The efficacy of conveying information through maps heavily depends on the quality of map generalization. However, automating map generalization poses a complex decision‐making challenge, requiring a profound understanding of the process—specifically, knowledge about the generalization procedure. Currently, there is a scarcity of research on the sequence of generalization operations, particularly for cartographic generalization involving symbolization and labeling. On the contrary, customary maps generated in practical applications consistently adhere to the specified generalization and symbolization protocol, which makes it feasible and credible to construct this overall process based on expert knowledge. To reconcile this incongruity, this paper presents a knowledge‐guided automated cartographic generalization process construction. Firstly, an exhaustive examination of the sequential procedures involved in manual generalization and a well‐applied automated generalization system are delineated, drawing upon map analysis methodologies, observations, and expert interviews. Then, elaborate guidelines governing each phase within this process, particularly concerning the symbolization and labeling of map features, are explored. Ultimately, details of the expert interview are described and a map generalized by the well‐applied system is analyzed. The results show that the automated generalization system follows the knowledge‐guided process in this paper can significantly improve production efficiency in practice, this study serves as a connection between cartographers and developers and may help achieve a higher level of automated cartographic generalization.
{"title":"Knowledge‐Guided Automated Cartographic Generalization Process Construction: A Case Study Based on Map Analysis of Public Maps of China","authors":"Xiaorong Gao, Haowen Yan, Zhongkui Chen, Panfei Yin","doi":"10.1111/tgis.13246","DOIUrl":"https://doi.org/10.1111/tgis.13246","url":null,"abstract":"The efficacy of conveying information through maps heavily depends on the quality of map generalization. However, automating map generalization poses a complex decision‐making challenge, requiring a profound understanding of the process—specifically, knowledge about the generalization procedure. Currently, there is a scarcity of research on the sequence of generalization operations, particularly for cartographic generalization involving symbolization and labeling. On the contrary, customary maps generated in practical applications consistently adhere to the specified generalization and symbolization protocol, which makes it feasible and credible to construct this overall process based on expert knowledge. To reconcile this incongruity, this paper presents a knowledge‐guided automated cartographic generalization process construction. Firstly, an exhaustive examination of the sequential procedures involved in manual generalization and a well‐applied automated generalization system are delineated, drawing upon map analysis methodologies, observations, and expert interviews. Then, elaborate guidelines governing each phase within this process, particularly concerning the symbolization and labeling of map features, are explored. Ultimately, details of the expert interview are described and a map generalized by the well‐applied system is analyzed. The results show that the automated generalization system follows the knowledge‐guided process in this paper can significantly improve production efficiency in practice, this study serves as a connection between cartographers and developers and may help achieve a higher level of automated cartographic generalization.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262716","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}
There have been very few studies done on measuring the influence of all prefecture‐level cities on a national scale due to the limited availability of public data, challenges in data collection, and insufficient data comprehensiveness. In this paper, we aim to fill this gap by investigating this problem in China. We first collected 692,859 news articles spanning one full year from the WeChat Official Accounts of 339 cities and Taiwan Province, which served as our study area and dataset. Then, we developed a city extractor module to reduce the ambiguity of place names and constructed a city interaction network. Then, we modeled the City Influence Index (CII) and the intensity of its influence. Finally, we proposed an analytical framework that examines the relationship between CII and Gross Domestic Product (GDP), compares it with the Global Cities Index, conducts influence analysis of cities at different levels, and more. The experimental results demonstrate that our analytical framework can effectively measure the influence of cities on a national scale and uncover the implicit relationships between cities. In doing so, our study offers a new perspective for measuring city influence. Code is available at: https://github.com/vczero/CII.
