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RoseTrajVis: Visual Analytics of Trajectories with Rose Diagrams
... This paper is an extended version of a paper presented in the 24th International Conference of Information Visualisation (IV 2020) [2]. All sections were improved, and we highlight that the related work in Section 2 was expanded, Section 3.1 was added concerning analyst' goals and tasks, and the design requirements were more thoroughly described in Section 3.2. ...
We propose RoseTrajVis, a visual analytics system for studying commuting behaviours using classic rose diagrams, which, given a location in a map, aggregate trajectories by direction, show additional summary information, such as average speed bands, and allow the application of spatial-temporal filters. The rose diagrams also include colored arrows on the border that point to the origin and destination of the trajectories and show the average speed. To support analytical work, the user can also adjust the aggregation radius, move a location marker to a different position on the map with automatic update of the corresponding rose diagram, and create multiple diagrams for the same location with different filters applied. We developed two prototypes of RoseTrajVis, which were evaluated through user studies covering various types of analytical tasks with trajectories. Results suggest that the concept of rose diagrams was well understood, that the system was easy to use, and reveal that most tasks were executed with no errors. RoseTrajVis provides an innovative way of aggregating trajectories and offers features that enable the analysis of commuting movements and other exploratory tasks on a map, to support urban planning and operation.
... traffic light switching to reduce traffic jam. A recent example was provided by Afonso et al. (see Fig.1) with so called rose diagrams [11]. ...
Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behaviour. New technologies will lead to a different mobility behaviour with other constraints. These changes in mobility behaviour require analytical systems to forecast the required information and probably appearing changes. These systems must consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are exemplary explained through two case studies.
In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the willingness of many government, researchers, and organizations are led by the initiates of open data to share their data and make them publicly accessible. Healthcare, disease, and epidemiological data, such as privacy-preserving statistics on patients who suffered from epidemic diseases such as Coronavirus disease 2019 (COVID-19), are examples of open big data. Analyzing these open big data can be for social good. For instance, people get a better understanding of the disease by analyzing and mining the disease statistics, which may inspire them to take part in preventing, detecting, controlling and combating the disease. Having a pictorial representation further enhances the understanding of the data and corresponding results for analysis and mining because a picture is worth a thousand words. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. The visualization and visual analytics of sequences of real-life COVID-19 epidemiological data illustrate the ideas. Through our solution, we enable users to visualize the COVID-19 epidemiological data over time. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. The effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data are demonstrated by evaluation of these real-life sequential COVID-19 epidemiological data.KeywordsInformation visualizationBig dataSequencesData scienceVisual data scienceData miningData analyticsVisual analyticsCOVID-19
To be able to explore visually large amounts of movement data, it is necessary to apply methods for aggregation and summarization of the data. The goal of our research has been to systemize the possible approaches to aggregation of movement data into a framework clearly defining what kinds of exploratory tasks each approach is suitable for. On the basis of a formal model of movement of multiple entities, we consider two possible views of movement data, situation-oriented and trajectory-oriented. For each view, we discuss the appropriate methods of data aggregation and the visualization techniques representing the results of aggregation and supporting data exploration. A special attention is given to dynamic aggregation working in combination with interactive filtering and classification of movement data.
Analyzing the occurrence of stops in transportation systems is an important challenge to better understand traffic congestion problems and find corresponding solutions. We propose an efficient system to analyze stop occurrences. It consists of two major parts: (1) an efficient clustering algorithm to partition the stops into groups based on strongly connected components (2) an interactive visual representation of the results to provide insights to domain experts.
Migration maps represent patterns of geographical movement by arrows or bands between places, using information arriving in "from-to" tables. In the most interesting cases the tables are of large size, suggesting that computer assistance would be useful in the preparation of the maps. A computer program prepared for this purpose shows that graphical representation is feasible for tables as large as fifty by fifty, and possibly larger. The program contains options for alternate forms of movement depiction, and rules are suggested for the parsing of migration tables prior to the cartographic display, without loss of spatial resolution.
