area, construct semantic connectors, enrich spatial semantics, and generate the CityJSON-formatted model. The case studies demonstrate that semantic 3D city models constructed by OSMsc are free from geometric and semantic errors, applicable to any city worldwide, and have potential for urban studies, such as urban morphology and urban microclimate analysis.Keywords: Semantic 3D city modelspatial semanticsCityJSONOpenStreetMap Authors’ contributionsRui Ma: conceptualization, data collection, coding design, analysis, manuscript writing and subsequent revisions. Jiayu Chen: conceptualization, manuscript review and subsequent revisions. Chendi Yang: data acquisition and visualization. Xin Li: project administration, conceptualization, manuscript writing, reviewing, and revisions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe source code for OSMsc is available at GitHub (https://github.com/ruirzma/osmsc) and the Semantic 3D City Models (S3CMs) of 25 cities in the US and Europe are available at Figshare (https://doi.org/10.6084/m9.figshare.21779507.v2).Additional informationNotes on contributorsRui MaRui Ma is a PhD candidate in the Department of Architecture and Civil Engineering, City University of Hong Kong. His research interests include urban energy modeling, GIS spatial analysis and semantic city modeling.Jiayu ChenJiayu Chen is an Associate Professor in the Department of Construction Management at Tsinghua University. His research focuses on human-centric intelligent construction systems, human-machine collaboration, and urban building digital modeling.Chendi YangChendi Yang is a PhD candidate in the Department of Architecture and Civil Engineering, City University of Hong Kong. Her main research interests include the built environment, spatial analysis, human behavior and urban analytics.Xin LiXin Li is an Associate Professor of Urban Planning at the Department of Architecture and Civil Engineering, City University of Hong Kong. Her research uses economic theories and statistical and GIS tools to study a wide range of urban issues, ","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141826","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}
AbstractFor urban growth modeling, assessment metrics derived from cell-by-cell comparisons are mainly related to the size of the study area and the urban growth rate. Non-urban areas always occupy an important part of the city to which cellular automata (CA) models do not contribute much, so the simulation accuracy is often exaggerated when this part is included. To enable comparing simulation results across models, regions, and time, we developed an improved equivalent area-based assessment (EQASS) method using cell-by-cell comparison metrics. As against existing assessment methods, EQASS is computed by including the same area of urban and suburban areas (i.e., equivalent areas). EQASS was tested in three Chinese coastal cities using a heuristic CA model and two spatial statistical CA models to simulate urban growth. The results show that EQASS can exclude correct rejections that are not attributable to CA models; these correct rejections have a significant impact on the model assessment. The improved assessment can better evaluate the performance of CA models across regions and over time than the conventional assessment method that accounts for the full study area. This study extends the simulation assessment method and provides a good solution for selecting the best CA model from many candidate models.Keywords: Model assessmentcellular automatabuffer analysisurban growthaccuracy comparison Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe software, codes and input datasets involved in this study are available at https://doi.org/10.6084/m9.figshare.21203147.Additional informationFundingSupported by the National Natural Science Foundation of China (42071371) and the National Key R&D Program of China (2018YFB0505400).Notes on contributorsChen GaoChen Gao received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2021. She is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Yongjiu FengYongjiu Feng received the Ph.D. degree in geomatics from Tongji University, Shanghai, China, in 2009. He is currently a Professor and Associate Dean with the College of Surveying and Geo-Informatics, Tongji University. His research interests include spatial modeling, synthetic aperture radar, and radar detection of the moon and deep space.Mengrong XiMengrong Xi received the B.E. degree in geomatics engineering from Tongji University, Shanghai, China, in 2022. He is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Rong WangRong Wang received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2022. She is currently working toward the Ph.D. degree in artificial intelligence with Tongji University, Shanghai, China.Pengshuo LiPengshuo Li received the B.E. degree in geomatics engineering from Tongj
