applied geovisual analytics and...

Linkoping Studies in Science and Technology

Dissertations, No. 1515

Applied Geovisual Analytics

and Storytelling

Patrik Lundblad

Department of Science and TechnologyLinkoping University

Norrkoping 2013

Applied Geovisual Analytics and Storytelling

Copyright c© 2013 Patrik Lundblad unless otherwise [email protected]

Department of Science and Technology, Linkoping UniversitySE-601 74 Norrkoping, Sweden

ISBN 978-91-7519-629-9ISSN 0345-7524

This thesis is available online through Linkoping University Electronic Press:www.ep.liu.se

Printed by LiU-Tryck, Linkoping, Sweden

Abstract

’Geovisual Analytics’ represents a cross-disciplinary research that looks for innovative meth-ods to interactively visualize and solve large spatio-temporal related visualization problems formultivariate data through a visual discovery, reasoning and collaborative process. The name em-phasizes the link with the well-known research discipline of Visual Analytics [76] and could beviewed as sub-area with its specific focus on space and time posing specific research problems.

This thesis focuses on the design, implementation and evaluation of interactive analyticalspatio-temporal and multivariate representations demonstrated in several application scenarioswhich contributes to our understanding of how technology, people and spatial representationsof information work effectively together. Data are analysed through the use of coordinated andtime-linked views controlled by a time slider. Trends are detected through several visual rep-resentations simultaneously, each of which is best suited to highlight different patterns and canhelp stimulate the analytical visual thinking process so characteristic for geovisual analytics rea-soning. Interactive features include tooltips, brushing, highlight, visual inquiry, and conditionedstatistics filter mechanisms that can discover outliers and simultaneously update all views.

To support knowledge capture and the communication and publishing of gained insights fromthe data exploration process, a visual storytelling concept with snapshots is introduced where theauthor can capture the visual data exploration process and share it. Snapshots are memorizedinteractive visualization views that are captured and later on recreated, so that the reader of thestory can see the same mental interactive scenario as the author of the story. These snapshotsare then part of a story where the author writes an explanatory text and uses the snapshots tohighlight key words. These highlights will allow the reader to recreate the data views used bythe author and will guide the reader to the visual discoveries made.

The contributions of this thesis are divided into two parts, where the first part includes ap-plications based on geovisual analytics methods for exploring complex weather data and findingpatterns and relationships within the data. Earlier, the use of data visualization has been verylimited and the introduction of geovisual analytics and the techniques used have significantlyimproved the visual analysis process as well as increased the flexibility. The results of this re-search are today used by SMHI to improve optimization and safety of voyages and monitoring ofthe weather along Swedish roads. Formative evaluations were performed with domain analystswith the purpose to explore qualitative usability issues with respect to visual representations andinteractive representation.

Furthermore, this thesis contributes with a visual storytelling approach which aims at givingdomain experts novel methods for capturing and sharing information discoveries in a way that the

reader can follow the process of visual exploration. This approach has been tested and verifiedwithin the domain of public statistics, where national and regional statistics is published to thepublic through the use of embedded interactive visualizations and a story that can engage thereader. The concept of visual storytelling has also been introduced to educators, where storiesare used as interactive teaching material for students to make national and regional statisticsinteractive and visually understandable to the students. It will also challenge the students toinvestigate new theories and then communicate them visually.

Popularvetenskaplig sammanfattning

Anvandande av geovisuell analys medhistorieberattande

Vi lever i en varld som svammas over av gigantiska mangder data och information, dar teknikerfor att samla in data forbattras och mojligheten att lagra okar for varje ar. For att kunna fa utkunskap ur denna information behovs analysmetoder som kan hjalpa en anvandare att struktureradatan. En av dessa metoder ar att anvanda visualisering for att skapa en mental bild av datan ochgenom farg och form kan visualisering stodja tolkning och analys som kan leda till slutsatser.

Denna avhandling presenterar geovisuella analysmetoder och teknik for att analysera kom-plex vaderdata for att fa kunskap om hur vadret paverkar resor inom sjofart och vaglaget pavagarna. Med grafiska gransnitt, interaktions- och filtreringstekniker kan en anvandare visuelltutforska datan och analysera handelser och forlopp for att dra slutsater. Dessa slutsatser kananvandas antingen direkt for att paverka ett fartygs resa, eller som beslutsstod vid analys av re-san. Avhandlingen visar aven hur dessa metoder kan anvandas till overvakning av prognoser forvaglaget och till analys i efterhand for att forbattra dessa prognoser.

For att sprida den kunskap som domanexperter besitter samt de slutsatser som dragits, harmetoder och verktyg utvecklats inom storytelling. Pa sa satt visas samband och slutsatser genomhistorier. Med visuella grafer och ogonblicksbilder kan samma mentala bild aterskapas for enlasare som den som experten sag nar analysen gjordes. Dessa ogonblicksbilder kompletteras medbeskrivande text, metadata och lankar till extern information. En lasare av denna historia kanlasa texten, fa information om bakgrunden till datan och aven aterskapa den bild som expertensag nar den skrev texten. Eftersom bilden ar en interaktiv visuell representation kan lasarenaven sjalv fritt utforska samma data som experten, med samma visuella representationer ochinteraktionstekniker.

De metoder som utvecklats inom ramen for avhandlingen underlattar effektiv dataanalysgenom att anvanda visuella representationer for explorativ utforskning for att identifiera intres-santa monster. Genom att kombinera dessa visuella representationer med interaktivitet blir detmojligt att att pa ett mer effektivt satt utforska datan och se monster som tidigare inte varit ty-dliga. Med storytelling kan man dels dra nytta av anvandarens domanexpertis da denne kandokumentera den analys som gors, dels lata en lasare ga in och avbryta historien och interaktivtagera med diagrammen och fortsatta utforskningen.

Acknowledgements

My first thanks goes to my two supervisors. To Mikael Jern for introducing me to the field ofgeovisual analytics and for his support, guidance and encouragement during these years. ToLennart Cederberg for inspiration and enthusiasm, and for making our collaboration a journey ofa life time.

Many thanks to my friends and colleagues at NCVA for making these years a fun experienceand for all their support which has been much appreciated. Especially to Ho Van Quan andTobias Astrom for working together with me during all these years, and together with me andMikael starting on a journey that keeps on growing. Also many thanks to Christoffer Carlssonand Miralem Drek for joining us.

To all my colleagues at SMHI for giving me new problems to solve and for stimulating mymind on a daily basis. Their encouraging feedback and support has helped me throughout myPhD studies, and their data has helped me putting visual exploration into interesting and relevantcontexts.

I would also like to thank all my friends and colleagues at MIT for feedback and support.Special thanks to Jimmy Johansson and Camilla Forsell for their support and collaboration, toEva Skarblom for helping and arranging all practical issues, to Karljohan Lundin Palmerius forcreating the LATEX template which this thesis is based on.

My special thanks go to my family for always supporting and believing in me. Last, but byfar the most important person in my life: my loving fiancee, Maria, for always supporting meand for being my best friend.

The main part of this work was supported by SMHI and a grant under the Swedish KnowledgeFoundations Visualization Programme.

Contents

A Context of the work 11 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1 Geovisual Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.2 Analytical Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Multivariate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 Space and Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.5 Visual Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.6 Research Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.7 Overview of Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.1 Aspects of Space and Time . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2 Interaction Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3 Time Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.4 Storytelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

B Contributions 233 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.1 Visual Voyage Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.1.1 Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.2 Road Weather Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . 333.2.1 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.2.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3 Storytelling Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.3.1 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.3.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.4 Storytelling in Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433.4.1 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.4.2 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.4.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.1 Summary of contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.3 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

C Publications included in the thesis 61I Voyage Analysis Applied to Geovisual Analytics . . . . . . . . . . . . . . . . . . 63II Interactive Visualization of Weather and Ship Data . . . . . . . . . . . . . . . . . 73III Geovisual Analytics Tools for Communicating Emergency and Early Warning . . 83IV Swedish Road Weather Visualization . . . . . . . . . . . . . . . . . . . . . . . . 103V Exploratory Visualization for Weather Data Verification . . . . . . . . . . . . . . 115VI A web-enabled visualization toolkit for geovisual analytics . . . . . . . . . . . . 125VII Visual Storytelling applied to Spatial-temporal multivariate statistical data . . . . 145

Complete list of publicationsPatrik Lundblad, Mikael Jern and Camilla Forsell. Voyage Analysis applied to Geovisual An-

alytics. In proceedings of 12th International Conference on Information Visualization, pages381-388, London, UK, 2008

Patrik Lundblad and Mikael Jern. Geovisual Analytics applied to a Swedish Road WarningPrediction System. In proceedings of GI4DM Conference on Geo-Information Systems forCrisis Management , Harbin, China, 2008

Patrik Lundblad, Oskar Eurenius and Tobias Heldring. Interactive Visualization of Weather andShip Data. In proceedings of 13th International Conference on Information Visualization,pages 379-386, London, UK, 2009

Patrik Lundblad and Mikael Jern. Weather and Ship Data Visualization applied to GeovisualAnalytics. 3rd ICA Workshop on Geospatial Analysis and Modelling, Gavle, Sweden, 2009

Mikael Jern, Monica Brezzi and Patrik Lundblad. Geovisual Analytics Tools for CommunicatingEmergency and Early Warning. Geographic Information and Cartography for Risk and CrisisManagement, pages 379-394, Springer 2010

Patrik Lundblad, Jonas Thoursie and Mikael Jern. Swedish Road Weather Visualization. Inproceedings of 14th International Conference on Information Visualization, pages 313-321,London, UK, 2011

Quan Ho, Patrik Lundblad, Tobias Astrom and Mikael Jern. A web-enabled visualization toolkitfor geovisual analytics. In Proceedings of Conference on Visualization and Data Analysis2011 (EI107), Vol. 7868: pp. 78680R-78680R-12, 2011.

Quan Ho, Patrik Lundblad, Tobias Astrom, and Mikael Jern. A web-enabled visualization toolkitfor geovisual analytics. Journal of Information Visualization, 11(1): pp.22-42, 2011.

Patrik Lundblad, Hanna Lofving, Annika Elovsson and Jimmy Johansson. Exploratory Visu-alization for Weather Data Verification. In proceedings of 15th International Conference onInformation Visualization, pages 306-313, London, UK, 2012

Patrik Lundblad, Tobias Astrom and Mikael Jern. Visual Storytelling with Snapshots appliedto Official Statistics. In proceedings of ICIW 2012, The Seventh International Conference onInternet and Web Applications and Services , pages 150-155, Stuttgart, Germany, 2012

Patrik Lundblad and Mikael Jern. Visual Storytelling in Education Applied to Spatial-TemporalMultivariate Statistics Data. Expanding the Frontiers of Visual Analytics and Visualization,pages 175-193, Springer 2012

Quan Ho, Patrik Lundblad, and Mikael Jern. Geovisual analytics framework integrated withstorytelling applied to HTML5. In Proceeding of 16th AGILE Conference on GeographicInformation Science (AGILE 2013), to appear.

