The impact of urban expansion on achieving sustainable development goals (SDGs) has become a significant research topic in the field of geographic information science. In this article, we describe a coupled cellular automata (CA)—‐What If? model to explore SDG11 “Sustainable cities and communities.” The model calculates overall residential land use demand based on historical data archives using the What If? planning support system (PSS), and then allocates it using a CA model that incorporates variables related to SDG11.2.1 and 11.7.1. Historical datasets for years 2016 and 2021 from Southwest Sydney, Australia were used to assess model accuracy, after which two residential expansion scenarios (years 2021 and 2026) were generated. Based on the modeling results, the SDG‐related spatial variables can improve the overall accuracy of CA sub‐models using an XGBoost machine learning training methodology. The simulation results of these scenarios confirm the effectiveness of the coupled CA‐What If? model, which has the potential to generate more reliable scenario results than the standalone What If? PSS for modeling urban growth of cities across Australia and internationally.
{"title":"Coupling cellular automata and What If? models for residential expansion simulation: A case study of Southwest Sydney, Australia","authors":"Yi Lu, Shawn Laffan, Christopher Pettit","doi":"10.1111/tgis.13198","DOIUrl":"https://doi.org/10.1111/tgis.13198","url":null,"abstract":"The impact of urban expansion on achieving sustainable development goals (SDGs) has become a significant research topic in the field of geographic information science. In this article, we describe a coupled cellular automata (CA)—‐What If? model to explore SDG11 “Sustainable cities and communities.” The model calculates overall residential land use demand based on historical data archives using the What If? planning support system (PSS), and then allocates it using a CA model that incorporates variables related to SDG11.2.1 and 11.7.1. Historical datasets for years 2016 and 2021 from Southwest Sydney, Australia were used to assess model accuracy, after which two residential expansion scenarios (years 2021 and 2026) were generated. Based on the modeling results, the SDG‐related spatial variables can improve the overall accuracy of CA sub‐models using an XGBoost machine learning training methodology. The simulation results of these scenarios confirm the effectiveness of the coupled CA‐What If? model, which has the potential to generate more reliable scenario results than the standalone What If? PSS for modeling urban growth of cities across Australia and internationally.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141342591","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}
Historical topographic maps are an important source of a visual record of the landscape, showing geographical elements such as terrain, elevation, rivers and water bodies, roads, buildings, and land use and land cover (LULC). Historical maps are scanned to their digital representation, a raster image. To quantify different classes of LULC, it is necessary to transform scanned maps to their vector equivalent. Traditionally, this has been done either manually, or by using (semi)automatic methods of clustering/segmentation. With the advent of deep neural networks, new horizons opened for more effective and accurate processing. This article attempts to use different deep‐learning approaches to detect and segment wetlands on historical Topographic Maps 1: 10000 (TM10), created during the 50s and 60s. Due to the specific symbology of wetlands, their processing can be challenging. It deals with two distinct approaches in the deep learning world, semantic segmentation and object detection, represented by the U‐Net and Single‐Shot Detector (SSD) neural networks, respectively. The suitability, speed, and accuracy of the two approaches in neural networks are analyzed. The results are satisfactory, with the U‐Net F1 score reaching 75.7% and the SSD object detection approach presenting an unconventional alternative.