Data about movements of various objects are collected in growing amounts by means of current tracking technologies. Traditional approaches to visualization and interactive exploration of movement data cannot cope with data of such sizes. In this research paper we investigate the ways of using aggregation for visual analysis of movement data. We define aggregation methods suitable for movement data and find visualization and interaction techniques to represent results of aggregations and enable comprehensive exploration of the data. We consider two possible views of movement, traffic-oriented and trajectory-oriented. Each view requires different methods of analysis and of data aggregation. We illustrate our argument with example data resulting from tracking multiple cars in Milan and example analysis tasks from the domain of city traffic management.
Remote experience of sporting events has thus far been limited mostly to watching video and the scores and statistics associated with the sport. However, a fast-developing trend is the use of visualization techniques to give new insights into performance, style, and strategy of the players. Automated techniques can extract accurate information from video about player performance that not even the most skilled observer is able to discern. When presented as static images or as a three-dimensional virtual replay, this information makes viewing a game an entirely new and exciting experience.This paper presents one such sports visualization system called LucentVision, which has been developed for the sport of tennis. LucentVision uses real-time video analysis to obtain motion trajectories of players and the ball, and offers a rich set of visualization options based on this trajectory data. The system has been used extensively in the broadcast of international tennis tournaments, both on television and the Internet.
Based on literature from the domains of organization science, marketing, accounting, and management information systems, this review article examines the theoretical basis of the information overload discourse and presents an overview of the main definitions, situations, causes, effects, and countermeasures. It analyzes the contributions from the last 30 years to consolidate the existing research in a conceptual framework and to identify future research directions.
The research reported here integrates computational, visual, and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial, and temporal dimensions via clustering, sorting, and visualization. Specifically, the approach involves a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices (including several ordering methods), a geographic small multiple display, and a 2-dimensional cartographic color design method. The coupling among these methods leverages their independent strengths and facilitates a visual exploration of patterns that are difficult to discover otherwise. The visualization system we developed supports overview of complex patterns and, through a variety of interactions, enables users to focus on specific patterns and examine detailed views. We demonstrate the system with an application to the IEEE InfoVis 2005 Contest data set, which contains time-varying, geographically referenced, and multivariate data for technology companies in the US.
The surveillance of large sea areas often generates huge amounts of multidimensional data. Exploring, analyzing and finding anomalous behavior within this data is a complex task. Confident decisions upon the abnormality of a particular vessel behavior require a certain level of situation awareness that may be difficult to achieve when the operator is overloaded by the available information. Based on a visual analytics process model, we present a novel system that supports the acquisition of situation awareness and the involvement of the user in the anomaly detection process using two layers of interactive visualizations. The system uses an interactive data mining module that supports the insertion of the user's knowledge and experience in the creation, validation and continuous update of the normal model of the environment.
In this paper we present a system for analyzing tracks using graphics inspired in wind roses. The elements of the rose include petals that aggregate tracks given their direction and that also show their average speeds within an area around a point in a map, as well as arrows that point to the origin and destination of the tracks. The user can interact with the rose by applying time filters and by adjusting the aggregation radius, allowing, for instance, the visualization of commuting patterns. An evaluation with users revealed that almost no errors were made regarding the answers to questions about the interpretation of track aggregation data, and that the response times did not differ significantly with gender or age band and suggested improvements in the graphical interface. The system offers a new way of analyzing tracks, based on a classic technic for aggregating wind data, contributing to the body of knowledge in the area of visual analytics.
Origin-destination (OD) movement data describe moves or trips between spatial locations by specifying the origins, destinations, start, and end times, but not the routes travelled. For studying the spatio-temporal patterns and trends of mass mobility, individual OD moves of many people are aggregated into flows (collective moves) by time intervals. Time-variant flow data pose two difficult challenges for visualization and analysis. First, flows may connect arbitrary locations (not only neighbors), thus making a graph with numerous edge intersections, which is hard to visualize in a comprehensible way. Even a single spatial situation consisting of flows in one time step is hard to explore. The second challenge is the need to analyze long time series consisting of numerous spatial situations. We present an approach facilitating exploration of long-term flow data by means of spatial and temporal abstraction. It involves a special way of data aggregation, which allows representing spatial situations by diagram maps instead of flow maps, thus reducing the intersections and occlusions pertaining to flow maps. The aggregated data are used for clustering of time intervals by similarity of the spatial situations. Temporal and spatial displays of the clustering results facilitate the discovery of periodic patterns and longer-term trends in the mass mobility behavior.
