Pub Date : 2023-10-26DOI: 10.1080/15230406.2023.2267419
Azelle Courtial, Guillaume Touya, Xiang Zhang
ABSTRACTThe automation of map generalization has been an important research subject for decades but is not fully solved yet. Deep learning techniques are designed for various image generation tasks, so one may think that it would be possible to apply these techniques to cartography and train a holistic model for end-to-end map generalization. On the contrary, we assume that map generalization is a task too complex to be learnt with a unique model. Thus, in this article, we propose to resort to past research on map generalization and to separate map generalization into simpler sub-tasks, each of which can be more easily resolved by a deep neural network. Our main contribution is a workflow of deep models, called DeepMapScaler, which achieves a step-by-step topographic map generalization from detailed topographic data. First, we implement this workflow to generalize topographic maps containing roads, buildings, and rivers at a medium scale (1:50k) from a detailed dataset. The results of each step are quantitatively and visually evaluated. Then the generalized images are compared with the generalization performed using a holistic model for an end-to-end map generalization and a traditional semi-automatic map generalization process. The experiment shows that the workflow approach is more promising than the holistic model, as each sub-task is specialized and fine-tuned accordingly. However, the results still do not reach the quality level of the semi-automatic traditional map generalization process, as some sub-tasks are more complex to handle with neural networks.KEYWORDS: Map generalizationgenerative adversarial networkdeep learningworkflowcartography Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available at the link https://doi.org/10.5281/zenodo.7957430.
{"title":"DeepMapScaler: a workflow of deep neural networks for the generation of generalised maps","authors":"Azelle Courtial, Guillaume Touya, Xiang Zhang","doi":"10.1080/15230406.2023.2267419","DOIUrl":"https://doi.org/10.1080/15230406.2023.2267419","url":null,"abstract":"ABSTRACTThe automation of map generalization has been an important research subject for decades but is not fully solved yet. Deep learning techniques are designed for various image generation tasks, so one may think that it would be possible to apply these techniques to cartography and train a holistic model for end-to-end map generalization. On the contrary, we assume that map generalization is a task too complex to be learnt with a unique model. Thus, in this article, we propose to resort to past research on map generalization and to separate map generalization into simpler sub-tasks, each of which can be more easily resolved by a deep neural network. Our main contribution is a workflow of deep models, called DeepMapScaler, which achieves a step-by-step topographic map generalization from detailed topographic data. First, we implement this workflow to generalize topographic maps containing roads, buildings, and rivers at a medium scale (1:50k) from a detailed dataset. The results of each step are quantitatively and visually evaluated. Then the generalized images are compared with the generalization performed using a holistic model for an end-to-end map generalization and a traditional semi-automatic map generalization process. The experiment shows that the workflow approach is more promising than the holistic model, as each sub-task is specialized and fine-tuned accordingly. However, the results still do not reach the quality level of the semi-automatic traditional map generalization process, as some sub-tasks are more complex to handle with neural networks.KEYWORDS: Map generalizationgenerative adversarial networkdeep learningworkflowcartography Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available at the link https://doi.org/10.5281/zenodo.7957430.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"48 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136382157","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}
Pub Date : 2023-10-17DOI: 10.1080/15230406.2023.2264749
Yu Lan, Eric Delmelle
The COVID-19 pandemic has had a profound impact worldwide and continues to spread due to various mutations of the virus. Many governmental and nonprofit agencies at different levels have quickly developed COVID-19 dashboards to disseminate information on the pandemic to the public. However, most of these systems have mainly distributed “plain” information (e.g. cases, death counts, vaccination), and rarely provided insights that can be gained from spatiotemporal analyses, such as the detection of emerging clusters. The results from these analyses hold tremendous potential for health policymakers as they try to identify ways to slow down transmission. We present a web-based geographic framework to detect and visualize space-time clusters of COVID-19. Our tightly coupled framework integrates the prospective space-time scan statistics and local indicators of spatial association (LISA) with novel 2D and 3D interactive visuals in a cyber environment (http://159.223.164.41/app/). We illustrate the applicability of our approach using COVID-19 data for the continental US. Our framework is portable to other regions that may experience infectious diseases but is also flexible to handle data of different spatial and temporal granularities. This paper fits within an effort to integrate space-time analytics for the monitoring of infectious diseases in web environment, ultimately improving health surveillance systems.