{"title":"City Influence Network: Mining and Analyzing the Influence of Chinese Cities Based on Social Media","authors":"Lihua Wang, Shengyi Jiang","doi":"10.1111/tgis.13249","DOIUrl":"https://doi.org/10.1111/tgis.13249","url":null,"abstract":"There have been very few studies done on measuring the influence of all prefecture‐level cities on a national scale due to the limited availability of public data, challenges in data collection, and insufficient data comprehensiveness. In this paper, we aim to fill this gap by investigating this problem in China. We first collected 692,859 news articles spanning one full year from the WeChat Official Accounts of 339 cities and Taiwan Province, which served as our study area and dataset. Then, we developed a city extractor module to reduce the ambiguity of place names and constructed a city interaction network. Then, we modeled the City Influence Index (CII) and the intensity of its influence. Finally, we proposed an analytical framework that examines the relationship between CII and Gross Domestic Product (GDP), compares it with the Global Cities Index, conducts influence analysis of cities at different levels, and more. The experimental results demonstrate that our analytical framework can effectively measure the influence of cities on a national scale and uncover the implicit relationships between cities. In doing so, our study offers a new perspective for measuring city influence. Code is available at: <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://github.com/vczero/CII\">https://github.com/vczero/CII</jats:ext-link>.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262717","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}
Geographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretations, GRF has already been used in many studies. The current GRF model, however, has limitations in its determination of the local model weight and bandwidth hyperparameters, potentially insufficient numbers of local training samples, and sometimes high local prediction errors. Also, implemented as an R package, GRF currently does not have a Python version which limits its adoption among machine learning practitioners who prefer Python. This work addresses these limitations by introducing theory‐informed hyperparameter determination, local training sample expansion, and spatially weighted local prediction. We also develop a Python‐based GRF model and package, PyGRF, to facilitate the use of the model. We evaluate the performance of PyGRF on an example dataset and further demonstrate its use in two case studies in public health and natural disasters.
{"title":"PyGRF: An Improved Python Geographical Random Forest Model and Case Studies in Public Health and Natural Disasters","authors":"Kai Sun, Ryan Zhenqi Zhou, Jiyeon Kim, Yingjie Hu","doi":"10.1111/tgis.13248","DOIUrl":"https://doi.org/10.1111/tgis.13248","url":null,"abstract":"Geographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretations, GRF has already been used in many studies. The current GRF model, however, has limitations in its determination of the local model weight and bandwidth hyperparameters, potentially insufficient numbers of local training samples, and sometimes high local prediction errors. Also, implemented as an R package, GRF currently does not have a Python version which limits its adoption among machine learning practitioners who prefer Python. This work addresses these limitations by introducing theory‐informed hyperparameter determination, local training sample expansion, and spatially weighted local prediction. We also develop a Python‐based GRF model and package, PyGRF, to facilitate the use of the model. We evaluate the performance of PyGRF on an example dataset and further demonstrate its use in two case studies in public health and natural disasters.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262760","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}
Bo Zhao, Jiaxin Feng, Yuzhi Sun, Xiaohui Chang, Shih‐Lung Shaw
As GIS progressively permeates our everyday lives, it becomes increasingly significant to incorporate emotional factors into the design and use of GIS technologies. While existing methods such as self‐report and AI‐enhanced techniques prevail in collecting emotional factors, they often miss real‐time emotional nuances and synchronization with geographically referenced and other accompanying data. To bridge this gap, we introduce “Neural Sensing,” a novel approach that combines Electroencephalogram (EEG) with GIS technologies to quantitatively measure emotional responses to places. This approach offers a unique lens to understand human dynamics in a hybrid physical–virtual realm, especially by delving into our mental worlds. To demonstrate the utility of Neural Sensing, we have designed a pilot empirical study to analyze emotional reactions to geographical environments. This approach broadens the domain of GIS methodologies from remote sensing and social sensing to a neural sensing approach. Through this study, we underscore the importance of the mental dimension of the hybrid physical–virtual world and encourage GIScientists to emphasize human experiences and feelings in GIS analyses.
