Visual Abstraction of Large Scale Geospatial Origin-Destination Movement Data

Zhou, Zhiguang; Meng, Linhao; Cheng, Tao; Zhao, Ying; Guo, Zhiyong; Hu, Miaoxin; Wei, Chen

doi:10.1109/tvcg.2018.2864503

Cited by 95 publications

(52 citation statements)

References 41 publications

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“…Besides enhancing the data-analysis experience, this acts as a platform for users without much machine learning background to reap the benefits of sophisticated machine learning models. Among the recent neural network-based models, CNN (Convolutional Neural Network) [63] and Word2Vec [36,66] models have been used to create interactive visual analysis systems for different application domains. Moreover, traditional models like SVM (Support Vector Machine) [62], LDA (Latent Dirichlet Allocation) [31,35], KNN (K Nearest Neighbor) [37], Bayes' rule [16], learning-from-crowds model [34], and online metric learning [32] have also been extensively utilized by visualization researchers to enhance the data-analysis experience in their systems.…”

Section: Related Workmentioning

confidence: 99%

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Hazarika

Wang

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Approach Overview: A trained neural network-based surrogate model acts as the backend analysis framework, driving our interactive visual analysis system for analyzing a computationally expensive yeast simulation model. Abstract-Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters, which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process. We performed two case studies, and discovered multiple new parameter configurations, which can trigger high cell polarization results in the original simulation model. We evaluated our results by comparing with the original simulation model outcomes as well as the findings from previous parameter analysis performed by our experts.

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Section: Related Workmentioning

confidence: 99%

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Hazarika

Wang

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…Retrieving trajectories via programming languages [78] is widely used in data analysis systems designed for programmers instead of casual users. Recently, visual query technology has been proven to be useful in helping users express their query requirements, such as defining spatial constraints on map [66,80], specifying Origin-Destination (OD) query [26], and performing road navigation [46]. Visual query systems also allow users to modify their query input by well-designed interactions.…”

Section: Query Condition Specificationmentioning

confidence: 99%

“…This has been proven useful to improve people's life quality [49], urban planning [72], and real-time monitoring [47,67,74]. A crucial problem in this process is to help data analysts express their query requirements intuitively and effectively, where interactive visual interfaces [16,80] can play an important role. However, specifying complex conditions in spatial and temporal dimensions is neither natural nor intuitive for domain users and practitioners, which can easily hinder their intention of utilizing the systems.…”

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confidence: 99%

“…The dashed line denotes a real human trace which is not achievable, while the solid line links the stations to form a trajectory that approximates the real trace. The spatially uncertain trajectories have been proved essential in many application scenarios [4] such as identification of commute patterns [67,80], analyzing people's activities [14,36], and spatial planning [44]. Similar to certain trajectories, the analytical goal is to quickly retrieve the trajectories based on spatiotemporal conditions and then discover patterns from the query results.…”

mentioning

confidence: 99%

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A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

Huang

Zhao

Chen

et al. 2019

IEEE Trans. Visual. Comput. Graphics

Self Cite

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Fig. 1. (a) The query condition specification view. (b) The relevance tree with the spatial keyword 'tourist attractions'. (c) The drop-down menu for changing the type of the input keyword. (d) The semantics view shows that the major region functional topic is 'residential related'. (e) The map view shows that the queried trajectories are mainly distributed in the northwest (named 'Jiangxin island'), the middle, and the east (named 'Wuhua building') of the city. (f) Most trajectories land the island from its east through ferry. (g) The region functional topics. (h) The rendering parameter widget. (i) The temporal graph view. (j) The detail result view of the queried trajectories. (k) Detail study of urban areas.Abstract-Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POIs and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.

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“…Martineez et al [34] consider a fleet size and bicycle relocation for a regular operating day and optimize the bike-station location through a mixed-integer linear program. Different GIS-based models like location-allocation [35] and origin-destination [36,37] are also popular methods for identifying suitable bike station locations.…”

Section: Introductionmentioning

confidence: 99%

Bike Station Suitability on University Campus Using Origin–Destination Matrix—A Morgan State University Case Study

et al. 2019

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In contemporary times, bike sharing programs are gaining importance as an influential transportation mode in both urban and rural areas. They are also used as a vital transportation mode on university campuses which serve as a healthy and environmentally-friendly transportation system. However, having an appropriate location for a bike station is important, so as to maximize the benefits of the service. This study used an origin–destination (O-D) matrix to identify appropriate bike station locations at the Morgan State University campus. The O-D matrix analysis identifies three locations Cumming Hall/University Health Center, Rawling Hall, and Center for Built Environment and Infrastructure Studies as the most appropriate locations to start a pilot, which will serve most of the campus (students, faculty, staff) and connect them to the maximum number of facilities at Morgan State University. The O-D matrix takes into account the occupancy or population of individual buildings based on enrollment over the past four years, the distance to the center of the campus where maximum facilities including the graduate and undergraduate offices are located, and the frequency of the university shuttle connecting most of the buildings. This methodology can be replicated and used on other university campuses and will help further bike sharing programs.

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Visual Abstraction of Large Scale Geospatial Origin-Destination Movement Data

Cited by 95 publications

References 41 publications

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

Bike Station Suitability on University Campus Using Origin–Destination Matrix—A Morgan State University Case Study

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