{"title":"A web-based analytical framework for the detection and visualization space-time clusters of COVID-19","authors":"Yu Lan, Eric Delmelle","doi":"10.1080/15230406.2023.2264749","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264749","url":null,"abstract":"The COVID-19 pandemic has had a profound impact worldwide and continues to spread due to various mutations of the virus. Many governmental and nonprofit agencies at different levels have quickly developed COVID-19 dashboards to disseminate information on the pandemic to the public. However, most of these systems have mainly distributed “plain” information (e.g. cases, death counts, vaccination), and rarely provided insights that can be gained from spatiotemporal analyses, such as the detection of emerging clusters. The results from these analyses hold tremendous potential for health policymakers as they try to identify ways to slow down transmission. We present a web-based geographic framework to detect and visualize space-time clusters of COVID-19. Our tightly coupled framework integrates the prospective space-time scan statistics and local indicators of spatial association (LISA) with novel 2D and 3D interactive visuals in a cyber environment (http://159.223.164.41/app/). We illustrate the applicability of our approach using COVID-19 data for the continental US. Our framework is portable to other regions that may experience infectious diseases but is also flexible to handle data of different spatial and temporal granularities. This paper fits within an effort to integrate space-time analytics for the monitoring of infectious diseases in web environment, ultimately improving health surveillance systems.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135994154","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}
Pub Date : 2023-10-17DOI: 10.1080/15230406.2023.2264750
Jacob Kruse, Song Gao, Yuhan Ji, Daniel P. Szabo, Kenneth R. Mayer
ABSTRACTRedistricting is the process by which electoral district boundaries are drawn so as to capture coherent communities of interest (COIs). While states rely on various proxies for community illustration, such as compactness and municipal split counts, to guide redistricting, recent legal challenges and scholarly works have shown the difficulty of balancing multiple criteria in district plan creation. To address these issues, we propose the use of spatial interaction to directly quantify the degree to which districts capture the underlying COIs. Using large-scale human mobility flow data, we condense spatial interaction community capture for a set of districts into a single number, the interaction ratio (IR), for redistricting plan evaluation. To compare the IR to traditional redistricting criteria (compactness and fairness), we employ a Markov chain-based regionalization algorithm (ReCom) to produce ensembles of valid plans and calculate the degree to which they capture spatial interaction communities. Furthermore, we propose two methods for biasing the ReCom algorithm towards different IR values. We perform a multi-criteria assessment of the space of valid maps, and present the results in an interactive web map. The experiments on Wisconsin congressional districting plans demonstrate the effectiveness of our methods for biasing sampling towards higher or lower IR values. Furthermore, the analysis of the districts produced with these methods suggests that districts with higher IR and compactness values tend to produce district plans that are more proportional with regard to seats allocated to each of the two major parties.KEYWORDS: Redistrictingregionalizationmobilityinteractive mapspatial interaction AcknowledgmentsWe would like to thank Gareth Baldrica-Franklin and Professor Robert Roth for their help and guidance in the development of the web map. We would also like to thank Professor Jin-Yi Cai for sharing his expertise on modifying the ensemble distribution in algorithmic design.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe mobility flow dataset used in this research is publicly available on GitHub: https://github.com/GeoDS/COVID19USFlows and from SafeGraph. The other aggregated data that support the findings of this study are available from the U.S. census bureau. Due to the privacy protection policies of the data providers, the voting data used here are not publicly available.Additional informationFundingThis project is supported by the University of Wisconsin 2020 WARF Discovery Initiative funded project: Multidisciplinary Approach for Redistricting Knowledge. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funder(s).