{"title":"Neural Sensing: Toward a New Approach to Understanding Emotional Responses to Place","authors":"Bo Zhao, Jiaxin Feng, Yuzhi Sun, Xiaohui Chang, Shih‐Lung Shaw","doi":"10.1111/tgis.13244","DOIUrl":"https://doi.org/10.1111/tgis.13244","url":null,"abstract":"As GIS progressively permeates our everyday lives, it becomes increasingly significant to incorporate emotional factors into the design and use of GIS technologies. While existing methods such as self‐report and AI‐enhanced techniques prevail in collecting emotional factors, they often miss real‐time emotional nuances and synchronization with geographically referenced and other accompanying data. To bridge this gap, we introduce “Neural Sensing,” a novel approach that combines Electroencephalogram (EEG) with GIS technologies to quantitatively measure emotional responses to places. This approach offers a unique lens to understand human dynamics in a hybrid physical–virtual realm, especially by delving into our mental worlds. To demonstrate the utility of Neural Sensing, we have designed a pilot empirical study to analyze emotional reactions to geographical environments. This approach broadens the domain of GIS methodologies from remote sensing and social sensing to a neural sensing approach. Through this study, we underscore the importance of the mental dimension of the hybrid physical–virtual world and encourage GIScientists to emphasize human experiences and feelings in GIS analyses.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262718","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}
Owing to the rapid development of Earth observation and Internet technology, researchers have acquired and shared a large amount of Earth observation data. However, traditional data sharing does not provide direct solutions to problems. The large amount of tacit knowledge contained in scientific data, scientific literature, analysis models, software/code, documentation, and other scientific resources on Earth observation applications has not been effectively organized and shared. To solve this problem, the Group on Earth Observations proposed an Earth Observation Knowledge Hub (EOKH); however, there is no unified and clear method for building an EOKH to date. This paper presents an automatic construction method for an EOKH on the basis of a knowledge graph, which describes scientific data, scientific literature, analysis models, software/code, documentation, and other scientific resources and their semantic relationships. An automatic discovery algorithm of scientific and technological resources was also constructed in this study on the basis of a knowledge graph from the Internet. This algorithm is capable of the automatic creation of knowledge packages and the construction of links between knowledge elements. Then, the knowledge discovery algorithm was evaluated through comparison with an existing method in relation to accuracy, and the results showed that our method outperforms the existing method. Lastly, the knowledge package was published on the Linked Open Data Cloud platform in the Resource Description Framework format, and an EOKH was created. Moreover, an application terminal based on SPARQL allowing users to search the EOKH was developed. A clear and operational method for the construction of an EOKH is proposed for the first time in this research, laying the foundation for the development of the EOKH.
{"title":"Construction of Earth Observation Knowledge Hub Based on Knowledge Graph","authors":"Kuangsheng Cai, Zugang Chen, Jin Li, Shaohua Wang, Guoqing Li, Jing Li, Hengliang Guo, Feng Chen, Liping Zhu","doi":"10.1111/tgis.13247","DOIUrl":"https://doi.org/10.1111/tgis.13247","url":null,"abstract":"Owing to the rapid development of Earth observation and Internet technology, researchers have acquired and shared a large amount of Earth observation data. However, traditional data sharing does not provide direct solutions to problems. The large amount of tacit knowledge contained in scientific data, scientific literature, analysis models, software/code, documentation, and other scientific resources on Earth observation applications has not been effectively organized and shared. To solve this problem, the Group on Earth Observations proposed an Earth Observation Knowledge Hub (EOKH); however, there is no unified and clear method for building an EOKH to date. This paper presents an automatic construction method for an EOKH on the basis of a knowledge graph, which describes scientific data, scientific literature, analysis models, software/code, documentation, and other scientific resources and their semantic relationships. An automatic discovery algorithm of scientific and technological resources was also constructed in this study on the basis of a knowledge graph from the Internet. This algorithm is capable of the automatic creation of knowledge packages and the construction of links between knowledge elements. Then, the knowledge discovery algorithm was evaluated through comparison with an existing method in relation to accuracy, and the results showed that our method outperforms the existing method. Lastly, the knowledge package was published on the Linked Open Data Cloud platform in the Resource Description Framework format, and an EOKH was created. Moreover, an application terminal based on SPARQL allowing users to search the EOKH was developed. A clear and operational method for the construction of an EOKH is proposed for the first time in this research, laying the foundation for the development of the EOKH.