As urbanization accelerates, cities become more complex, coming along with more complex urban issues. Agent-based model (ABM) is a traditional method to simulate activities in a complex system, which has been widely applied in urban studies. However, due to its rigid initial settings, ABM has been criticized for its lack of intelligence, especially in dealing with modern urban issues. With the success of artificial intelligence (AI) and complexity science, it is generally agreed that ABM can be enhanced with AI agents, a promising technology that can bridge the gaps. For that, this article provides a systematic review, in which 10 subsections correspond to 10 different ways that AI can work with ABM in the methodological framework. The sections include that (1) ABM is Al; (2) ABM provides training data for Al; (3) Al provides data for ABM; (4) ABM is a submodule in the ensemble Al; (5) Al leads an optimization framework with ABM participation; (6) Al tunes ABM initialization parameters; (7) Al provides the environment for ABM; (8) Al aids in choosing the agent's attributes; (9) Al provides behaviors for agents in ABM; (10) Al helps to evaluate the performance of ABM. For each case, some typical works are examined for illustration. Finally, we discuss some of the current limitations and prospects for future development.
{"title":"How artificial intelligence cooperating with agent-based modeling for urban studies: A systematic review","authors":"Zijian Guo, Xintao Liu","doi":"10.1111/tgis.13152","DOIUrl":"https://doi.org/10.1111/tgis.13152","url":null,"abstract":"As urbanization accelerates, cities become more complex, coming along with more complex urban issues. Agent-based model (ABM) is a traditional method to simulate activities in a complex system, which has been widely applied in urban studies. However, due to its rigid initial settings, ABM has been criticized for its lack of intelligence, especially in dealing with modern urban issues. With the success of artificial intelligence (AI) and complexity science, it is generally agreed that ABM can be enhanced with AI agents, a promising technology that can bridge the gaps. For that, this article provides a systematic review, in which 10 subsections correspond to 10 different ways that AI can work with ABM in the methodological framework. The sections include that (1) ABM is Al; (2) ABM provides training data for Al; (3) Al provides data for ABM; (4) ABM is a submodule in the ensemble Al; (5) Al leads an optimization framework with ABM participation; (6) Al tunes ABM initialization parameters; (7) Al provides the environment for ABM; (8) Al aids in choosing the agent's attributes; (9) Al provides behaviors for agents in ABM; (10) Al helps to evaluate the performance of ABM. For each case, some typical works are examined for illustration. Finally, we discuss some of the current limitations and prospects for future development.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"34 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140047776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prior research has defined cancer service areas (CSAs) anchored by major cancer centers in the U.S., such as members of the Association of American Cancer Institutes (AACI). Those CSAs have discrete boundaries and may not capture the increasingly interwoven cancer care markets. This is the first attempt to delineate possible overlapping CSAs. Specifically, we integrate the concept of shared memberships and other spatial attributes into the Speaker‐Listener Label Propagation (SLPA) algorithm, termed the spatially constrained SLPA (or ScSLPA), and apply it to six representative areas in the U.S. The results show that overlapping CSAs tend to form in areas that are more urbanized, with higher localization index (LI) values, larger populations, and shorter travel times than discrete CSAs. Two CSAs in Los Angeles and San Diego are consistent with the catchment areas (CAs) of the National Cancer Institute (NCI)‐designated cancer centers, and other CSAs are much smaller than the CAs of their anchoring cancer centers. The study has important implications for public health policy to advance cancer control and prevention efforts.