{"title":"Bringing spatial interaction measures into multi-criteria assessment of redistricting plans using interactive web mapping","authors":"Jacob Kruse, Song Gao, Yuhan Ji, Daniel P. Szabo, Kenneth R. Mayer","doi":"10.1080/15230406.2023.2264750","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264750","url":null,"abstract":"ABSTRACTRedistricting is the process by which electoral district boundaries are drawn so as to capture coherent communities of interest (COIs). While states rely on various proxies for community illustration, such as compactness and municipal split counts, to guide redistricting, recent legal challenges and scholarly works have shown the difficulty of balancing multiple criteria in district plan creation. To address these issues, we propose the use of spatial interaction to directly quantify the degree to which districts capture the underlying COIs. Using large-scale human mobility flow data, we condense spatial interaction community capture for a set of districts into a single number, the interaction ratio (IR), for redistricting plan evaluation. To compare the IR to traditional redistricting criteria (compactness and fairness), we employ a Markov chain-based regionalization algorithm (ReCom) to produce ensembles of valid plans and calculate the degree to which they capture spatial interaction communities. Furthermore, we propose two methods for biasing the ReCom algorithm towards different IR values. We perform a multi-criteria assessment of the space of valid maps, and present the results in an interactive web map. The experiments on Wisconsin congressional districting plans demonstrate the effectiveness of our methods for biasing sampling towards higher or lower IR values. Furthermore, the analysis of the districts produced with these methods suggests that districts with higher IR and compactness values tend to produce district plans that are more proportional with regard to seats allocated to each of the two major parties.KEYWORDS: Redistrictingregionalizationmobilityinteractive mapspatial interaction AcknowledgmentsWe would like to thank Gareth Baldrica-Franklin and Professor Robert Roth for their help and guidance in the development of the web map. We would also like to thank Professor Jin-Yi Cai for sharing his expertise on modifying the ensemble distribution in algorithmic design.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe mobility flow dataset used in this research is publicly available on GitHub: https://github.com/GeoDS/COVID19USFlows and from SafeGraph. The other aggregated data that support the findings of this study are available from the U.S. census bureau. Due to the privacy protection policies of the data providers, the voting data used here are not publicly available.Additional informationFundingThis project is supported by the University of Wisconsin 2020 WARF Discovery Initiative funded project: Multidisciplinary Approach for Redistricting Knowledge. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funder(s).","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136033097","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}
Pub Date : 2023-10-17DOI: 10.1080/15230406.2023.2264748
Ming-Hsiang Tsou, Christian Mejia
ABSTRACTDr. Buttenfield led the user evaluation and user interface design/improvement tasks in the Alexandra Digital Library (ADL) Project from 1994 to 1998 funded by the National Science Foundation in the U.S. Her research efforts in the ADL extended the role of cartographers from traditional map designers to web-based user interface and user experiences (UI/UX) designers. This paper will highlight key concepts and design principles (including user center design (UCD), usability testing, and user interface evaluation) in the ADL project and re-investigate these pioneering research topics in cartography with recent development of the Metaverse and human-computer interaction (HCI) using Extended Reality (XR), including Virtual Reality, (VR), Augmented Reality (AR), and Mixed Reality (MR). The new role of cartographers today includes user interface designers and usability evaluators using XR-enabled mapping tools and technologies. This paper will discuss cartographic research challenges and opportunities in the era of the Metaverse and highlight some software issues for cartographers to develop 3D or 4D web GIS applications.KEYWORDS: User interface designextended realityvirtual realityaugmented realitythe Metaverse AcknowledgmentsThe Virtual Reality research component is partially funded by the Safety through Disruption (Safe-D) National University Transportation Center, a grant from the U.S. Department of Transportation. The authors thank for insightful comments and suggestions from anonymous reviewers.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article as no new data were created or analyzed in this study.Additional informationFundingThis work was supported by the National University Transportation Center (UTC) [Safe-D: Evaluating the Safe Routes to School (SR2S)].
