An Interactive Radial Visualization Of Geoscience Observation Data
An Interactive Radial Visualization of GeoscienceObservation DataJie Li1, Zhao-Peng Meng1, Mao-Lin Huang1,3 and Kang Zhang212School of Computer Software, Tianjin University, Tianjin, ChinaDepartment of Computer Science, The University of Texas at Dallas, USA3School of Software, The University of Technology, Sydney, Australia{vassilee, zpmeng, mlhuang}@tju.edu.cn, kzhang@utdallas.eduABSTRACTGeoscience observation data refers to the datasets consisting oftime series of multiple parameters generated from the sensors atfixed locations. Although a few works have attempted to visualizefeatures of these data, none of them views these data as a specifictype and attempts to show the overview in all the space, time andattribute aspects. It is important for domain experts to selectinterested subsets from huge amounts of observation dataaccording to the high level patterns shown in the overview. Wepresent a novel approach to visualizing geoscience observationdata in a compact radial view. Our solution consists of three visualelements. A map showing the spatial aspect is in the center of thevisualization, while temporal and attribute aspects are seamlesslycombined with the spatial information. Our approach is equippedwith interactive mechanisms for highlighting the selected features,adjusting the display range, as well as interactively generating afisheye view. We demonstrate the effectiveness and usability ofour approach with a usability experiment. Eye tracking recordsand user feedbacks obtained in the experiment prove theeffectiveness of our approach.KeywordsSpatiotemporal visualization, geovisualization,observation data, radial layout, visual analytics.observation station is built at a representative location to obtainthe long term temporal variation trend of multiple environmentalparameters. A large amount of stations have been built throughoutthe world to obtain the data for overall distributions.How to process and analyze the large amount of collectedobservation data is a challenge. Recently, an increasing number ofscientists attempt to use information visualization techniques toanalyze such data in different domains [1, 2, 3].An overview of the observation data is usually the start to helpusers quickly gain high-level characteristics of the data [4].Because the observation data are intrinsically multi-dimensional,time-oriented and geo-related, it is hard to show all theinformation facets in a compact view [5]. When visualizing thedata, space, time and data attributes should all be consideredtogether due to the strong coupling of the three aspects. Tocomprehensively show the spatial, temporal and multidimensional features of observation data, visual designers oftenjointly use a map and other visualization techniques. Theseapproaches depend on interactions and cross-reference amongdifferent representations, rather than showing all the data aspectsin a single view.geoscience1. INTRODUCTIONObservation is one of the most common means for experts indifferent geoscience domains, such as atmosphere, ocean,environment, etc., to monitor the variations of naturalenvironment or physical phenomena, as in Fig. 1. Due to theautomatic and continuous observation capabilities, huge amountof observation data can be collected, indispensable for domainstudies and result evaluation. In a typical usage scenario, anPermission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and thatcopies bear this notice and the full citation on the first page. To copyotherwise, or republish, to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.VINCI 2015, August 24–26, 2015, Tokyo, Japan.Copyright 2015 ACM 978-1-4503-3482-2 15.00.(a)(b)(c)(d)Figure 1. Four typical observation activities. (a) Meteorologicalobservation. (b) Air quality observation. (c) Oceanographicobservation. (d) Fixed traffic flow monitoring.