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262722","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}
Mitko Aleksandrov, Kimia Amoozandeh, Stephan Winter, Sisi Zlatanova, Martin Tomko
In an era where localization is increasingly vital for daily activities, determining an individual's location and providing suggestions is crucial for successful navigation. Central to our method is the concept of visibility areas, defined as spaces from which a landmark is at least partially visible. We tessellate space into a grid and use raycasting to determine these visibility areas. Visibility areas are further subdivided into sub‐spaces, each representing a unique set of visible landmarks. To efficiently manage and query these sub‐spaces, we create a tree of options for each landmark and merge them into one compact multitree. This structure represents a hierarchical composition of sub‐spaces and enhances query efficiency while storing each sub‐space only once in the memory. The multitree is designed to support the rapid identification of sub‐spaces based on observed landmarks, resulting in efficient localization within the environment is presented to calculating the optimal localization point in a visibility area based on suggested visible landmarks. The paper concludes with a discussion of the approach targeting indoor localization but highlights also its potential for further research and some limitations.
{"title":"A Visibility‐Based Multitree of a Space Subdivision for Indoor Localization","authors":"Mitko Aleksandrov, Kimia Amoozandeh, Stephan Winter, Sisi Zlatanova, Martin Tomko","doi":"10.1111/tgis.13239","DOIUrl":"https://doi.org/10.1111/tgis.13239","url":null,"abstract":"In an era where localization is increasingly vital for daily activities, determining an individual's location and providing suggestions is crucial for successful navigation. Central to our method is the concept of visibility areas, defined as spaces from which a landmark is at least partially visible. We tessellate space into a grid and use raycasting to determine these visibility areas. Visibility areas are further subdivided into sub‐spaces, each representing a unique set of visible landmarks. To efficiently manage and query these sub‐spaces, we create a tree of options for each landmark and merge them into one compact multitree. This structure represents a hierarchical composition of sub‐spaces and enhances query efficiency while storing each sub‐space only once in the memory. The multitree is designed to support the rapid identification of sub‐spaces based on observed landmarks, resulting in efficient localization within the environment is presented to calculating the optimal localization point in a visibility area based on suggested visible landmarks. The paper concludes with a discussion of the approach targeting indoor localization but highlights also its potential for further research and some limitations.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225060","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 technological advancements continue to blur the boundaries between cyber and physical spaces, individuals' activities are not limited to physical space and increasingly transcend singular space. Prior research on interactions between cyber and physical spaces oversimplified or even overlooked the interactions between the two spaces due to limited access to human activity big data, for instance, the implications of cyber–physical interactions for tourism. Consequently, this study proposes an overtourism index framework intended to capture travel behaviors in both cyber and physical spaces to address the research gap. Based on extensive social media data from Hong Kong, the proposed framework is tested and validated efficiently to emulate tourism interactions between cyber and physical spaces at fine spatiotemporal resolutions. The results indicate that there is strong convergent interaction among the public in both cyber and physical spaces. Moreover, an online WebGIS interactive platform (https://arcg.is/0mzHyH) has been developed for visualizing these interactions and provides better decision‐making regarding tourism policy in Hong Kong.