ABSTRACTDr。从1994年到1998年，Buttenfield领导了由美国国家科学基金会资助的亚历山德拉数字图书馆(ADL)项目的用户评估和用户界面设计/改进任务。她在ADL中的研究工作将制图师的角色从传统的地图设计师扩展到基于web的用户界面和用户体验(UI/UX)设计师。本文将重点介绍ADL项目中的关键概念和设计原则(包括用户中心设计(UCD)、可用性测试和用户界面评估)，并结合最近使用扩展现实(XR)(包括虚拟现实(VR)、增强现实(AR)和混合现实(MR))的虚拟世界和人机交互(HCI)的发展，重新研究这些开创性的制图研究课题。如今，制图师的新角色包括使用支持xr的制图工具和技术的用户界面设计师和可用性评估员。本文将讨论元宇宙时代的地图研究挑战和机遇，并重点介绍制图师开发3D或4D web GIS应用程序的一些软件问题。关键字:用户界面设计、扩展现实、虚拟现实、增强现实、虚拟世界致谢虚拟现实研究部分由美国交通部授予的安全中断(Safe-D)国立大学交通中心资助。作者感谢来自匿名审稿人的有见地的评论和建议。披露声明作者未报告潜在的利益冲突。数据可用性声明数据共享不适用于本文，因为本研究没有创建或分析新的数据。本研究得到了国立大学交通中心(UTC) [Safe- d:评估安全的上学路线(SR2S)]的支持。
{"title":"Beyond mapping: extend the role of cartographers to user interface designers in the Metaverse using virtual reality, augmented reality, and mixed reality","authors":"Ming-Hsiang Tsou, Christian Mejia","doi":"10.1080/15230406.2023.2264748","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264748","url":null,"abstract":"ABSTRACTDr. Buttenfield led the user evaluation and user interface design/improvement tasks in the Alexandra Digital Library (ADL) Project from 1994 to 1998 funded by the National Science Foundation in the U.S. Her research efforts in the ADL extended the role of cartographers from traditional map designers to web-based user interface and user experiences (UI/UX) designers. This paper will highlight key concepts and design principles (including user center design (UCD), usability testing, and user interface evaluation) in the ADL project and re-investigate these pioneering research topics in cartography with recent development of the Metaverse and human-computer interaction (HCI) using Extended Reality (XR), including Virtual Reality, (VR), Augmented Reality (AR), and Mixed Reality (MR). The new role of cartographers today includes user interface designers and usability evaluators using XR-enabled mapping tools and technologies. This paper will discuss cartographic research challenges and opportunities in the era of the Metaverse and highlight some software issues for cartographers to develop 3D or 4D web GIS applications.KEYWORDS: User interface designextended realityvirtual realityaugmented realitythe Metaverse AcknowledgmentsThe Virtual Reality research component is partially funded by the Safety through Disruption (Safe-D) National University Transportation Center, a grant from the U.S. Department of Transportation. The authors thank for insightful comments and suggestions from anonymous reviewers.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article as no new data were created or analyzed in this study.Additional informationFundingThis work was supported by the National University Transportation Center (UTC) [Safe-D: Evaluating the Safe Routes to School (SR2S)].","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135995899","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}
Pub Date : 2023-10-17DOI: 10.1080/15230406.2023.2264759
Tomasz Opach, Jan Ketil Rød, Bjørn Erik Munkvold
Emergency events such as floods and wildfires are handled by various responders and at various levels: strategic, tactical, and operational. To facilitate situational awareness, emergency responders require customized map-based decision support systems that are tailored to specific needs depending on the responders’ organizational affiliation, role, objectives, and occupationally specific knowledge. As a result, the systems are equipped with manifold map functions. However, the diversity of map-based emergency tools in use impedes gaining common user skills among their target audiences and thus, requires a systematic overview. Through a multistep research process, this study was to: investigate the requirements for support from map-based tools expressed by various emergency responders in Norway, identify desired map functions, and categorize those functions to facilitate an overview. Six stages constituted our workflow: meetings with Norwegian emergency responders, survey on selected map-based tools, interviews with designers and users of tools, a table-top exercise, theoretical considerations, and validation with stakeholders. This study contributes to the state of the art by systematizing and structuring knowledge about map functions that facilitate situational awareness. In turn, it helps developing and optimizing functionality of map-based tools depending on needs of specific emergency responders.