{"title":"Overlapping cancer service areas: Delineation and implications","authors":"Changzhen Wang, Tracy Onega, Fahui Wang","doi":"10.1111/tgis.13140","DOIUrl":"https://doi.org/10.1111/tgis.13140","url":null,"abstract":"Prior research has defined cancer service areas (CSAs) anchored by major cancer centers in the U.S., such as members of the Association of American Cancer Institutes (AACI). Those CSAs have discrete boundaries and may not capture the increasingly interwoven cancer care markets. This is the first attempt to delineate possible overlapping CSAs. Specifically, we integrate the concept of shared memberships and other spatial attributes into the Speaker‐Listener Label Propagation (SLPA) algorithm, termed the spatially constrained SLPA (or ScSLPA), and apply it to six representative areas in the U.S. The results show that overlapping CSAs tend to form in areas that are more urbanized, with higher localization index (LI) values, larger populations, and shorter travel times than discrete CSAs. Two CSAs in Los Angeles and San Diego are consistent with the catchment areas (CAs) of the National Cancer Institute (NCI)‐designated cancer centers, and other CSAs are much smaller than the CAs of their anchoring cancer centers. The study has important implications for public health policy to advance cancer control and prevention efforts.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"257 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140011227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Transit‐oriented development (TOD) promotes sustainable urban growth by integrating land use and transportation systems around public transportation nodes. Many high‐density Chinese cities have effectively applied TOD. However, studies on TODness in cities with relatively less‐developed metro networks are still nascent. This study developed a new geographic information system‐based method to assess TODness utilizing data from official departments and open sources. We employed 15 sub‐indicators of “Transit (T),” “Oriented (O),” and “Development (D)” dimensions to assess the TODness of Shenyang metro station areas (MSAs). We tested the validity of this tool by examining the relationship between TODness and urban vitality using an ordinary least squares model. Each MSA was comprehensively quantified and organized by categorizing their TODness into five types. The results indicate that the TODness index was closely associated with urban vitality, thereby validating the reasonableness of the proposed tool. Furthermore, the TODness of Shenyang's MSAs presents a spatial distribution of “high, medium, and low” from the urban center to the periphery. The proposed method is expected to enhance the effectiveness of TOD planning by providing a comprehensive and data‐driven approach.
{"title":"Measuring metro station area's TODness: An exploratory study of Shenyang based on multi‐source urban data","authors":"Zheng Yi, Zhehao Zhang, Haiming Wang","doi":"10.1111/tgis.13148","DOIUrl":"https://doi.org/10.1111/tgis.13148","url":null,"abstract":"Transit‐oriented development (TOD) promotes sustainable urban growth by integrating land use and transportation systems around public transportation nodes. Many high‐density Chinese cities have effectively applied TOD. However, studies on TODness in cities with relatively less‐developed metro networks are still nascent. This study developed a new geographic information system‐based method to assess TODness utilizing data from official departments and open sources. We employed 15 sub‐indicators of “Transit (T),” “Oriented (O),” and “Development (D)” dimensions to assess the TODness of Shenyang metro station areas (MSAs). We tested the validity of this tool by examining the relationship between TODness and urban vitality using an ordinary least squares model. Each MSA was comprehensively quantified and organized by categorizing their TODness into five types. The results indicate that the TODness index was closely associated with urban vitality, thereby validating the reasonableness of the proposed tool. Furthermore, the TODness of Shenyang's MSAs presents a spatial distribution of “high, medium, and low” from the urban center to the periphery. The proposed method is expected to enhance the effectiveness of TOD planning by providing a comprehensive and data‐driven approach.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"6 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The simplification of lines plays a crucial role in map generalization and multiscale representation; however, addressing inconsistent sidedness relationships between the simplified lines and their neighboring features poses a persistent challenge. In order to preserve correct and consistent sidedness relationships following simplification, this study introduces a novel line simplification method. This method incorporates topological constraints pertaining to left–right sidedness relationships, formulated as an optimization procedure based on the proposed partial total least squares method with constraints. The primary objective is to minimize the positional difference between the line features before and after simplification, with the sidedness relationship represented as an inequality constraint. Moreover, an optimization algorithm is derived to address the simplification problem effectively while adhering to the specified constraints. The proposed method is then applied to simplify the lines within three distinct datasets. Experimental results validate the efficacy of the proposed method in maintaining sidedness consistency across all tested datasets. In comparison to the Douglas–Peucker (DP) method, the proposed method exhibits minimal vertical displacement variance in the line feature points post-simplification. Additionally, it achieves a smaller overall positional difference between the simplified and original lines compared to the DP method. These findings underscore the superior performance of the proposed method in maintaining sidedness relationships and minimizing positional differences during the line simplification process.