{"title":"An improved assessment method for urban growth simulations across models, regions, and time","authors":"Chen Gao, Yongjiu Feng, Mengrong Xi, Rong Wang, Pengshuo Li, Xiaoyan Tang, Xiaohua Tong","doi":"10.1080/13658816.2023.2264942","DOIUrl":"https://doi.org/10.1080/13658816.2023.2264942","url":null,"abstract":"AbstractFor urban growth modeling, assessment metrics derived from cell-by-cell comparisons are mainly related to the size of the study area and the urban growth rate. Non-urban areas always occupy an important part of the city to which cellular automata (CA) models do not contribute much, so the simulation accuracy is often exaggerated when this part is included. To enable comparing simulation results across models, regions, and time, we developed an improved equivalent area-based assessment (EQASS) method using cell-by-cell comparison metrics. As against existing assessment methods, EQASS is computed by including the same area of urban and suburban areas (i.e., equivalent areas). EQASS was tested in three Chinese coastal cities using a heuristic CA model and two spatial statistical CA models to simulate urban growth. The results show that EQASS can exclude correct rejections that are not attributable to CA models; these correct rejections have a significant impact on the model assessment. The improved assessment can better evaluate the performance of CA models across regions and over time than the conventional assessment method that accounts for the full study area. This study extends the simulation assessment method and provides a good solution for selecting the best CA model from many candidate models.Keywords: Model assessmentcellular automatabuffer analysisurban growthaccuracy comparison Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe software, codes and input datasets involved in this study are available at https://doi.org/10.6084/m9.figshare.21203147.Additional informationFundingSupported by the National Natural Science Foundation of China (42071371) and the National Key R&D Program of China (2018YFB0505400).Notes on contributorsChen GaoChen Gao received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2021. She is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Yongjiu FengYongjiu Feng received the Ph.D. degree in geomatics from Tongji University, Shanghai, China, in 2009. He is currently a Professor and Associate Dean with the College of Surveying and Geo-Informatics, Tongji University. His research interests include spatial modeling, synthetic aperture radar, and radar detection of the moon and deep space.Mengrong XiMengrong Xi received the B.E. degree in geomatics engineering from Tongji University, Shanghai, China, in 2022. He is currently working toward the Ph.D. degree in surveying and geoinformation with Tongji University, Shanghai, China.Rong WangRong Wang received the M.S. degree in marine sciences from Shanghai Ocean University, Shanghai, China, in 2022. She is currently working toward the Ph.D. degree in artificial intelligence with Tongji University, Shanghai, China.Pengshuo LiPengshuo Li received the B.E. degree in geomatics engineering from Tongj","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591481","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 : 2023-10-03DOI: 10.1080/13658816.2023.2254382
Stefano De Sabbata, Pengyuan Liu
Geodemographic classifications are exceptional tools for geographic analysis, business and policy-making, providing an overview of the socio-demographic structure of a region by creating an unsupervised, bottom-up classification of its areas based on a large set of variables. Classic approaches can require time-consuming preprocessing of input variables and are frequently a-spatial processes. In this study, we present a groundbreaking, systematic investigation of the use of graph neural networks for spatial geodemographic classification. Using Greater London as a case study, we compare a range of graph autoencoder designs with the official London Output Area Classification and baseline classifications developed using spatial fuzzy c-means. The results show that our framework based on a Node Attributes-focused Graph AutoEncoder (NAGAE) can perform similarly to classic approaches on class homogeneity metrics while providing higher spatial clustering. We conclude by discussing the current limitations of the proposed framework and its potential to develop into a new paradigm for creating a range of geodemographic classifications, from simple, local ones to complex classifications able to incorporate a range of spatial relationships into the process.
