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Mota R, Ferreira N, Silva JD, Horga M, Lage M, Ceferino L, Alim U, Sharlin E, Miranda F. A Comparison of Spatiotemporal Visualizations for 3D Urban Analytics. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:1277-1287. [PMID: 36166521 DOI: 10.1109/tvcg.2022.3209474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Recent technological innovations have led to an increase in the availability of 3D urban data, such as shadow, noise, solar potential, and earthquake simulations. These spatiotemporal datasets create opportunities for new visualizations to engage experts from different domains to study the dynamic behavior of urban spaces in this under explored dimension. However, designing 3D spatiotemporal urban visualizations is challenging, as it requires visual strategies to support analysis of time-varying data referent to the city geometry. Although different visual strategies have been used in 3D urban visual analytics, the question of how effective these visual designs are at supporting spatiotemporal analysis on building surfaces remains open. To investigate this, in this paper we first contribute a series of analytical tasks elicited after interviews with practitioners from three urban domains. We also contribute a quantitative user study comparing the effectiveness of four representative visual designs used to visualize 3D spatiotemporal urban data: spatial juxtaposition, temporal juxtaposition, linked view, and embedded view. Participants performed a series of tasks that required them to identify extreme values on building surfaces over time. Tasks varied in granularity for both space and time dimensions. Our results demonstrate that participants were more accurate using plot-based visualizations (linked view, embedded view) but faster using color-coded visualizations (spatial juxtaposition, temporal juxtaposition). Our results also show that, with increasing task complexity, plot-based visualizations perform better in preserving efficiency (time, accuracy) compared to color-coded visualizations. Based on our findings, we present a set of takeaways with design recommendations for 3D spatiotemporal urban visualizations for researchers and practitioners. Lastly, we report on a series of interviews with four practitioners, and their feedback and suggestions for further work on the visualizations to support 3D spatiotemporal urban data analysis.
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Deng Z, Weng D, Liu S, Tian Y, Xu M, Wu Y. A survey of urban visual analytics: Advances and future directions. COMPUTATIONAL VISUAL MEDIA 2022; 9:3-39. [PMID: 36277276 PMCID: PMC9579670 DOI: 10.1007/s41095-022-0275-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/08/2022] [Indexed: 06/16/2023]
Abstract
Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.
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Affiliation(s)
- Zikun Deng
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058 China
| | - Di Weng
- Microsoft Research Asia, Beijing, 100080 China
| | - Shuhan Liu
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058 China
| | - Yuan Tian
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058 China
| | - Mingliang Xu
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
- Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450001 China
| | - Yingcai Wu
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou, 310058 China
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Jamonnak S, Bhati D, Amiruzzaman M, Zhao Y, Ye X, Curtis A. VisualCommunity: a platform for archiving and studying communities. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2022; 5:1257-1279. [PMID: 35602668 PMCID: PMC9109455 DOI: 10.1007/s42001-022-00170-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 04/21/2022] [Indexed: 06/15/2023]
Abstract
VisualCommunity is a platform designed to support community or neighborhood scale research. The platform integrates mobile, AI, visualization techniques, along with tools to help domain researchers, practitioners, and students collecting and working with spatialized video and geo-narratives. These data, which provide granular spatialized imagery and associated context gained through expert commentary have previously provided value in understanding various community-scale challenges. This paper further enhances this work AI-based image processing and speech transcription tools available in VisualCommunity, allowing for the easy exploration of the acquired semantic and visual information about the area under investigation. In this paper we describe the specific advances through use case examples including COVID-19 related scenarios.