{"title":"Deep learning approaches for delineating wetlands on historical topographic maps","authors":"Jakub Vynikal, Jana Müllerová, Jan Pacina","doi":"10.1111/tgis.13193","DOIUrl":"https://doi.org/10.1111/tgis.13193","url":null,"abstract":"Historical topographic maps are an important source of a visual record of the landscape, showing geographical elements such as terrain, elevation, rivers and water bodies, roads, buildings, and land use and land cover (LULC). Historical maps are scanned to their digital representation, a raster image. To quantify different classes of LULC, it is necessary to transform scanned maps to their vector equivalent. Traditionally, this has been done either manually, or by using (semi)automatic methods of clustering/segmentation. With the advent of deep neural networks, new horizons opened for more effective and accurate processing. This article attempts to use different deep‐learning approaches to detect and segment wetlands on historical Topographic Maps 1: 10000 (TM10), created during the 50s and 60s. Due to the specific symbology of wetlands, their processing can be challenging. It deals with two distinct approaches in the deep learning world, semantic segmentation and object detection, represented by the U‐Net and Single‐Shot Detector (SSD) neural networks, respectively. The suitability, speed, and accuracy of the two approaches in neural networks are analyzed. The results are satisfactory, with the U‐Net F1 score reaching 75.7% and the SSD object detection approach presenting an unconventional alternative.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372762","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}
Xiao Chen, Tao Pei, Ci Song, Hua Shu, Sihui Guo, Xi Wang, Yaxi Liu, Jie Chen
Understanding individual's socioeconomic status (SES) can provide supporting information for designing political and economic policies. Acquiring large‐scale economic survey data is time‐consuming and laborious. The widespread mobile phone data, which can reflect human mobility and social network characteristics, has become a low‐cost data source for researchers to infer SES. However, previous studies often oversimplify human mobility features and social network features extracted from mobile phone data into general statistical features, resulting in discounting some important temporal and relational information. Therefore, we propose a comprehensive framework for individual SES prediction that effectively utilizes a combination of human mobility and social relationships. In this framework, Word2Vec module extracts human mobility features from mobile phone positioning data, and graph neural network (GNN) module GraphSAGE captures social network characteristics constructed from call detail records. We evaluated the effectiveness of our proposed approach by training the model with real‐world data in Beijing. According to the experimental results, our proposed hybrid approach outperformed the other methods evidently, demonstrating that human mobility and social links are complementary in the characterization of SES. Coupling human mobility and social links can further deepen our understanding of cities' economic geography.
了解个人的社会经济地位（SES）可以为制定政治和经济政策提供辅助信息。获取大规模经济调查数据费时费力。广泛使用的手机数据可以反映人的流动性和社会网络特征，已成为研究人员推断 SES 的低成本数据来源。然而，以往的研究往往将从手机数据中提取的人员流动特征和社会网络特征过度简化为一般的统计特征，从而忽略了一些重要的时间和关系信息。因此，我们提出了一个综合框架来预测个人的社会经济地位，有效地将人员流动性和社会关系相结合。在这个框架中，Word2Vec 模块从手机定位数据中提取人员流动特征，而图神经网络（GNN）模块 GraphSAGE 则从通话详情记录中捕捉社会网络特征。我们利用北京的真实数据对模型进行了训练，评估了我们提出的方法的有效性。实验结果表明，我们提出的混合方法明显优于其他方法，这表明在描述社会经济地位时，人员流动和社会联系是互补的。将人员流动和社会联系结合起来，可以进一步加深我们对城市经济地理的理解。
{"title":"Coupling human mobility and social relationships to predict individual socioeconomic status: A graph neural network approach","authors":"Xiao Chen, Tao Pei, Ci Song, Hua Shu, Sihui Guo, Xi Wang, Yaxi Liu, Jie Chen","doi":"10.1111/tgis.13189","DOIUrl":"https://doi.org/10.1111/tgis.13189","url":null,"abstract":"Understanding individual's socioeconomic status (SES) can provide supporting information for designing political and economic policies. Acquiring large‐scale economic survey data is time‐consuming and laborious. The widespread mobile phone data, which can reflect human mobility and social network characteristics, has become a low‐cost data source for researchers to infer SES. However, previous studies often oversimplify human mobility features and social network features extracted from mobile phone data into general statistical features, resulting in discounting some important temporal and relational information. Therefore, we propose a comprehensive framework for individual SES prediction that effectively utilizes a combination of human mobility and social relationships. In this framework, Word2Vec module extracts human mobility features from mobile phone positioning data, and graph neural network (GNN) module GraphSAGE captures social network characteristics constructed from call detail records. We evaluated the effectiveness of our proposed approach by training the model with real‐world data in Beijing. According to the experimental results, our proposed hybrid approach outperformed the other methods evidently, demonstrating that human mobility and social links are complementary in the characterization of SES. Coupling human mobility and social links can further deepen our understanding of cities' economic geography.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375746","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}
Correlation between burglary crime and urban environmental characteristics is crucial for understanding the causes of crime events. Mathematical relationships can be linked between crime and crime‐causing events with the help of the machine learning (ML) model and geographic information system (GIS). The main objective of this research is to analyze and predict burglary crime events by applying ML‐based GIS models for Trabzon and Turkey. Random forest regression (RFR) and support vector regression (SVR) were implemented to predict crime. Correlation between crime and urban physical environmental metrics was used in the prediction model. Due to the result of the analysis, the R2 value was measured as 0.78 with the RFR and 0.71 with the SVR algorithm. The height of the building, the proportion of floor area, the density of buildings, and the density of intersection of streets are the four most important variables that affect the burglary crime rate positively. Conversely, the variable with the lowest effect on burglary crime is the ratio of the park to the residential area.