With the prevalence of mobile computing systems and location based services, large amounts of spatio-temporal data are nowadays being collected, representing the mobility of people performing various activities. However, despite the increasing interest in the exploration of these data, there are still open challenges in various application contexts, e.g. related to visualisation and human–computer interaction. In order to support the extraction of useful and relevant information from the spatio-temporal and the thematic properties associated with human trajectories, it is crucial to develop and study adequate interactive visualisation techniques. In addition to the properties of the visualisations themselves, it is important to take into consideration the types of information present within the data and, more importantly, the types of tasks that a user might need to consider in order to achieve a given goal. The understanding of these factors may, in turn, simplify the development and the assessment of a given interactive visualisation. In this paper, we present and analyse the most relevant concepts associated to these topics. In particular, our analysis addresses the main properties associated with (human) trajectory data, the main types of visualisation tasks/objectives that the users may require in order to analyse that data and the high-level classes of techniques for visualising trajectory data. In addition, this paper also presents an overview on a user study, conducted in function of this analysis, to compare two classes of visualisation techniques, namely static maps and space-time cubes, regarding their adequacy in helping users completing basic visualisation tasks.
It is very useful to understand metropolitan human movement patterns for better city planning and traffic management. As the most accessible and wide-coverage data source for probing the laws behind
city pulse and human movement, taxi O/D data have been receiving more and more attention from road traffic administration offices. In this paper, we design a visual analysis system for big taxis O/D data for assisting understanding the spatio-temporal patterns of human mobility. The system first helps users determine the regions of interest for further investigation by the global heat map view of O/D distributions; visually encodes the spatio-temporal patterns of the O/D data of the to-be-analyzed regions chosen by lasso or rectangle region selection tools; and provides a multi-dimensidoneonal analysis of the latent spatio-temporal patterns of taxis O/D data through interactions between multiple coordinated views of visualizations including circular pixel graph, spatio-temporal stacked graph and nested pixel bar. The proposed system of taxis O/D data visual analysis gets interesting findings about the metropolitan residents’ movement behavior when applied to large-scale real taxis GPS data in Hangzhou and receives good user feedbacks.
Graphical abstract
Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this book. As the authors show, modern visual analytics techniques are ready to tackle the enormous challenges brought about by movement data, and the technology and software needed to exploit them are available today. The authors start by illustrating the different kinds of data available to describe movement, from individual trajectories of single objects to multiple trajectories of many objects, and then proceed to detail a conceptual framework, which provides the basis for a fundamental understanding of movement data. With this basis, they move on to more practical and technical aspects, focusing on how to transform movement data to make it more useful, and on the infrastructure necessary for performing visual analytics in practice. In so doing they demonstrate that visual analytics of movement data can yield exciting insights into the behavior of moving persons and objects, but can also lead to an understanding of the events that transpire when things move. Throughout the book, they use sample applications from various domains and illustrate the examples with graphical depictions of both the interactive displays and the analysis results. In summary, readers will benefit from this detailed description of the state of the art in visual analytics in various ways. Researchers will appreciate the scientific precision involved, software technologists will find essential information on algorithms and systems, and practitioners will profit from readily accessible examples with detailed illustrations for practical purposes. © Springer-Verlag Berlin Heidelberg 2013. All rights are reserved.
