Pub Date : 2022-11-17DOI: 10.1080/13658816.2022.2146120
Peixiao Wang, Yan Zhang, Tao Hu, Tong Zhang
Abstract Accurate traffic flow prediction on the urban road network is an indispensable function of Intelligent Transportation Systems (ITS), which is of great significance for urban traffic planning. However, the current traffic flow prediction methods still face many challenges, such as missing values and dynamic spatial relationships in traffic flow. In this study, a dynamic temporal graph neural network considering missing values (D-TGNM) is proposed for traffic flow prediction. First, inspired by the Bidirectional Encoder Representations from Transformers (BERT), we extend the classic BERT model, called Traffic BERT, to learn the dynamic spatial associations on the road structure. Second, we propose a temporal graph neural network considering missing values (TGNM) to mine traffic flow patterns in missing data scenarios for traffic flow prediction. Finally, the proposed D-TGNM model can be obtained by integrating the dynamic spatial associations learned by Traffic BERT into the TGNM model. To train the D-TGNM model, we design a novel loss function, which considers the missing values problem and prediction problem in traffic flow, to optimize the proposed model. The proposed model was validated on an actual traffic dataset collected in Wuhan, China. Experimental results showed that D-TGNM achieved good prediction results under four missing data scenarios (15% random missing, 15% block missing, 30% random missing, and 30% block missing), and outperformed ten existing state-of-the-art baselines.
{"title":"Urban traffic flow prediction: a dynamic temporal graph network considering missing values","authors":"Peixiao Wang, Yan Zhang, Tao Hu, Tong Zhang","doi":"10.1080/13658816.2022.2146120","DOIUrl":"https://doi.org/10.1080/13658816.2022.2146120","url":null,"abstract":"Abstract Accurate traffic flow prediction on the urban road network is an indispensable function of Intelligent Transportation Systems (ITS), which is of great significance for urban traffic planning. However, the current traffic flow prediction methods still face many challenges, such as missing values and dynamic spatial relationships in traffic flow. In this study, a dynamic temporal graph neural network considering missing values (D-TGNM) is proposed for traffic flow prediction. First, inspired by the Bidirectional Encoder Representations from Transformers (BERT), we extend the classic BERT model, called Traffic BERT, to learn the dynamic spatial associations on the road structure. Second, we propose a temporal graph neural network considering missing values (TGNM) to mine traffic flow patterns in missing data scenarios for traffic flow prediction. Finally, the proposed D-TGNM model can be obtained by integrating the dynamic spatial associations learned by Traffic BERT into the TGNM model. To train the D-TGNM model, we design a novel loss function, which considers the missing values problem and prediction problem in traffic flow, to optimize the proposed model. The proposed model was validated on an actual traffic dataset collected in Wuhan, China. Experimental results showed that D-TGNM achieved good prediction results under four missing data scenarios (15% random missing, 15% block missing, 30% random missing, and 30% block missing), and outperformed ten existing state-of-the-art baselines.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"885 - 912"},"PeriodicalIF":5.7,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42253934","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 : 2022-11-15DOI: 10.1080/13658816.2022.2143504
José Duarte, Paulo Dias, José Moreira
Abstract The region interpolation methods proposed in the moving objects databases literature impose restrictions that can have a significant impact on the representation of the evolution of moving regions, in particular, when a rotation occurs between two observations. In this paper, we propose a data model for moving regions that allows moving segments to rotate and change their length during their evolution between two observations and uses quadratic Bezier curves to define the trajectories of their endpoints. This introduces a new class of moving regions called rotating moving regions (rmregions). We present algorithms for operations involving rmregions and we propose a strategy to allow different interpolation methods to be used in the context of moving objects databases by approximating the interpolations they create using rmregions. We demonstrate our strategy using a reference implementation and compare results obtained when using the strategy presented here and the region interpolation methods and the spatiotemporal operations proposed in the state-of-the-art. Experimental results show that our strategy can be used to complement the region interpolation methods proposed in the moving objects databases literature.