Part A

Context of the work

Chapter 1

Introduction

With the advances in computer technology during the last decades more and more data is beingproduced every day, and we are starting to live in an age of too much data. New data sources arebeing discovered, and new methods for capturing data are being invented on a daily basis. Thecapacity to store this new data is continuously increasing, which means that even more data canbe collected. As a result of this, a major challenge of today is avoiding information overload andinstead focusing on the extraction of useful knowledge from this huge amount of complex datathat are becoming available. To aid in the process of gaining knowledge, new methods are beingdeveloped that can help in the effort to explain and explore the data. Visualization is one of thesemethods and can be described as the process of forming a mental image of the data, which is acognitive activity [59] that can assist us when analysing and exploring this complex data.

Visualization is today used as a support for decision making and has been defined as ”Theuse of computer-supported, interactive, visual representations of data to amplify cognition” [14].The use of visual representations to support decision making [71] helps us in the creation of amental image that facilitates the extraction of patterns from within the data. Combining thevisual representation with advanced interactive techniques, analysts are aided in finding a goodrepresentation when exploring and trying to gain insights from the data. A key challenge hereis to find new intuitive representations and interaction techniques, which can be used in dataexploration to gain insight. Another key challenge is the support of knowledge capture and thecommunication and publishing of gained insights from the data exploration process.

Storytelling is as the term implies, to tell a story. As social creatures, we love telling storiesand sharing them with others. What better way to share gained insights than using a story, asa good story can interest and engage a reader and transform them from being passive viewersto active participants. Visual storytelling can be used to represent the data with colourful visualviews that a reader can interact with when taking part of earlier gained insights.

This chapter will provide a background on geovisual analytics, and its focus on space andtime, and also how the analytical reasoning process is used to gain knowledge. It will alsoaddress multivariate data and describe the aspect of space and time and how it distinguishesitself and possesses its own challenges. Furthermore, it will provide an introduction to visualrepresentations and briefly present some of the fundamental visual representations. Finally, theresearch challenges motivating the work of this thesis are presented.

4 Introduction

1.1 Geovisual Analytics’Geovisual Analytics’ represents a cross-disciplinary research that looks for innovative meth-ods to interactively visualize and solve large spatio-temporal related visualization problems formultivariate data through a visual discovery, reasoning and collaborative process [5, 74]. Thename emphasizes the link with the well-known research discipline of Visual Analytics [76] andcould be viewed as sub-area with its specific focus on space and time posing specific researchproblems.

”Visual Analytics is the science of analytical reasoning facilitated by interactive visual in-terfaces” [64] and has been in use since the publishing of the book ”Illuminating the path” in2005. However, the research, methods and approaches that are now named Visual Analytics arenot new and emerged much earlier. Visual Analytics connects to the fields of information visual-ization and scientific visualization, but focuses on analytical reasoning and how visual interfacescan aid in obtaining insights. There is no clear boundary between these fields, but commonly in-formation visualization and scientific visualization are distinguished by the properties of the datathat are analysed. Scientific visualization mainly deals with data that have a natural geometricstructure [60] such as MRI data and molecules. For scientific visualization, the representationsused often relate to the physical properties of the data. Information visualization deals more withabstract data without any obvious spatial mapping, and the representations used rarely requirethe actual physical object to be represented. The field of visual analytics is especially concernedwith sense making and reasoning, and to develop knowledge, methods, techniques and practicesthat combine the strengths of human processing with the techniques from computational trans-formation and analysis of data. Through visualizations, human and computers can cooperate toachieve the most effective result.

Visual analytics is a multidisciplinary field that according to Thomas and Cook [64] includesthe following focus areas:

• Analytical reasoning techniques that enable users to obtain deep insights that directly sup-port assessment, planning, and decision making

• Visual representations and interaction techniques that take advantage of the human eye’sbroad bandwidth pathway into the mind to allow users to see, explore, and understandlarge amounts of information at once

• Data representations and transformations that convert all types of conflicting and dynamicdata [53] in ways that support visualization and analysis

• Techniques to support production, presentation, and dissemination of the results of ananalysis to communicate information in the appropriate context to a variety of audiences.

Geovisual analytics puts the focus on space and time and takes experience from GIS andgeovisualization. As people use the spatial dimension in their daily life when driving to work orplanning a trip, it is not a dimension reserved just for the highly qualified experts. The challenge,due to this, is to learn and understand the users and find the potential areas where cartographers

1.2 Analytical Reasoning 5

Figure 1.1: The dynamic visual analytics process combines automatic and visual analysis meth-ods. Knowledge is gained through human interaction, where the analyst alternates betweenvisual and automatic methods.

and geographic information system (GIS) experts can benefit from the techniques from geovisualanalytics. As there is nothing spatial that is not temporal, the time dimension is of importanceto see the spatial change over time and to answer questions regarding where the change is goingand whether the change can be predicted. In some cases, the time dimension contains cycles orevents that re-occur. Some of these are regular and easy to predict, such as seasons. Others areless regular and harder to predict, like hurricanes. These cycles and events are of importance toanalyse, as the prediction of them plays a big role in the decision making process.

1.2 Analytical Reasoning

The visual analytics process combines automatic and visual analysis methods with human in-teraction in order to gain knowledge from data. Figure 1.1 shows the different stages and thetransitions in the visual analytics process that goes from data to knowledge, using either auto-mated analysis, visual data exploration or a combination of both. The first step in the processis the transformation and preprocessing of the data to derive different representations for furtherexploration. Typical tasks are data cleaning, normalization, grouping or integration of heteroge-neous data. After the transformation, the analyst uses either a data mining method [19, 25] or avisual mapping of the data. For the data mining method, the analyst has to refine and evaluatethe method [20]. In the visual exploration process, the analyst interacts with the visualization toreveal insightful information by looking at the data and transforming the visualization to formnew mental images. Alternating between visual and automatic methods is characteristic for the

6 Introduction

Table 1.1: A multivariate data set with three data items representing three municipalities (rows),each consisting of three variables (columns).

Population Average income Tax148,521 252,000 30.25132,124 241,000 31.3084,800 251,000 31.31

visual analytics process and leads to a continuous refinement of the result [29]. In the end knowl-edge can be gained from visualization or model, as well as the interaction between visualization,model, and the human analyst.

For visual exploration, the guide proposed by Shneiderman 1996, describes how data shouldbe presented on screen using ”Overview first, zoom/filter, details on demand” [56]. However,within visual analytics the possibility to create the overview visualization may be difficult with-out losing interesting patterns. Within visual analytics, the guide can therefore be extended to”Analyse first, show the important, zoom/filter, analyse further, details on demand” [30]. Thus,is not sufficient to just retrieve and display the data using visual metaphors. It is necessary toanalyse the data according to what is of interest to the analysts and where the most relevant as-pects of the data is shown. With interaction methods, the analyst can then get details of the dataon demand in the visual representation or from other representations.

1.3 Multivariate DataA data set can be defined as a collection of data items [63], where an item in the data set mayrepresent a car in a collection of cars, a region in a nation or an observation station in a networkof observation stations. Throughout this thesis and included publications, the term for the dataset may change as the different areas have their own naming convention, but data and data setwill be mostly used. Although in some areas, the data set will be defined as if it is containsobservation or forecast data.

An illustration of a data set containing three data items can be seen in table 1.1. In this tab-ular representation a data item corresponds to a row and each variable corresponds to a column.This representation will be used throughout the thesis. The items in table 1.1 are municipalitiesin Sweden, where each municipality is represented by a number of variables (population size,tax, unemployment) and they describe the different properties of the municipality. These prop-erties may vary from one another and may also be time-varying, meaning that the data changesover time. Variables are sometimes also referred to as attributes, dimensions or indicators. Inthis thesis and included publications, the terms variables, attributes and indicators are used inter-changeably, depending on the domain.

A multivariate data set is a data set that contains two or more variables. The items of a multi-variate data set can be thought of as points in a multidimensional space, where each dimension isrepresented by a variable. Thus, for a multivariate data set each extra variable adds to the amount

1.4 Space and Time 7

Table 1.2: An example of a multidimensional data set including three data items, each represent-ing a country, and four variables of different type, each representing a property of the country(columns).

Continent HDI HDI rank Life expectancy (years)Africa Low HDI 150 47.31Asia Medium HDI 100 67.28

Europe High HDI 50 73.21

of dimensions available in the data. A standard format for structuring multivariate data is to usean m-by-n matrix where m corresponds to the rows, usually the data items, and n represents thecolumns, usually the variables.

Data variables are often separated into different types, where the initial separation is whetherthey are categorical (qualitative) or numerical (quantitative). An example of a mixed data setwith both categorical and numerical data variable is seen in table 1.2. Categorical data is definedas being either nominal, where the data values only contains enough information to distinguishthem from another, or ordinal, where the data values also provide information to order them.In table 1.2, the variable Continent is an example of a nominal variable, as there is no orderinginformation within the variable. The variable HDI can take the value of Low, Medium andHigh, and is an ordinal variable as the variable can be ordered from Low to High. The numericalvariables can be either continuous or integer values [63], and separated into interval data and ratiodata. For interval data, only the difference in the value is of interest, while ratio data uses boththe difference and the ratio. In table 1.2, the variable HDI rank represents an interval variable,whereas Life expectancy is a ratio variable. Throughout this thesis, the data is mainly dividedinto numerical or categorical.

How the variables are defined is important for the method of representation to be used. Thereis rarely one method available for representing all the different types of variables. Numericalvariables are often used in techniques for finding numerical differences or similarity betweendata items. Categorical data does not include any distance measure, so techniques for categoricaldata are more often based on grouping data items into a category.

1.4 Space and TimeWhat distinguishes geovisual analytics from the other fields is the importance of space and timedimensions, which are often present in geographical data sets. Decisions are often based onwhere we are and when. We also often have to take into consideration what is around us and howthe surrounding is evolving. The main components behind space and time are the data item, timeand location. The data item may, for example, refer to a vehicle, a location or a region. The basicassumption is that a data item may only exist in one location at a time, and that time passing canbe measured.

8 Introduction

Figure 1.2: A three dimensional data cube where the dimensions are region, time and indicators.

The spatial and time dimensions are additions to the multivariate data set, and for each dataitem a time and location can be provided. The most essential part is the distinction betweenthe characteristic and referential components of the data set: the former reflect observations ormeasurements of the data item, while the latter specify the context in which the observations ormeasurements were made, for example place and/or time. In the representation of the multivari-ate data set this adds new columns to the m-by-n matrix, where the new columns contain thereferential components of the data item.