{"title":"A graph neural network framework for spatial geodemographic classification","authors":"Stefano De Sabbata, Pengyuan Liu","doi":"10.1080/13658816.2023.2254382","DOIUrl":"https://doi.org/10.1080/13658816.2023.2254382","url":null,"abstract":"Geodemographic classifications are exceptional tools for geographic analysis, business and policy-making, providing an overview of the socio-demographic structure of a region by creating an unsupervised, bottom-up classification of its areas based on a large set of variables. Classic approaches can require time-consuming preprocessing of input variables and are frequently a-spatial processes. In this study, we present a groundbreaking, systematic investigation of the use of graph neural networks for spatial geodemographic classification. Using Greater London as a case study, we compare a range of graph autoencoder designs with the official London Output Area Classification and baseline classifications developed using spatial fuzzy c-means. The results show that our framework based on a Node Attributes-focused Graph AutoEncoder (NAGAE) can perform similarly to classic approaches on class homogeneity metrics while providing higher spatial clustering. We conclude by discussing the current limitations of the proposed framework and its potential to develop into a new paradigm for creating a range of geodemographic classifications, from simple, local ones to complex classifications able to incorporate a range of spatial relationships into the process.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135739388","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 : 2023-09-26DOI: 10.1080/13658816.2023.2257262
Yao Yao, Qia Zhu, Zijin Guo, Weiming Huang, Yatao Zhang, Xiaoqin Yan, Anning Dong, Zhangwei Jiang, Hong Liu, Qingfeng Guan
AbstractRapid land-use change detection (LUCD) is pivotal for refined urban planning and management. In this paper, we investigate LUCD through learning embeddings of points of interest (POIs) from multiple temporalities. There are several prominent challenges: (1) the co-occurrence problem of multi-temporal POIs, (2) the heterogeneity of POI categorization, and (3) The lack of human-crafted labels. Therefore, multi-temporal POIs need to be aligned in the embedding space for effective LUCD. This study proposes a multi-temporal POI embedding (MT-POI2Vec) technique for LUCD in a fully unsupervised manner. In MT-POI2Vec, we first utilize random walks in POI networks to capture their single-period co-occurrence patterns; then, we leverage manifold learning to capture (1) single-period categorical semantics of POIs to enforce semantically similar POI embedding to be close and (2) cross-period categorical semantics to align multi-temporal POI embedding in a unified embedding space. We conducted experiments in Shenzhen, China, which demonstrates that the proposed method is effective. Compared with several baseline models, MT-POI2Vec can better align multi-temporal POIs and thus achieve higher performance in LUCD. In addition, our model can effectively identify areas with unchanged land use and land use changes in residential and industrial areas at a fine scale.Keywords: Land-use changeembedding space alignmentpoints of interestPOI embedding AcknowledgementsWe would like to acknowledge the comments and insights from the editors and three anonymous reviewers that helped lift the quality of the article.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementWe share the codes and the sub-sampled data of the study at https://doi.org/10.6084/m9.figshare.24081699.Additional informationFundingThis work was supported by the National Key Research and Development Program of China [2019YFB2102903], the National Natural Science Foundation of China [41801306, 42101421 and 42171466]; the “CUG Scholar” Scientific Research Funds at China University of Geosciences (Wuhan) [2022034], a grant from Alibaba Innovative Research Project [20228670], a Guangdong-Hong Kong-Macau Joint Laboratory Program [2020B1212030009], and a grant from State Key Laboratory of Resources and Environmental Information System. W.H. acknowledges the financial support from the Knut and Alice Wallenberg Foundation.Notes on contributorsYao YaoYao Yao is a professor at China University of Geosciences (Wuhan), a researcher from the Center for Spatial Information Science at the University of Tokyo, and a visiting scholar at Alibaba Group. His research interests are geospatial big data mining, analysis, and computational urban science.Qia ZhuQia Zhu is a graduate student at China University of Geosciences (Wuhan). His research interests are spatial representation learning and urban land use change detection.Zijin GuoZijin Guo is a graduate
