Pub Date : 2023-01-23DOI: 10.1080/15230406.2022.2157879
Crystal J. Bae, S. Dodge
ABSTRACT This paper evaluates cognitively plausible geovisualization techniques for mapping movement data. With the widespread increase in the availability and quality of space-time data capturing movement trajectories of individuals, meaningful representations are needed to properly visualize and communicate trajectory data and complex movement patterns using geographic displays. Many visualization and visual analytics approaches have been proposed to map movement trajectories (e.g. space-time paths, animations, trajectory lines, etc.). However, little is known about how effective these complex visualizations are in capturing important aspects of movement data. Given the complexity of movement data which involves space, time, and context dimensions, it is essential to evaluate the communicative efficiency and efficacy of various visualization forms in helping people understand movement data. This study assesses the effectiveness of static and dynamic movement displays as well as visual variables in communicating movement parameters along trajectories, such as speed and direction. To do so, a web-based survey is conducted to evaluate the understanding of movement visualizations by a nonspecialist audience. This and future studies contribute fundamental insights into the cognition of movement visualizations and inspire new methods for the empirical evaluation of geovisualizations.
{"title":"Assessing the cognition of movement trajectory visualizations: interpreting speed and direction","authors":"Crystal J. Bae, S. Dodge","doi":"10.1080/15230406.2022.2157879","DOIUrl":"https://doi.org/10.1080/15230406.2022.2157879","url":null,"abstract":"ABSTRACT This paper evaluates cognitively plausible geovisualization techniques for mapping movement data. With the widespread increase in the availability and quality of space-time data capturing movement trajectories of individuals, meaningful representations are needed to properly visualize and communicate trajectory data and complex movement patterns using geographic displays. Many visualization and visual analytics approaches have been proposed to map movement trajectories (e.g. space-time paths, animations, trajectory lines, etc.). However, little is known about how effective these complex visualizations are in capturing important aspects of movement data. Given the complexity of movement data which involves space, time, and context dimensions, it is essential to evaluate the communicative efficiency and efficacy of various visualization forms in helping people understand movement data. This study assesses the effectiveness of static and dynamic movement displays as well as visual variables in communicating movement parameters along trajectories, such as speed and direction. To do so, a web-based survey is conducted to evaluate the understanding of movement visualizations by a nonspecialist audience. This and future studies contribute fundamental insights into the cognition of movement visualizations and inspire new methods for the empirical evaluation of geovisualizations.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"143 - 161"},"PeriodicalIF":2.5,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44691458","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 : 2023-01-10DOI: 10.1080/15230406.2022.2152098
N. Yang, Guojia Wu, A. MacEachren, Xujing Pang, Hao Fang
ABSTRACT Tag weight differences in tag maps are usually reflected by different font sizes. With this strategy, low weighted tags may be ignored and tag sizes may be misjudged due to differing word length, character height and word width. To address these shortcomings, this paper improves the layout method of tag maps by presetting anchor points. We construct weight contours based on corner points and center points of tag outer envelope rectangles and adopt hypsometric tinting as background color to reflect tag weight differences. Finally, we compare and analyze the background color strategy with the font size strategy from the perspectives of tag selection, tag recognition, tag recall, confidence, readability, and preference through eye movement experiments and questionnaire surveys. The results show that both strategies exhibit advantages (when tags are devoid of semantics), with the font size strategy being favored slightly in this abstract case. We provide tag map designers with a new visualization scheme for the expression of tag weight.
