Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices,such as HTC VIVE,Oculus Rift,and Microsoft HoloLens.These immersive devices have the potential to significantly ext...Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices,such as HTC VIVE,Oculus Rift,and Microsoft HoloLens.These immersive devices have the potential to significantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence.In this paper,we propose a theoretical model to characterize the visualizations in immersive urban analytics.Furthermore,based on our comprehensive and concise model,we contribute a typology of combination methods of 2D and 3D visualizations that distinguishes between linked views,embedded views,and mixed views.We also propose a supporting guideline to assist users in selecting a proper view under certain circumstances by considering visual geometry and spatial distribution of the 2D and 3D visualizations.Finally,based on existing work,possible future research opportunities are explored and discussed.展开更多
2019年科睿唯安旗下科学信息研究所(Institute for Scientific Information,ISI)发表《全面画像,而非简单指标》的全球研究报告。阐述了四类误用的常见分析并提出相应地可视化选项,用于解读每个度量指标下蕴含的更丰富的信息,以及支持...2019年科睿唯安旗下科学信息研究所(Institute for Scientific Information,ISI)发表《全面画像,而非简单指标》的全球研究报告。阐述了四类误用的常见分析并提出相应地可视化选项,用于解读每个度量指标下蕴含的更丰富的信息,以及支持开展全面的、负责任的科研管理。本文基于此报告的核心内容,围绕科研管理最常见的三种分析对象——机构、学科和个人,结合更多的案例展开详细分析。案例分析进一步表明相比于单一指标,"全面画像"可展示更多有价值的信息,能更有力支撑适当的、负责任的科研管理。因此,建议利用"全面画像"的可视化方式来替代简单指标对科研活动进行分析。展开更多
Geographical information systems (GIS) are often used to design environmental justice (EJ) policy interventions. Leveraging GIS and other graphics, overburdened EJ communities can learn from maps that geographically l...Geographical information systems (GIS) are often used to design environmental justice (EJ) policy interventions. Leveraging GIS and other graphics, overburdened EJ communities can learn from maps that geographically link environmental burden (EB) and social disparity (SD) data. Visually representing EB and SD data concretizes the unjust distributions of environmental and broader inequitable societal policies. These maps can be used to efficaciously assess EJ disparities created by such policies through exploring socioeconomic characteristics with local communities. Given the great variation in how GIS EJ applications measure and visualize EB and SD, we present a community-based participatory design (CBPD) lens to collaboratively work across overburdened communities and support making EJ data accessible to all stakeholders. Our location proximity approach is a powerful way to assess overburdened EJ communities because it relies on user-predefined boundaries, and it doesn’t use a single fixed unit of reference to prioritize areas of intervention. Moreover, most areal unit applications use ordinal measures, such as percentiles, and multidimensional indexes, which are intelligible to understand by many residents. Leveraging a community-based participatory design methodology, we present our novel Proximity to Hazards Dashboard (PHD) that includes data on asphalt plants and industrial corridors, hazards often missing from state-level dashboards but very relevant for city policymaking, as well as more traditionally used environmental hazard sources. The use of the tool by policymakers and community members suggests that EJ categorization should focus less on procedural benchmarks and more on systemic change for policy impacts in ways that sustain the participatory nature of our approach.展开更多
This paper utilizes three popular semantic segmentation networks,specifically DeepLab v3+,fully convolutional network(FCN),and U-Net to qualitively analyze and identify the key components of cutting slope images in co...This paper utilizes three popular semantic segmentation networks,specifically DeepLab v3+,fully convolutional network(FCN),and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection.The elements of cutting slope images are divided into 7 categories.In order to determine the best algorithm for pixel level classification of cutting slope images,the networks are compared from three aspects:a)different neural networks,b)different feature extractors,and c)2 different optimization algorithms.It is found that DeepLab v3+with Resnet18 and Sgdm performs best,FCN 32s with Sgdm takes the second,and U-Net with Adam ranks third.This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization.Results show that the contour generated by DeepLab v3+(combined with Resnet18 and Sgdm)is closest to the ground truth,while the resulting contour of U-Net(combined with Adam)is closest to the input images.展开更多
This paper sought to surface visualizations of“older adult selves”(OAS)of junior college students studying development communication after an interaction with older adults in Bagac,Bataan in the Philippines.It was d...This paper sought to surface visualizations of“older adult selves”(OAS)of junior college students studying development communication after an interaction with older adults in Bagac,Bataan in the Philippines.It was done to figure out the ways by which such visualizations could be achieved through institutional efforts within an academic setting.Twenty-three students,mostly females who volunteered to participate in a reflection paper writing activity a week after the youth-older adult interaction,wrote their visions or projections of their life as older adults informed by their social exchanges with older adults in a rural community in the Philippines(Bagac,Bataan).The results indicated that an image of the well-connected older adult was shared across all narratives and it is characterized by youthfulness,openness and flexibility,and sense of accomplishment.Necessary conditions were drawn out from the data to help facilitate the fulfillment of such visions through the possible initiatives of St.Paul University Manila.展开更多
基金The work was supported by National 973 Program of China(2015CB352503)National Natural Science Foundation of China(61772456,U1609217)+5 种基金NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(U1609217)NSFC(61502416)Zhejiang Provincial Natural Science Foundation(LR18F020001)the Fundamental Research Funds for Central Universities(2016QNA5014)the research fund of the Ministry of Education of China(188170-170160502)the 100 Talents Program of Zhejiang University.This project is also partially funded by Microsoft Research Asia.