This chapter describes various efforts to visualize, query, and summarize personal spatiotemporal data. A highly interactive approach that allows the simultaneous manipulation of space and time can offer insight at the exploratory stage of work and altering the spatial and temporal scales of analysis can reveal different processes that may drive or constrain individual behavior. The spatial history explorer (SHE) has been developed specifically to analyze datasets in a highly interactive environment that places equal importance upon both the spatial and temporal location of data. Approaches for interactive spatio-temporal querying, visualizing mobility attributes, and representing the cyclical nature of time are suggested. This approach of linking spatial and temporal representations is then contrasted with a single 3D spatiotemporal representation based upon a time geography approach. Several surface representations that summarize large volumes of point data are also described, in particular for identifying interesting locations associated with destinations, transport links and constraints. The insight gained from these techniques has led to several approaches to improve the way in which mobile individuals access digital information; this work is ongoing and described briefly in the conclusion.
Nowadays, big cities are suffering from severe traffic congestion as a result of the continuing increase in vehicles. Taxis equipped with GPS can be viewed as sensors of the traffic situation in city. However, trajectory data generated by taxi's GPS traces are often high-dimensional and contain large spatial and temporal attributes, which pose challenges for analysts. In this paper, based on taxi trajectory data, we present an interactive visual analytics system, T-Watcher, for monitoring and analyzing complex traffic situations in big cities. Users are able to use a carefully designed interface to monitor and inspect data interactively from three levels (region, road and vehicle views). We develop a visualization method to monitor and analyze traffic patterns for abnormal behaviors detection. In the region view of our system, global temporal changes in spatial evolution will be presented to users and can be interactively explored. The road view shows temporal changes to the traffic situations of significant segments of roads. The vehicle view uses a novel visualization method to track individual vehicles. Furthermore, the three views integrate important statistical and historical information related to traffic, which illustrate temporal changes of the traffic. We find that this design can help users explore historical information while monitoring traffic. We test our system on a real-life vehicle dataset collected from thousands of taxis and obtained some interesting findings. The experimental results confirm the effectiveness and efficiency of the proposed visual detection method. The analysis of the results also shows that our system is capable of effectively monitoring traffic and detecting abnormal traffic patterns.
Sports analysts live in a world of dynamic games flattened into tables of numbers, divorced from the rinks, pitches, and courts where they were generated. Currently, these professional analysts use R, Stata, SAS, and other statistical software packages for uncovering insights from game data. Quantitative sports consultants seek a competitive advantage both for their clients and for themselves as analytics becomes increasingly valued by teams, clubs, and squads. In order for the information visualization community to support the members of this blossoming industry, it must recognize where and how visualization can enhance the existing analytical workflow. In this paper, we identify three primary stages of today's sports analyst's routine where visualization can be beneficially integrated: 1) exploring a dataspace; 2) sharing hypotheses with internal colleagues; and 3) communicating findings to stakeholders.Working closely with professional ice hockey analysts, we designed and built SnapShot, a system to integrate visualization into the hockey intelligence gathering process. SnapShot employs a variety of information visualization techniques to display shot data, yet given the importance of a specific hockey statistic, shot length, we introduce a technique, the radial heat map. Through a user study, we received encouraging feedback from several professional analysts, both independent consultants and professional team personnel.
As increasing volumes of urban data are captured and become available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based decision making and policies. In this paper, we focus on a particularly important urban data set: taxi trips. Taxis are valuable sensors and information associated with taxi trips can provide unprecedented insight into many different aspects of city life, from economic activity and human behavior to mobility patterns. But analyzing these data presents many challenges. The data are complex, containing geographical and temporal components in addition to multiple variables associated with each trip. Consequently, it is hard to specify exploratory queries and to perform comparative analyses (e.g., compare different regions over time). This problem is compounded due to the size of the data-there are on average 500,000 taxi trips each day in NYC. We propose a new model that allows users to visually query taxi trips. Besides standard analytics queries, the model supports origin-destination queries that enable the study of mobility across the city. We show that this model is able to express a wide range of spatio-temporal queries, and it is also flexible in that not only can queries be composed but also different aggregations and visual representations can be applied, allowing users to explore and compare results. We have built a scalable system that implements this model which supports interactive response times; makes use of an adaptive level-of-detail rendering strategy to generate clutter-free visualization for large results; and shows hidden details to the users in a summary through the use of overlay heat maps. We present a series of case studies motivated by traffic engineers and economists that show how our model and system enable domain experts to perform tasks that were previously unattainable for them.