{"title":"Unsupervised land-use change detection using multi-temporal POI embedding","authors":"Yao Yao, Qia Zhu, Zijin Guo, Weiming Huang, Yatao Zhang, Xiaoqin Yan, Anning Dong, Zhangwei Jiang, Hong Liu, Qingfeng Guan","doi":"10.1080/13658816.2023.2257262","DOIUrl":"https://doi.org/10.1080/13658816.2023.2257262","url":null,"abstract":"AbstractRapid land-use change detection (LUCD) is pivotal for refined urban planning and management. In this paper, we investigate LUCD through learning embeddings of points of interest (POIs) from multiple temporalities. There are several prominent challenges: (1) the co-occurrence problem of multi-temporal POIs, (2) the heterogeneity of POI categorization, and (3) The lack of human-crafted labels. Therefore, multi-temporal POIs need to be aligned in the embedding space for effective LUCD. This study proposes a multi-temporal POI embedding (MT-POI2Vec) technique for LUCD in a fully unsupervised manner. In MT-POI2Vec, we first utilize random walks in POI networks to capture their single-period co-occurrence patterns; then, we leverage manifold learning to capture (1) single-period categorical semantics of POIs to enforce semantically similar POI embedding to be close and (2) cross-period categorical semantics to align multi-temporal POI embedding in a unified embedding space. We conducted experiments in Shenzhen, China, which demonstrates that the proposed method is effective. Compared with several baseline models, MT-POI2Vec can better align multi-temporal POIs and thus achieve higher performance in LUCD. In addition, our model can effectively identify areas with unchanged land use and land use changes in residential and industrial areas at a fine scale.Keywords: Land-use changeembedding space alignmentpoints of interestPOI embedding AcknowledgementsWe would like to acknowledge the comments and insights from the editors and three anonymous reviewers that helped lift the quality of the article.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementWe share the codes and the sub-sampled data of the study at https://doi.org/10.6084/m9.figshare.24081699.Additional informationFundingThis work was supported by the National Key Research and Development Program of China [2019YFB2102903], the National Natural Science Foundation of China [41801306, 42101421 and 42171466]; the “CUG Scholar” Scientific Research Funds at China University of Geosciences (Wuhan) [2022034], a grant from Alibaba Innovative Research Project [20228670], a Guangdong-Hong Kong-Macau Joint Laboratory Program [2020B1212030009], and a grant from State Key Laboratory of Resources and Environmental Information System. W.H. acknowledges the financial support from the Knut and Alice Wallenberg Foundation.Notes on contributorsYao YaoYao Yao is a professor at China University of Geosciences (Wuhan), a researcher from the Center for Spatial Information Science at the University of Tokyo, and a visiting scholar at Alibaba Group. His research interests are geospatial big data mining, analysis, and computational urban science.Qia ZhuQia Zhu is a graduate student at China University of Geosciences (Wuhan). His research interests are spatial representation learning and urban land use change detection.Zijin GuoZijin Guo is a graduate","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134887204","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}
AbstractInvestigating the relationship between built environment factors and roadway safety is crucial for preventing road traffic accidents. Although studies have analyzed traffic-related built environment factors based on pre-determined zonal units, conclusive evidence regarding the relationship between streetscape features and traffic accidents at a fine-grained road segment level is still lacking. With the widespread availability of large-scale street view images, automatically analyzing urban built environments on a large scale is possible. Therefore, the aim of this study was to investigate the relationship between streetscape features and traffic accidents at a fine-grained road segment level using street view images. Specifically, we employed semantic image segmentation to extract streetscape elements from urban street view images, and then created traffic crash-related variables, including the street-level built environment variables, traffic variables, land-use indices, and proximity characteristics, at the road-segment level. Finally, we adopted a classification-then-regression strategy to model the number of traffic crashes while considering the zero-inflated and spatial heterogeneity issues. Our findings suggest that streetscape features can effectively reflect built-environment characteristics at the road-segment level. Moreover, a comparison of our proposed modeling method with existing models demonstrates its superior performance. The results provide insight into the development of effective planning strategies to improve traffic safety.Keywords: Traffic crashesstreet view imagesstreetscape featuresgeographically weighted Poisson regression AcknowledgmentsWe are grateful to Prof. May Yuan, Prof. Christophe Claramunt, and the anonymous referees for their valuable comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe sample data and codes that support the findings of this study are available on ‘figshare.com’ with the identifier at the permanent link: https://doi.org/10.6084/m9.figshare.21384024.v1Additional informationFundingThis work was supported by the National Natural Science Foundation of China [41971406, 42271470, 42001340]; Guangdong Basic and Applied Basic Research Foundation [2022A1515011586]; State Key Laboratory of Geo-Information Engineering [No. SKLGIE2021-M-4-1]; and the China Scholarship Council (CSC) during a visit by Sheng Hu to National University of Singapore.Notes on contributorsSheng HuSheng Hu is a Postdoctoral Scholar at the Beidou Research Institute, South China Normal University. He is also a Distinguished Associated Research Fellow at South China Normal University. His research interests include geospatial artificial intelligence and geospatial data science.Hanfa XingHanfa Xing is a Professor for Geoinformatics at South China Normal University. He is also an Associated Dean of the Beidou Research Institute, Sout