{"title":"Predicting and analyzing crime—Environmental design relationship via GIS‐based machine learning approach","authors":"G. Bediroglu, Husniye Ebru Colak","doi":"10.1111/tgis.13195","DOIUrl":"https://doi.org/10.1111/tgis.13195","url":null,"abstract":"Correlation between burglary crime and urban environmental characteristics is crucial for understanding the causes of crime events. Mathematical relationships can be linked between crime and crime‐causing events with the help of the machine learning (ML) model and geographic information system (GIS). The main objective of this research is to analyze and predict burglary crime events by applying ML‐based GIS models for Trabzon and Turkey. Random forest regression (RFR) and support vector regression (SVR) were implemented to predict crime. Correlation between crime and urban physical environmental metrics was used in the prediction model. Due to the result of the analysis, the R2 value was measured as 0.78 with the RFR and 0.71 with the SVR algorithm. The height of the building, the proportion of floor area, the density of buildings, and the density of intersection of streets are the four most important variables that affect the burglary crime rate positively. Conversely, the variable with the lowest effect on burglary crime is the ratio of the park to the residential area.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383390","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}
Publication of raster tile services is a widely adopted method for presenting and sharing geographically referenced data, and enhancing geographic information systems (GIS) by serving as either base layers or featured layers. However, the establishment of raster tile services can still be improved in terms of data fusion efficiency and diversity from various raster and vector sources. In addition, addressing data security concerns while maintaining flexibility to meet the requirements and expectations of clients and publishers is crucial. HTile is proposed as a solution to efficiently publish high‐performance real‐time raster tile services. This solution incorporates an innovative tile generation process that enables customized data fusion and data‐hiding and offers dynamic styling while utilizing minimal storage space, ensuring rapid response time to meet the objectives of satisfactory data protection and visualization. Implementations of HTile leverage commonly used raster and vector data, which demonstrate compelling evidence of data‐fusion and data‐hiding capacities with exceptional performance. This study makes a significant contribution to the innovation strategy in publishing raster tile services, proving a novel approach that holds promising potential for GIS paradigms in data management and sharing flexibility.
{"title":"HTile: A high‐performance real‐time raster tile service with data‐fusion and data‐hiding approaches","authors":"Jyun‐Yuan Chen, C. Kuo","doi":"10.1111/tgis.13194","DOIUrl":"https://doi.org/10.1111/tgis.13194","url":null,"abstract":"Publication of raster tile services is a widely adopted method for presenting and sharing geographically referenced data, and enhancing geographic information systems (GIS) by serving as either base layers or featured layers. However, the establishment of raster tile services can still be improved in terms of data fusion efficiency and diversity from various raster and vector sources. In addition, addressing data security concerns while maintaining flexibility to meet the requirements and expectations of clients and publishers is crucial. HTile is proposed as a solution to efficiently publish high‐performance real‐time raster tile services. This solution incorporates an innovative tile generation process that enables customized data fusion and data‐hiding and offers dynamic styling while utilizing minimal storage space, ensuring rapid response time to meet the objectives of satisfactory data protection and visualization. Implementations of HTile leverage commonly used raster and vector data, which demonstrate compelling evidence of data‐fusion and data‐hiding capacities with exceptional performance. This study makes a significant contribution to the innovation strategy in publishing raster tile services, proving a novel approach that holds promising potential for GIS paradigms in data management and sharing flexibility.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387409","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}
Changbin Wu, Xinyang Yu, Can Ma, Rongkai Zhong, Xinxin Zhou
3D urban building modeling is a vital foundational step for building Digital Twins and Smart Cities. In response to existing challenges, such as high time costs, complex production processes, and low consistency with real‐world textures in large‐scale 3D urban building modeling methods, this research proposes a reconstructing 3D urban building models (3DUBM) approach that integrates geospatial data and street view. The approach achieves an enhanced generation of large‐scale 3DUBMs. Based on open geospatial data and street‐view imagery (SVI), the approach was tested in modeling experiments conducted in Shanghai, Hongkong, and Nanjing. Furthermore, a dataset covering unique blocks of 30 cities in China was constructed to demonstrate the approach's characteristics of large coverage, high time efficiency, high model quality and low economic cost. The accuracy of texture mapping from SVI to 3DUBM reached 85%. This achievement has significant economic value in bridging the gap in the production of large‐scale and low‐cost 3DUBM data, promoting the construction of Digital Twins, Smart Cities, and Real‐world 3D modeling.