Wind-data presentations are varied. Some serve one purpose better than another. A relatively new type of wind rose is presented in order that its potential may be exploited. This is the standard vector-deviation wind rose. It is an easily reproduced rose and may be constructed from values of the vector mean wind, standard deviations of the latitudinal and meridional wind components, and the correlation between the components. If the distribution is circular, rather than elliptical, only the vector mean and the standard vector deviation are needed. Methods to test for ellipticity and to construct the roses are given.
Florence Nightingale is usually pictured as an angelic nurse tending to British sol- diers in military hospitals during the Crimean War. Although Nightingale was indeed a tender of soldiers, she was also an administrator, advocate for the common soldier, and proponent of the use of statistics and information design. This article examines Nightingale's rose diagrams, which she designed following her service as the direc- tor of nurses at a field hospital in the Crimean War. When the war ended, Nightingale was asked by the queen to write a report on the poor sanitary conditions and make recommendations for reform. When, after six months, the government did not act on the reforms, Nightingale decided to write an annex to the report, in which she would include her invention, the rose diagrams. Nightingale's ultimate success in persuad- ing the government to institute reforms is an illustration of the power of visual rheto- ric, as well as an example of Nightingale's own passionate resolve to right what she saw as a grievous wrong. In this article I analyze Nightingale's use of visual and verbal rhetoric in the de- sign and presentation of her rose diagrams. This analysis is important not only because it highlights a woman's role in the early development of information de- sign, but also because it examines all three of the rose diagrams that appeared in the annex to her report on poor sanitary conditions in military hospitals on the front during the Crimean War. Nearly all of the accounts of the rose diagrams now in print discuss only the second rose diagram that Nightingale designed be- cause it is the most dramatic (Cohen, "Nightingale"; Dossey; Goldie; Wainer). However, from a rhetorical point of view, it is important to trace her visual argu- ment as it progressed from the first rose diagram to the third because this pro- gression provides insight into her visual rhetorical approach to persuading resis- tant audiences.
Determination of a runway orientation is a critical task in the planning and design of an airport. The direction of the runway controls the layout of the other airport facilities, such as passenger terminals, taxiways/apron configurations, circulation roads, and parking facilities. This paper presents a geographic information systems (GIS)-based system called airport runway optimization (ARO) that determines the best runway orientation for the effective layout of air-port facilities. The paper first provides an overview of the previous research efforts with regards to runway orientation and investigates Federal Aviation Administration standards, wind conditions, airspace obstructions, topographic settings, and other factors that apply to runway design. It then addresses a GIS-based wind rose method that automates the determination of runway placement. The ARO system uses the GIS-based wind rose method and customized GIS functions and database management tools to successfully solve the "partial coverage" problem. The ARO system significantly improves the current efforts of using "trial and error" and computation-intensive methods to determine runway alignment.
Choosing effective colour schemes for thematic maps is surprisingly difficult. ColorBrewer is an online tool designed to take some of the guesswork out of this process by helping users select appropriate colour schemes for their specific mapping needs by considering: the number of data classes; the nature of their data (matched with sequential, diverging and qualitative schemes); and the end-use environment for the map (e.g., CRT, LCD, printed, projected, photocopied). ColorBrewer contains 'learn more' tutorials to help guide users, prompts them to test-drive colour schemes as both map and legend, and provides output in five colour specification systems.
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We present a new technique for the visual exploration of origins (O) and destinations (D) arranged in geographic space. Previous attempts to map the flows between origins and destinations have suffered from problems of occlusion usually requiring some form of generalisation, such as aggregation or flow density estimation before they can be visualized. This can lead to loss of detail or the introduction of arbitrary artefacts in the visual representation. Here, we propose mapping OD vectors as cells rather than lines, comparable with the process of constructing OD matrices, but unlike the OD matrix, we preserve the spatial layout of all origin and destination locations by constructing a gridded two-level spatial treemap. The result is a set of spatially ordered small multiples upon which any arbitrary geographic data may be projected. Using a hash grid spatial data structure, we explore the characteristics of the technique through a software prototype that allows interactive query and visualisation of 10⁵-10⁶ simulated and recorded OD vectors. The technique is illustrated using US county to county migration and commuting statistics.