{"title":"Approximating the evolution of rotating moving regions using Bezier curves","authors":"José Duarte, Paulo Dias, José Moreira","doi":"10.1080/13658816.2022.2143504","DOIUrl":"https://doi.org/10.1080/13658816.2022.2143504","url":null,"abstract":"Abstract The region interpolation methods proposed in the moving objects databases literature impose restrictions that can have a significant impact on the representation of the evolution of moving regions, in particular, when a rotation occurs between two observations. In this paper, we propose a data model for moving regions that allows moving segments to rotate and change their length during their evolution between two observations and uses quadratic Bezier curves to define the trajectories of their endpoints. This introduces a new class of moving regions called rotating moving regions (rmregions). We present algorithms for operations involving rmregions and we propose a strategy to allow different interpolation methods to be used in the context of moving objects databases by approximating the interpolations they create using rmregions. We demonstrate our strategy using a reference implementation and compare results obtained when using the strategy presented here and the region interpolation methods and the spatiotemporal operations proposed in the state-of-the-art. Experimental results show that our strategy can be used to complement the region interpolation methods proposed in the moving objects databases literature.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"839 - 863"},"PeriodicalIF":5.7,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46797166","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 : 2022-11-11DOI: 10.1080/13658816.2022.2143503
Peng Ti, Tao Xiong, Yuhong Qiu, Liying Wang, Zhilin Li
Abstract Landmark buildings are salient features for spatial cognition on maps. Distinctive outlines are the major visual characteristics that separate landmark buildings from their surrounding environments. The automatic symbolization of landmark outlines facilitates recognition and map production. As users often recognize landmarks by the outlines of their façades from a street view, this study proposes an automatic method for automatically generating representations of the outlines of landmark buildings in four steps: (1) extract outlines from street-view photographs using GrabCut method, (2) vectorize the extracted building outlines, (3) simplify outline shapes, and (4) symbolize the simplified building outlines in three dimensions (3D). We used the proposed method to generate test data with symbolized outlines for eight buildings in a real-world environment for a wayfinding experiment in which the subjects used the building representations to identify landmark buildings and evaluated their perception of the generated maps. The subjects successfully recognized these buildings based on the symbolized outlines on a map, expressed satisfaction with the manually generated 3D symbols, and reported the same or similar ease of building recognition using 2D or 3D symbolized outlines.
{"title":"Automatic generation of outline-based representations of landmark buildings with distinctive shapes","authors":"Peng Ti, Tao Xiong, Yuhong Qiu, Liying Wang, Zhilin Li","doi":"10.1080/13658816.2022.2143503","DOIUrl":"https://doi.org/10.1080/13658816.2022.2143503","url":null,"abstract":"Abstract Landmark buildings are salient features for spatial cognition on maps. Distinctive outlines are the major visual characteristics that separate landmark buildings from their surrounding environments. The automatic symbolization of landmark outlines facilitates recognition and map production. As users often recognize landmarks by the outlines of their façades from a street view, this study proposes an automatic method for automatically generating representations of the outlines of landmark buildings in four steps: (1) extract outlines from street-view photographs using GrabCut method, (2) vectorize the extracted building outlines, (3) simplify outline shapes, and (4) symbolize the simplified building outlines in three dimensions (3D). We used the proposed method to generate test data with symbolized outlines for eight buildings in a real-world environment for a wayfinding experiment in which the subjects used the building representations to identify landmark buildings and evaluated their perception of the generated maps. The subjects successfully recognized these buildings based on the symbolized outlines on a map, expressed satisfaction with the manually generated 3D symbols, and reported the same or similar ease of building recognition using 2D or 3D symbolized outlines.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"864 - 884"},"PeriodicalIF":5.7,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48221937","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 : 2022-11-11DOI: 10.1080/13658816.2022.2143505
Pengbo Li, Haowen Yan, Xiaomin Lu
Abstract Measuring similarity is essential for classifying, clustering, retrieving, and matching linear features in geospatial data. However, the complexity of linear features challenges the formalization of characteristics and determination of the weight of each characteristic in similarity measurements. Additionally, traditional methods have limited adaptability to the variety of linear features. To address these challenges, this study proposes a metric learning model that learns similarity metrics directly from linear features. The model’s ability to learn allows no pre-determined characteristics and supports adaptability to different levels of complex linear features. LineStringNet functions as a feature encoder that maps vector lines to embeddings without format conversion or feature engineering. With a Siamese architecture, the learning process minimizes the contrastive loss, which brings similar pairs closer and pushes dissimilar pairs away in the embedding space. Finally, the proposed model calculates the Euclidean distance to measure the similarity between learned embeddings. Experiments on common linear features and building shapes indicated that the learned similarity metrics effectively supported retrieving, matching, and classifying lines and polygons, with higher precision and accuracy than traditional measures. Furthermore, the model ensures desired metric properties, including rotation and starting point invariances, by adjusting labeling strategies or preprocessing input data.