{"title":"Map functions to facilitate situational awareness during emergency events","authors":"Tomasz Opach, Jan Ketil Rød, Bjørn Erik Munkvold","doi":"10.1080/15230406.2023.2264759","DOIUrl":"https://doi.org/10.1080/15230406.2023.2264759","url":null,"abstract":"Emergency events such as floods and wildfires are handled by various responders and at various levels: strategic, tactical, and operational. To facilitate situational awareness, emergency responders require customized map-based decision support systems that are tailored to specific needs depending on the responders’ organizational affiliation, role, objectives, and occupationally specific knowledge. As a result, the systems are equipped with manifold map functions. However, the diversity of map-based emergency tools in use impedes gaining common user skills among their target audiences and thus, requires a systematic overview. Through a multistep research process, this study was to: investigate the requirements for support from map-based tools expressed by various emergency responders in Norway, identify desired map functions, and categorize those functions to facilitate an overview. Six stages constituted our workflow: meetings with Norwegian emergency responders, survey on selected map-based tools, interviews with designers and users of tools, a table-top exercise, theoretical considerations, and validation with stakeholders. This study contributes to the state of the art by systematizing and structuring knowledge about map functions that facilitate situational awareness. In turn, it helps developing and optimizing functionality of map-based tools depending on needs of specific emergency responders.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136032938","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}
Pub Date : 2023-07-20DOI: 10.1080/15230406.2023.2224584
E. Tomai, M. Kokla, Christos Charcharos, Katerina Pastra, F. Liarokapis, Maria Bezerianou, K. Cheliotis, Athanasia Darra, M. Kavouras
{"title":"Investigating the relationship between spatial skills and web mapping application performance among university students","authors":"E. Tomai, M. Kokla, Christos Charcharos, Katerina Pastra, F. Liarokapis, Maria Bezerianou, K. Cheliotis, Athanasia Darra, M. Kavouras","doi":"10.1080/15230406.2023.2224584","DOIUrl":"https://doi.org/10.1080/15230406.2023.2224584","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47290182","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}
Pub Date : 2023-06-22DOI: 10.1080/15230406.2023.2221454
Yucheng Shu, Zihao Tang, S. Yue, Y. Wen, Min Chen
ABSTRACT The increasing demands of presenting large numbers of points in maps have promoted the progress of rendering point symbols in GPUs. Although the drawing efficiency issue can be handled with texture mapping methods, the rendering quality problem due to the fixed resolution that affects map renders’ visual experiences remains. The method of directly drawing vector paths of a point symbol can be used to satisfy the sharper effect of point symbols. However, it requires high memory cost and affects the drawing efficiency. This paper proposes a point symbol rendering method using the idea of reinterpreted textures. The rendering data used in this method are based on vectors to achieve refined results. Vector properties of symbols are encoded and organized into the texture structure with specific layout schemes. In the rendering phase, an instanced pipeline is launched to accept the texture and decode the required attributes. The proposed method takes advantage of fast access and continuity of textures while retaining geometric transformations. These features allow all symbols to be drawn in one single draw call and rotated or scaled arbitrarily. Experiments on drawing quality and efficiency demonstrate that the proposed method achieves fast and stable performance while maintaining the rendering quality.
{"title":"A reinterpreted-texture strategy for rendering point symbols based on graphics processing unit","authors":"Yucheng Shu, Zihao Tang, S. Yue, Y. Wen, Min Chen","doi":"10.1080/15230406.2023.2221454","DOIUrl":"https://doi.org/10.1080/15230406.2023.2221454","url":null,"abstract":"ABSTRACT The increasing demands of presenting large numbers of points in maps have promoted the progress of rendering point symbols in GPUs. Although the drawing efficiency issue can be handled with texture mapping methods, the rendering quality problem due to the fixed resolution that affects map renders’ visual experiences remains. The method of directly drawing vector paths of a point symbol can be used to satisfy the sharper effect of point symbols. However, it requires high memory cost and affects the drawing efficiency. This paper proposes a point symbol rendering method using the idea of reinterpreted textures. The rendering data used in this method are based on vectors to achieve refined results. Vector properties of symbols are encoded and organized into the texture structure with specific layout schemes. In the rendering phase, an instanced pipeline is launched to accept the texture and decode the required attributes. The proposed method takes advantage of fast access and continuity of textures while retaining geometric transformations. These features allow all symbols to be drawn in one single draw call and rotated or scaled arbitrarily. Experiments on drawing quality and efficiency demonstrate that the proposed method achieves fast and stable performance while maintaining the rendering quality.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"403 - 420"},"PeriodicalIF":2.5,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48339862","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}
Pub Date : 2023-06-20DOI: 10.1080/15230406.2023.2213446
Sven Gedicke, Lukas Arzoumanidis, J. Haunert
ABSTRACT In this article, we address the time-critical work of emergency services in the field of disaster and emergency response. Aiming at saving valuable human and time resources during emergency operations, we present one exact and one heuristic approach for the automatic placement of tactical symbols in situation maps. Such maps are used to establish situational awareness and to convey mission-relevant information to emergency personnel. Usually, the information is communicated through the visualization of descriptive symbols which are predominantly placed in a manual process. We automate this process based on an established map layout used by emergency services in Germany that distributes the symbols to the map boundaries. Following general principles and observations from existing literature, we formalize the symbol placement as an optimization problem. We take into account the relevance of tactical symbols as well as short and crossing-free leaders and allow the grouped representation of symbols of similar semantics and spatially close map locations. In experiments with real-world data, we determine a balance between the optimization criteria and show that our heuristic generates high-quality results in less than a second. In an assessment by an expert, we get confirmation that our maps are suitable for use in emergency scenarios.