We have previously developed a visualization approach tostudy climate changes, called Vismate [5], which includes threeinterrelated visualization views. Vismate focuses on analyzingclimate changes, without viewing the geoscience observation dataas a specific data type and evaluating its usability in otherapplication domains. This paper focuses on the majorenhancements to the main view of Vismate to assist users toquickly identify high-level spatiotemporal patterns of datasets andspecify interesting subsets for further in-depth studies. Theenhancements include three aspects: Interactive operations, such as highlighting interrelatedobjects between different visual elements, map operationstypically used in GIS platforms, and focus context views. A comprehensive usability evaluation, in which eye trackingdata and user feedbacks are collected and analyzed.The remaining part of this paper is organized as follows.Section 2 reviews related work. The approach overview isdescribed in Section 3, followed the visualization technique,interactive design in Sections 4 and 5. Section 6 describes theimplementation details and a usability experiment. Finally, weconclude the paper in Section 7.2. RELATED WORK2.1 Visualizing Geoscience Observation DataThere exist numerous classical visualization techniques, such ascontour line [6], standard coloration [7], rose diagram, etc., usedto represent several facets of geoscience observation data. Thesemethods, however, are usually simple and can only show analysisresults, lacking interactions and support for discovering thepotential knowledge from big observation data.With the advance of display technologies, researchers haveapplied the state-of-the-art visualization techniques to domainstudies. Qu et al. [2] utilized observation data generated frommultiple air quality observation stations to analyze the pollutionsources in Hong Kong. Drocourt et al. [3] designed a visualizationto intuitively show the advance and retreat of calving glaciersbased on the data generated from the observation stationsdistributed along the coastline of Greenland. Nocke et al. [8, 9]used multiple information visualization techniques to analyzemeteorological observation data. Landesberger et al. [10]visualized the cluster features of the weather data of 111 weatherstations over Germany and Slovakia.The temporal series of data generated from numerous sensorsinstalled at fixed locations serve similar purposes as theobservation data. For example, Wang et al. [11] used multipletransportation cells in Nanjing (a city in South China) for macrotraffic analysis. Guo et al. [12] visualized the road intersectiondata captured by roadside laser scanners. Although the aboveworks have resolved different domain problems, they do not viewthe analyzed data as a proper data type and offer a generic andscalable approach for visualizing data of different domains. Theseapproaches either involve limited number of stations and temporalpoints, or are only used to find the spatiotemporal patterns in aspecified field. In contrast, we view geoscience observation dataas a new standalone data type and offer an overview visualizationapproach that can accommodate almost any number of stations,cover any temporal span, and clearly show the boundaries withdifferent geometric shapes of countries or regions.2.2 Radial VisualizationRadial visualization, or displaying data in a circular or ellipticallayout, is a common technique in information visualization [13],which often encode time dimension by several concentric rings [3,14]. However, one often ignores its advantage of representingorientation and position due to its non-directional shape. Forexample, Qu et al. [2] designed an s-shaped axis used in parallelcoordinates to represent wind direction, and Malik et al. [15] usedmultiple regular polygons for comparative visualization. Toimprove the effective space usage, one could utilize the inside andoutside of a radial visualization. For example, Draper et al. [16]put search inputs in the central space, while Wu et al. [17] utilizedthe internal space to place a triangular ScatterPlot. A human bodychart was laid in radar plot by Zhang et al. [18] to visualize aperson’s health condition. Burch [14] drew many thumbnailsoutside the outermost ring of TimeRadar. Inspired by thesemethods, our approach also uses a radial layout. We encode timedimension by different concentric rings along radial direction witha map in the center, so that our approach can visualize spatial andtemporal dimensions at the same time. Moreover, the outside areaof the radial plot is utilized to represent clustered information.2.3 Space, Time, and Attributes VisualizationAlthough many visualization techniques have been proposed toshow space [19, 20], time [21, 22] and multi-dimensionalattributes [23, 24, 25] separately, showing numerous data featuresin a single view is still a challenge for visual designers. Tosimultaneously show the space and attribute aspects, Kim et al.[26] improved the classic thematic map by drawing short linesalong the roads in a map to represent spatial uncertainty, andcalled it BristleMap. Similarly, Gratzl et al. [27] proposed anapproach to visually analyzing multi-attribute rankings and used itto visualize the world university ranking, showing time andattribute information together.To simultaneously show the space, time and attribute aspects,most researchers attempted to jointly use a map and other basicvisualization techniques. Malik et al. [15] used map, bar chart,line chart and pie chart to analyze the correlation between urbancrime activities and spatiotemporal dimensions. Landesberger etal. [10] designed a dynamic categorical data view (DCDV) tovisualize human position transitions in one day, and associated itwith a geography view. Parallel coordinates [23, 28, 29],ThemeRiver [30, 31], stacked bar charts [8], and perhaps all theexisting time-series visualization techniques could be combinedwith a map to make effective spatiotemporal visualization.However, these loosely coupled spatiotemporal visualizationmethods do not provide an overview of spatial and temporaldimensions. They lack intuitive and compact methods forvisualizing spatiotemporal data in a single view. Different fromsuch multi-view methods, our approach offers a global yetcompact view, integrating both spatial and temporal features.3D visualization is another way for simultaneously showingspace, time and attribute information. Tominski et al. [32] andAndrienko et al. [33] proposed a stack-based trajectory wallmethod for exploring multiple trajectories in a 3D context. Twosimilar methods, Great Wall of Space-Time [34] and Space-TimeCube [35], have been proposed by different researchers. Theseapproaches are, however, only suitable for trajectory datavisualization and have inherit the drawbacks of 3D visualization,such as clutter, overlapping, and slow interaction.In summary, although various approaches have been used tovisualize multi-dimensional spatiotemporal data, few works are