{"title":"Uncovering the association between traffic crashes and street-level built-environment features using street view images","authors":"Sheng Hu, Hanfa Xing, Wei Luo, Liang Wu, Yongyang Xu, Weiming Huang, Wenkai Liu, Tianqi Li","doi":"10.1080/13658816.2023.2254362","DOIUrl":"https://doi.org/10.1080/13658816.2023.2254362","url":null,"abstract":"AbstractInvestigating the relationship between built environment factors and roadway safety is crucial for preventing road traffic accidents. Although studies have analyzed traffic-related built environment factors based on pre-determined zonal units, conclusive evidence regarding the relationship between streetscape features and traffic accidents at a fine-grained road segment level is still lacking. With the widespread availability of large-scale street view images, automatically analyzing urban built environments on a large scale is possible. Therefore, the aim of this study was to investigate the relationship between streetscape features and traffic accidents at a fine-grained road segment level using street view images. Specifically, we employed semantic image segmentation to extract streetscape elements from urban street view images, and then created traffic crash-related variables, including the street-level built environment variables, traffic variables, land-use indices, and proximity characteristics, at the road-segment level. Finally, we adopted a classification-then-regression strategy to model the number of traffic crashes while considering the zero-inflated and spatial heterogeneity issues. Our findings suggest that streetscape features can effectively reflect built-environment characteristics at the road-segment level. Moreover, a comparison of our proposed modeling method with existing models demonstrates its superior performance. The results provide insight into the development of effective planning strategies to improve traffic safety.Keywords: Traffic crashesstreet view imagesstreetscape featuresgeographically weighted Poisson regression AcknowledgmentsWe are grateful to Prof. May Yuan, Prof. Christophe Claramunt, and the anonymous referees for their valuable comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe sample data and codes that support the findings of this study are available on ‘figshare.com’ with the identifier at the permanent link: https://doi.org/10.6084/m9.figshare.21384024.v1Additional informationFundingThis work was supported by the National Natural Science Foundation of China [41971406, 42271470, 42001340]; Guangdong Basic and Applied Basic Research Foundation [2022A1515011586]; State Key Laboratory of Geo-Information Engineering [No. SKLGIE2021-M-4-1]; and the China Scholarship Council (CSC) during a visit by Sheng Hu to National University of Singapore.Notes on contributorsSheng HuSheng Hu is a Postdoctoral Scholar at the Beidou Research Institute, South China Normal University. He is also a Distinguished Associated Research Fellow at South China Normal University. His research interests include geospatial artificial intelligence and geospatial data science.Hanfa XingHanfa Xing is a Professor for Geoinformatics at South China Normal University. He is also an Associated Dean of the Beidou Research Institute, Sout","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135436958","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}
Existing intervisibility analysis methods suffer from computational inefficiency due to redundant sampling points. To address this issue, we propose a new approximate method called line-of-sight (LoS) zoning, which leverages continuous terrain relief to identify potentially obscuring zones (POZ) of LoS. By limiting the sampling range to a much smaller POZ, the number of sampling points is significantly reduced. The optimal sampling interval of 6 is determined by striking a balance between computational efficiency and accuracy. Through experiments in both mountainous and plain areas, regardless of the height range and resolution conditions, we demonstrate the high efficiency of the LoS zoning method, especially in scenarios with a high proportion of visible LoS. To account for potential visibility errors caused by sharp peaks in the terrain, we conducted experiments under fixed time intervals to assess the calculation quality of different methods. The results show that in mountainous and plain areas, the improvement in detection rate compared to the hopping strategy method is around 4–6 times in most scenarios. This significant performance enhancement highlights the superiority of the LoS zoning method, and shows great promise in terrain avoidance, path planning in the military, and detection of dangerous targets.