{"title":"Comparison of font size and background color strategies for tag weights on tag maps","authors":"N. Yang, Guojia Wu, A. MacEachren, Xujing Pang, Hao Fang","doi":"10.1080/15230406.2022.2152098","DOIUrl":"https://doi.org/10.1080/15230406.2022.2152098","url":null,"abstract":"ABSTRACT Tag weight differences in tag maps are usually reflected by different font sizes. With this strategy, low weighted tags may be ignored and tag sizes may be misjudged due to differing word length, character height and word width. To address these shortcomings, this paper improves the layout method of tag maps by presetting anchor points. We construct weight contours based on corner points and center points of tag outer envelope rectangles and adopt hypsometric tinting as background color to reflect tag weight differences. Finally, we compare and analyze the background color strategy with the font size strategy from the perspectives of tag selection, tag recognition, tag recall, confidence, readability, and preference through eye movement experiments and questionnaire surveys. The results show that both strategies exhibit advantages (when tags are devoid of semantics), with the font size strategy being favored slightly in this abstract case. We provide tag map designers with a new visualization scheme for the expression of tag weight.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"162 - 177"},"PeriodicalIF":2.5,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45045563","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 : 2023-01-02DOI: 10.1080/15230406.2022.2154271
Qiaosong Hei, Weihua Dong, Bowen Shi
ABSTRACT Visual attention detection, as an important concept for human visual behavior research, has been widely studied. However, previous studies seldom considered the feature integration mechanism to detect visual attention and rarely considered the differences due to different geographical scenes. In this paper, we use an augmented reality aided (AR-aided) navigation experimental dataset to study human visual behavior in a dynamic AR-aided environment. Then, we propose a multi-feature integration fully convolutional network (M-FCN) based on a self-adaptive environment weight (SEW) to integrate RGB-D, semantic, optical flow and spatial neighborhood features to detect human visual attention. The result shows that the M-FCN performs better than other state-of-the-art saliency models. In addition, the introduction of feature integration mechanism and the SEW can improve the accuracy and robustness of visual attention detection. Meanwhile, we find that RGB-D and semantic features perform best in different road routes and road types, but with the increase in road type complexity, the expressiveness of these two features weakens, and the expressiveness of optical flow and spatial neighborhood features increases. The research is helpful for AR-device navigation tool design and urban spatial planning.
{"title":"Detecting dynamic visual attention in augmented reality aided navigation environment based on a multi-feature integration fully convolutional network","authors":"Qiaosong Hei, Weihua Dong, Bowen Shi","doi":"10.1080/15230406.2022.2154271","DOIUrl":"https://doi.org/10.1080/15230406.2022.2154271","url":null,"abstract":"ABSTRACT Visual attention detection, as an important concept for human visual behavior research, has been widely studied. However, previous studies seldom considered the feature integration mechanism to detect visual attention and rarely considered the differences due to different geographical scenes. In this paper, we use an augmented reality aided (AR-aided) navigation experimental dataset to study human visual behavior in a dynamic AR-aided environment. Then, we propose a multi-feature integration fully convolutional network (M-FCN) based on a self-adaptive environment weight (SEW) to integrate RGB-D, semantic, optical flow and spatial neighborhood features to detect human visual attention. The result shows that the M-FCN performs better than other state-of-the-art saliency models. In addition, the introduction of feature integration mechanism and the SEW can improve the accuracy and robustness of visual attention detection. Meanwhile, we find that RGB-D and semantic features perform best in different road routes and road types, but with the increase in road type complexity, the expressiveness of these two features weakens, and the expressiveness of optical flow and spatial neighborhood features increases. The research is helpful for AR-device navigation tool design and urban spatial planning.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"63 - 78"},"PeriodicalIF":2.5,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45941614","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 : 2023-01-02DOI: 10.1080/15230406.2022.2156389
Kirsty Watkinson, Jonathan J. Huck, Angela Harris
ABSTRACT Volunteered geographic information (VGI) offers a solution to inequalities in authoritative map data that can limit our response to humanitarian crises. However, sustaining voluntary contributions of map data can be difficult and hybrid machine learning-VGI (ML-VGI) workflows developed to encourage sustained volunteer contributions have been demonstrated to be insufficient. Gamification can be used to encourage volunteers to map for longer, however evaluations of gamification to increase humanitarian mapping contributions are rare. Here we develop a gamified humanitarian ML-VGI mapping platform (“Map Safari”) and evaluate the use of game elements to encourage sustained volunteer contributions without reducing contribution quality. Our results suggest that gamification makes mapping more fun, particularly for first time mappers, without degrading map data quality. Competition is demonstrated to be important for encouraging enjoyment of game elements and increasing map data contributions. Future gamified mapping platforms should emphasize competition and ensure there are enough game elements to make platform use feel game-like. This research demonstrates that gamification can be used to encourage continued voluntary contributions of map data thereby increasing the amount of map data available to humanitarian organizations.