{"title":"A Siamese neural network for learning the similarity metrics of linear features","authors":"Pengbo Li, Haowen Yan, Xiaomin Lu","doi":"10.1080/13658816.2022.2143505","DOIUrl":"https://doi.org/10.1080/13658816.2022.2143505","url":null,"abstract":"Abstract Measuring similarity is essential for classifying, clustering, retrieving, and matching linear features in geospatial data. However, the complexity of linear features challenges the formalization of characteristics and determination of the weight of each characteristic in similarity measurements. Additionally, traditional methods have limited adaptability to the variety of linear features. To address these challenges, this study proposes a metric learning model that learns similarity metrics directly from linear features. The model’s ability to learn allows no pre-determined characteristics and supports adaptability to different levels of complex linear features. LineStringNet functions as a feature encoder that maps vector lines to embeddings without format conversion or feature engineering. With a Siamese architecture, the learning process minimizes the contrastive loss, which brings similar pairs closer and pushes dissimilar pairs away in the embedding space. Finally, the proposed model calculates the Euclidean distance to measure the similarity between learned embeddings. Experiments on common linear features and building shapes indicated that the learned similarity metrics effectively supported retrieving, matching, and classifying lines and polygons, with higher precision and accuracy than traditional measures. Furthermore, the model ensures desired metric properties, including rotation and starting point invariances, by adjusting labeling strategies or preprocessing input data.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"684 - 711"},"PeriodicalIF":5.7,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48700013","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 : 2022-11-09DOI: 10.1080/13658816.2022.2141751
Hiroyuki Usui
Abstract Measuring building setbacks and heights along streets is important for evaluating the variability of streetscape skeletons, the 3D spaces of streets defined by the arrangement of surrounding buildings. Its evaluation requires computing the streetscape width, defined as the front road width of a building plus the setbacks of both sides of the front roads, the building heights and the ratio of the streetscape width to the building height, known as the streetscape width-to-height ratio. However, measuring building setbacks and streetscape widths with geographical information systems (GIS) workstations remains theoretically and technically challenging because conventional methods fail to define the ambiguous boundaries of streetscape skeletons. To address this issue, we developed a new method for defining and measuring building setbacks and streetscape widths automatically and in a consistent way. A new basic spatial unit was also developed for evaluating the variability in building setbacks, heights, streetscape widths and streetscape width-to-height ratios not only in plots focusing on classical urban morphologies but also along streets focusing on a pedestrian perspective. The method contributes practically to the measurement and evaluation of streetscape skeletons in a bottom-up way at fine intervals without the need for setting predetermined spatial units. KEY POLICY HIGHLIGHTS Measuring building setbacks and heights along roads is important for evaluating the variability of streetscape skeletons. However, measuring these in an actual complex urban space without vagueness on a GIS workstation is difficult. We have developed a new method for defining and measuring building setbacks and streetscape widths automatically. A new basic spatial unit for evaluating streetscape skeletons is proposed focusing on the plot geometry and a pedestrian perspective. The method contributes to the evaluation of streetscapes in a bottom-up way at fine intervals without setting predetermined spatial units.