三维城市建筑建模是建设数字孪生城市和智能城市的重要基础步骤。针对大规模三维城市建筑建模方法存在的时间成本高、制作过程复杂、与真实世界纹理一致性低等挑战，本研究提出了一种整合地理空间数据和街景的重构三维城市建筑模型（3DUBM）方法。该方法实现了大规模三维城市建筑模型的增强生成。基于开放的地理空间数据和街景图像（SVI），该方法在上海、香港和南京进行了建模实验。此外，还构建了一个覆盖中国 30 个城市独特街区的数据集，以证明该方法具有覆盖范围大、时间效率高、模型质量高和经济成本低的特点。从 SVI 到 3DUBM 的纹理映射准确率达到 85%。这一成果对于弥补大规模、低成本 3DUBM 数据生产的空白，促进数字孪生、智慧城市和真实世界三维建模的建设，具有重要的经济价值。
{"title":"Integrating geospatial data and street‐view imagery to reconstruct large‐scale 3D urban building models","authors":"Changbin Wu, Xinyang Yu, Can Ma, Rongkai Zhong, Xinxin Zhou","doi":"10.1111/tgis.13192","DOIUrl":"https://doi.org/10.1111/tgis.13192","url":null,"abstract":"3D urban building modeling is a vital foundational step for building Digital Twins and Smart Cities. In response to existing challenges, such as high time costs, complex production processes, and low consistency with real‐world textures in large‐scale 3D urban building modeling methods, this research proposes a reconstructing 3D urban building models (3DUBM) approach that integrates geospatial data and street view. The approach achieves an enhanced generation of large‐scale 3DUBMs. Based on open geospatial data and street‐view imagery (SVI), the approach was tested in modeling experiments conducted in Shanghai, Hongkong, and Nanjing. Furthermore, a dataset covering unique blocks of 30 cities in China was constructed to demonstrate the approach's characteristics of large coverage, high time efficiency, high model quality and low economic cost. The accuracy of texture mapping from SVI to 3DUBM reached 85%. This achievement has significant economic value in bridging the gap in the production of large‐scale and low‐cost 3DUBM data, promoting the construction of Digital Twins, Smart Cities, and Real‐world 3D modeling.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387833","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}
Knowledge embedding for geographic knowledge graphs can effectively improve computational efficiency and provide support for knowledge reasoning, knowledge answering and other applications of knowledge graphs. To maintain a more comprehensive understanding of spatial features through knowledge embedding, it is crucial to integrate the representation and computation of various entity types, encompassing points, lines, and polygons. This article proposes a geographic entities uniformly explicit knowledge embedding model (GEUKE). In GEUKE, spatial data of point, line, and polygon‐type geographic entities are expressed in the form of subgraphs, and space embedding is generated using a SubGNN‐based uniform spatial feature encoder. GEUKE improves the energy function in TransE to train spatial feature‐based embedding and structural‐based embedding of geographic entities into a unified vector space. Experimental results show that GEUKE has higher performance than TransE, TransH, TransD, and TransE‐GDR on link prediction and triple classification task. Within the spatial feature embedding process, GEUKE effectively preserves the inherent features of entities, encompassing location, neighborhood, and structural attributes, while simultaneously ensuring a coherent spatial data representation across all three entity types: points, lines, and polygons. By maintaining the spatial features of geographic entities and their interrelations, this capability unleashes the full potential of applications such as knowledge reasoning and geospatial question answering in a manner that is conducive to diverse geospatial scenarios.