{"title":"A line-of-sight zoning method for intervisibility computation by considering terrain relief","authors":"Zengjie Wang, Xiaoyu Niu, Zhenxia Liu, Wen Luo, Zhaoyuan Yu, Jiyi Zhang, Linwang Yuan","doi":"10.1080/13658816.2023.2254825","DOIUrl":"https://doi.org/10.1080/13658816.2023.2254825","url":null,"abstract":"Existing intervisibility analysis methods suffer from computational inefficiency due to redundant sampling points. To address this issue, we propose a new approximate method called line-of-sight (LoS) zoning, which leverages continuous terrain relief to identify potentially obscuring zones (POZ) of LoS. By limiting the sampling range to a much smaller POZ, the number of sampling points is significantly reduced. The optimal sampling interval of 6 is determined by striking a balance between computational efficiency and accuracy. Through experiments in both mountainous and plain areas, regardless of the height range and resolution conditions, we demonstrate the high efficiency of the LoS zoning method, especially in scenarios with a high proportion of visible LoS. To account for potential visibility errors caused by sharp peaks in the terrain, we conducted experiments under fixed time intervals to assess the calculation quality of different methods. The results show that in mountainous and plain areas, the improvement in detection rate compared to the hopping strategy method is around 4–6 times in most scenarios. This significant performance enhancement highlights the superiority of the LoS zoning method, and shows great promise in terrain avoidance, path planning in the military, and detection of dangerous targets.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981579","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 : 2023-09-05DOI: 10.1080/13658816.2023.2250838
M. Sachdeva, A. Fotheringham, Ziqi Li, Hanchen Yu
Abstract A recent addition to the suite of techniques for local statistical modeling is the implementation of the multiscale geographically weighted regression (MGWR), a multiscale extension to geographically weighted regression (GWR). Using a back-fitting algorithm, MGWR relaxes the restrictive assumption in GWR that all processes being modeled operate at the same spatial scale and allows the estimation of a unique indicator of scale, the bandwidth, for each process. However, the current MGWR framework is limited to use with continuous data making it unsuitable for modeling data that do not typically exhibit a Gaussian distribution. This study expands the application of the MGWR framework to scenarios involving discrete response outcomes (count data following a Poisson’s distribution). Use of this new MGWR Poisson regression (MGWPR) model is demonstrated with a simulated data set and then with COVID-19 case counts within New York City at the zip code level. The results from the simulated data underscore the superiority of the MGWPR model in effectively capturing spatial processes that influence count data patterns, particularly those operating across diverse spatial scales. For empirical data, the results reveal significant spatial variations in relationships between socio-ecological factors and COVID-19 cases – variations often missed by traditional ‘global’ models.