{"title":"Automatic measurement of building setbacks and streetscape widths and their spatial variability along streets and in plots: integration of streetscape skeletons and plot geometry","authors":"Hiroyuki Usui","doi":"10.1080/13658816.2022.2141751","DOIUrl":"https://doi.org/10.1080/13658816.2022.2141751","url":null,"abstract":"Abstract Measuring building setbacks and heights along streets is important for evaluating the variability of streetscape skeletons, the 3D spaces of streets defined by the arrangement of surrounding buildings. Its evaluation requires computing the streetscape width, defined as the front road width of a building plus the setbacks of both sides of the front roads, the building heights and the ratio of the streetscape width to the building height, known as the streetscape width-to-height ratio. However, measuring building setbacks and streetscape widths with geographical information systems (GIS) workstations remains theoretically and technically challenging because conventional methods fail to define the ambiguous boundaries of streetscape skeletons. To address this issue, we developed a new method for defining and measuring building setbacks and streetscape widths automatically and in a consistent way. A new basic spatial unit was also developed for evaluating the variability in building setbacks, heights, streetscape widths and streetscape width-to-height ratios not only in plots focusing on classical urban morphologies but also along streets focusing on a pedestrian perspective. The method contributes practically to the measurement and evaluation of streetscape skeletons in a bottom-up way at fine intervals without the need for setting predetermined spatial units. KEY POLICY HIGHLIGHTS Measuring building setbacks and heights along roads is important for evaluating the variability of streetscape skeletons. However, measuring these in an actual complex urban space without vagueness on a GIS workstation is difficult. We have developed a new method for defining and measuring building setbacks and streetscape widths automatically. A new basic spatial unit for evaluating streetscape skeletons is proposed focusing on the plot geometry and a pedestrian perspective. The method contributes to the evaluation of streetscapes in a bottom-up way at fine intervals without setting predetermined spatial units.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"810 - 838"},"PeriodicalIF":5.7,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42737107","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 : 2022-11-08DOI: 10.1080/13658816.2022.2140808
Binyu Lei, R. Stouffs, F. Biljecki
Abstract 3D city models are omnipresent in urban management and simulations. However, instruments for their evaluation have been limited. Furthermore, current instances are scattered worldwide and developed independently, hampering their comparison and understanding practices. While there are developed assessment frameworks in open data, such efforts are generic and not applied to geospatial data. We establish a holistic and comprehensive four-category framework ‘3D City Index’, encompassing 47 criteria to identify key properties of 3D city models, enabling their assessment and benchmarking, and suggesting usability. We evaluate 40 authoritative 3D city models and derive quantitative and qualitative insights. The framework implementation enables a comprehensive and structured understanding of the landscape of semantic 3D geospatial data, as well as doubles as an evaluated collection of open 3D city models. For example, datasets differ substantially in their characteristics, having heterogeneous properties influenced by their different purposes. There are further applications of this first endeavour to standardise the characterisation of 3D data: monitoring developments and trends in 3D city modelling, and enabling researchers and practitioners to find the most appropriate datasets for their needs. The work is designed to measure datasets continuously and can also be applied to other instances in spatial data infrastructures.
{"title":"Assessing and benchmarking 3D city models","authors":"Binyu Lei, R. Stouffs, F. Biljecki","doi":"10.1080/13658816.2022.2140808","DOIUrl":"https://doi.org/10.1080/13658816.2022.2140808","url":null,"abstract":"Abstract 3D city models are omnipresent in urban management and simulations. However, instruments for their evaluation have been limited. Furthermore, current instances are scattered worldwide and developed independently, hampering their comparison and understanding practices. While there are developed assessment frameworks in open data, such efforts are generic and not applied to geospatial data. We establish a holistic and comprehensive four-category framework ‘3D City Index’, encompassing 47 criteria to identify key properties of 3D city models, enabling their assessment and benchmarking, and suggesting usability. We evaluate 40 authoritative 3D city models and derive quantitative and qualitative insights. The framework implementation enables a comprehensive and structured understanding of the landscape of semantic 3D geospatial data, as well as doubles as an evaluated collection of open 3D city models. For example, datasets differ substantially in their characteristics, having heterogeneous properties influenced by their different purposes. There are further applications of this first endeavour to standardise the characterisation of 3D data: monitoring developments and trends in 3D city modelling, and enabling researchers and practitioners to find the most appropriate datasets for their needs. The work is designed to measure datasets continuously and can also be applied to other instances in spatial data infrastructures.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"26 24-25","pages":"788 - 809"},"PeriodicalIF":5.7,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41307044","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 : 2022-10-31DOI: 10.1080/13658816.2022.2131790
Ramón Giraldo, V. Leiva, G. Christakos
Abstract Regression is often conducted assuming independent model errors. The detection of atypical values in regression (leverage and influential points) assumes independent errors. However, such independence could be unrealistic in geostatistics. In this article, we propose a methodology based on least squares and geostatistics to identify such values in spatial regression. Our procedure uses the hat matrix to detect leverage points. A modified Cook distance is employed to confirm whether these points are influential. The methodology is evaluated with stationary and non-stationary geostatistical data. We apply this methodology to real georeferenced data related to depth, dissolved oxygen, and temperature. First, an autoregressive model is fitted to depth data. Second, a regression between oxygen and temperature is estimated. In both models, spatial correlation is assumed to determine the parameters, leverage, and influential observations. Our methodology can be used in regression with geographical information to avoid misinterpreted results. Not considering this information may under- or over-estimate geographical indicators, such as the mean depth, which can affect the circulation of water masses or dissolved oxygen variability. Our results reveal that including spatial dependence to identify high leverage points is relevant and must be considered in any geostatistical analysis.