{"title":"Line simplification with sidedness relationship consistency using the constrained total least squares method","authors":"Yanmin Jin, Lejingyi Zhou, Zhengxiang Song, Lingling Wei, Xinyi Zheng, Xiaohua Tong, Xiongfeng Yan","doi":"10.1111/tgis.13151","DOIUrl":"https://doi.org/10.1111/tgis.13151","url":null,"abstract":"The simplification of lines plays a crucial role in map generalization and multiscale representation; however, addressing inconsistent sidedness relationships between the simplified lines and their neighboring features poses a persistent challenge. In order to preserve correct and consistent sidedness relationships following simplification, this study introduces a novel line simplification method. This method incorporates topological constraints pertaining to left–right sidedness relationships, formulated as an optimization procedure based on the proposed partial total least squares method with constraints. The primary objective is to minimize the positional difference between the line features before and after simplification, with the sidedness relationship represented as an inequality constraint. Moreover, an optimization algorithm is derived to address the simplification problem effectively while adhering to the specified constraints. The proposed method is then applied to simplify the lines within three distinct datasets. Experimental results validate the efficacy of the proposed method in maintaining sidedness consistency across all tested datasets. In comparison to the Douglas–Peucker (DP) method, the proposed method exhibits minimal vertical displacement variance in the line feature points post-simplification. Additionally, it achieves a smaller overall positional difference between the simplified and original lines compared to the DP method. These findings underscore the superior performance of the proposed method in maintaining sidedness relationships and minimizing positional differences during the line simplification process.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"282 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139981266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Competition among tourism enterprises is an ineluctable component of sustainable tourism growth, requiring comprehensive studies to understand its dynamic and develop appropriate strategies. The literature employs text mining or statistical analyses to identify correlations between tourism areas as competitive relationships. However, this approach may not be fully applicable, due to the sparsity of crucial coexistence phenomena, and may fail to investigate fine-grained attractions' competition inside destination using large-scale geospatial data. To overcome the limitations, this study proposes a knowledge-driven competitive intelligence framework for tourism management, utilizing knowledge graph (KG) construction and inference technologies. First, multi-mode heterogeneous tourism data are integrated into a unified KG, including tourist check-in, online text, and basic geographic information. Second, the spatial-dependent GNN-based model absorbing abundant spatial semantic knowledge from tourism-oriented KG can enhance the performance of competition reasoning. Third, with multiple analyses via symbolic queries on KG, a comprehensive panorama of competition situations can be revealed.
旅游企业之间的竞争是旅游业可持续增长不可避免的组成部分，需要进行全面研究以了解其动态并制定适当的战略。文献采用文本挖掘或统计分析的方法来确定旅游领域之间的相关竞争关系。然而，由于关键共存现象的稀缺性，这种方法可能并不完全适用，也可能无法利用大规模地理空间数据研究目的地内部细粒度的景点竞争关系。为了克服上述局限性，本研究利用知识图谱（KG）构建和推理技术，提出了一种知识驱动的旅游管理竞争情报框架。首先，将多模式异构旅游数据整合到统一的知识图谱中，包括游客签到、在线文本和基础地理信息。其次，基于空间依赖的 GNN 模型从面向旅游的知识图谱中吸收了丰富的空间语义知识，从而提高了竞争推理的性能。其三，通过符号查询对 KG 进行多重分析，可以揭示全面的竞争情况全景。
{"title":"Knowledge-driven spatial competitive intelligence for tourism","authors":"Jialiang Gao, Peng Peng, Feng Lu, Shu Wang, Xiaowei Xie, Christophe Claramunt","doi":"10.1111/tgis.13145","DOIUrl":"https://doi.org/10.1111/tgis.13145","url":null,"abstract":"Competition among tourism enterprises is an ineluctable component of sustainable tourism growth, requiring comprehensive studies to understand its dynamic and develop appropriate strategies. The literature employs text mining or statistical analyses to identify correlations between tourism areas as competitive relationships. However, this approach may not be fully applicable, due to the sparsity of crucial coexistence phenomena, and may fail to investigate fine-grained attractions' competition inside destination using large-scale geospatial data. To overcome the limitations, this study proposes a knowledge-driven competitive intelligence framework for tourism management, utilizing knowledge graph (KG) construction and inference technologies. First, multi-mode heterogeneous tourism data are integrated into a unified KG, including tourist check-in, online text, and basic geographic information. Second, the spatial-dependent GNN-based model absorbing abundant spatial semantic knowledge from tourism-oriented KG can enhance the performance of competition reasoning. Third, with multiple analyses via symbolic queries on KG, a comprehensive panorama of competition situations can be revealed.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"282 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139981253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhong Wang, Kai Cao, Yu Lung Marcus Chiu, Qiushi Feng
Primary healthcare plays a pivotal role in enhancing health conditions. In Singapore, such services are predominantly manifested through the implementation of the Community Health Assistance Scheme (CHAS). CHAS is an initiative aimed at providing fundamental preventive and therapeutic services, especially for those seniors and low-income adults with chronic diseases. In spite of considerable efforts in policy and research in this domain, there is a dearth of studies focusing on the spatial optimization of these primary healthcare services. In this study, an innovative multi-objective medical service facility siting model has been developed based on coarse-grained parallel genetic algorithm to address the intricate challenges associated with the optimization of locations for CHAS clinics. The proposed optimization model aims to simultaneously maximize accessibility, minimize inequity, and minimize the number of clinics. The successful application of this model in the siting of CHAS clinics in Singapore demonstrates its effectiveness in enhancing residents' access to healthcare services. Apart from its novel academic contributions to the field of spatial optimization of primary healthcare facilities in general, we have also discussed the inherent limitations and identified certain aspects as the future directions of this research.