capable of showing the overview of space, time, and attributeinformation in a compact and intuitive form. This is the mainmotivation of our work in this paper.3. APPROACH OVERVIEW3.1 Data CharacteristicsOne of the primary characteristics of geoscience observationdata is their multiple types by nature. First, data are continuouslygenerated from observation stations having fixed spatialcoordinates, therefore are geo-related and time-oriented. The datavalues only represent the temporal variations of environmentalparameters at the locations where the data are generated. Second,records of the same data structure are output from observationdevices at a fixed temporal interval, each often containingmultiple parameters. Multi-dimension is therefore anothercharacteristic of geoscience observation data. Finally, geoscienceobservation data are inherently hierarchical, because each recordis associated with an observation station positioned in ahierarchically administrative area. For example a country can bedivided into multiple administrative levels, such as province (orstate), city, county, etc. Table 1 illustrates typical attributes andthe corresponding data characteristics of geoscience observationdata, although the parameters of different types of such data varygreatly.Table 1. Characteristics of Geoscience Observation DataAttributeData CharacteristicStation Code, Continent, Country,Province, CountyHierarchicalLongitude, Latitude, AltitudeSpatialDate, TimeTemporalPrecipitation, Air Pressure, WindSpeed, Air Temperature, TemperatureAnomaly, Sunshine HoursMulti-dimensionalsubsets from a target dataset, our approach shouldaccommodate the data of the overall S and T. Retrieval: This requirement contains two aspects, i.e.identifying the S and T of particular spatiotemporal patterns(A— S T) and detecting the attribute distribution of asubarea or a subinterval (S T — A). To support thebidirectional visual reasoning, our approach should be able tohighlight the specified attribute values and demonstrate theattribute distribution at a selected spatiotemporal scale. Comparison: Compare the attribute distribution in different Sand T, or in different subsets of the dataset. Our approachallows users to interactively generate the visualization byadjusting the parameters.3.3 Design DecisionsWe believe that full awareness, investigation, andunderstanding of both time and spatial patterns at the same time isextremely important, and therefore design our approach tovisualize spatiotemporal patterns in a compact view. Theframework of our approach consists of: A map in the center conveying the geospatial information. A ring-band outside the map, encoding time series changes. Multiple concentric cluster rings outside the ring-band, eachshowing a cluster generated by a clustering algorithm overmultiple attributes of the dataset.The observation stations are placed on the circumferences ofdifferent cluster rings at equal angular intervals. This arrangementaims to effectively condense a large number of widely distributedstations in a single view, while emphasizing time series andclustered changes. Through interactive operations, such a layoutfacilitates the explorations and comparisons among differentstations, regions and clusters over time.4. VISUALIZATION COMPONENTS3.2 Visualization RequirementsWe focus on how to effectively generate the overview of agiven dataset consisting of big observation data, for whichintuitively visualizing all the characteristics is an importantcriterion. Our objective is to help analysts better understand thevariations of attributes (A) with respect to the spatial (S) andtemporal (T) dimensions [36]. We classify the most importantrequirements into three major categories: Exploration: Show attribute (A) distribution at differentspatiotemporal scales. This requirement is important foranalysts to identify different spatiotemporal patterns, such asconstant, gradual or abrupt changes, outliers, and repetitionsin space and time. Because the analysts may use our approachas the preliminary step of data analysis to select interestingThis section provides details on all the visual components andtheir designs, including the map, cluster rings, section-based ringband, and mapping of stations on the rings.Map. As the longitude/latitude equidistance projection wastestoo much space, we select Mercator projection [37] featured withshape-preserving to convert a world map to a rectangle within abounding box (longitude:-180 180 , latitude:-85.05 85.5 ), asin Fig. 3. To accommodate any geographic shapes of differentcountries, map drawing has to compute the map’s centralcoordinate. Drocourt [3] used a modified standard formula tocompute the map gravity center of Greenland, however, it is moreeffective to use the bounding-box’s center as the map center.Furthermore, to facilitate manual browsing of different subareasof a map and distinguish the attribute values of target areas, ourapproach also supports two types of interactive operations, to bediscussed in Section 4.3. To keep the map style intuitive andminimize the viewer’s cognitive effort, we use the online toolColorBrewer [38] with a recommended color solution to assign acolor to each area. The color solution is also used to color thestations the same as the areas they belong to.