{"title":"Exploring the Convergence of Cyber–Physical Space: Multidimensional Modeling of Overtourism Interactions","authors":"Kaiwing Tai, Minglei Liao, Xintao Liu","doi":"10.1111/tgis.13245","DOIUrl":"https://doi.org/10.1111/tgis.13245","url":null,"abstract":"As technological advancements continue to blur the boundaries between cyber and physical spaces, individuals' activities are not limited to physical space and increasingly transcend singular space. Prior research on interactions between cyber and physical spaces oversimplified or even overlooked the interactions between the two spaces due to limited access to human activity big data, for instance, the implications of cyber–physical interactions for tourism. Consequently, this study proposes an overtourism index framework intended to capture travel behaviors in both cyber and physical spaces to address the research gap. Based on extensive social media data from Hong Kong, the proposed framework is tested and validated efficiently to emulate tourism interactions between cyber and physical spaces at fine spatiotemporal resolutions. The results indicate that there is strong convergent interaction among the public in both cyber and physical spaces. Moreover, an online WebGIS interactive platform (<jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://arcg.is/0mzHyH\">https://arcg.is/0mzHyH</jats:ext-link>) has been developed for visualizing these interactions and provides better decision‐making regarding tourism policy in Hong Kong.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196780","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}
Accessibility between fire hydrants and buildings is paramount in emergency response, significantly influencing the efficiency and effectiveness of firefighting operations in the event of an incident. However, assessing this relationship within a geographic information system (GIS) framework presents challenges on two fronts. Obtaining the path avoiding building and parcel obstructions to hydrants is not trivial. Further, determining the furthest extent around a building exterior from hydrants is complicated, yet it is critically important given the spatial limitations of equipment reach. To assess furthest extent, an analytical framework is introduced based on the Euclidean shortest path problem. The proposed approach offers a comprehensive, automated GIS‐based methodology tailored to evaluate the dynamic relationship between hydrants and buildings. The developed methods are able to accurately and precisely identify the furthest point around a building structure from hydrant, facilitating risk assessment as well as fire code compliance. This enables a comprehensive evaluation of potential loss and structure vulnerability at property, street, neighborhood, and regional levels.
{"title":"Emergency Response Planning: A Framework to Assess Hydrant–Structure Access","authors":"Jiwon Baik, Alan T. Murray","doi":"10.1111/tgis.13243","DOIUrl":"https://doi.org/10.1111/tgis.13243","url":null,"abstract":"Accessibility between fire hydrants and buildings is paramount in emergency response, significantly influencing the efficiency and effectiveness of firefighting operations in the event of an incident. However, assessing this relationship within a geographic information system (GIS) framework presents challenges on two fronts. Obtaining the path avoiding building and parcel obstructions to hydrants is not trivial. Further, determining the furthest extent around a building exterior from hydrants is complicated, yet it is critically important given the spatial limitations of equipment reach. To assess furthest extent, an analytical framework is introduced based on the Euclidean shortest path problem. The proposed approach offers a comprehensive, automated GIS‐based methodology tailored to evaluate the dynamic relationship between hydrants and buildings. The developed methods are able to accurately and precisely identify the furthest point around a building structure from hydrant, facilitating risk assessment as well as fire code compliance. This enables a comprehensive evaluation of potential loss and structure vulnerability at property, street, neighborhood, and regional levels.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196781","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}
Sandy beaches, widespread in coastal areas, provide valuable economic and ecological benefits. However, a substantial proportion of sandy beaches are undergoing erosion caused by marine disasters and human interventions. Advanced sandy beach extraction (SBE) approaches are indispensable to sandy beach observation and protection. This study proposes a novel two‐step SBE approach using remote sensing images and digital elevation models. First, sea–land segmentation is performed as a preparatory work. We model sea–land segmentation as an optimization problem and develop an improved NSGA‐II, SCS‐NSGA‐II, to solve it, considering both topographical and spectral costs. Second, a region growing algorithm is applied to generate the final sandy beach extents. The assessment results verify that (1) our approach effectively reduces the false‐positive rate, thereby resulting in more accurate SBE results compared with existing approaches. (2) SCS‐NSGA‐II ensures the diversity of individuals in spatial patterns and exhibits superior performance compared with NSGA‐II in this task.