{"title":"Spatial multi-objective optimization of primary healthcare facilities: A case study in Singapore","authors":"Zhong Wang, Kai Cao, Yu Lung Marcus Chiu, Qiushi Feng","doi":"10.1111/tgis.13147","DOIUrl":"https://doi.org/10.1111/tgis.13147","url":null,"abstract":"Primary healthcare plays a pivotal role in enhancing health conditions. In Singapore, such services are predominantly manifested through the implementation of the Community Health Assistance Scheme (CHAS). CHAS is an initiative aimed at providing fundamental preventive and therapeutic services, especially for those seniors and low-income adults with chronic diseases. In spite of considerable efforts in policy and research in this domain, there is a dearth of studies focusing on the spatial optimization of these primary healthcare services. In this study, an innovative multi-objective medical service facility siting model has been developed based on coarse-grained parallel genetic algorithm to address the intricate challenges associated with the optimization of locations for CHAS clinics. The proposed optimization model aims to simultaneously maximize accessibility, minimize inequity, and minimize the number of clinics. The successful application of this model in the siting of CHAS clinics in Singapore demonstrates its effectiveness in enhancing residents' access to healthcare services. Apart from its novel academic contributions to the field of spatial optimization of primary healthcare facilities in general, we have also discussed the inherent limitations and identified certain aspects as the future directions of this research.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"18 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139981268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As one of the classic tasks in information retrieval, the core of image retrieval is to identify the images sharing similar features with a query image, aiming to enable users to find the required information from a large number of images conveniently. Street view image retrieval, in particular, finds extensive applications in many fields, such as improvements to navigation and mapping services, formulation of urban development planning scheme, and analysis of historical evolution of buildings. However, the intricate foreground and background details in street view images, coupled with a lack of attribute annotations, render it among the most challenging issues in practical applications. Current image retrieval research mainly uses the visual model that is completely dependent on the image visual features, and the multimodal learning model that necessitates additional data sources (e.g., annotated text). Yet, creating annotated datasets is expensive, and street view images, which contain a large amount of scene texts themselves, are often unannotated. Therefore, this paper proposes a deep unsupervised learning algorithm that combines visual and text features from image data for improving the accuracy of street view image retrieval. Specifically, we employ text detection algorithms to identify scene text, utilize the Pyramidal Histogram of Characters encoding predictor model to extract text information from images, deploy deep convolutional neural networks for visual feature extraction, and incorporate a contrastive learning module for image retrieval. Upon testing across three street view image datasets, the results demonstrate that our model holds certain advantages over the state‐of‐the‐art multimodal models pre‐trained on extensive datasets, characterized by fewer parameters and lower floating point operations. Code and data are available at https://github.com/nwuSY/svtRetrieval.