{"title":"GEUKE: A geographic entities uniformly explicit knowledge embedding model","authors":"Yongquan Yang, Dehui Kong, Min Cao, Min Chen","doi":"10.1111/tgis.13191","DOIUrl":"https://doi.org/10.1111/tgis.13191","url":null,"abstract":"Knowledge embedding for geographic knowledge graphs can effectively improve computational efficiency and provide support for knowledge reasoning, knowledge answering and other applications of knowledge graphs. To maintain a more comprehensive understanding of spatial features through knowledge embedding, it is crucial to integrate the representation and computation of various entity types, encompassing points, lines, and polygons. This article proposes a geographic entities uniformly explicit knowledge embedding model (GEUKE). In GEUKE, spatial data of point, line, and polygon‐type geographic entities are expressed in the form of subgraphs, and space embedding is generated using a SubGNN‐based uniform spatial feature encoder. GEUKE improves the energy function in TransE to train spatial feature‐based embedding and structural‐based embedding of geographic entities into a unified vector space. Experimental results show that GEUKE has higher performance than TransE, TransH, TransD, and TransE‐GDR on link prediction and triple classification task. Within the spatial feature embedding process, GEUKE effectively preserves the inherent features of entities, encompassing location, neighborhood, and structural attributes, while simultaneously ensuring a coherent spatial data representation across all three entity types: points, lines, and polygons. By maintaining the spatial features of geographic entities and their interrelations, this capability unleashes the full potential of applications such as knowledge reasoning and geospatial question answering in a manner that is conducive to diverse geospatial scenarios.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189144","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 delineation of two‐dimensional ascending and descending manifolds represents the theoretical basis for a large number of applications in which functions are used to describe phenomena related to climate, economy, or engineering, to mention only a few. Whereas the applications are related to the pits, passes, peaks, courses, ridges, basins, and hills, of mathematical interest are the corresponding critical points, separatrices as well as two‐dimensional ascending and descending manifolds. The present article demonstrates how the boundaries of the latter, which represent the pre‐images of basins and hills, can be characterized in a graph‐theoretic way. An algorithm for their extraction, which is based on a newly proved theorem, is presented together with its implementation in C#. Finally, the modus operandi of the algorithm is illustrated by two examples, thereby demonstrating how it works even in the case of surfaces with topologically complicated structures.
{"title":"Delineation of basins and hills by Morse theory and critical nets","authors":"Gert W. Wolf","doi":"10.1111/tgis.13161","DOIUrl":"https://doi.org/10.1111/tgis.13161","url":null,"abstract":"The delineation of two‐dimensional ascending and descending manifolds represents the theoretical basis for a large number of applications in which functions are used to describe phenomena related to climate, economy, or engineering, to mention only a few. Whereas the applications are related to the pits, passes, peaks, courses, ridges, basins, and hills, of mathematical interest are the corresponding critical points, separatrices as well as two‐dimensional ascending and descending manifolds. The present article demonstrates how the boundaries of the latter, which represent the pre‐images of basins and hills, can be characterized in a graph‐theoretic way. An algorithm for their extraction, which is based on a newly proved theorem, is presented together with its implementation in C#. Finally, the <jats:italic>modus operandi</jats:italic> of the algorithm is illustrated by two examples, thereby demonstrating how it works even in the case of surfaces with topologically complicated structures.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172801","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}
Methods for evaluating cognitively inspired geospatial interfaces have been important for revealing and helping solve their cognitive and usability issues. We argue that this is now true of interfaces in GIScience that deliver narrative visualizations, including 3D virtual narrative environments. These spaces allow for controlled conditions and realistic natural settings, where spatio‐temporal data can be collected and used to ascertain how well an interface design fulfilled a given narrative function. This study investigates the function of a cognitively inspired geospatial interface (Future Vision) that aimed to determine how mental images can be situated in geospatial environments and used to convey narratives that improve user cognition and decision‐making. The results of a two‐alternative forced‐choice (2AFC) decision‐making task showed that participants using future thinking guidance (mental images as a split‐second display of correct path choice) had statistically significant improvements in their task completion times, movement speeds and 2AFC decision‐making, compared to the unguided control group. Implications of the results include benefits for cue‐based navigation of real and conceptual spaces in GIScience. Future research can improve the interface design by modifying the interface code to reduce visual loss caused by eye blinks and saccades.