{"title":"On the local modeling of count data: multiscale geographically weighted Poisson regression","authors":"M. Sachdeva, A. Fotheringham, Ziqi Li, Hanchen Yu","doi":"10.1080/13658816.2023.2250838","DOIUrl":"https://doi.org/10.1080/13658816.2023.2250838","url":null,"abstract":"Abstract A recent addition to the suite of techniques for local statistical modeling is the implementation of the multiscale geographically weighted regression (MGWR), a multiscale extension to geographically weighted regression (GWR). Using a back-fitting algorithm, MGWR relaxes the restrictive assumption in GWR that all processes being modeled operate at the same spatial scale and allows the estimation of a unique indicator of scale, the bandwidth, for each process. However, the current MGWR framework is limited to use with continuous data making it unsuitable for modeling data that do not typically exhibit a Gaussian distribution. This study expands the application of the MGWR framework to scenarios involving discrete response outcomes (count data following a Poisson’s distribution). Use of this new MGWR Poisson regression (MGWPR) model is demonstrated with a simulated data set and then with COVID-19 case counts within New York City at the zip code level. The results from the simulated data underscore the superiority of the MGWPR model in effectively capturing spatial processes that influence count data patterns, particularly those operating across diverse spatial scales. For empirical data, the results reveal significant spatial variations in relationships between socio-ecological factors and COVID-19 cases – variations often missed by traditional ‘global’ models.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"2238 - 2261"},"PeriodicalIF":5.7,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44807777","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 : 2023-09-01DOI: 10.1080/13658816.2023.2253453
Yilang Shen, Rong Zhao, Tinghua Ai, Fengfeng Han, Su Ding
Abstract Computer screens often constrain the level of detail and clarity of displays. High-density data require a predefined strategy to select significant features hierarchically to allow interactive data zooming. Although many methods are available for hierarchically selecting rivers from vector data, some approaches for raster data are better than others for maintaining accuracy when the original river data are in a raster format during generalization. In this study, a raster-based approach is proposed to allow hierarchical superpixel selection in river networks. Linear spectral clustering segmentation was applied to divide the original raster river networks into superpixels at multiple levels. A graph was constructed to organize the generated river network superpixels based on the distances between adjacent superpixels by considering the weights determined by the four types of rules. Finally, the total weight values were ranked, the river-network superpixels were selected according to their weights, and the redundant pixels at the river-network intersections were removed. Compared with the traditional vector selection method, the proposed superpixel river network selection method can effectively consider the characteristics of river width without artificial river grading and preserve the main structure and connectivity features during hierarchical mapping. Notably, the average geometry and density changes decreased by 15.8% and 5.1%, respectively.
{"title":"A raster-based method for the hierarchical selection of river networks based on stream characteristics","authors":"Yilang Shen, Rong Zhao, Tinghua Ai, Fengfeng Han, Su Ding","doi":"10.1080/13658816.2023.2253453","DOIUrl":"https://doi.org/10.1080/13658816.2023.2253453","url":null,"abstract":"Abstract Computer screens often constrain the level of detail and clarity of displays. High-density data require a predefined strategy to select significant features hierarchically to allow interactive data zooming. Although many methods are available for hierarchically selecting rivers from vector data, some approaches for raster data are better than others for maintaining accuracy when the original river data are in a raster format during generalization. In this study, a raster-based approach is proposed to allow hierarchical superpixel selection in river networks. Linear spectral clustering segmentation was applied to divide the original raster river networks into superpixels at multiple levels. A graph was constructed to organize the generated river network superpixels based on the distances between adjacent superpixels by considering the weights determined by the four types of rules. Finally, the total weight values were ranked, the river-network superpixels were selected according to their weights, and the redundant pixels at the river-network intersections were removed. Compared with the traditional vector selection method, the proposed superpixel river network selection method can effectively consider the characteristics of river width without artificial river grading and preserve the main structure and connectivity features during hierarchical mapping. Notably, the average geometry and density changes decreased by 15.8% and 5.1%, respectively.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"2262 - 2287"},"PeriodicalIF":5.7,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46581846","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 : 2023-08-28DOI: 10.1080/13658816.2023.2249968
M. Deng, Kaiqi Chen, Kaiyuan Lei, Yuanfang Chen, Yan Shi
Abstract Fine-grained urban traffic data are often incomplete owing to limitations in sensor technology and economic cost. However, data-driven traffic analysis methods in intelligent transportation systems (ITSs) heavily rely on the quality of input data. Thus, accurately estimating missing traffic observations is an essential data engineering task in ITSs. The complexity of underlying node-wise correlation structures and various missing scenarios presents a significant challenge in achieving high-precision estimation. This study proposes a novel multiview neural network termed MVCV-Traffic, equipped with a cross-view learning mechanism, to improve traffic estimation. The contributions of this model can be summarized into two parts: multiview learning and cross-view fusing. For multiview learning, several specialized neural networks are adopted to fit diverse correlation structures from different views. For cross-view fusing, a new information fusion strategy merges multiview messages at both feature and output levels to enhance the learning of joint correlations. Experiments on two real-world datasets demonstrate that the proposed model significantly outperforms existing traffic speed estimation methods for different types and rates of missing data.