{"title":"Using gamification to increase map data production during humanitarian volunteered geographic information (VGI) campaigns","authors":"Kirsty Watkinson, Jonathan J. Huck, Angela Harris","doi":"10.1080/15230406.2022.2156389","DOIUrl":"https://doi.org/10.1080/15230406.2022.2156389","url":null,"abstract":"ABSTRACT Volunteered geographic information (VGI) offers a solution to inequalities in authoritative map data that can limit our response to humanitarian crises. However, sustaining voluntary contributions of map data can be difficult and hybrid machine learning-VGI (ML-VGI) workflows developed to encourage sustained volunteer contributions have been demonstrated to be insufficient. Gamification can be used to encourage volunteers to map for longer, however evaluations of gamification to increase humanitarian mapping contributions are rare. Here we develop a gamified humanitarian ML-VGI mapping platform (“Map Safari”) and evaluate the use of game elements to encourage sustained volunteer contributions without reducing contribution quality. Our results suggest that gamification makes mapping more fun, particularly for first time mappers, without degrading map data quality. Competition is demonstrated to be important for encouraging enjoyment of game elements and increasing map data contributions. Future gamified mapping platforms should emphasize competition and ensure there are enough game elements to make platform use feel game-like. This research demonstrates that gamification can be used to encourage continued voluntary contributions of map data thereby increasing the amount of map data available to humanitarian organizations.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"79 - 95"},"PeriodicalIF":2.5,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42796345","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}
ABSTRACT Eye movement is a new type of data for cartography and geographic information science (GIS) research. However, previous studies rarely built eye movement datasets with geospatial images. In this paper, we firstly proposed a geospatial image-based eye movement dataset called GeoEye, a publicly shared, widely available eye movement dataset. This dataset consists of 110 college-aged participants who freely viewed 500 images, including thematic maps, remote sensing images, and street view images. In addition, we used the dataset for geospatial image saliency prediction and map user identification. Results demonstrated the scientific benefits and applications of the proposed dataset. GeoEye dataset will not only promote the application of eye-tracking data in cartography and GIS research but also intelligence and customization of geographic information services.
{"title":"A geospatial image based eye movement dataset for cartography and GIS","authors":"Bing He, Weihua Dong, Hua Liao, Qi Ying, Bowen Shi, Jiping Liu, Yong Wang","doi":"10.1080/15230406.2022.2153172","DOIUrl":"https://doi.org/10.1080/15230406.2022.2153172","url":null,"abstract":"ABSTRACT Eye movement is a new type of data for cartography and geographic information science (GIS) research. However, previous studies rarely built eye movement datasets with geospatial images. In this paper, we firstly proposed a geospatial image-based eye movement dataset called GeoEye, a publicly shared, widely available eye movement dataset. This dataset consists of 110 college-aged participants who freely viewed 500 images, including thematic maps, remote sensing images, and street view images. In addition, we used the dataset for geospatial image saliency prediction and map user identification. Results demonstrated the scientific benefits and applications of the proposed dataset. GeoEye dataset will not only promote the application of eye-tracking data in cartography and GIS research but also intelligence and customization of geographic information services.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"96 - 111"},"PeriodicalIF":2.5,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47253599","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 : 2022-12-16DOI: 10.1080/15230406.2022.2142849
Jinjin Yan, S. Zlatanova, J. Lee
ABSTRACT Indoor navigation has been studied for many years, but it still has many limitations. In current navigation, pedestrians need to tell navigation systems the destination, because it is one of the preconditions for path planning. However, in some indoor cases, pedestrians cannot specify a destination because they have no information about where it is or even cannot be sure if there is a desired one. We believe that determining a service area is a possible way to handle such cases. For example, a service area that can be reached within a 2-min walk. In this paper, we propose an indoor service area determination approach for pedestrian navigation path planning. We demonstrate this approach in a shopping mall with multi-floors. The results show that it can successfully compute the reachable spaces and thereby helping people to select and arrive at the most appropriate destination. This approach is also useful for other indoor navigation applications within public buildings like offices, airports, theaters, hospitals, and museums where pedestrians would like to make a choice between multiple facilities of the same type, such as printers, registration desks, ATMs, AED, garbage bins, even exits.