{"title":"Automating the external placement of symbols for point features in situation maps for emergency response","authors":"Sven Gedicke, Lukas Arzoumanidis, J. Haunert","doi":"10.1080/15230406.2023.2213446","DOIUrl":"https://doi.org/10.1080/15230406.2023.2213446","url":null,"abstract":"ABSTRACT In this article, we address the time-critical work of emergency services in the field of disaster and emergency response. Aiming at saving valuable human and time resources during emergency operations, we present one exact and one heuristic approach for the automatic placement of tactical symbols in situation maps. Such maps are used to establish situational awareness and to convey mission-relevant information to emergency personnel. Usually, the information is communicated through the visualization of descriptive symbols which are predominantly placed in a manual process. We automate this process based on an established map layout used by emergency services in Germany that distributes the symbols to the map boundaries. Following general principles and observations from existing literature, we formalize the symbol placement as an optimization problem. We take into account the relevance of tactical symbols as well as short and crossing-free leaders and allow the grouped representation of symbols of similar semantics and spatially close map locations. In experiments with real-world data, we determine a balance between the optimization criteria and show that our heuristic generates high-quality results in less than a second. In an assessment by an expert, we get confirmation that our maps are suitable for use in emergency scenarios.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"385 - 402"},"PeriodicalIF":2.5,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48938760","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}
Pub Date : 2023-06-13DOI: 10.1080/15230406.2023.2218106
Xiongfeng Yan, Min Yang
{"title":"A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints","authors":"Xiongfeng Yan, Min Yang","doi":"10.1080/15230406.2023.2218106","DOIUrl":"https://doi.org/10.1080/15230406.2023.2218106","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48844398","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}
Pub Date : 2023-06-13DOI: 10.1080/15230406.2023.2215449
Taisheng Chen, Weixing Jiang, Menglin Chen, Kun Hu, Xi Lv, Xiaomin Mu
ABSTRACT Hundreds of millions of people suffer from color vision deficiency, leading to confusion in the perception of maps. Barrier-free colors can reduce confusion and improve the readability of maps. However, most of these colors are manually designed by experts based on extensive experience. For most mapmakers, especially novices, creating barrier-free map colors is a challenge. In this paper, we focus on qualitative schemes, a color type that easily causes confusion for people with color vision deficiency, and propose an approach to automatically generate barrier-free colors. The proposed approach consists of two steps: 1) extracting the factors of barrier-free qualitative schemes, including color vision deficiency factors and cartographic rule factors, and characterizing them, and 2) building an optimization model using these factors to generate barrier-free qualitative schemes. The approach was tested with two experimental maps: a metro map for public use and a special-use land cover map. Twenty-two students with color vision deficiency were invited to read these maps and complete tasks. The results suggested that the map features using the generated barrier-free schemes were easy to distinguish for people with color vision deficiency. In addition, we recruited twenty-eight students with normal color vision to read the maps, and the results suggested that the generated schemes are effective for people with normal color vision as well.
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