Another approach on how to structure the data is to use the data cube commonly acceptedas a method for abstracting and summarising relational databases and data warehouses [23].Data cubes categorise the components of the data set into two classes, namely dimensions andmeasures. Dimensions are the independent variables, or in our case referrers, and the measuresare dependent variables, in our case the attributes or indicators. Within the data cube, the dataare abstractly structured as a multidimensional cube, where each axis corresponds to a dimensionand consists of every possible value for that dimension. Every cell in the data cube is a uniquecombination of values for the dimensions, and contains one value per measure. An exampleof a three dimensional data cube is illustrated in figure 1.2. The dimensions are region, timeand indicators and each cell corresponds to a measured value for an indicator at a location at acertain time step. In this example, a cell could be population size in a region for a certain year.The dimensions of a data cube may also have hierarchies, for example, the time dimension can,depending on aggregation, store data in seconds, minutes, hours or days [62].

1.5 Visual Representations 9

Figure 1.3: A scatter plot where each bubble is a country.

Figure 1.4: A bar chart used to display the population in each municipality.

1.5 Visual Representations

The primary purpose of visualisation is to make data and the corresponding phenomena percep-tible to the mind or imagination of the analyst [18, 67]. Examples of features that may be ofinterest are relations, patterns, outliers and predictions, which can be visualized using differentmethods for visual representations [12]. The scatter plot (figure 1.3), uses one variable for eachaxis, and each data item is represented by a bubble. These bubbles can also differ in size andcolour to add two more variables to the representation. With a bar chart (figure 1.4), one or morevariables can be displayed for a range of data items where the value of the data item is repre-sented by the length or height of the bar.

10 Introduction

Figure 1.5: A parallel coordinates plot displaying each country with a polyline.

Figure 1.6: A table lens where each country is represented by a row and each variable is acolumn.

Figure 1.7: A distribution plot displaying each country with a bubble and grouped based oncontinent.

For multivariate data, the field of information visualization provides a range of representa-tions [24], and the ones used within this thesis are the parallel coordinates [26, 73] and the tablelens [46]. In the parallel coordinates representation (figure 1.5), the variables of the data setare mapped to parallel axes. Each data item is then represented by a polyline intersecting theaxes at the value of the variable. The table lens (figure 1.6), maps each variable to a column

1.6 Research Challenges 11

Figure 1.8: A map consisting of four layers stacked on top of each other.

and each data item to a row. For each column and row a line length represents the value of thatcombination.

In the field of geovisualization, the main visual representation is the map [35, 38, 58], butother representations can also use the spatial dimension, and within this thesis the distributionplot has commonly been used. The map consists of a combination of layers stacked on top ofeach other (figure 1.8), where each layer has a specific purpose. For example, a polygon layercan be used to colour an area after a chosen variable, while a glyph layer can use one or morevariables and represent them using icons. The distribution plot uses a categorical variable, or inthe spatial example a region group, to divide the data items into different rows. Then on eachrow, the data items are represented by a bubble that is positioned along the axes depending onthe value of the chosen variable. This is a representation that is often used to see how a regioncompares to its neighbours within the same group.

1.6 Research ChallengesThe nature of spatio-temporal and multivariate data creates challenges in both the understandingof the data as well as the representation of it. One of the focuses of the work presented here ison the design, implementation and evaluation of interactive analytical spatio-temporal and multi-variate representations. The understanding of how technology, people and spatial representationsof information work effectively together is also of importance, since it has a wider applicabil-ity. For this, the overall goal and the main challenge of this part of the thesis is the creation of

12 Introduction

interactive approaches for visual exploration of complex weather data and methods for findingpatterns and relationships within the data. The process of identifying patterns and relationshipsis a challenge with respect to the choice of method appropriate to the task and data at hand. Thecreation of representations able to give both overview and detail is a challenge, as the representa-tions should facilitate interaction techniques [31] that can freely explore the data. It is importantthat the new methods are evaluated with usability studies, to confirm advantages and limitationsof novel usage of visualization and interaction techniques within the new domains.

The second focus of this thesis is the challenge of supporting knowledge capture and thecommunication and publishing of gained insights from the visual data exploration process. Toooften this is overlooked and the focus is on finding methods for achieving a result, rather thanthe communication of it. Another challenge which arises is how production, presentation anddissemination can be seamless and integrated with the visualization and analysis. The capture ofan analytical result should be generic so that the analytical result can be tailored for the intendedreceiver and situation. To summarize, the main research challenges addressed in this thesis are:

• the design and development of interactive visualization environments that facilitates anal-ysis and exploration of complex weather data

• the mapping between data and visual representations so that patterns and trends can beeasily detected, confirmed and understood

• the use of methods for supporting knowledge capture

• the integration of production, presentation and dissemination of the knowledge capture ina seamless environment

1.7 Overview of PapersThe research work presented in this thesis is concerned with the design, implementation and eval-uation of interactive analytical spatio-temporal and multivariate representations. Furthermore,research on support for knowledge capture and the communication and publishing of gained in-sights from the data exploration process is presented. Seven research papers have been createdaround this theme and are the core of this thesis. They are included in this thesis, and a morethorough description of the contributions will be presented in chapter 3. Unless stated otherwise,the author of this thesis is the first author and main contributor.

Paper I and Paper II address issues within the area of voyage analysis, where data fromship voyages is combined with weather data, to either monitor the voyage of a ship or to do per-formance analysis to improve future voyages. The formative evaluation in paper I was made incollaboration with Camilla Forsell. The implementation of paper II was made in collaborationwith Oskar Eurenius and Tobias Heldring.

Paper III continues the work in geovisual analytics, describing three demonstrators devel-oped in close collaboration with industry partners. The author of this thesis is the third author of

1.7 Overview of Papers 13

this paper and has contributed with two of the demonstrators.

Paper IV and Paper V describes how visualization techniques and methods have been devel-oped and used for monitoring the weather along the roads and also how to analyse the forecastsafterwards and find areas of improvement. The implementation of paper V was made in collab-oration with Hanna Lofving and Annika Elovsson.

Paper VI describes the approach of creating the concepts of an integrated storytelling sys-tem into a GeoVisual Analytics Framework. The author of this thesis is the second author of thispaper and has contributed with the storytelling system.

Paper VII presents how the storytelling system has been used within two areas of research.The first area is the one of official statistics, where storytelling has been used to present statisticaldata together with the knowledge of the statistician for the public. Within the area of education,the storytelling concept has been used by teachers as an aid inside the classroom when teachingstudents geography and history.

14 Introduction

Chapter 2

Background

This chapter will give the background of the research work presented in this thesis. Through-out this entire research work, the concept of space and time has been a constant and substantialinfluence. The representations and interaction features developed have been inspired by earlierwork within the field of geovisual analytics, visual analytics and geovisualization. Followingthis, different techniques have been applied for extracting knowledge from the data using visualrepresentations and interaction techniques. This chapter concludes with some background infor-mation within the area of storytelling, and methods used today for production, presentation anddissemination of the exploration process.

2.1 Aspects of Space and Time

When working with space and time data there are some properties of this data that differs fromother types of data [8], which can be used as an advantage. The first aspect is that of spatialdependence, often referred to as ”the first law of geography” or ”Tobler’s first law”, which statesthat everything is related to everything else, but near things are more related than distant things[65]. Spatial and temporal dependences can serve as a source of information and enhance dataprocessing and analysis [7], such as interpolation and extrapolation of data which can be usedto fill gaps in incomplete data or to fill in extra time steps for change over time. As example,weather forecasts can be interpolated to calculate a probable value between two forecasts periodif they are close enough in time.

The second aspect is that of scale, as the spatio-temporal data and processes operate at dif-ferent spatial and temporal extents. For example, the weather a ship encounters during an hourof a long voyage is both local and short-term, whereas the weather for an entire voyage is bothglobal and long-term. The scale of the spatial analysis is reflected in the size of the units whichis measured, and the scale of the analysis may significantly affect the results, as patterns on onescale may not be detected when analysed on another scale (figure 2.1). The same applies to thetemporal analysis, as the resolution in time can be aggregated or divided into larger or smallerunits. The choice of temporal scale may affect the analysis in the same way as the choice ofspatial scale [36].

Keeping these two aspects in mind is of utter importance when performing analysis on thespatio-temporal data, as the resolution of the data may affect the analysis. As there is no sys-

16 Background

Figure 2.1: Example of presenting the weather on two different scales. The left side is aggre-gated and is used for an overview. The right side is the original data and is used for detailedstudies.

tematic method to detect appropriate scales in both space and time, analysts have used a ”trial-and-error” approach. Interactive visual interfaces facilitates for a combination of algorithmicprocessing and human judgement to aid the analysts in choosing the appropriate scale of theanalysis to fulfil the goal of the analysis [6]. Within this thesis and included papers, the spatialdependence and the aspect of scale has been applied in the research on exploration and analysisof weather data.

2.2 Interaction Techniques

The use of multiple coordinated views is a standard technique within information visualizationthat today is used in many fields [11, 49, 70]. The concept of multiple coordinated views isbased on the usage of two or more views that are linked together (figure 2.2). The benefit ofusing multiple views is that one view can display one aspect of the data, while another viewdisplays another aspect. The integration between the views is the coordination between them,meaning that any changes made in one view is reflected in the other views. Linking and relatinginformation in one view to that of other views will assist the analyst in the exploration process,and may provide additional insights into underlying information. Since different views havedifferent limitations and possibilities, the use of multiple views can enhance the different aspectsof the data to help with the knowledge discovery. Operations such as filtering, dynamic queries[1] and selections applied in one view can simultaneously affect the other views so that not onlythe same information in the view is updated, but more effectively the collections of different datain all views.

Interaction is a core part of multiple coordinated views and a large variety of interactiontechniques may often be used within a system. These interaction techniques can be defined aseither indirect, where the data is manipulated outside of the visual representation using buttonsor sliders, or direct, where manipulation is performed directly in the visual representation itself.Examples of indirect interaction is the querying concept introduced by Shneiderman [55], where

2.2 Interaction Techniques 17

Figure 2.2: Example of two views linked together. The colours used is reflected in both viewsand the selected countries in the bar chart have been highlighted in the map.

Figure 2.3: Indirect manipulation of the visual representation where the interaction is performedoutside of the visual representation using sliders.

18 Background

sliders and buttons are included in a graphical user interface and used as an alternative to astandard databases query (figure 2.3). Direct manipulation can be exemplified by the concept ofbrushing [9] where objects, such as lines in a parallel coordinates plot, are selected in the visualrepresentation using a brushing operation. This operation can result in highlighting or labellingof the selected items, which in a multiple coordinated systems context often propagates to allviews.

Multiple coordinated views are also used for the Overview+Detail [15] technique, where oneview presents the whole data set, while other views only display a subset of the data set in moredetail. The overview is then used to control which part of the data is represented in the detailedviews [50]. This method is used in the analysis of the forecasts for the winter season in paper V,where there is an interest in studying both the performance of the entire season, as well as theindividual hours.