{"title":"An indoor service area determination approach for pedestrian navigation path planning","authors":"Jinjin Yan, S. Zlatanova, J. Lee","doi":"10.1080/15230406.2022.2142849","DOIUrl":"https://doi.org/10.1080/15230406.2022.2142849","url":null,"abstract":"ABSTRACT Indoor navigation has been studied for many years, but it still has many limitations. In current navigation, pedestrians need to tell navigation systems the destination, because it is one of the preconditions for path planning. However, in some indoor cases, pedestrians cannot specify a destination because they have no information about where it is or even cannot be sure if there is a desired one. We believe that determining a service area is a possible way to handle such cases. For example, a service area that can be reached within a 2-min walk. In this paper, we propose an indoor service area determination approach for pedestrian navigation path planning. We demonstrate this approach in a shopping mall with multi-floors. The results show that it can successfully compute the reachable spaces and thereby helping people to select and arrive at the most appropriate destination. This approach is also useful for other indoor navigation applications within public buildings like offices, airports, theaters, hospitals, and museums where pedestrians would like to make a choice between multiple facilities of the same type, such as printers, registration desks, ATMs, AED, garbage bins, even exits.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"321 - 332"},"PeriodicalIF":2.5,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47152160","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}
ABSTRACT The hidden Markov model-based map matching algorithm (HMM-MM) is an effective method for online vehicle navigation and offline trajectory position correction. Common HMM-MMs are susceptible to the influence of adjacent road segment endpoints and similar parallel roads, because the multi-index probability model may ignore some indexes when the probability of other indexes is high. This makes the map-matching result not meet the assumption that vehicles always travel the shortest or optimal path, and it cannot guarantee that the trajectory points can match to the nearest position of the maximum likelihood road segment, resulting in poor accuracy. In this paper, an IHMM-MM is proposed. IHMM-MM (1) modifies the definition of transition probability and no longer takes the straight-line distance between trajectory points as the reference for the shortest path length between candidate point pairs. (2) supplements the definition of observation probability and introduces the point-line relation function to screen and group candidate points. (3) adds additional logic outside the HMM probability model to consider the trajectory connectivity and fill in the key trajectory points where the vehicles travel. Experiments show that the IHMM-MM can effectively improve the sampling frequency of trajectory data and has better performance in complex urban road environments.
{"title":"An improved hidden Markov model-based map matching algorithm considering candidate point grouping and trajectory connectivity","authors":"Bozhao Li, Zhongliang Cai, Mengjun Kang, Shiliang Su, Lili Jiang, Yong Ge, Yan-Liang Niu","doi":"10.1080/15230406.2022.2135023","DOIUrl":"https://doi.org/10.1080/15230406.2022.2135023","url":null,"abstract":"ABSTRACT The hidden Markov model-based map matching algorithm (HMM-MM) is an effective method for online vehicle navigation and offline trajectory position correction. Common HMM-MMs are susceptible to the influence of adjacent road segment endpoints and similar parallel roads, because the multi-index probability model may ignore some indexes when the probability of other indexes is high. This makes the map-matching result not meet the assumption that vehicles always travel the shortest or optimal path, and it cannot guarantee that the trajectory points can match to the nearest position of the maximum likelihood road segment, resulting in poor accuracy. In this paper, an IHMM-MM is proposed. IHMM-MM (1) modifies the definition of transition probability and no longer takes the straight-line distance between trajectory points as the reference for the shortest path length between candidate point pairs. (2) supplements the definition of observation probability and introduces the point-line relation function to screen and group candidate points. (3) adds additional logic outside the HMM probability model to consider the trajectory connectivity and fill in the key trajectory points where the vehicles travel. Experiments show that the IHMM-MM can effectively improve the sampling frequency of trajectory data and has better performance in complex urban road environments.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"351 - 370"},"PeriodicalIF":2.5,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47314086","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 : 2022-11-15DOI: 10.1080/15230406.2022.2127123
Jin Yan, Tiansheng Xu, Jing Gao, Ni Li, Guanghong Gong
ABSTRACT Map projections are imaging procedures used to depict geographic features. We adopt the traditional differential metric and exploit the intrinsic image properties of map projections to establish an image-based differential metric for evaluating distortions in map projections, obtaining an effective, practical, and relatively accurate metric. We use bivariate polynomial functions to approximate the forward and inverse formulae of map projections. Thereafter, the proposed metric is conveniently calculated using the partial derivatives of the approximate forward functions based on polynomial functions, while complicated differential calculations are avoided. Moreover, multiple sampling and image filters mitigate the influence of imaging noise and achieve a high computation precision. Experiments were conducted using the NASA G.Projector mapping software to generate images from more than 200 map projections. Explicit equations of map projections were not required owing to the use of the mapping software. These images were then evaluated using the proposed metric through an implementation in the Julia programming language. The corresponding results confirmed that the proposed metric avoided the drawbacks of the great circle arc metric and provided considerably low errors (1.12° on average) and high consistency (0.999 on average) with respect to the traditional differential metric. Although there were errors, experimental results indicated that feasibility and high usability were achieved by the image-based method for evaluating distortions in small-scale map projections.