{"title":"Leverage and Cook distance in regression with geostatistical data: methodology, simulation, and applications related to geographical information","authors":"Ramón Giraldo, V. Leiva, G. Christakos","doi":"10.1080/13658816.2022.2131790","DOIUrl":"https://doi.org/10.1080/13658816.2022.2131790","url":null,"abstract":"Abstract Regression is often conducted assuming independent model errors. The detection of atypical values in regression (leverage and influential points) assumes independent errors. However, such independence could be unrealistic in geostatistics. In this article, we propose a methodology based on least squares and geostatistics to identify such values in spatial regression. Our procedure uses the hat matrix to detect leverage points. A modified Cook distance is employed to confirm whether these points are influential. The methodology is evaluated with stationary and non-stationary geostatistical data. We apply this methodology to real georeferenced data related to depth, dissolved oxygen, and temperature. First, an autoregressive model is fitted to depth data. Second, a regression between oxygen and temperature is estimated. In both models, spatial correlation is assumed to determine the parameters, leverage, and influential observations. Our methodology can be used in regression with geographical information to avoid misinterpreted results. Not considering this information may under- or over-estimate geographical indicators, such as the mean depth, which can affect the circulation of water masses or dissolved oxygen variability. Our results reveal that including spatial dependence to identify high leverage points is relevant and must be considered in any geostatistical analysis.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"607 - 633"},"PeriodicalIF":5.7,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48051352","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 : 2022-10-25DOI: 10.1080/13658816.2022.2137879
Wenkai Liu, Qiliang Liu, Jie Yang, M. Deng
Abstract For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement patterns. Because of spatial heterogeneity, the effective detection of inhomogeneous and irregularly shaped bivariate clusters from bivariate OD movement data remains a challenge. To fill this gap, we propose a network-constrained method for clustering two types of individual OD movements on road networks. To adaptively estimate the densities of inhomogeneous OD movements, we first define a new network-constrained density based on the concept of the shared nearest neighbor. A fast Monte Carlo simulation method is then developed to statistically estimate the density threshold for each type of OD movements. Finally, bivariate clusters are constructed using the density-connectivity mechanism. Experiments on simulated datasets demonstrate that the proposed method outperformed three state-of-the-art methods in identifying inhomogeneous and irregularly shaped bivariate clusters. The proposed method was applied to taxi and ride-hailing service datasets in Xiamen. The identified bivariate clusters successfully reveal competition patterns between taxi and ride-hailing services.