A potential model, which allows one to estimate a net flow table from observed geographical changes, is derived. The relation of this model to Poisson's equation is established; numerical evaluation then permits the computation of a continuous spatial flow field. The model may have promise as a device for the forecasting of net geographical migration. -Author
The analysis of recently collected wind data at five sites in Saudi Arabia namely, Dhulum, Arar, Yanbu, Gassim and Dhahran is presented. The five sites represent different geographically and climatologically conditions. The data collected over a period spanned between 1995 and 2002 with different collection periods for each site. Daily, monthly and frequency profiles of the wind speed at the sites showed that Dhulum and Arar sites have higher wind energy potential with annual wind speed average of 5.7 and 5.4 m/s and speeds higher than 5 m/s for 60 and 47% of the time, respectively. The two sites are candidates for remote area wind energy applications. The costal site's, i.e. Yanbu and Dhahran wind speed data indicated that the two sites have lower annual wind speed averages and wind blows at speed higher than 5 m/s during afternoon hours. That makes the two sites candidates for grid connected wind systems for electrical load peak shaving. The data of Gassim site showed that the site has the lowest wind energy potential compared to the others. The annual energy produced by a Nordex N43 wind machine is estimated to be 1080, 990, 730, 454 and 833 MWh for Dhulum, Arar, Yanbu, Gassim and Dhahran, respectively. The analysis showed that the estimated annual energy produced by the machine based on 10 min averaged data is 2.5% higher than the estimated energy based on 30 min averaged data.
Information that contains a geographic component is becoming increasingly prevalent and can be used both to analyse relatively complex behaviours in time and space and to combat the potential for information overload by assessing the geographic relevance of information. Such analysis can be combined with mobile communications technology to fuel location-based services that offer information pertinent in terms of geography, time, experience and preference. This paper aims to raise some issues relating to these advances and describes novel representations designed for interactive graphical exploratory data analysis (EDA). A number of graphical techniques and representation methods are introduced to establish the nature of the kinds of data that are being collected and the suitability of visualization for EDA of spatio-temporal data. These include the interactive views provided by the Location Trends Extractor, ‘spotlights’—continuous density surfaces of recorded spatio-temporal activity, networks of morphometric features derived from continuous surfaces representing density of activity and geocentric parallel plots presented in a spatial multimedia environment for data exploration. Some of the benefits and limitations of the techniques are outlined along with suggestions as to how the visualization tools might be utilized and developed to improve our understanding of behaviour in time and space and evaluate and model geographic relevance.
We present a method to interactively explore multiple attributes in trajectory data using density maps, i.e., images that show an aggregate overview of massive amounts of data. So far, density maps have mainly been used to visualize single attributes. Density maps are created in a two-way procedure; first smoothed trajectories are aggregated in a density field, and then the density field is visualized. In our approach, the user can explore attributes along trajectories by calculating a density field for multiple subsets of the data. These density fields are then either combined into a new density field or first visualized and then combined. Using a widget, called a distribution map, the user can interactively define subsets in an effective and intuitive way, and, supported by high-end graphics hardware the user gets fast feedback for these computationally expensive density field calculations. We show the versatility of our method with use cases in the maritime domain: to distinguish between periods in the temporal aggregation, to find anomalously behaving vessels, to solve ambiguities in density maps via drill down in the data, and for risk assessments. Given the generic framework and the lack of domain-specific assumptions, we expect our concept to be applicable for trajectories in other domains as well.