Sector-based RingBandCluster RingsFigure 2. Scheme of our approach consisting of three visual elements, i.e. a map, a sector-based ring band and several cluster rings. Thisvisualization shows the climate changes in China during the period of 1981 - 0,-90)(-180,-85.05)(180,-90)AsiaOceaniaSouth America(180,-85.05)AntarcticaNorth AmericaAfricaEuropeFigure 3. Comparison of traditional longitude/latitudeequidistance projection (left) and Mercator projection (right).Cluster rings. The stations are divided into multiple clustersusing the K-means algorithm [41]. Each cluster ring (see Fig. 2)represents a cluster. The thickness and color of a ring indicate thevalue and direction (positive or negative) of the correspondingcluster respectively. Using Fig. 2 as an example, pink and greyrepresent the warming and cooling trends respectively, while aring’s thickness represents the absolute value of the averageclimate variation trend of the cluster. The K-Means clusteringalgorithm (see Section 4.3) divides stations into several clustersthat are drawn on the corresponding cluster rings. To clearly showthe overall state, the cluster rings are also sorted outward onaverage values of the clusters.Sector-based Ring Band. We use a sector-based ring band toencode the parameter values of all stations over a period of time.
Outward direction indicates the time axis (see the Sector-basedring band in Fig. 2). Each sector indicates the time series of onestation, while a radial bin is colored to represent the attributevalue at a time point. An intuitive and rational color legend is thekey to temporal mapping. We design the color legend accordingto the domain research standard and also support color filtering[32] to allow the user to emphasize or de-emphasize selectedintervals, as in Fig. 4. This effect can be triggered by clicking onthe corresponding interval on the color legend.5. INTERACTIVITYTo clearly display the detailed information of any area withdifferent geometric shapes, our approach provides three types ofinteractive operations.Figure 4. Color filtering effect. The width of the selected intervalof legend (See Fig. 2) is decreased for reducing observation load.Station Mapping. In the basic view, all the stations with(longitude, latitude) Cartesian coordinates are mapped to (𝜃, 𝑟)polar coordinates, which creates extra space to represent time andclustering information. Using a fixed angular interval, 𝜃 of astation can be determined by simply adding an equal angularinterval to the angular coordinate of the previous station.Therefore, obtaining the angular coordinate of the first station isthe key to this step. To avoid two geographically distant stationsto be too close to each other on the ring, we use a hierarchicalmapping strategy. We first divide a map into several subareas andassign an angular span to each subarea in proportion to thenumber of the stations in that area, as in Fig. 5. Then the stationsare projected in each area onto the ring according to a reference.We select latitude as the reference for determining the sequence ofthe stations in a subarea, while other references can also be chosenupon the domain requirements. Parameter r of the polarcoordinate of a station can be obtained by determining the clusterring that the station should be drawn.(a)Zoom InZoom InZoom OutZoom OutFixed angularintervalPan(b)Figure 5. An example of station mapping which contains 4subareas and 2 cluster rings.Figure 6. Interactive operations supported by our approach. (a)Polar coordinate system based fisheye view. (b) Interactiveoperations used in GIS, such as Zoom in, Zoom Out, Pan, etc.