{"title":"A network-constrained clustering method for bivariate origin-destination movement data","authors":"Wenkai Liu, Qiliang Liu, Jie Yang, M. Deng","doi":"10.1080/13658816.2022.2137879","DOIUrl":"https://doi.org/10.1080/13658816.2022.2137879","url":null,"abstract":"Abstract For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement patterns. Because of spatial heterogeneity, the effective detection of inhomogeneous and irregularly shaped bivariate clusters from bivariate OD movement data remains a challenge. To fill this gap, we propose a network-constrained method for clustering two types of individual OD movements on road networks. To adaptively estimate the densities of inhomogeneous OD movements, we first define a new network-constrained density based on the concept of the shared nearest neighbor. A fast Monte Carlo simulation method is then developed to statistically estimate the density threshold for each type of OD movements. Finally, bivariate clusters are constructed using the density-connectivity mechanism. Experiments on simulated datasets demonstrate that the proposed method outperformed three state-of-the-art methods in identifying inhomogeneous and irregularly shaped bivariate clusters. The proposed method was applied to taxi and ride-hailing service datasets in Xiamen. The identified bivariate clusters successfully reveal competition patterns between taxi and ride-hailing services.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"767 - 787"},"PeriodicalIF":5.7,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44178826","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 : 2022-10-20DOI: 10.1080/13658816.2022.2123488
A. Courtial, G. Touya, X. Zhang
Abstract Map generalisation is a process that transforms geographic information for a cartographic at a specific scale. The goal is to produce legible and informative maps even at small scales from a detailed dataset. The potential of deep learning to help in this task is still unknown. This article examines the use case of mountain road generalisation, to explore the potential of a specific deep learning approach: generative adversarial networks (GAN). Our goal is to generate images that depict road maps generalised at the 1:250k scale, from images that depict road maps of the same area using un-generalised 1:25k data. This paper not only shows the potential of deep learning to generate generalised mountain roads, but also analyses how the process of deep learning generalisation works, compares supervised and unsupervised learning and explores possible improvements. With this experiment we have exhibited an unsupervised model that is able to generate generalised maps evaluated as good as the reference and reviewed some possible improvements for deep learning-based generalisation, including training set management and the definition of a new road connectivity loss. All our results are evaluated visually using a four questions process and validated by a user test conducted on 113 individuals.
{"title":"Deriving map images of generalised mountain roads with generative adversarial networks","authors":"A. Courtial, G. Touya, X. Zhang","doi":"10.1080/13658816.2022.2123488","DOIUrl":"https://doi.org/10.1080/13658816.2022.2123488","url":null,"abstract":"Abstract Map generalisation is a process that transforms geographic information for a cartographic at a specific scale. The goal is to produce legible and informative maps even at small scales from a detailed dataset. The potential of deep learning to help in this task is still unknown. This article examines the use case of mountain road generalisation, to explore the potential of a specific deep learning approach: generative adversarial networks (GAN). Our goal is to generate images that depict road maps generalised at the 1:250k scale, from images that depict road maps of the same area using un-generalised 1:25k data. This paper not only shows the potential of deep learning to generate generalised mountain roads, but also analyses how the process of deep learning generalisation works, compares supervised and unsupervised learning and explores possible improvements. With this experiment we have exhibited an unsupervised model that is able to generate generalised maps evaluated as good as the reference and reviewed some possible improvements for deep learning-based generalisation, including training set management and the definition of a new road connectivity loss. All our results are evaluated visually using a four questions process and validated by a user test conducted on 113 individuals.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"499 - 528"},"PeriodicalIF":5.7,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42024207","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 : 2022-10-17DOI: 10.1080/13658816.2022.2133125
Cillian Berragan, A. Singleton, A. Calafiore, J. Morley
Abstract Place names embedded in online natural language text present a useful source of geographic information. Despite this, many methods for the extraction of place names from text use pre-trained models that were not explicitly designed for this task. Our paper builds five custom-built Named Entity Recognition (NER) models and evaluates them against three popular pre-built models for place name extraction. The models are evaluated using a set of manually annotated Wikipedia articles with reference to the F1 score metric. Our best performing model achieves an F1 score of 0.939 compared with 0.730 for the best performing pre-built model. Our model is then used to extract all place names from Wikipedia articles in Great Britain, demonstrating the ability to more accurately capture unknown place names from volunteered sources of online geographic information.
{"title":"Transformer based named entity recognition for place name extraction from unstructured text","authors":"Cillian Berragan, A. Singleton, A. Calafiore, J. Morley","doi":"10.1080/13658816.2022.2133125","DOIUrl":"https://doi.org/10.1080/13658816.2022.2133125","url":null,"abstract":"Abstract Place names embedded in online natural language text present a useful source of geographic information. Despite this, many methods for the extraction of place names from text use pre-trained models that were not explicitly designed for this task. Our paper builds five custom-built Named Entity Recognition (NER) models and evaluates them against three popular pre-built models for place name extraction. The models are evaluated using a set of manually annotated Wikipedia articles with reference to the F1 score metric. Our best performing model achieves an F1 score of 0.939 compared with 0.730 for the best performing pre-built model. Our model is then used to extract all place names from Wikipedia articles in Great Britain, demonstrating the ability to more accurately capture unknown place names from volunteered sources of online geographic information.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"747 - 766"},"PeriodicalIF":5.7,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41386424","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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