{"title":"Image-based approximation of derivatives of traditional differential metrics of angular distortion in map projections","authors":"Jin Yan, Tiansheng Xu, Jing Gao, Ni Li, Guanghong Gong","doi":"10.1080/15230406.2022.2127123","DOIUrl":"https://doi.org/10.1080/15230406.2022.2127123","url":null,"abstract":"ABSTRACT Map projections are imaging procedures used to depict geographic features. We adopt the traditional differential metric and exploit the intrinsic image properties of map projections to establish an image-based differential metric for evaluating distortions in map projections, obtaining an effective, practical, and relatively accurate metric. We use bivariate polynomial functions to approximate the forward and inverse formulae of map projections. Thereafter, the proposed metric is conveniently calculated using the partial derivatives of the approximate forward functions based on polynomial functions, while complicated differential calculations are avoided. Moreover, multiple sampling and image filters mitigate the influence of imaging noise and achieve a high computation precision. Experiments were conducted using the NASA G.Projector mapping software to generate images from more than 200 map projections. Explicit equations of map projections were not required owing to the use of the mapping software. These images were then evaluated using the proposed metric through an implementation in the Julia programming language. The corresponding results confirmed that the proposed metric avoided the drawbacks of the great circle arc metric and provided considerably low errors (1.12° on average) and high consistency (0.999 on average) with respect to the traditional differential metric. Although there were errors, experimental results indicated that feasibility and high usability were achieved by the image-based method for evaluating distortions in small-scale map projections.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"44 - 62"},"PeriodicalIF":2.5,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41992387","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 : 2022-11-15DOI: 10.1080/15230406.2022.2125078
Yifan Zhang, Wenhao Yu
{"title":"Detecting common features from point patterns for similarity measurement using matrix decomposition","authors":"Yifan Zhang, Wenhao Yu","doi":"10.1080/15230406.2022.2125078","DOIUrl":"https://doi.org/10.1080/15230406.2022.2125078","url":null,"abstract":"","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48274550","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 : 2022-11-15DOI: 10.1080/15230406.2022.2125077
M. Galvão, J. Krukar, A. Schwering
ABSTRACT Car drivers can benefit from schematized maps because they require a different level and type of information from different areas of the map. The technical challenge of creating such maps is that a schematic car route map should be optimized for the individual route, and yet simultaneously present the surrounding street network to support orientation. Existing schematization algorithms focus either on routes (without including the surrounding street network) or on the street network (without optimizing the route schematic layout). This paper addresses this lack of methods in schematization research and proposes an algorithm that is able to schematize both the route and the surrounding street network while resolving their conflicting layout criteria. We follow a two-step approach: we optimize the route layout criteria and afterward add the surrounding street network adapting it to the schematic route distortions. Our schematic ‘route + network’ maps aim to satisfy three requirements: (i) better readability of the route with respect to its decision points, (ii) preserving the qualitative characteristics of the surrounding street network while adapting it to route distortions, (iii) better visibility of alternative routes within the street network. A user study with six example maps validates our layout.
{"title":"Schematizing car routes with their surrounding street network","authors":"M. Galvão, J. Krukar, A. Schwering","doi":"10.1080/15230406.2022.2125077","DOIUrl":"https://doi.org/10.1080/15230406.2022.2125077","url":null,"abstract":"ABSTRACT Car drivers can benefit from schematized maps because they require a different level and type of information from different areas of the map. The technical challenge of creating such maps is that a schematic car route map should be optimized for the individual route, and yet simultaneously present the surrounding street network to support orientation. Existing schematization algorithms focus either on routes (without including the surrounding street network) or on the street network (without optimizing the route schematic layout). This paper addresses this lack of methods in schematization research and proposes an algorithm that is able to schematize both the route and the surrounding street network while resolving their conflicting layout criteria. We follow a two-step approach: we optimize the route layout criteria and afterward add the surrounding street network adapting it to the schematic route distortions. Our schematic ‘route + network’ maps aim to satisfy three requirements: (i) better readability of the route with respect to its decision points, (ii) preserving the qualitative characteristics of the surrounding street network while adapting it to route distortions, (iii) better visibility of alternative routes within the street network. A user study with six example maps validates our layout.","PeriodicalId":47562,"journal":{"name":"Cartography and Geographic Information Science","volume":"50 1","pages":"20 - 43"},"PeriodicalIF":2.5,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42435812","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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