5.1 Highlighting Interrelated Visual ElementsSimultaneously showing the spatial, temporal and attributeaspects of a selected subset is a common task when performinganalysis tasks. We therefore design this an interactive mechanismto help the user simultaneously understand the three aspects of theselected subset. The three visual elements of our approach (Map,Sector-based Ring Band, and Cluster Ring) respectively showspace, time and attribute aspects of a given dataset. When the userfocuses on an area of one of the three visual elements, ourapproach can highlight the corresponding areas on the other twovisual elements For example, when the mouse hovers on a circleon the cluster rings, the name of the station represented by thiscircle appears at the mouse location and the sector for showingtemporal information and the geographic location on the map isalso highlighted on the other two visual elements. Similarly, whenwe put the mouse on any other element, the corresponding areason the remaining two elements are also highlighted. Furthermore,if the mouse hovers on a cluster ring, all the correspondingstations are also highlighted to show the overall distribution.5.2 Polar Coordinate Based FisheyeAlthough the user can quickly find the areas of all the threeaspects by highlighting interrelated visual elements, it is still achallenge to accurately judge the color and location of eachgeographic shape on different elements. This is especially difficultwhen the view accommodates too many stations or covers a longtemporal interval. Therefore, a focus context model is used togenerate the fisheye view to enlarge the target area whilemaintaining the contextual information. Because our approach hasa radial shape, the focus context model is based on polarcoordinate system, which features shape-preserving for maptransformation [39]. Fig. 6a shows an example of the fisheye view,in which Xinjiang province is selected as the focus area.When a focus point is selected, each point of the three visualelements (Map, Sector-baser Ring Band and Cluster Ring) (x, y)istransformed to the fisheye coordinate (xfeye , yfeye ) by recomputing the distance d between the focus point (xfocus , yfocus )and this point. After obtaining the new distance, fisheyecoordinates can be computed using a simple trigonometricfunction. The three visual elements use the same transformationequation with different distortion factors [40]. For maptransformation, each geographic vertexes move toward or awayfrom the focus point along the line connecting the focus point andthis geographic vertex (angle is unchanged), while both angularinterval and radius interval are changed for the Sector-based RingBand and Cluster Ring. Keeping the focus point, old vertex andnew vertex along a line could maximally retain the geometricshape of each map feature.5.3 Interactive Operations of GIS PlatformApart from visualizing the datasets generated from a fixed area,our approach is generic, such as showing over a long time withany number of stations, which may be generated from anycountries or regions of the world. Inspired by GIS platforms, weuse several interactive operations, such as pan, zoom-in, zoom-out,etc., to operate maps containing any countries and areas, as in Fig.6b. Through these interactions, users could flexibly interactivelyexplore various geographical areas at any scales, which isparticularly useful for visualizing big data obtained from a hugeamount of stations.In summary, our approach provides three complementaryinteractive operations to support seamless exploration.6. IMPLEMENTATION ANDEXPERIMENT6.1 ImplementationThe current implementation of our approach is written in WPF(Windows Presentation Foundation), utilizing the client-serverarchitecture. The server program is responsible for loadingdatasets and performing automatic clustering, while the clientprogram presents the visualization and handles user interactions.Separating the computationally intensive tasks can minimize theCPU and memory usage of the client-side computer, and the clientprogram can smoothly run on a laptop.Our approach uses the K-means [41] clustering algorithm,while any other clustering algorithm can be selected according tothe domain requirements. To support clustering, we use the slopeof the linear regression line of a station as the clustering reference.Let X {x1, x2, , xN} be the set of time points in the selected timeinterval, Y {y1, y2, , yN} the set of attribute values at thecorresponding time points, and x̅ and y̅ the average values of Xand Y. The slope is calculated as follows:𝑠𝑙𝑜𝑝𝑒 𝑁𝑖 1(𝑥𝑖 𝑥) (𝑦𝑖 𝑦)2 𝑁i 1(𝑥𝑖 𝑥)(1)6.2 Objectives and Tasks of ExperimentWe conduct a usability experiment to evaluate the effectivenessand interactivity of our approach. Our approach is embedded intomulti-view visual analytics tool Vismate, presented in ourprevious paper [5], which does not report any experiment on itsusability, nor applications in other domains. This section analyzesthe influence of each individual visual element on the overallcognitive effect, and the effectiveness of embedding our approachinto an analysis tool consisting of multiple views for domain users.The experiment results could also guide users to better setvisualization parameters to meet their application needs.To perform the experiment, we proposed three interrelatedtasks regarding the climate changes in East China, which requirecomprehensive uses of all the three visual elements: Find the cluster containing most of the stations of East China. Identify two turning points of climate changes in East China. Determine the stations having different variation trends.In addition to the routine experimental parameters, such asaccuracy and completion times, as an important means ofevaluating visualization techniques, eye tracking has also beenutilized in the experiment. Eye movements are recordedcontinuously throughout the visualization task and can provideinsight into the process of working with a visualizationenvironment [46]. Therefore, the measurement ofspatiotemporal eye movement data may be more diagnostic thansummative experimental parameters. To analyze the cooperativeeffects of the four views (our approach is one of them) in the tool,we set 6 AOIs (Areas of Interest) on the interface, each on a visualelement (Map, Sector-based Ring Band, Cluster Ring) as in Fig.13. By observing the direct transitions of the subjects between theAOIs, multiple usability patterns can be identified.