{"title":"Multimodal learning with only image data: A deep unsupervised model for street view image retrieval by fusing visual and scene text features of images","authors":"Shangyou Wu, Wenhao Yu, Yifan Zhang, Mengqiu Huang","doi":"10.1111/tgis.13146","DOIUrl":"https://doi.org/10.1111/tgis.13146","url":null,"abstract":"As one of the classic tasks in information retrieval, the core of image retrieval is to identify the images sharing similar features with a query image, aiming to enable users to find the required information from a large number of images conveniently. Street view image retrieval, in particular, finds extensive applications in many fields, such as improvements to navigation and mapping services, formulation of urban development planning scheme, and analysis of historical evolution of buildings. However, the intricate foreground and background details in street view images, coupled with a lack of attribute annotations, render it among the most challenging issues in practical applications. Current image retrieval research mainly uses the visual model that is completely dependent on the image visual features, and the multimodal learning model that necessitates additional data sources (e.g., annotated text). Yet, creating annotated datasets is expensive, and street view images, which contain a large amount of scene texts themselves, are often unannotated. Therefore, this paper proposes a deep unsupervised learning algorithm that combines visual and text features from image data for improving the accuracy of street view image retrieval. Specifically, we employ text detection algorithms to identify scene text, utilize the Pyramidal Histogram of Characters encoding predictor model to extract text information from images, deploy deep convolutional neural networks for visual feature extraction, and incorporate a contrastive learning module for image retrieval. Upon testing across three street view image datasets, the results demonstrate that our model holds certain advantages over the state‐of‐the‐art multimodal models pre‐trained on extensive datasets, characterized by fewer parameters and lower floating point operations. Code and data are available at <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://github.com/nwuSY/svtRetrieval\">https://github.com/nwuSY/svtRetrieval</jats:ext-link>.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"33 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fenli Jia, Jian Yang, Linfang Ding, Guangxia Wang, Guomin Song
Map images with various themes and cartographic representations have become ubiquitous on the Internet. Such ubiquitously and openly accessible data, named ubiquitous map images in this study, are a potential resource for many geographic information applications such as cartographic design. However, there is a semantic gap between the simple physical form and the complex connotation of ubiquitous map images, which hinders their further applications. To mitigate such barrier, this article develops an ontology‐based semantic description model for ubiquitous map images. First, we discuss the design concerns and principles of the semantic description model of ubiquitous map images. Second, three semantic layers of the semantic description model are proposed, that is, image semantic description layer, cognitive tool layer, and information source layer, and detailed semantic description items are defined for each layer. Furthermore, a formalized semantic description model for ubiquitous map images is developed using ontology construction tools, which lays the foundation for automated and fine‐grained reasoning with the information embedded in map images. We construct a small test dataset consisting of weather maps, and use three types of constraints, namely “time‐topic,” “region‐topic,” and “map auxiliary elements” for the semantic retrieval experiments. The experiments show that the proposed semantic ontology model can enable complex semantic retrieval of ubiquitous map images. Finally, the scalability of the model is discussed from three perspectives: the depth of description, the combination with intelligent methods, and the integration with other open knowledge bases. The proposed model provides a semantic label system for applying data‐driven approaches to decode ubiquitous map images, which also paves the path to the development of cartographic theory in the era of information and communications technologies.