2.3 Time Representations

There are many interactive techniques for visualising data sets with temporal components avail-able in the field of information visualization [2, 3, 41], although the majority of the methodsmostly treat time as another numerical variable. The representation of time is then performedusing a linear time axis, and does not take into consideration the complexity of time and thecharacteristics available in the time dimension. Within visual analysis of spatial and spatio-temporal data, interactive maps are predominant [7, 47]. Temporal data can be characterized byhaving the following characteristics [5].

• Linear time versus cyclic time

• Time points versus time intervals

• Ordered time versus branching time versus time with multiple perspectives

Each of these characteristics is important to take into consideration when choosing the ap-propriate visual representation to solve the analytical task.

If the data is likely to contain a cyclical pattern, a representation where the analyst can takeadvantage of interactivity and change the pattern may reveal additional insights that would notshow up in a linear time representation. The use of visual representations for cyclical timedata can help highlight patterns of periodic behaviour of the data that would not show up ina common line graph, which is more useful for showing general trends and outliers. Spiralvisualizations [66] address the cyclical aspect of time-related data by using a representationmethod suitable for a cyclical behaviour, although the use of this representation requires anappropriate parametrisation of the data. The detection of patterns can use either an analyticalmethod or interaction techniques where the analyst can go through different cycle lengths anddiscover patterns.

The difference between conceptually modelling the temporal attributes as time points or astime intervals is of importance, as the visual representation chosen needs to take advantage of

2.4 Storytelling 19

the characteristics of the temporal attribute. Today, most of the techniques for representing time-oriented data uses a time point model within the representation. There is therefore a challengeto represent data that have an interval boundary, such as uncertainty data [40] like the start of asnowfall, within a weather forecast. Also, the temporal granularity needs to be part of the un-certainty representation, as the uncertainty of the starting time may be represented on a differentlevel than the actual time point (e.g. minutes vs. hours).

Ordered time is the most common characteristic compared to branching time and time withmultiple perspectives. Therefore, most visualization techniques are designed to represent orderedtime, and only a few exists that consider the alternative to ordered time [2]. Branching time andtime with multiple perspectives are, however, very relevant for an analyst when working withforecasted meteorological data. For this type of data, it is often not only the latest forecast that isrelevant to analyse, as the forecast has been updated multiple times and the data from earlier fore-casts are still relevant. Today few techniques exist for representation of time-oriented data withbranching time and time with multiple perspectives, that can be used with visual representationsof space. Within the research on exploration and analysis of weather data, the characteristics ofthe time-oriented data has played a crucial role for the choice of methods used by the analyst.

2.4 Storytelling

Presenting the result of the visual exploration process is one of the major challenges for ananalyst. Too often this is overlooked, and the focus is on finding methods for achieving a resultrather than the communication of it [37]. Within the visual analytics context, sharing of analyticalresults is divided into production, presentation and dissemination [64].

• Production is defined as the creation of materials that summarize the results of an analyticaleffort

• Presentation is packaging of those materials in a way that helps an audience understandthe analytical results in a context, using terms that are meaningful to them

• Dissemination is the process of sharing the presentation with the intended audience

To achieve the most efficient process possible, the production, presentation and disseminationshould be seamless and integrated with the visualization and analysis [39]. Also, the methodol-ogy and tools that enable the capture of an analytical result should be generic so that the analyticalresult can be tailored for the intended receiver and situation. In the production process, the endresult has to be in focus and the analysts needs to be able to build on this result during the courseof the exploration process so that the associated analytical reasoning that led to the conclusion iscaptured [27]. For the presentation, there is a need to be able to link the presentation to the sourceand add additional information regarding the certainty of the data and put the data into context.In the dissemination process, the result has to be shared using the same graphical representationsthat were used during the production process to avoid potential misuse of the representation usedfor dissemination, which would obscure the analytical result.

20 Background

The main method for storytelling [22] and presentation of analytical results is using slide-shows such as Microsoft PowerPoint, which are used for reporting analytical results in a mean-ingful order using text and images captured from the exploration process. Only in recent yearshave new systems emerged that can show how effective visualization and animation can be fortelling compelling stories about data. Although, the use of animation can lead to errors [51].The GapMinder system designed by Hans Rosling shows how data can be turned into stories thatengage the audience using visualization and animation. Within visual analytics, there is still alack of mechanism within most of the existing systems, to produce, present and disseminate theanalytical process, except for producing images.

A common method for storing the exploration process is the use of interaction history mech-anisms such as undo or ”time-travel”, which enables the user to revisit an earlier stage of inter-action in the application. The model used for history storing is often based on either storing theactions of the user, or the state of the application. A state is defined by the settings of the interfaceand the application content, while actions are defined by the transform used when moving fromone state into another. Logging the user’s action is often referred to as command object model[21] and typically provides do and undo methods that apply an operation or its inverse on a state.To traverse the history of an exploration process then involves a sequence of commands that areeither done or undone in order. For this approach both do and undo operations have to be definedfor all actions. State logging instead stores the individual state of the application, and traversingthe exploration process then involves restoring the application state to a stored configuration.This approach requires that all parameters of the application can be stored. Hybrid approachesare also available and for example the WebQuilt web logging system [72] stores URLs (states)but also information regarding the previously performed action.

Since visualization is data-driven, the application states are depending on the data set used,which may result in changes to the exploration process if data is updated. If the analysis needs tobe current, the historical states need to be updated to take the change of data into account. If not,an extract of the data needs to be stored together with the interaction history. The organizationof these history items can be performed in various ways. Using a stack model the history isplaced in a stack that can be traversed. The downside is that when going back to an earlier pointin history and performing a new action, the stack is cleared from that action forwards. Whenstoring the states, the alternative is to use either a timeline model that stores items in the linearorder in which they occur, or a branching model [69], which stores the history items in a treestructure. To enable presentation of the exploration process using the interaction history, it isoften important to only export and share selected parts of the history, as the full explorationprocess may be too cumbersome to disseminate.

Instead, some systems have added mechanisms for capturing parts of the analytical processfor re-use in presentations, utilizing different methods. Oculus nSpace is a system designed tohelp the exploration process and has support for creating presentations to show the analyticalprocess, but this presentation is neither interactive, nor linked to the actual exploration [45, 77].VisTrails support the linking between images and the exploration process and explorations per-formed in VisTrails can be distributed and replayed [13]. MAYA Viz has developed the Com-mand Post of the Future (CPOF) system, that allows the user to feed real-time situational aware-

2.4 Storytelling 21

ness into the system and have that information available in text and graphics representation [33].Within the mapping tool CCMaps, users can save the state of the application with snapshotsand reuse them for presentational purposes [17]. ReVise uses the method of ”Re-Visualization”,introduced by Robinson, which captures and reuses the analysis session [52]. Keel describesa visual analytics system that supports the exchange among team members of task-relevant in-formation for incremental discoveries of relationships and knowledge, used in the sense-makingprocess [28].

For dissemination, many commercial tools such as Tableau [61], QlikView and IBM’s ManyEyes [68] can publish visualization dashboards as interactive web pages, and also embed vi-sualizations in external web sites. These visualizations can support interaction techniques forexploration to enable follow-up analysis by the users. A problem that may arise in allowing theuser to continue with the exploration is the lack of domain knowledge. For traditional domains,the exploration process may be well understood, but for other domains analysts may need to de-velop guides for newcomers, to introduce them to the domain. In recent years, journalists havestarted using narrative visualization [54] by structuring interactive graphics to tell stories withdata. News sources such as The New York Times, Washington Post and The Guardian oftenguides the viewers through the visualization using supporting text and annotations. At the endof this guide the visualizations provide controls for further freeform exploration, and as the in-troduction has provided the viewer with key observations of the data and available interactionsthe viewer can easier get into the exploration process [75]. The use of narrative visualizationdemonstrates how guided visual analytics can help disseminate stories to a general audience.

There is still a lack of methods and tools that support a seamless integration of visual anal-ysis where the exploration process can be captured, presented and disseminated within one en-vironment. The use of storytelling and snapshots may be a method for creating this seamlessintegration, and is addressed in papers VI and VII of this thesis.

22 Background

Part B

Contributions

Chapter 3

Contributions

The previous chapters have introduced the reader to the research and concepts of this thesis. Thischapter will present an overview of the main contributions of this thesis, put the developed toolsand concepts into context and highlight the contributions from the appended papers. This chapterhas been divided into four sections, where two of the sections highlight contributions withingeovisual analytics applied to weather data, and the other two sections highlight the contributionswithin storytelling in support of knowledge capture.

Section 3.1 describes the contributions within the area of voyage analysis, where data fromship voyages is combined with weather data, to either monitor the voyage of a ship, or to doperformance analysis to improve future voyages, presented in papers I, II and part of paper III.This is followed by section 3.2, which describes how visualization techniques have been devel-oped and used for monitoring the weather along the roads in paper IV, and part of paper III.This section is then continued with paper V, where visualization techniques have been devel-oped to analyse forecasts, to find areas of improvement. Section 3.3 describes the approach ofcreating the concepts for an integrated storytelling system into a GeoVisual Analytics (GAV)Framework, which includes the contributions of paper VI. The last section, 3.4, describes howthe storytelling system has been used within two areas of research. The first area is the one of of-ficial statistics where storytelling has been used to present statistical data for the public, togetherwith the knowledge of the statistician. The second area is storytelling within education, wherethe storytelling concept has been used by teachers as an aid inside the classroom when teachingstudents geography and history, as described in paper VII.

3.1 Visual Voyage Analysis

This part of the research concerns ways of using visualization and interaction techniques tofacilitate exploration and analysis of weather data, and in particular in combination with shipvoyages. The focus of the research has been divided into two parts, where the first part concernsthe ability to look at ongoing voyages for a fleet, and see what methods and techniques could beused for monitoring the weather development along a voyage. The second part of this researchconcerns the analysis of concluded voyages, and especially how the ships have performed inrelation to weather. The area of voyage analysis is a good example of where data fusion isneeded, as the spatial and temporal dimensions often differs between the reports from a ship and

26 Contributions

the weather forecast. To be able to interact with the fused data within this domain, there is a needfor interactive and integrated tools that aid the analysts when they manage, process, visualize andinteract with the information space.

The main reasons for using voyage analysis are dived into two different areas. First and fore-most it is of utter importance that a ship, travelling from one port to another, does not encountera weather situation it can not handle. This differs very much between ship types, as well asthe cargo the ship is handling. For this, tools for voyage analysis need to be able to handle thedifferent weather scenarios, as well as the different types of ships. Today, shipping companiesconsults with meteorological organizations that specialize in weather routing and have access tothe latest forecast scenarios. For a single ship, tools for visualizing the route in regards to theweather scenario exists. On the other hand, for a shipping company that handles one or morefleets, there is a lack of tools for visually exploring the weather scenario and the effects it willhave on the fleet.

