Pub Date : 2022-09-02DOI: 10.1080/23729333.2022.2124733
W. Cartwright, A. Ruas
understanding of our discipline.
理解我们的学科。
{"title":"Issue 8.3, 2022 International Journal of Cartography","authors":"W. Cartwright, A. Ruas","doi":"10.1080/23729333.2022.2124733","DOIUrl":"https://doi.org/10.1080/23729333.2022.2124733","url":null,"abstract":"understanding of our discipline.","PeriodicalId":36401,"journal":{"name":"International Journal of Cartography","volume":"140 1","pages":"271 - 271"},"PeriodicalIF":0.5,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86610711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-13DOI: 10.1080/23729333.2022.2049106
Chenyu Zuo, L. Ding, Xiaoyu Liu, Hui Zhang, L. Meng
ABSTRACT Open government data has great potential to support various stakeholders for multiple purposes, such as participatory planning, smart city services, and strategic decision-making. However, many barriers stand in the way of efficient learning and analysis of the data. Suitable tools are needed to overcome these barriers. In this study, we designed and implemented a map-based dashboard called InDash to represent the spatial and semantic information of the industrial innovation environment at different levels of detail. We collected the open data from the statistical yearbook in 2015 Jiangsu, China, and selected 24 relevant factors from the categories of economy, inhabitance, infrastructure, and research & development to illustrate the design. To ensure the usefulness of InDash, we first analyzed and summarized the information needs and design requirements from the potential users. We then proposed the design requirements and designed the interface of InDash. Moreover, we evaluated the effectiveness of InDash using the think-aloud approach with 30 participants. The experiment results show that the users can efficiently learn and reason about the industrial innovation environment through InDash without intensive training.
{"title":"Map-based dashboard design with open government data for learning and analysis of industrial innovation environment","authors":"Chenyu Zuo, L. Ding, Xiaoyu Liu, Hui Zhang, L. Meng","doi":"10.1080/23729333.2022.2049106","DOIUrl":"https://doi.org/10.1080/23729333.2022.2049106","url":null,"abstract":"ABSTRACT Open government data has great potential to support various stakeholders for multiple purposes, such as participatory planning, smart city services, and strategic decision-making. However, many barriers stand in the way of efficient learning and analysis of the data. Suitable tools are needed to overcome these barriers. In this study, we designed and implemented a map-based dashboard called InDash to represent the spatial and semantic information of the industrial innovation environment at different levels of detail. We collected the open data from the statistical yearbook in 2015 Jiangsu, China, and selected 24 relevant factors from the categories of economy, inhabitance, infrastructure, and research & development to illustrate the design. To ensure the usefulness of InDash, we first analyzed and summarized the information needs and design requirements from the potential users. We then proposed the design requirements and designed the interface of InDash. Moreover, we evaluated the effectiveness of InDash using the think-aloud approach with 30 participants. The experiment results show that the users can efficiently learn and reason about the industrial innovation environment through InDash without intensive training.","PeriodicalId":36401,"journal":{"name":"International Journal of Cartography","volume":"26 1","pages":"97 - 113"},"PeriodicalIF":0.5,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90648484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-15DOI: 10.1080/23729333.2022.2072082
A. Moore
{"title":"Atlas of the invisible: maps and graphics that will change how you see the world","authors":"A. Moore","doi":"10.1080/23729333.2022.2072082","DOIUrl":"https://doi.org/10.1080/23729333.2022.2072082","url":null,"abstract":"","PeriodicalId":36401,"journal":{"name":"International Journal of Cartography","volume":"164 11 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86681756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-04DOI: 10.1080/23729333.2022.2087617
W. Cartwright, A. Ruas
{"title":"Dissemination of the outcomes of research and endeavour of the inter-national Cartography and GIScience community","authors":"W. Cartwright, A. Ruas","doi":"10.1080/23729333.2022.2087617","DOIUrl":"https://doi.org/10.1080/23729333.2022.2087617","url":null,"abstract":"","PeriodicalId":36401,"journal":{"name":"International Journal of Cartography","volume":"74 1","pages":"169 - 170"},"PeriodicalIF":0.5,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83758866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-12DOI: 10.1080/23729333.2022.2062662
P. Vujaković
{"title":"New directions in radical cartography: Why the map is never the territory","authors":"P. Vujaković","doi":"10.1080/23729333.2022.2062662","DOIUrl":"https://doi.org/10.1080/23729333.2022.2062662","url":null,"abstract":"","PeriodicalId":36401,"journal":{"name":"International Journal of Cartography","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82341028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-12DOI: 10.1080/23729333.2021.2024649
Rahul Ranade
ABSTRACT The Cartosat-1 satellite provides relatively high-resolution elevation data, which is publicly available for free, for most parts of India. Yet, published works applying such data in the local geographical context are few. This paper illustrates the application of CartoDEM, an elevation dataset based on Cartosat-1, to develop a coarse geographic narrative of the terrain at the tehsil level. Kotra tehsil in Udaipur district of Rajasthan, India, was used as a case study. It was found that the data, along with the calibration and analysis methods used here, allows a fairly well-resolved understanding of the terrain of the tehsil, including identification of major landforms and quantification of terrain metrics such as elevation and roughness. The free and easy availability of such data shows offers the potential for such studies to be performed for any local area for which CartoDEM or similar data exists.