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Affiliation(s)
| | - Deepshikha Bhati
- Department of Computer Science, Kent State University, Kent, OH USA
| | - Md Amiruzzaman
- Department of Computer Science, West Chester University, West Chester, PA USA
| | - Ye Zhao
- Department of Computer Science, Kent State University, Kent, OH USA
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX USA
| | - Andrew Curtis
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH USA
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Shao L, Chu Z, Chen X, Lin Y, Zeng W. Modeling layout design for multiple-view visualization via Bayesian inference. J Vis (Tokyo) 2021. [DOI: 10.1007/s12650-021-00781-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Zikirya B, He X, Li M, Zhou C. Urban Food Takeaway Vitality: A New Technique to Assess Urban Vitality. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18073578. [PMID: 33808267 PMCID: PMC8036972 DOI: 10.3390/ijerph18073578] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/28/2021] [Accepted: 03/29/2021] [Indexed: 02/07/2023]
Abstract
As one of the most important criteria for measuring the quality of urban life and the environment, urban vitality has become the focus of urban-related research and related disciplines with an increasing number of advocates for the rapid and harmonious development of urban cities. Urban takeaway can represent urban vitality, but studies have not investigated this in a quantitative manner. Furthermore, current studies rarely focus on or even mention the urban food takeaway vitality generated by the spatial distribution of urban takeaway. This study first calculated the vitality of urban takeaways based on the urban takeaway distribution, building footprint, Open Street Map (OSM) data, and the Rapidly Exploring Random Tree (RRT). Then, the urban vitality was obtained using Tencent-Yichuxing data and night-time light data, followed by a spatial correlation analysis between the urban takeaway vitality and urban vitality. Finally, the results for Beijing, Shanghai, and Guangzhou were compared, and the following conclusions were drawn: (1) there is a significant spatial correlation between the urban takeaway vitality and urban vitality, but the correlation varies in different cities at different times; and (2) even in the same city, different road and building densities have an impact on the correlation. The urban takeaway vitality proposed in this study can be used as a new index to evaluate the urban vitality, which has important theoretical and practical significance for the sustainable development of future urban cities.
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Affiliation(s)
- Bahram Zikirya
- College of Tourism, Xinjiang University, Urumqi 830049, China;
- School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China; (X.H.); (M.L.)
- Key Laboratory of the Sustainable Development of Xinjiang’s Historical and Cultural Tourism, Xinjiang University, Urumqi 830046, China
| | - Xiong He
- School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China; (X.H.); (M.L.)
| | - Ming Li
- School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China; (X.H.); (M.L.)
| | - Chunshan Zhou
- College of Tourism, Xinjiang University, Urumqi 830049, China;
- School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China; (X.H.); (M.L.)
- Key Laboratory of the Sustainable Development of Xinjiang’s Historical and Cultural Tourism, Xinjiang University, Urumqi 830046, China
- Correspondence:
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Weng D, Zheng C, Deng Z, Ma M, Bao J, Zheng Y, Xu M, Wu Y. Towards Better Bus Networks: A Visual Analytics Approach. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:817-827. [PMID: 33048743 DOI: 10.1109/tvcg.2020.3030458] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts. Index Terms-Bus route planning, spatial decision-making, urban data visual analytics.
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Huang KT. Mapping the Hazard: Visual Analysis of Flood Impact on Urban Mobility. IEEE COMPUTER GRAPHICS AND APPLICATIONS 2021; 41:26-34. [PMID: 33253115 DOI: 10.1109/mcg.2020.3041371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
With the growing influence of climate change, the intensity of flood disasters has significantly increased throughout the world over the past decades. Among the various types of hazards caused by floods, disruption of the road network has a particularly severe impact on the mobility of emergency responders, and therefore, poses a difficult challenge to damage mitigation, especially in the urban environment. The aim of this article is to present a mapping model for analyzing the spatial pattern of flood impact on urban mobility. Specifically, by incorporating the theory of space syntax, this model focuses on two dimensions of the analysis: the performance of the road network and the relationship between the factors behind it. The former can demonstrate the extent to which the city is affected by flooding in terms of mobility, whereas the latter can provide valuable reference for enhancing the efficiency of evacuation and rescue operations.
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Wu M, Zeng W, Fu CW. FloorLevel-Net: Recognizing Floor-Level Lines With Height-Attention-Guided Multi-Task Learning. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:6686-6699. [PMID: 34310282 DOI: 10.1109/tip.2021.3096090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The ability to recognize the position and order of the floor-level lines that divide adjacent building floors can benefit many applications, for example, urban augmented reality (AR). This work tackles the problem of locating floor-level lines in street-view images, using a supervised deep learning approach. Unfortunately, very little data is available for training such a network - current street-view datasets contain either semantic annotations that lack geometric attributes, or rectified facades without perspective priors. To address this issue, we first compile a new dataset and develop a new data augmentation scheme to synthesize training samples by harassing (i) the rich semantics of existing rectified facades and (ii) perspective priors of buildings in diverse street views. Next, we design FloorLevel-Net, a multi-task learning network that associates explicit features of building facades and implicit floor-level lines, along with a height-attention mechanism to help enforce a vertical ordering of floor-level lines. The generated segmentations are then passed to a second-stage geometry post-processing to exploit self-constrained geometric priors for plausible and consistent reconstruction of floor-level lines. Quantitative and qualitative evaluations conducted on assorted facades in existing datasets and street views from Google demonstrate the effectiveness of our approach. Also, we present context-aware image overlay results and show the potentials of our approach in enriching AR-related applications. Project website: https://wumengyangok.github.io/Project/FloorLevelNet.
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