Visual analytics has seen unprecedented growth in its first 5 years of mainstream existence. Great progress has been made in a short time, yet significant challenges must be met in the next decade to provide new technologies that will be widely accepted throughout the world. This article explains some of those challenges in an effort to provide a stimulus for research, both basic and applied, that can realize or even exceed the potential envisioned for visual analytics technologies. We start with a brief summary of the initial challenges, followed by a discussion of the initial driving domains and applications. These are followed by a selection of additional applications and domains that have been a part of recent rapid expansion of visual analytics usage. We then look at the common characteristics of several tools illustrating emerging visual analytics technologies and conclude with the top 10 challenges for the field of study. We encourage feedback and continued participation by members of the research community, the wide array of user communities and private industry.
Visualizing flows between origins and destinations can be straightforward when dealing with small numbers of journeys or simple geographies. Lines embedded in geographic space have commonly been used in mapping transport flows, especially when geographic patterns are important, as they are when characterizing cities or managing transportation. For larger numbers of flows, however, this approach requires careful design to avoid problems of occlusion, salience bias, and information overload. Driven by the requirements identified by users and managers of the London Bicycle Hire scheme, we present three methods of representation of bicycle-hire use and travel patterns. Flow maps with curved flow symbols are used to show overviews in flow structures. Gridded views of docking-station locations that preserve geographic relationships are used to explore docking-station status over space and time in a graphically efficient manner. Origin-Destination maps that visualize the OD matrix directly while maintaining geographic context are used to provide visual details on demand. We use these approaches to identify changes in travel behaviour over space and time, to aid station rebalancing, and to provide a framework for incorporating travel modelling and simulation.
At the end of the sixties Hägerstrand introduced a space-time model which included features such as a Space-Time-Path, and a Space-Time-Prism. His model is often seen as the start of the time-geography studies. Throughout the years his model has been applied and improved to understand our movements through space. Problems studied can be found in different fields of geography, and range from those on an individual movement to whole theories to optimize transportation. From a visualization perspective the Space-Time-Cube was the most prominent element in Hagerstrand’s approach. In its basic appearance these images consist of a cube with on its base a representation of geography (along the x- and y-axis), while the cube’s height represents time (z-axis). A typical Space-Time-Cube could contain the space time-paths of for instance individuals or bus routes. However, when the concept was introduced the options to create the graphics were limited to manual methods and the user could only experience the single view created by the draftsperson. An alternative view on the cube would mean to go through a laborious drawing exercise. Today’s software has options to automatically create the cube and its contents from a database. Data acquisition of space-time paths for both individuals and groups is also made easier using GPS. Today, the user’s viewing environment is, by default, interactive and allows one to view the cube from any direction. In this paper an extended interactive and dynamic visualization environment is proposed, and demonstrated, in which the user has full flexibility to view, manipulate and query the data in a Space-Time-Cube. Included are options to move slider planes along each of the axes
One of the most important challenges of the emerging Information Age is to effectively utilize the immense wealth of data and information acquired, computed and stored by modem information systems. On the one hand, the intelligent use of available data volumes and information extracted thereof offers large potential torealize technological progress and business success. On the other hand, there exists the severe danger that users and analysts get lost in irrelevant, or inappropriately processed or presented information, a problem, which is generally called the information overload problem. Visual Analytics is an emerging research discipline developing technology to make the best possible use of huge information loads in a wide variety of applications. The basic idea is to appropriately combine the strengths of intelligent automatic data analysis with the visual perception and analysis capabilities of the human user. In this paper, we will outline the most important challenges of the young research field.
Trajgraph: A graph-based visual analytics approach to studying urban network centralities using taxi trajectory data
- X Huang
- Y Zhao
- C Ma
- J Yang
- X Ye
- C Zhang
X. Huang, Y. Zhao, C. Ma, J. Yang, X. Ye, and C. Zhang, "Trajgraph:
A graph-based visual analytics approach to studying urban network
centralities using taxi trajectory data," IEEE transactions on visualization
and computer graphics, vol. 22, no. 1, pp. 160-169, 2015.
Courtvision: New visual and spatial analytics for the nba
- K Goldsberry
K. Goldsberry, "Courtvision: New visual and spatial analytics for the
nba," in 2012 MIT Sloan Sports Analytics Conference, 2012.