{"title":"A Two‐Step Approach to Extracting Sandy Beaches Through Integrating Spatial Semantic Information From Open‐Source Geospatial Datasets","authors":"Zhe Wang, Zhixiang Fang, Jiayi Chang, Zhongyuan Wang, Weiming Shen","doi":"10.1111/tgis.13231","DOIUrl":"https://doi.org/10.1111/tgis.13231","url":null,"abstract":"Sandy beaches, widespread in coastal areas, provide valuable economic and ecological benefits. However, a substantial proportion of sandy beaches are undergoing erosion caused by marine disasters and human interventions. Advanced sandy beach extraction (SBE) approaches are indispensable to sandy beach observation and protection. This study proposes a novel two‐step SBE approach using remote sensing images and digital elevation models. First, sea–land segmentation is performed as a preparatory work. We model sea–land segmentation as an optimization problem and develop an improved NSGA‐II, SCS‐NSGA‐II, to solve it, considering both topographical and spectral costs. Second, a region growing algorithm is applied to generate the final sandy beach extents. The assessment results verify that (1) our approach effectively reduces the false‐positive rate, thereby resulting in more accurate SBE results compared with existing approaches. (2) SCS‐NSGA‐II ensures the diversity of individuals in spatial patterns and exhibits superior performance compared with NSGA‐II in this task.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196782","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}
With the rapid urbanization in China, urban land resources gradually become the core of urban development. This study spatially evaluated the urban land resource carrying capacity (LRCC) with a case study of the built‐up area in Wuhan from 2015 to 2020. Following an evaluation index system, five critical LRCC indicators, including population density, GDP per land area, plot ratio, building density, and road network density, were selected by an analytical hierarchical process. The synthesis of indicators, however, is usually challengeable due to homogeneous assumptions of traditional techniques. In this study, we adopted a local technique, geographically weighted principal component analysis, to calculate a comprehensive carrying pressure (CCP) concerning spatially varying contributions of each indicator on their synthesis across different geographic locations. On mapping these spatial outputs of the built‐up area in Wuhan, the highest CCP was found in the central areas, where population size tends to be influential and the dominant variable in 62.69% of subdistricts. Furthermore, increased construction over the 5 years has led to an increased CCP in some of the peripheries of the built‐up area, and 55.22% of subdistricts show rising changes. With the GWPCA technique, this framework works well in evaluating and analyzing urban LRCC from a new local perspective.
{"title":"Evaluating Urban Land Resource Carrying Capacity With Geographically Weighted Principal Component Analysis: A Case Study in Wuhan, China","authors":"Binbin Lu, Yilin Shi, Sixian Qin, Peng Yue, Jianghua Zheng, Paul Harris","doi":"10.1111/tgis.13241","DOIUrl":"https://doi.org/10.1111/tgis.13241","url":null,"abstract":"With the rapid urbanization in China, urban land resources gradually become the core of urban development. This study spatially evaluated the urban land resource carrying capacity (LRCC) with a case study of the built‐up area in Wuhan from 2015 to 2020. Following an evaluation index system, five critical LRCC indicators, including population density, GDP per land area, plot ratio, building density, and road network density, were selected by an analytical hierarchical process. The synthesis of indicators, however, is usually challengeable due to homogeneous assumptions of traditional techniques. In this study, we adopted a local technique, geographically weighted principal component analysis, to calculate a comprehensive carrying pressure (CCP) concerning spatially varying contributions of each indicator on their synthesis across different geographic locations. On mapping these spatial outputs of the built‐up area in Wuhan, the highest CCP was found in the central areas, where population size tends to be influential and the dominant variable in 62.69% of subdistricts. Furthermore, increased construction over the 5 years has led to an increased CCP in some of the peripheries of the built‐up area, and 55.22% of subdistricts show rising changes. With the GWPCA technique, this framework works well in evaluating and analyzing urban LRCC from a new local perspective.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196783","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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