{"title":"An ontology‐based semantic description model of ubiquitous map images","authors":"Fenli Jia, Jian Yang, Linfang Ding, Guangxia Wang, Guomin Song","doi":"10.1111/tgis.13144","DOIUrl":"https://doi.org/10.1111/tgis.13144","url":null,"abstract":"Map images with various themes and cartographic representations have become ubiquitous on the Internet. Such ubiquitously and openly accessible data, named ubiquitous map images in this study, are a potential resource for many geographic information applications such as cartographic design. However, there is a semantic gap between the simple physical form and the complex connotation of ubiquitous map images, which hinders their further applications. To mitigate such barrier, this article develops an ontology‐based semantic description model for ubiquitous map images. First, we discuss the design concerns and principles of the semantic description model of ubiquitous map images. Second, three semantic layers of the semantic description model are proposed, that is, image semantic description layer, cognitive tool layer, and information source layer, and detailed semantic description items are defined for each layer. Furthermore, a formalized semantic description model for ubiquitous map images is developed using ontology construction tools, which lays the foundation for automated and fine‐grained reasoning with the information embedded in map images. We construct a small test dataset consisting of weather maps, and use three types of constraints, namely “time‐topic,” “region‐topic,” and “map auxiliary elements” for the semantic retrieval experiments. The experiments show that the proposed semantic ontology model can enable complex semantic retrieval of ubiquitous map images. Finally, the scalability of the model is discussed from three perspectives: the depth of description, the combination with intelligent methods, and the integration with other open knowledge bases. The proposed model provides a semantic label system for applying data‐driven approaches to decode ubiquitous map images, which also paves the path to the development of cartographic theory in the era of information and communications technologies.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"4 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Youjun Tu, Zihan Shu, Wenjun Wu, Zongyi He, Junli Li
With accelerating globalization, the complexity of the global grain trade network structure is increasing. Traditional network analysis approaches have certain limitations in capturing these dynamic changes and hidden topological structures in data. Based on global import and export trade data for rice, wheat, and corn from 1988 to 2022, this study has proposed a novel method for the topological clustering of temporal multilayer networks based on topological data analysis in order to systematically assess the topological structure evolution of temporal multilayer networks. The results indicate that different agricultural trade networks reveal hidden clustering characteristics in different years. In addition, this study combines principles from landscape ecology to construct a dynamic community spatiotemporal change model of grain trade networks, aiming to comprehensively reveal potential patterns and dynamic trends in grain trade networks and provide valuable information for grain trade decision‐making.
{"title":"Spatiotemporal analysis of global grain trade multilayer networks considering topological clustering","authors":"Youjun Tu, Zihan Shu, Wenjun Wu, Zongyi He, Junli Li","doi":"10.1111/tgis.13149","DOIUrl":"https://doi.org/10.1111/tgis.13149","url":null,"abstract":"With accelerating globalization, the complexity of the global grain trade network structure is increasing. Traditional network analysis approaches have certain limitations in capturing these dynamic changes and hidden topological structures in data. Based on global import and export trade data for rice, wheat, and corn from 1988 to 2022, this study has proposed a novel method for the topological clustering of temporal multilayer networks based on topological data analysis in order to systematically assess the topological structure evolution of temporal multilayer networks. The results indicate that different agricultural trade networks reveal hidden clustering characteristics in different years. In addition, this study combines principles from landscape ecology to construct a dynamic community spatiotemporal change model of grain trade networks, aiming to comprehensively reveal potential patterns and dynamic trends in grain trade networks and provide valuable information for grain trade decision‐making.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"23 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of GIS to enhance student learning in geographical education has garnered broad recognition. Notwithstanding this, the diffusion of GIS technology into class teaching has been slow. This study endeavored to examine the effects of GIS usage in air pollution teaching on learning outcomes of secondary school students. To this end, two parallel classes in the same academic year were chosen as the control and experimental groups. A quasi‐experimental research design was used to compare the learning outcomes of the experimental group who were exposed to the use of GIS in air pollution teaching with those of the control group who were not. The results show that GIS‐based teaching does lead to improvement in students' learning outcomes, although not uniformly. More specifically, GIS‐based teaching enhances high‐order cognitive abilities related to application and analysis, highlighting the effectiveness of GIS as a tool in educational settings, especially for developing advanced cognitive abilities.
{"title":"Does the use of GIS in geographical education yield better learning outcomes? Evidence from a quasi‐experimental study on air pollution teaching","authors":"Daihu Yang, Chuanbing Wang, Liqing Qian","doi":"10.1111/tgis.13142","DOIUrl":"https://doi.org/10.1111/tgis.13142","url":null,"abstract":"The use of GIS to enhance student learning in geographical education has garnered broad recognition. Notwithstanding this, the diffusion of GIS technology into class teaching has been slow. This study endeavored to examine the effects of GIS usage in air pollution teaching on learning outcomes of secondary school students. To this end, two parallel classes in the same academic year were chosen as the control and experimental groups. A quasi‐experimental research design was used to compare the learning outcomes of the experimental group who were exposed to the use of GIS in air pollution teaching with those of the control group who were not. The results show that GIS‐based teaching does lead to improvement in students' learning outcomes, although not uniformly. More specifically, GIS‐based teaching enhances high‐order cognitive abilities related to application and analysis, highlighting the effectiveness of GIS as a tool in educational settings, especially for developing advanced cognitive abilities.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"6 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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