The second part of voyage analysis concerns voyage optimization, which is used for planningthe fastest route depending on the safety and consumption criteria. The effect from weather ona ship can be calculated both for performance speed as well as how it affects the safety of theship and its cargo. For a single voyage, it is easy to see how a ship has performed compared toexpectations, but for the voyages of a fleet spanning over multiple years, it is harder to detectcomplex patterns within the data [4, 48].

3.1.1 AimsThe aim for the visual voyage analysis research has been to develop geovisual analytics toolsfor expert analysts, who can interact with the complex data of voyage planning and voyageoptimization using these tools. With the aid of advanced visualization techniques and interaction,an analyst should be able to retrieve the information needed to facilitate decision making basedon the given data.

For voyage optimization and analysis of a fleet’s performance, the following key tasks werechosen.

• Allow the analysts to see the difference between the actual result from a voyage and thetheoretically calculated result

• Compare this output with data stored from earlier voyages, in order to find trends or dif-ferences

• Define an aggregated method to calculate an average performance value for a ship or aroute, which can then be used in comparisons

• Integrate route and weather information to facilitate decision-making

For weather monitoring of a fleet, the following criteria were put up for the tool.

• Detailed monitoring of voyages based on planned and reported waypoints

3.1 Visual Voyage Analysis 27

• Easy exploration of weather forecast according to both geographic positions and plannedroutes

• Using parallel coordinates together with geographic map visualization to find interestingvoyages according to weather parameters

• Using highly interactive linked visual representations to facilitate exploration of data

3.1.2 Results

The functionality and visual representations needed for performing voyage optimization andanalysis have been implemented within the interactive geovisual analytics tool VISPER, VISu-alize PERformance data, as shown i figure 3.1. The research aims given have been discussed inPaper I, where the research on voyage analysis is presented.

In VISPER, a map representation has been implemented for the spatial dimension of the data,where each voyage is plotted as a track on the map, using the way points for the voyage and thereported positions from the ships during the voyage. The map representation with the voyagelayer showing the voyages, gives an overview of all the voyages as well as details on the selectedones. In this overview, each track in the map is coloured according to the chosen numericalattribute. This can be used to identify voyages that have a high value in for example, bunkerusage, or have had a high weather factor against them during the entire voyage. For the selectedvoyages, each voyage is broken down into segments which are then coloured according to thechosen numerical attribute as seen in figure 3.2. This allows for detailed studies of each segment,to see for example when the worst weather was encountered, or during which part of the voyagethe ship was going faster or slower than ordered. This representation combines an overview ofall the voyages, together with details on the selected ones where each track of the voyage can bestudied further.

Categorical attributes in the data for the voyages are represented in a list box view. Withinthis representation all the unique values for each categorical attribute is listed. Thus, a selectionof voyages can be made choosing one or more data items. This makes it possible to narrowdown the data in the form of looking at voyages with similar values. Examples of this could be:choosing voyages that have the same departure and destination port, or looking at all voyagesthat were made by the same ship type or model.

The parallel coordinates representation [10] is used for visualizing the numerical attributes ofthe voyages, which makes it possible to look for relations between attributes. A comparison ofthe visual profiles of voyages can tell if the results of the voyages are different or similar, and alsoshow the trend of the voyages. Also, as the minimum and maximum values of each numericalattribute are used in the creation of each axis in the parallel coordinates representation, one caneasily identify outliers.

In VISPER, interaction technique that allows an analyst to work with the data and retrievedetails on demand when doing brushing or picking have been implemented. Also, as the rep-resentations in the application are linked to each other, any brushing action performed in onerepresentation is propagated to the other representations. This allows exploration of voyages

28 Contributions

Figure 3.1: The VISPER user interface with its three components; the world map view the listbox view and the parallel coordinates view (please note that the list box view is minimized dueto secrecy).

Figure 3.2: Voyages plotted as lines in the Mediterranean where weather factor is used asattribute.

where the analyst starts exploring the data in one view, and then uses the other views for moreinformation. Filtering techniques have also been implemented so that the analyst can either filteron the categorical or the numerical data. The categorical data filtering can be used to narrowdown the data to a certain operator, ship or ship type so that a visual analysis can be done ona subset of the data. The numerical data can be filtered using the range sliders of the parallel


Figure 3.3: The SWIM overview. In the world map ships are visualized using glyphs and theirroutes are plotted as lines. Significant wave height is displayed using an iso-surface wherewarmer color indicates higher waves. The current time step can be changed using the timeslider positioned underneath the world map. Using the weather parameter menu to the left theanalyst can select which parameter to visualize and which representation to use. At the bottomweather parameters are plotted in the parallel coordinates plot.

coordinates, which can help in removing outliers or remove voyages that do not fulfil a searchcriterion for the analyst, such as which ships had the highest overconsumption. VISPER alsocontains functionality for calculating average performances, which is done by selecting all thevoyages, or a subset, and then making an average calculation for each numerical attribute. Thiscreates a new line in the parallel coordinates plot, which can be used as a performance value forcomparison.

For the task of weather monitoring of a fleet, the interactive geovisual analytics tool Ship andWeather Information Monitoring, SWIM, was developed as seen in figure 3.3. The aims for thisresearch are presented in paper II, where the goal was to implement a tool and techniques formonitoring a fleets performance and weather development along a planned route.

As with VISPER, the implementation of SWIM also uses a map for the spatial representationof data in which the routes of the on-going voyages are represented by lines on the map. Since

30 Contributions

Figure 3.4: Filtering using time step parallel coordinates plot. In the world map significant waveheight is visualized using an iso-surface. The left side shows parallel coordinates plot withcorresponding map before filtering. The right side shows filtering of ships according to significantwave height.

the voyages are on-going, it is of importance to look at the temporal aspect to see where a shipcurrently is, as well as where it is projected to be in the future. For this, a glyph along the routerepresents the position of the ship at a given time, and the line of the route is divided into a solidline for the distance travelled, and a dotted line for the planned route. For weather parameters, themap uses the techniques of iso-line, iso-surface and glyphs, to represent the weather attributes atthe chosen time step. Each weather attribute is listed, and the analyst can choose which techniqueto use for which attribute. They are then animated as the time changes. As the weather forecastsare global, has a high resolution and there are many time steps in the forecast, a level of detailimplementation had to be adapted. The method chosen was a mean value calculation based withrespect to all interesting neighbours of each visible grid point. This allows the analyst to zoomin on the map and get the weather in a higher resolution in the chosen area.

For the representation of the numerical attributes, the parallel coordinates were chosen in asimilar way as the VISPER implementation. Since the voyage data in this case has temporalvalues, the parallel coordinates updates when the time step changes, so that the analyst can seewhich lines stand out and, for example, spot which ships are going to encounter harsh weather.The parallel coordinates also gives a good overview of all voyages and parameters at the chosentime step, so that the analyst can see the whole picture. To represent the temporal data a timegraph was chosen, which can visualize the weather parameters over time for a chosen voyage, so


Figure 3.5: Highlighting in the map view. The left side shows the map before selection and theright side after.

Figure 3.6: Selection area tool. The left side show the world map view with ships and routeswhere the grey circle is the selection area tool. When using the selection area tool ships thattravel through the selected area at any time during the forecast period will be highlighted asshown on the right side.

that the situation change from good to bad weather can be found.Selection and filtering techniques were also tested and implemented within SWIM. As with

VISPER, the analyst can do dynamic filtering using the parallel coordinates as shown in figure3.4. The implementation of filtering uses the global max and min values of an attribute so thatthe parallel coordinates lines can be normalized over time. With filtering, the analyst can set athreshold in the parallel coordinates, which is then saved, so when the time step changes onlyvoyages surpassing that limit is shown. As there is uncertainty in a planned route, an area se-lection method (figure 3.6) was deemed appropriate so that the analyst could select all voyagespassing within an area for all the time steps. This complements the normal selection (figure 3.5)and if a weather situation would arise, the analyst can immediately chose an area and select allvoyages that will go through that area.

For paper I and II controlled formative evaluations were performe, with the purpose to ex-plore qualitative usability issues, with respect to visual representations and interactive represen-

32 Contributions

tation. The evaluations were based on inspection methods [42], and involved participants fromthe domain field, who were asked to perform tasks representing the aims for the research [16, 78].The tasks were chosen to test the explorative methods of the implementations, in order to locateand identify relations between attributes, specific areas of interests and outliers. The evaluationshowed that the ability to do a visual analysis exploration was highly appreciated compared totraditional methods. The concept of starting an analytical process with the big picture first andthen looking at patterns and narrowing down the data, had major advantages. Furthermore, theability to interact directly with data, the dynamic linking between the representations and thatdynamic queries immediately reflected the visual content in all views, was considered very use-ful. In regards to the visual representations, the spatial organization and choice of representationswere logical and clear, although there was a lack of functionality to reduce the cognitive com-plexity when there were many attributes. A method for eliminating attributes that were deemednot useful in the exploration would decrease the cognitive complexity of the representations, inthe same manner that a filtering operation reduces the amount of voyages. Considering the inter-action mechanism, the participants were overall pleased with the functionalities, although theythought that the functionalities should be better highlighted to make them easier to discover.

3.1.3 ContributionsThe main contribution within visual analysis of voyages is the VISPER and SWIM applicationspresented in the results section. In the area of shipping and weather, the use of visualizationhas so far been very limited. The introduction of the geovisual analytics applications and thetechniques used is a significant contribution. This contribution has later on been further adaptedin the form of systems developed within this area, and is today used on a daily basis for decisionsupport and knowledge gain.

Paper I shows how visual analytics can be used to explore data from performed voyages tofind patterns and trends that can influence the planning of future voyages to improve optimiza-tion. The data is displayed on a voyage level keeping the focus on the individual ship and voyage,while at the same time giving a general overview of the fleets performance. With appropriate in-teraction techniques, such as filtering and highlighting, trends are visually enhanced, which playsa significant role in the understanding of the data. Also, the synergy between multivariate datain the parallel coordinates and the logistics visualization in the map, has been a new area to beexplored. The usability study has demonstrated that VISPER gives the analysts a new powerfulgeovisual analytics tool to explore the multi-source, time-varying and geospatial digital infor-mation that has for years been available to the shipping companies, but never previously fusedtogether and analysed visually.

Paper II complements the contribution within the voyage analysis area by applying the tech-niques and lessons learned in the area of monitoring of voyages on a fleet level. Voyages whoseships are being exposed to harsh weather can easily be brought to focus, and detailed route spe-cific weather and voyage information can be retrieved. Discovering voyages that fulfils certainweather criteria is an import feature, which is made possible using filtering and picking tech-niques that allows analysts of different experience level to explore and make sense of the spatialdimension in the data. Both known and previously unknown issues regarding visual and interac-

3.2 Road Weather Visualization 33

tive representation brought strengths and weaknesses to light during the evaluation. These issueswill constitute the foundation for further development.

As conclusion, both of these systems provide an effective and intuitive approach to under-standing and exploring the data, to gain knowledge that has never been experienced within thisdomain. The ability to combine traditional analysis methods with novel interactive ones givesthe analysts a better understanding of the impact of weather on voyages. With visualization tech-niques and interactions, the analysts get immediate feedback during the process and can performthe analysis more effective and efficient.