{"title":"Incorporating mental imagery into geospatial environments for narrative visualizations","authors":"Ronny A. Rowe, Antoni B. Moore","doi":"10.1111/tgis.13187","DOIUrl":"https://doi.org/10.1111/tgis.13187","url":null,"abstract":"Methods for evaluating cognitively inspired geospatial interfaces have been important for revealing and helping solve their cognitive and usability issues. We argue that this is now true of interfaces in GIScience that deliver narrative visualizations, including 3D virtual narrative environments. These spaces allow for controlled conditions and realistic natural settings, where spatio‐temporal data can be collected and used to ascertain how well an interface design fulfilled a given narrative function. This study investigates the function of a cognitively inspired geospatial interface (Future Vision) that aimed to determine how mental images can be situated in geospatial environments and used to convey narratives that improve user cognition and decision‐making. The results of a two‐alternative forced‐choice (2AFC) decision‐making task showed that participants using future thinking guidance (mental images as a split‐second display of correct path choice) had statistically significant improvements in their task completion times, movement speeds and 2AFC decision‐making, compared to the unguided control group. Implications of the results include benefits for cue‐based navigation of real and conceptual spaces in GIScience. Future research can improve the interface design by modifying the interface code to reduce visual loss caused by eye blinks and saccades.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188910","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}
Viewshed analysis is an important research content of digital terrain analysis. The terrain viewshed refers to the range that can be seen at the current position, which varies with the nature of the observer. When the observer is a wireless signal tower, it is the communication viewshed, which refers to the area consisted of grid cells where the receiving antennas can receive the signals from a transmitting antenna set up on a grid cell of terrain. The core of base station location problem includes two aspects: combinatorial optimization and the calculation of the coverage rate of signal. The calculation of communication viewshed is an important research content for determining signal coverage range. In this article, we propose an accurate communication viewshed computation algorithm for wireless signal (CVCWS) using the projection curve of 3D Fresnel zone analysis based on DEM. The CVCWS method can calculate the signal reception quality at different locations more precisely. Besides, a signal attenuation model is proposed to compute the theoretical attenuation value according to the signal receiving quality index. The proposed algorithm is compared with the existing DEM‐based communication viewshed algorithms, and the theoretical attenuation values are compared with the measured values. The experimental results show that the theoretical values gained by the CVCWS algorithm are close to the measured values, indicating high accuracy of the CVCWS algorithm. The proposed method can provide theoretical support for communication tower location planning and other related applications.
视角分析是数字地形分析的一项重要研究内容。地形视角是指在当前位置所能看到的范围，因观测者的性质而异。当观测者是无线信号塔时，它就是通信视域，指的是由网格单元组成的区域，在该区域内，接收天线可以接收到设置在地形网格单元上的发射天线发出的信号。基站选址问题的核心包括两个方面：组合优化和信号覆盖率计算。通信视角的计算是确定信号覆盖范围的重要研究内容。本文利用基于 DEM 的三维菲涅尔区分析投影曲线，提出了一种精确的无线信号通信视角计算算法（CVCWS）。CVCWS 方法能更精确地计算不同地点的信号接收质量。此外，还提出了一个信号衰减模型，根据信号接收质量指标计算理论衰减值。将提出的算法与现有的基于 DEM 的通信视角算法进行比较，并将理论衰减值与测量值进行比较。实验结果表明，CVCWS 算法获得的理论值与实测值接近，表明 CVCWS 算法具有较高的精度。所提出的方法可为通信塔选址规划和其他相关应用提供理论支持。
{"title":"Accurate calculation method of terrain viewshed for wireless signal as line of sight","authors":"Yiwen Wang, Wanfeng Dou","doi":"10.1111/tgis.13188","DOIUrl":"https://doi.org/10.1111/tgis.13188","url":null,"abstract":"Viewshed analysis is an important research content of digital terrain analysis. The terrain viewshed refers to the range that can be seen at the current position, which varies with the nature of the observer. When the observer is a wireless signal tower, it is the communication viewshed, which refers to the area consisted of grid cells where the receiving antennas can receive the signals from a transmitting antenna set up on a grid cell of terrain. The core of base station location problem includes two aspects: combinatorial optimization and the calculation of the coverage rate of signal. The calculation of communication viewshed is an important research content for determining signal coverage range. In this article, we propose an accurate communication viewshed computation algorithm for wireless signal (CVCWS) using the projection curve of 3D Fresnel zone analysis based on DEM. The CVCWS method can calculate the signal reception quality at different locations more precisely. Besides, a signal attenuation model is proposed to compute the theoretical attenuation value according to the signal receiving quality index. The proposed algorithm is compared with the existing DEM‐based communication viewshed algorithms, and the theoretical attenuation values are compared with the measured values. The experimental results show that the theoretical values gained by the CVCWS algorithm are close to the measured values, indicating high accuracy of the CVCWS algorithm. The proposed method can provide theoretical support for communication tower location planning and other related applications.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119424","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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