{"title":"MVCV-Traffic: multiview road traffic state estimation via cross-view learning","authors":"M. Deng, Kaiqi Chen, Kaiyuan Lei, Yuanfang Chen, Yan Shi","doi":"10.1080/13658816.2023.2249968","DOIUrl":"https://doi.org/10.1080/13658816.2023.2249968","url":null,"abstract":"Abstract Fine-grained urban traffic data are often incomplete owing to limitations in sensor technology and economic cost. However, data-driven traffic analysis methods in intelligent transportation systems (ITSs) heavily rely on the quality of input data. Thus, accurately estimating missing traffic observations is an essential data engineering task in ITSs. The complexity of underlying node-wise correlation structures and various missing scenarios presents a significant challenge in achieving high-precision estimation. This study proposes a novel multiview neural network termed MVCV-Traffic, equipped with a cross-view learning mechanism, to improve traffic estimation. The contributions of this model can be summarized into two parts: multiview learning and cross-view fusing. For multiview learning, several specialized neural networks are adopted to fit diverse correlation structures from different views. For cross-view fusing, a new information fusion strategy merges multiview messages at both feature and output levels to enhance the learning of joint correlations. Experiments on two real-world datasets demonstrate that the proposed model significantly outperforms existing traffic speed estimation methods for different types and rates of missing data.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"2205 - 2237"},"PeriodicalIF":5.7,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43738981","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 : 2023-08-24DOI: 10.1080/13658816.2023.2246154
Xiao-liang Zhou, R. Assunção, H. Shao, Cheng-Chia Huang, Mark V. Janikas, H. Asefaw
Abstract Multi-scale geographically weighted regression (MGWR) is among the most popular methods to analyze non-stationary spatial relationships. However, the current model calibration algorithm is computationally intensive: its runtime has a cubic growth with the sample size, while its memory use grows quadratically. We propose calibrating MGWR with gradient-based optimization. This is obtained by analytically deriving the gradient vector and the Hessian matrix of the corrected Akaike information criterion (AICc) and wrapping them with a trust-region optimization algorithm. We evaluate the model quality empirically. Our method converges to the same coefficients and produces the same inference as the current method but it has a substantial computational gain when the sample size is large. It reduces the runtime to quadratic convergence and makes the memory use linear with respect to sample size. Our new algorithm outperforms the existing alternatives and makes MGWR feasible for large spatial datasets.
{"title":"Gradient-based optimization for multi-scale geographically weighted regression","authors":"Xiao-liang Zhou, R. Assunção, H. Shao, Cheng-Chia Huang, Mark V. Janikas, H. Asefaw","doi":"10.1080/13658816.2023.2246154","DOIUrl":"https://doi.org/10.1080/13658816.2023.2246154","url":null,"abstract":"Abstract Multi-scale geographically weighted regression (MGWR) is among the most popular methods to analyze non-stationary spatial relationships. However, the current model calibration algorithm is computationally intensive: its runtime has a cubic growth with the sample size, while its memory use grows quadratically. We propose calibrating MGWR with gradient-based optimization. This is obtained by analytically deriving the gradient vector and the Hessian matrix of the corrected Akaike information criterion (AICc) and wrapping them with a trust-region optimization algorithm. We evaluate the model quality empirically. Our method converges to the same coefficients and produces the same inference as the current method but it has a substantial computational gain when the sample size is large. It reduces the runtime to quadratic convergence and makes the memory use linear with respect to sample size. Our new algorithm outperforms the existing alternatives and makes MGWR feasible for large spatial datasets.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"2101 - 2128"},"PeriodicalIF":5.7,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42101458","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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