3.2 Road Weather Visualization

With the knowledge gained from the previous research within voyage analysis, the techniqueswere now applied to a different time-varying geospatial information source, that of the stationaryweather sensor network. Within this area, the focus was on the weather stations used for moni-toring the weather along the Swedish roads, and the forecasts for these stations. These forecastare for a spatial point where there also is a sensor that can measure observational data, whichallows for a verification of the forecasts.

Paper IV focuses on the implementation of a real time monitoring system of the currentweather situation and forecasts. Since preventive methods are taken using these forecasts thesystem has to be able to give a clear overview of the situation, while at the same time allowingfor a deeper study of the details. As inaccurate weather forecasts can mean unnecessary costsin either giving false alarms, or worse missing an alarm, it is important that these forecasts areas accurate as possible. This is addressed in paper V, where geovisual analytics techniques havebeen applied to improve the weather forecasts from previous paper. The objective in this paperhas been to create methods for analysing the forecasts and comparing them to observational data.To see if there are any trends in forecasts errors, and also be able to communicate the accuracy ofthe forecasts for the different parameters and on the different time scales. This verification canhelp meteorologists find specific scenarios where the forecast models can be improved and leadto safer roads.

3.2.1 Aim

The aim with this part of the research has been the design and implementation of an interactivevisual exploration approach, which facilitates the exploration of complex weather data and theeffect it has on the safety on the roads.

The following key user motivated and task-based designs for monitoring of the road situationwere defined in cooperation with domain experts:

• Find warning scenarios and alert the user of the risks

• Deploy multiple-linked visual representations to allow for a simultaneous analysis andvisualization

34 Contributions

• Demonstrate how interaction techniques can facilitate exploration and discovery

• Apply iterative design and frequent usability inspections to gather insights for enhance-ments and future development

• Furthermore, the exploration of data should be performed in real time with the latest fore-cast data available, and automatically look for and download updated data without inter-rupting the exploration process

For evaluation of the forecasts during the winter season, the following major exploratorytasks were chosen:

• Investigation of significant trends and patterns for verification of the underlying forecastmodel

• Communication of accuracy measurements for competitive comparisons

• Visual analysis on multiple levels of detail; from the full winter season down to individualhours

3.2.2 Results

For real time monitoring of weather forecasts, the application RoadVis (figure 3.7) was imple-mented, which uses the data from the weather stations placed alongside the Swedish roads. Theimplementation of this system is described in Paper IV. For exploratory weather data verifica-tion, forecasts and observational data from the weather stations have been used in an applicationdeveloped for facilitating identification of significant trends and patterns within weather data,which is described in Paper V.

Monitoring systems for weather stations have been used earlier, but have largely been basedon the old web technology and lacked the interactivity and functionality that represents a goodgeovisual analytics implementation. The standard has been to use tables that list the stations andweather parameters, together with a map showing the current situation using text annotations.The RoadVis implementation uses an interactive map as the main representation, where eachweather station is plotted using its position, and coloured according to the attribute selected. Theattributes for the colour are based on the different scenarios that can occur depending on theweather, such as ice slipperiness or condensation, and for each attribute the different conditionsare calculated. If more than one condition applies, then the one that has the highest severity ischosen. The temporal dimension has been added to the map with a time slider so that the analystcan change time step during their analysis, thus making it possible for the analyst to see how theweather evolves hour by hour. This functionality replaces the old systems method of using a signon the text annotation which then indicated that a condition would occur in the next hour.

To complement the map view a meteorological diagram, meteogram, was chosen as themethod for analysing one station over many parameters and time steps, as shown in figure 3.8.The benefit of this view is that it has been used as a standard within the domain, although not in


Figure 3.7: The RoadVis multi-linked visual user interface, where stations, represented by acircle, are here coloured according to temperature. Meteorological diagrams are shown for twoselected stations in the map. A parallel coordinates plot allows for dynamic inquires in themultivariate weather data. A time slider controls time steps for the time-linked views.

an interactive form, but rather as generated images. Unlike other views that could show the samedata, the meteogram does not add too much complexity. The implementation of this meteogramview uses an overview and details approach, where the analyst can chose to either focus in detailon a few stations, or have a broader overview of multiple stations. This functionality is based onthe approach of using an area of the screen that is dedicated to the meteograms where they arescaled depending on the available space. Thus, when two meteograms are selected the area issplit evenly between them and so on. Zoom functionality has been added so that when sharingthe area between many meteograms, the analyst can maximize one meteogram so that it coversthe entire area, and then minimize it back down.

For visual analysis of multiple attributes for all stations, the parallel coordinates view waschosen. The parallel coordinates facilitates exploration of relations between the attributes, com-parison of profiles between the stations (figure 3.9) and identification of extreme values. Theparallel coordinates view is here also used as a dynamic filtering mechanism, where the analystcan chose to remove stations based on an attribute and threshold. The main advantage here is thatas the analyst changes the threshold, the parallel coordinates view shows the stations remaining,and the analysts can immediately get feedback on what is being removed. Taking advantage ofthe results from the visual analysis of voyages research, the parallel coordinates view here has

36 Contributions

Figure 3.8: A scenario where the analyst has filtered in the parallel coordinates so that thesouthern part of Sweden is in focus. Four different stations throughout this area have beenselected and are shown in the meteograms view. These meteograms display the current weathercondition and the forcast for the next six hours. From this, the analyst can get both an overviewof the area, but also detailed weather information, both present and forecasted, for the selectedstations.

Figure 3.9: In the parallel coordinates plot three stations are highlighted and the analyst cancompare them against several indicators. Patterns never before discovered are visually nowobvious to the analyst.


Figure 3.10: The application is developed to assist the weather forecast verification work atSMHI. Weather data can be examined in different components and on different levels of detail.The application consists of four overview components: table lens (a), glyph map (b), SOM (c)and frost time shift plot (d), and two detail components: detail data plot (e) and time line plot (inthe second tab, next to e). In f, three tabs contain extra information about the selected stations.

the option of removing or adding attributes so that the complexity of the view can be changed.

Paper V continues the research from Paper IV, and focuses on the analysis of forecast toverify the parameters in regard to the observed values measured by the stations (figure 3.10).Since the data is on an hourly level, and the verification should be performed on an entire season,the concept of overview and detail has been a key approach in the development of an applicationfor exploratory visualization for weather data verification. The data used in the application hasbeen pre-processed so that for each station, both forecast error and accuracy parameters havebeen calculated. These parameters are then used in the overview where the entire season isanalysed and where the analyst can find which stations are constantly getting bad forecasts.

For the spatial dimension there are two aspects that are of importance, the location of thestation, and the environment of the station. In regard to the location, a map representation is usedso that the analyst can see if there is any pattern in errors that could be based on the geographicalposition, such as a certain area on the map constantly having too warm forecasts. The secondaspect is the environment of the station. Since these stations are often located in geographicalpositions where bad weather conditions have a higher chance to occur, it is of interest to seeif there are any patterns in the forecast errors due to environment. As example, stations nearrivers are often colder than the area around them which makes it easier for condensation to turninto frost. To examine if there are any environmental patterns, a clustering approach has been

38 Contributions

(a) All parameters (b) Surface tempera-ture

(c) Frost (d) Distance to neigh-bours

Figure 3.11: A SOM is used to examine clusters using a specific parameter category. In (a) themap is calculated from all parameters, in (b) it is based on the surface temperature parametersand in (c) the frost parameters. In (d) colour is used to convey similarities between neighbouringnodes, from green (small values) to brown (large values).

adopted. In this implementation, the self-organizing map (SOM) [32] representation was chosen,as it uses a spatial clustering approach which complements the map. The input for the SOMcan be changed so that clustering is only performed on a specific parameter category, such astemperature forecasts. The visual output from the SOM shows the clustering and can potentiallyhighlight groups of stations that have similar forecast errors, but are geographically distant fromeach other. From this, environment factors of the stations can be examined to see if they have anyinfluence on the forecast errors of the group, thus showing that certain factors can create trendsin the errors of the forecasts.

As the analysts can find trends and patterns in the overview, there is also a need to go intothe details and see when and how the forecast errors occur. For this, the analyst can use a de-tailed view that either shows the raw data day by day, or the analyst can choose to look at thecalculated mean values for a timespan. Within this timespan, the analyst can see if the accuracyof the weather forecasts differ during certain hours of the day, and also how the forecasts createdduring the day differ depending on when they were made.

For both these papers, a formative evaluation was carried out. These formative evaluationswere based on the same techniques used in the earlier papers. The results from these evaluationsshow that the tasks were completed by both test groups with little or no difficulty, and that thegeneral opinion was that the techniques were easy to use after a short introduction. In comparisonto traditional methods, the usage of interactive visualizations speeds up the decision makingprocess, and increases the flexibility of the analysts, and is a good foundation for knowledgeconstruction.

3.2.3 Contributions

The main contribution lies in the implementation of the two systems described in paper IV andpaper V, and the introduction of geovisual analytics in the domain area to speed up the pro-

3.3 Storytelling Framework 39

cess and results compared to traditional methods. This contribution has later on been furtheradapted in the form of systems developed within this area that are today used on a daily basis forimproving the safety on the roads.

Paper IV demonstrates how real time monitoring of weather forecasts can use geovisual an-alytics methods for exploration of spatio temporal data with multiple attributes for comparingprofiles and identification of extreme values. Through the flexible exploration interface, the ana-lyst can use overview and details techniques for monitoring of the stations. This approach givesthe analyst the freedom to decide the direction of the exploration depending on the exploratorytask [34], while at the same time keeping the overview open with the latest data for the monitor-ing of changes in the new data. Using a real-time visualization solution, compared to the earlieruse of images and tables, the focus of the analyst has lifted from being short and narrow, to beingable to look at the bigger picture over a longer time.

Paper V continues the research within the area focusing on verification and analysis of roadweather conditions working with hourly data from one or more seasons. The results from aninformal user study with meteorologists showed that the application has great potential to helpthem in their verification work. The use of interactive visualization was for them a new wayof working with weather data verification, and was seen as a good complement to the tradi-tional manual methods currently used. The use of novel methods for visual exploration of large,multivariate and spatial temporal data has simplified the process of finding spatial and temporalregions where weather forecasts models needs to be improved, and has helped identify inaccurateweather stations.

Overall, the combination of tools for monitoring and verification of weather forecasts usinglinked views and interaction techniques have shown good potential for improving the speed andresults compared to earlier processes. The intuitive exploration interface has enhanced the abilityof the analyst. Being able to see both the overview and details, and the techniques for comparisonof stations with a spatial spread, has helped to uncover new trends not seen earlier in weatherverification. Furthermore, evaluations show that visual analysis methods can augment an analystand decision-maker capabilities to assimilate complex situations and reach important knowledge.