文摘Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices,such as HTC VIVE,Oculus Rift,and Microsoft HoloLens.These immersive devices have the potential to significantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence.In this paper,we propose a theoretical model to characterize the visualizations in immersive urban analytics.Furthermore,based on our comprehensive and concise model,we contribute a typology of combination methods of 2D and 3D visualizations that distinguishes between linked views,embedded views,and mixed views.We also propose a supporting guideline to assist users in selecting a proper view under certain circumstances by considering visual geometry and spatial distribution of the 2D and 3D visualizations.Finally,based on existing work,possible future research opportunities are explored and discussed.
文摘2019年科睿唯安旗下科学信息研究所(Institute for Scientific Information,ISI)发表《全面画像,而非简单指标》的全球研究报告。阐述了四类误用的常见分析并提出相应地可视化选项,用于解读每个度量指标下蕴含的更丰富的信息,以及支持开展全面的、负责任的科研管理。本文基于此报告的核心内容,围绕科研管理最常见的三种分析对象——机构、学科和个人,结合更多的案例展开详细分析。案例分析进一步表明相比于单一指标,"全面画像"可展示更多有价值的信息,能更有力支撑适当的、负责任的科研管理。因此,建议利用"全面画像"的可视化方式来替代简单指标对科研活动进行分析。
文摘Geographical information systems (GIS) are often used to design environmental justice (EJ) policy interventions. Leveraging GIS and other graphics, overburdened EJ communities can learn from maps that geographically link environmental burden (EB) and social disparity (SD) data. Visually representing EB and SD data concretizes the unjust distributions of environmental and broader inequitable societal policies. These maps can be used to efficaciously assess EJ disparities created by such policies through exploring socioeconomic characteristics with local communities. Given the great variation in how GIS EJ applications measure and visualize EB and SD, we present a community-based participatory design (CBPD) lens to collaboratively work across overburdened communities and support making EJ data accessible to all stakeholders. Our location proximity approach is a powerful way to assess overburdened EJ communities because it relies on user-predefined boundaries, and it doesn’t use a single fixed unit of reference to prioritize areas of intervention. Moreover, most areal unit applications use ordinal measures, such as percentiles, and multidimensional indexes, which are intelligible to understand by many residents. Leveraging a community-based participatory design methodology, we present our novel Proximity to Hazards Dashboard (PHD) that includes data on asphalt plants and industrial corridors, hazards often missing from state-level dashboards but very relevant for city policymaking, as well as more traditionally used environmental hazard sources. The use of the tool by policymakers and community members suggests that EJ categorization should focus less on procedural benchmarks and more on systemic change for policy impacts in ways that sustain the participatory nature of our approach.
文摘This paper utilizes three popular semantic segmentation networks,specifically DeepLab v3+,fully convolutional network(FCN),and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection.The elements of cutting slope images are divided into 7 categories.In order to determine the best algorithm for pixel level classification of cutting slope images,the networks are compared from three aspects:a)different neural networks,b)different feature extractors,and c)2 different optimization algorithms.It is found that DeepLab v3+with Resnet18 and Sgdm performs best,FCN 32s with Sgdm takes the second,and U-Net with Adam ranks third.This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization.Results show that the contour generated by DeepLab v3+(combined with Resnet18 and Sgdm)is closest to the ground truth,while the resulting contour of U-Net(combined with Adam)is closest to the input images.
文摘This paper sought to surface visualizations of“older adult selves”(OAS)of junior college students studying development communication after an interaction with older adults in Bagac,Bataan in the Philippines.It was done to figure out the ways by which such visualizations could be achieved through institutional efforts within an academic setting.Twenty-three students,mostly females who volunteered to participate in a reflection paper writing activity a week after the youth-older adult interaction,wrote their visions or projections of their life as older adults informed by their social exchanges with older adults in a rural community in the Philippines(Bagac,Bataan).The results indicated that an image of the well-connected older adult was shared across all narratives and it is characterized by youthfulness,openness and flexibility,and sense of accomplishment.Necessary conditions were drawn out from the data to help facilitate the fulfillment of such visions through the possible initiatives of St.Paul University Manila.