{"title":"Use of Cartosat-1 elevation data for local-scale terrain studies in India: a case study","authors":"Rahul Ranade","doi":"10.1080/23729333.2021.2024649","DOIUrl":"https://doi.org/10.1080/23729333.2021.2024649","url":null,"abstract":"ABSTRACT The Cartosat-1 satellite provides relatively high-resolution elevation data, which is publicly available for free, for most parts of India. Yet, published works applying such data in the local geographical context are few. This paper illustrates the application of CartoDEM, an elevation dataset based on Cartosat-1, to develop a coarse geographic narrative of the terrain at the tehsil level. Kotra tehsil in Udaipur district of Rajasthan, India, was used as a case study. It was found that the data, along with the calibration and analysis methods used here, allows a fairly well-resolved understanding of the terrain of the tehsil, including identification of major landforms and quantification of terrain metrics such as elevation and roughness. The free and easy availability of such data shows offers the potential for such studies to be performed for any local area for which CartoDEM or similar data exists.","PeriodicalId":36401,"journal":{"name":"International Journal of Cartography","volume":"47 1","pages":"87 - 96"},"PeriodicalIF":0.5,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78219887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1080/23729333.2022.2047402
Tome Marelić
{"title":"The origin of distance and bearing navigational data contained in portolani for the Adriatic Sea basin","authors":"Tome Marelić","doi":"10.1080/23729333.2022.2047402","DOIUrl":"https://doi.org/10.1080/23729333.2022.2047402","url":null,"abstract":"","PeriodicalId":36401,"journal":{"name":"International Journal of Cartography","volume":"11 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79481250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-02DOI: 10.1080/23729333.2022.2031554
S. Christophe, Samuel Mermet, Morgan Laurent, G. Touya
ABSTRACT Neural Style Transfer is a Computer Vision topic intending to transfer the visual appearance or the style of images to other images. Developments in deep learning nicely generate stylized images from texture-based examples or transfer the style of a photograph to another one. In map design, the style is a multi-dimensional complex problem related to recognizable visual salient features and topological arrangements, supporting the description of geographic spaces at a specific scale. The map style transfer is still at stake to generate a diversity of possible new styles to render geographical features. Generative adversarial Networks (GANs) techniques, well supporting image-to-image translation tasks, offer new perspectives for map style transfer. We propose to use accessible GAN architectures, in order to experiment and assess neural map style transfer to ortho-images, while using different map designs of various geographic spaces, from simple-styled (Plan maps) to complex-styled (old Cassini, Etat-Major, or Scan50 B&W). This transfer task and our global protocol are presented, including the sampling grid, the training and test of Pix2Pix and CycleGAN models, such as the perceptual assessment of the generated outputs. Promising results are discussed, opening research issues for neural map style transfer exploration with GANs.
{"title":"Neural map style transfer exploration with GANs","authors":"S. Christophe, Samuel Mermet, Morgan Laurent, G. Touya","doi":"10.1080/23729333.2022.2031554","DOIUrl":"https://doi.org/10.1080/23729333.2022.2031554","url":null,"abstract":"ABSTRACT Neural Style Transfer is a Computer Vision topic intending to transfer the visual appearance or the style of images to other images. Developments in deep learning nicely generate stylized images from texture-based examples or transfer the style of a photograph to another one. In map design, the style is a multi-dimensional complex problem related to recognizable visual salient features and topological arrangements, supporting the description of geographic spaces at a specific scale. The map style transfer is still at stake to generate a diversity of possible new styles to render geographical features. Generative adversarial Networks (GANs) techniques, well supporting image-to-image translation tasks, offer new perspectives for map style transfer. We propose to use accessible GAN architectures, in order to experiment and assess neural map style transfer to ortho-images, while using different map designs of various geographic spaces, from simple-styled (Plan maps) to complex-styled (old Cassini, Etat-Major, or Scan50 B&W). This transfer task and our global protocol are presented, including the sampling grid, the training and test of Pix2Pix and CycleGAN models, such as the perceptual assessment of the generated outputs. Promising results are discussed, opening research issues for neural map style transfer exploration with GANs.","PeriodicalId":36401,"journal":{"name":"International Journal of Cartography","volume":"11 1","pages":"18 - 36"},"PeriodicalIF":0.5,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77699513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：