3.3 Storytelling Framework

During a visual exploration process, the steps taken and the results achieved are often forgottenunless they can be documented. For this, the analyst needs a method to capture the knowledgegained and store it in a way that when the capture is later on recreated it should allow the analystto continue from the point they were. Many systems today does not support this, and the capturebecomes a static result that can no longer be continued on or changed. Therefore, a dynamiccapture method is needed where it is the state of the exploration process that is captured and notjust the result.

Geovisual analytics is not only for exploration and discovery, but should also support thecommunication and publishing of the visual exploration process. For this, a mechanism is neededto create a coherent cognitive workspace that holds the achieved discoveries for organizationaland navigational purposes. This mechanism also need to allow for the discoveries to be edited,

40 Contributions

so that they may be improved and at later time published.For publishing, storytelling is one of the most powerful ways to teach, learn and persuade,

and dates back to the tradition of telling stories and sharing them. It lets the user find the samemeaning in the data as the domain experts who have the knowledge behind the numbers. With agood story, the data becomes more interesting and engaging and holds the audience’s attentionand can leave a lasting impression.

3.3.1 AimThe aim of this part of the research was to design and implement a generic system for knowledgecapture of the visual exploration process. The primary goal, as presented in paper VI, was tocreate methods for snapshots that can capture steps in the exploration process. The second goalwas to enhance these snapshots with storytelling so that the snapshots can be organized andassociated explanatory text can complement the snapshot.

• Snapshot: Support the storage of interactive events in an analytical reasoning processthrough ”memorized interactive visualization views” or ”snapshots,” that can be capturedat any time during an explorative data analysis process

• Storytelling: Composition of texts and visual representations together with snapshots,metadata, hyperlinks and references to create a cognitive workspace for organization ofthe snapshots

To evaluate the process of storytelling the methods will be implemented in the GeovisualAnalytics Visualization (GAV) Framework, to facilitate capture of the visual exploration processwithin this environment.

3.3.2 ResultsStorytelling functionality has been added as part of the GAV Framework as described in PaperVI, so that the applications using the framework are now no longer exploration and discoverytools, but also supports the creation of a social style in the use of geovisual analytics. Storytellingis achieved through a mechanism in GAV that supports the storage of interactive events in ananalytical reasoning process through ”memorized interactive visualization views” or ”snapshots”that can be captured at any time during an exploration process. A series of snapshot captures thencreates the foundation for a story which allows for understanding to be shared as the snapshotcan now be commented. This story can be followed by a reader, and as they go through thesnapshots they can see the same view as the author. The reader may then also make changes tothe visualizations such as highlighting other areas of interest. This new discovery may then bestored as a new snapshot which can be inserted into the story so that the story evolves.

A snapshot contains the collection of representations used in the visual exploration process.For each snapshot, the snapshot system scans through all active views and gathers their state.This is achieved by going through every representation and for each representation request theproperties that are to be captured. Each of these properties will then be stored in a node tree

3.3 Storytelling Framework 41

Figure 3.12: The state of of each component is stored in a node tree. These states can later beapplied back to each component and thus an analytical result can be restored.

so that when a snapshot is later on activated, the saved state of the snapshot can be read fromthe node tree and applied on the representations. This node tree can also be stored as seen infigure 3.12 for later use. Together with the state of the representations, the data and a referenceto the geographical representation is stored in the snapshot. In a typical scenario, the analysthas selected the relevant data and representations for the snapshot, e.g., time step, variables, dataitems, selections, colour scale and filter settings. Then as the analyst captures the snapshot allcomponents within the application stores their properties, thus saving the state of the application,which later on can be recreated.

When storing the snapshots, the analyst can add an associated explanatory text that helpsprovide a richer functionality, as the snapshots are inserted into the text as hyperlinks, the textcan be read as a story (figure 3.13). This aids the reader as the text describes the snapshot andcan help the reader to understand what has been captured, and the hyperlinks can be clicked andthe snapshot associated to the hyperlink is activated and restores the application to the state itwas when the snapshot was captured as demonstrated in figure 3.14. Web hyperlinks can also beinserted so that further information can be obtained from external resources, creating a broaderbase for understanding. For a reader, the use of snapshots and a few associated words make thedifference between pretty pictures and understanding.

3.3.3 Contributions

The paper presented in this section contributes with methods for how an integration betweenvisualizations and storytelling can be made. This integration and usage of storytelling makesthe visual discoveries more comprehensible and accessible for a greater audience. The approachshown in this paper allows the analyst (author) to communicate in a new way, where the visualdiscoveries are highlighted, and associated text describes them. Unlike many earlier implemen-tations, the snapshots also allows for the readers to be in control of the story and explore thedata on their own, guided by the stories from the experts. With interactive data visualization andstorytelling, the user can interrupt the story, and as interaction is available, the user can verifythat the story represents what it claims. Allowing users to interact with the visualization takesthem from being passive viewers to active participants and increases their involvement.

42 Contributions

Figure 3.13: Example of a use of the Snapshot mechanism, where eight different snapshotshave been linked to the text in the story editor.

Figure 3.14: Example of a snapshot linked to the metadata in the story text. A few associatedwords and a snapshot can provide the difference between a pretty picture and understanding.

3.4 Storytelling in Context 43

Figure 3.15: Multiple views dashboard visualization with associated storytelling panel basedon statistical national data from the World Bank. A compelling story about the world ageingpopulation problem during 1960-2010; map (age 65+) with 4 map layers including the pie chartlayer (age 0-14 vs. 65+), treemap showing total population (rectangle size) and age 65+ (colour)and bar chart (65+).

3.4 Storytelling in Context

In paper VII, the usage of storytelling is exemplified through the use of statistical data, andexamples of the implementation of snapshots and storytelling is shown. In addition to this,a publishing system is described in which a Vislet is generated, and the use of the Vislet isexplored. A Vislet is a simple dashboard (widget) assembled from the representations available.The Vislet facilitates the translation from an exploration and analytical context, to heterogeneousand communicative sense making news. Thus, improving on the publishing process of geovisualanalytics as the author of the story can adapt the Vislet to the environment the story is published.

For the use of storytelling within the statistics community, the 4Ws ”What-When-Where-Why” concept (figure 3.15) is used to create the story. The ”What” are the data, and the repre-sentations used to represent one or more variables in the data. ”When” is the time step the data isshown for and also the animation of change over time, either by a time representation or throughthe animated representations. ”Where” takes in the spatial dimension and is used for showingwhich region is considered. Finally the ”Why” is the most important aspect and is explained inthe associated text that describes the story.

To introduce the use of storytelling within the statistics community, and later on also as an

44 Contributions

educational tool within the classrooms, a process had to be adopted where a user could importdata, assign representations, create a story and finally publish the story. This process lays thefoundation of the creation of the exploration and publishing concept.

3.4.1 AimThe conceptual approach and framework of the geovisual analytics storytelling implementationis based around three complementary characteristics:

• Authoring: data provider and manager, motion visual representations including choroplethmap, scatter plot, table lens, parallel axes, time graph, data grid, coordinated views, maplayers, analytic tools (dynamic query, filter, regional categorization, profiles and highlight),and dynamic colour scale

• Tell-a-story: snapshot mechanism that captures an interactive scenario (active views, in-dicators, attributes, time step and regions), metadata with hyperlinks, story and chapters,edit, capture, save, and export story

• Publisher (Vislet): import stories and create HTML code for embeddable interactive mo-tion visual, representations for publishing on a web site or blog

3.4.2 ResultThe result of the research is an introduction of a geovisual analytics tool for integrated statis-tics analysis. Statistical news content is created in an automatic authoring process involving dataloading, analysis and storytelling which facilitates collaboration and publishing. Within this con-text, storytelling is about sharing the knowledge of the statistics data, and the related reasoningprocess behind the knowledge discovery performed by statisticians. The knowledge gained andspread in the form of the story is consumed by policy makers, teachers and also informed citizenswho with an interactive web context are more easily engaged, and with a better knowledge caneasier achieve informed decisions. With stories around the statistics data, the user can easier un-derstand how the statistical indicators may influence society. The process utilised for exploring,presentation, communication and publishing of statistics data is divided into the following tasks:

• Import statistical data through Excel, World Databank or other statistical formats

• Explore and make discoveries through trends and patterns found within the representationsand derive insight

• Document the visual exploration and knowledge discovery process through snapshots andassociated descriptive meta text. Connect the snapshots to the text through hyperlinks

• Share the stories with colleagues and reach consensus and trust through collaboration andinvolvement. With the feedback and continuation of the story from the colleagues, thestory can adapt and evolve as the analytical reasoning process is shared


Figure 3.16: The analytical storytelling loop.

• Publish the story to the community using a ”Vislet” that is embedded in blogs or web pages

With this process, statisticians with a diverse background and expertise can participate inthe creative discovery process (figure 3.16) that transforms the statistical data into knowledge.With the ability to collaborate, a shared understanding between statisticians is formed and whena consensus is reached the story can be shared to the public domain. With the snapshots in thehyperlinks, the statisticians can guide the readers to important visual discoveries.

When a story has been created, and the understanding has been shared with other statisticians,the story is ready to be published (figure 3.17). In the publishing process, the story is importedfrom the analytical tool into a publisher where a Vislet is generated. For the Vislet, the authorfirst selects an appropriate layout for the visual representation, which depends on where theVislet is published. This layout can be changed between one or multiple visualizations and alsothe orientation of the layout. When the layout is chosen, the author selects the visualizations tobe used, Vislet size, metadata options and sharing methods. Finally, the publisher generates anHTML code that represents the Visle,t which can then manually be embedded into a webpage.

46 Contributions

Figure 3.17: Storytelling facilitates three complementary characteristics: explore and gain in-sight, tell-a-story and publish (Vislet) which finally can be embedded in a web page/blog usingthe HTML code.

The reader can then open the webpage, and the story is dynamically communicated throughthe web browser. The story file is maintained on the publisher server while the Vislet runslocally in the clients web browser and can, therefore, achieve interactive performance where thelimit is only on the client the Vislet is running. The Vislet maintains the features of the visualexploration process where all visualizations are dynamically linked, and interactivity is availablewith tooltips, brushing, highlight and filters that can discover outliers.

The code of practice for European statistics, with regard to dissemination, states that ”Eu-ropean Statistics should be presented in a clear and understandable form, disseminated in asuitable and convenient manner, available and accessible on an impartial basis with supportingmetadata and guidance”. This is a principle of accessibility and clarity. To comply with this,in collaboration with OECD, the interactive Web document ”Regions-at-a-Glance” (figure 3.18)was developed. This document presents statistical indicators ranging from economic, social, de-mographic and environmental fields for more than 2000 local OECD regions from all over theworld. An interactive document is based on a wider storytelling concept, where Vislets play therole of images and figures. This adds another depth to reports or publications by making dia-grams interactive, which allows the reader to reach a deeper understanding and further explorethe subject. Readers can run animations, change indicators and view more details on specificfigures. For the interactive document, the analysts at OECD created snapshots for the regionsat a glance data dividing the snapshots into chapters and sections. This was then the foundationof the document where text was added to the sections and sections were combined to form achapter. An interactive web document was then created where the reader could navigate throughthe chapters and for section see the associated snapshots. Extra features were added so that thereader could download the originally printed document as well as the data behind the visual-izations. To facilitate communication, methods for sharing were added so that the reader couldembed the Vislet on their own web page or blog.