(a) 2001-2012(b) 2001-2008Figure 12. Climate Changes in different interval of the 2000s.541236Figure 14. GazaPlot with 6 AOIs. Each subject is mapped to a different color generated by the integrated eye tracking software and all gazetrajectories are overlapped.
6.4 Procedure6.3 Experiment PreparationDatasets. China surface meteorological observation data wasused to the experiment. This dataset contains the long-termobservation records of 206 stations distributed throughout thecountry. Within the China meteorological observation network,these stations could automatically and continuously obtain 23types of meteorological attributes on the land surface. Thedataset covers 1951-2012, containing about 4 million dailyaverage records, 135000 monthly average records and 47000yearly average records. The entire set has been checked forconsistency by the meteorological authority, and thus is reliableto use.Visualization. We first analyze the climate change situationduring the period of 2001-2012.We first divided China into 7areas using the customary geographical division method inclimate studies, and assign a color to each area. Furthermore, aChinese meteorological industry standard [42] is used to definethe color codes of multiple meteorological attributes, as depictedin Fig. 2. We divided the stations into 9 clusters according to thetemperature change rates using the K-Means algorithm. Whenall the stations were mapped on the cluster rings, spatiotemporalpatterns were clearly shown, as in Fig. 12a. By observing thecolor filtering view of the sector-based ring band, we found thewarming process mainly appeared in the first half of the 2000s.Therefore, we selected the period of 2001-2008 as the targetinterval and generated the visualization again, in which almostall the cluster rings have reddish colors, representing a strongnational warming trend, as in Fig. 12b.Stimuli. As in Fig. 13, the tool used in the experimentcontains 4 views, in which our approach offers the overview(AOIs 1, 2 and 3), while other three views are specialized infinding turning points (AOI 4), analyzing temporal variationtrends (AOI 5) and detecting anomaly cases (AOI 6). The threeAOIs marked on our approach are on the three visual elementsrespectively, each covering the corresponding area of the threeelements of East China. To support interactive operations, anexecutable program with the predefined parameters was used,which was shown to each subject after a calibration procedure.We used the dataset of climate observation data, selected theperiod of 2001-2012 as the target interval, and set 9 cluster rings.Subjects. We chose a within-subjects study design with 10subjects. Eight subjects are students from the School ofComputer Software, Tianjin University, and two others have artbackgrounds. All the subjects were graduate students and hadexperiences in visualization. Seven of them were male and threewere female, aged 24.6 years on average (between 19 and 31).None of the subjects had used our approach. All the subjectswere confident with mouse and keyboard interaction and hadheard about global warming.Environment. All the trials were conducted in a laboratoryenvironment during a vacation to minimize distractions fromoutside. We used a Tobii T60 XL eye tracking system with aTFT screen resolution of 1920 1200 pixels. The fixationduration is chosen to be 60ms and the fixation size 10 pixels onthe eye t
An Interactive Radial Visualization of Geoscience Observation Data Jie Li1, Zhao-Peng Meng1, Mao-Lin Huang1,3 and Kang Zhang2 1School of Computer Software, Tianjin University, Tianjin, China 2Department of Computer Science, The University of Texas at Dallas, USA 3 School of Software, The University of Technology, Sydney, Australia {vassilee, zpmeng, mlhuang}@tju.edu.cn, kzhang@utdallas.edu
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