Figure 3.18: The interactive web document Regions-at-a-Glance published by OECD.

Educators and students are generally familiar with the storytelling paradigm, and the intro-duction of visual storytelling gives them an option of enhancing their learning and knowledgeconstruction. To evaluate the usage of visual storytelling in school the project Visual StorytellingIn Education (VISE) was started which involved public junior high schools in a municipality inSweden. The aim for the project was to see if the teaching in social science could be improved,as well as a study of the teachers’ and students’ experience. In the beginning of the project, theteachers are introduced to visual storytelling and how to create stories. During classes, the teach-ers produces stories, and the pedagogical aspects of how to use visual storytelling are discussed.At the end, the teachers have produced multiple Vislets to be used in teaching where they havechosen, for example, a geographical area or theme available from the statistical data. The Visletis also complemented with additional teaching material from the web, such as texts, images andvideo. With the usage of Vislets, the students can follow the lesson planned out by the educator,but they may also explore and create questions on their own. The aim with this project is todemonstrate that visual storytelling will give educators innovative tools that can make nationaland regional statistics interactive, visually understandable and usable to the students. It will alsochallenge the students as new theories can be studied and communicated back visually.

48 Contributions

3.4.3 ContributionsBased on the idea of making use of both the knowledge of the statistics data as well as the relatedanalytics reasoning process behind the knowledge discovery performed by statisticians, the paperdescribed in this section contributes to a seamless environment for production, presentation anddissemination of knowledge insights. Firstly, by providing a production environment for the vi-sual exploration process with integrated methods for knowledge capture based on the snapshotstechnique, thus providing the analyst with methods to build on the analytical result during thecourse of the exploration process. Secondly, by integrating the captured snapshots into a storyused for presentation where associated explanatory texts, hyperlinks and other metadata can beadded providing a structure for guiding the reader in the knowledge building process. Finally,the paper contributes with methods for dissemination where the analyst can share the informationwith the intended audience using a Vislet. The usefulness of this seamless environment is demon-strated through case-scenarios involving statisticians creating statistical news content sharing theknowledge discovery performed by statisticians. This environment has also been introduced inthe form of visual storytelling within education to give educators innovative tools that can makenational and regional statistics interactive, visually understandable and useable to the students.

Chapter 4

Conclusion

The research described in this thesis has focused on two areas, where the first area is the explo-ration and analysis of complex weather data and its effect within analysis of voyages and roadsafety. Even though the methods implemented and presented are mainly used within these twofields, they are applicable to a wider range of analysis of weather. The second area is that ofstorytelling, and how the analytical reasoning process can be captured and shared to a wider au-dience. Although this has been evaluated in one system as a seamless environment, the methodsand functionalities can be applied within other systems. This chapter will first summarize themain contributions of this thesis work, conclusions will then be drawn, and some suggestions forfuture directions will be discussed.

4.1 Summary of contributions

The main contributions of the presented research work, which have been described in detail inthe included papers and chapter 3, can be summarized as:

• geovisual analytics systems that facilitate the interactive exploration of complex weatherdata in analysis and monitoring of voyages

• the use of geovisual analytics techniques and methods to facilitate monitoring of roadweather and weather forecast verification

• formative evaluations with domain experts studying the performance of the use of geovi-sual analytics for visual exploration of patterns and trends in complex weather data

• a framework for capturing the analytical reasoning process utilizing the approach of snap-shot to save the state of the exploration and highlight visual discoveries

• the use of storytelling to organize the visual discoveries and add associated explanatorytext and links, for providing a structure for guiding the reader in the knowledge buildingprocess

• case-scenarios involving statisticians creating statistical news content, sharing the knowl-edge discovery performed by statisticians

50 Conclusion

Several of the contributions are based on a stepwise evolvement, where the knowledge gainedfrom one of the contributions has been the inspiration and motivation for a following contribu-tion, and are part of a longer learning process. The knowledge gained from each individualcontribution is therefore also part of the contribution of this research.

In the context of applying geovisual analytics, the research has been built upon the theoreti-cal foundations of reasoning, sense-making, cognition, and perception, in order to create visuallyenabled tools to support collaborative analytic reasoning about complex weather data. These newtools provide efficient and effective methods for analysis of spatial and temporal data, within adomain area where there has been a lack of methods facilitating visual exploration. The tools andmethods presented in papers I and II have been further refined and incorporated into the dailyoperation of fleets, for monitoring and analysis of ship voyages by operators and meteorologists.Paper IV contributes with a new monitoring tool for real time analysis of the weather situation,and the impact of weather on the road situation, which has further been refined and used as deci-sion support during the winter season. In paper V, the methods used are being further evaluatedby meteorologists in their work to find areas of improvement in the weather forecasts.

The concept of snapshots and associated text, where snapshots can be hyperlinked, were thebasis for the storytelling concept, for capturing the analytical reasoning process, introduced inpaper VI. This has then been further expanded upon with a structure for guiding the reader inthe knowledge building process. In paper VII a pipeline for visual exploration, packaging, pre-sentation and dissemination in the integrated exploration, collaboration and publication process,is presented. Multiple case-scenarios involving statisticians creating statistical news content, ispresented as a proof of concept where they share statistical knowledge as news content usingVislets as the method for dissemination. This advanced storytelling technology can be very use-ful for educational purposes, where teachers can communicate with interested students throughvisual discoveries captured in snapshots with descriptive text.

4.2 Conclusions

Part of this thesis has been primarily concerned with developing and evaluating methods of usinggeovisual analytics techniques and methods with weather data, in particular the fusion of weatherdata with voyage and road traffic information. The use of visual exploration for analysing thistype of data has so far been limited, and it is therefore a threshold that needs to be overcomewhen adapting the methods to this field. For this, formative evaluations have been used with thepurpose to explore qualitative usability issues, with respect to visual representations and inter-active representations, and highlight what adaptations are needed and what the user’s responsewas to these novel methods. From the evaluations it has been shown that the visual analysisexploration process was highly appreciated. The ability to look at the big picture first and thenlooking at patterns and narrowing down the data also had major advantages compared to tradi-tional methods. The evaluations have, in addition, revealed limitations that have then further onbeen addressed.

With appropriate interaction techniques, such as filtering and highlighting, trends are visuallyenhanced, which plays a significant role in the understanding of the data in ways that have not

4.3 Future work 51

been seen before within the field of voyage analysis. The methods have given the analysts theoption of doing an explorative analysis, meaning that the analyst has no clear hypothesis of whatto look for, which has resulted in findings that earlier have not been noticed. The synergy betweenmultivariate data in the parallel coordinates and the logistics visualization in the map, has been anew area to be explored. This synergy has been further adopted by other implementations withinthe voyage analysis field, and is today part of multiple newly developed systems.

For exploration and analysis of road weather, as discussed in this thesis, the combination ofoverview and detail together with interactive methods enables the use of analysis of data on abigger scale than before. The analyst can with their domain knowledge chose to either look atthe overview of the data to find correlations and patterns, or dig down deeper once somethinghas been discovered, and analyse the reasons for the result. Furthermore, the temporal aspect hasbeen highlighted, and the analyst can choose to look at cyclical patterns to see how the accuracyof the forecasts changes during the time of the day, and depending on which time the forecastwas made. This gives the analyst a deeper understanding of the temporal aspect, and how itrelates to the spatial dimension as these cyclical patterns may change depending on the locationof the forecast.

Considering visual analysis as a method for gaining insights and creating knowledge, theutilization of methods for capturing and sharing this knowledge may often be necessary, asit enables the analysts to communicate what they know through the use of appropriate visualmetaphors and graphic representations. Too often this step in the analytical process is over-looked, but it should be part of the process as the output is the only part that is visible to theconsumers of the analysis. The use of storytelling and snapshots creates a seamless environmentfor production and dissemination of results, as the consumer takes part of the same process asthe analysts, and can follow the exploration process as it is highlighted in the story. Thus, theinsights gained can be shared and may in the end contribute to a further exploration process bythe consumer, taking them from being passive viewers to active participants, and increasing theirinvolvement.

4.3 Future work

This research thesis has addressed several aspects concerning the exploration of complex weatherdata within the two discussed fields, but there are still many interesting future fields where theintroduction of visual analysis would improve the efficiency and performance of daily work.While new and more efficient techniques for representing data are developed, there is a riskthat the distance between researchers and users keeps growing, and that the innovative methodsnever comes to use. Consequently, further research can be done within the area of identifyingthe best practices for inserting geovisual analytics technologies into operational environments.Formative evaluations have helped to identify the usability of these new systems, but even furtherevaluations can be performed focusing on the usability of systems and techniques within this, andother task and domain based contexts [57]. The visual representations within these systems havebeen identified based on the tasks they need to support, but other representations and methodsmay add to the analytical reasoning process and uncover further patterns in the data.

52 Conclusion

As the size and complexity of data continues to increase, the issue of scalability in the repre-sentations arises. This thesis has mainly focused on the visual exploration of the data availabletoday, and in a near time, but in the future the methods and representations need to be adapted tohandle the ever increasing amount of data that can be collected. The combination of algorithmsand visualization methods will help in this process, and a more integrated combination of datamining and visualization will be the first step of the exploration. With this combination, there isa risk that the interfaces will not be as intuitive as today. Thus, the user may need to be guided inthe analysis with suggestions and possible analytical paths and interactions that can be performedon the data [43].

The use of storytelling has many interesting areas where it can be applied, where focus so farhas been on using text and pictures to convey the insights gained. The ability to do continuouscaptures during the exploration process gives the analyst a higher flexibility when presenting theresult. With the snapshots, the analyst can go back to a part of the exploration process and duringpresentation recreate the steps taken to gain insights. It also allows for ”what if” questions,where the analyst can see how taking another path in the exploration could have given additionalinsights.

Geovisual analytics storytelling tools together with Open Data, opens up for opportunities forjournalists, educators and researchers, and can quickly become a key technology for tomorrow’seditorial [44]. By simply accessing open data, the author of a story can be a narrator describingthe data and what it actually means to the world. So far, with the huge manual effort required,only a few pioneers have shown how data sources can be drilled down to create deeper insightsinto the dynamics of complex situations. The integrated exploration, collaboration and publica-tion process opens up for the owners of data to share their insight and knowledge, and it can alsoenable the users to take a more active role in the discovery process. The use cases with statisticaldata has only been a first step in using storytelling and many more areas can benefit from it.

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