SkiVis: Visual Exploration and Route Planning in Ski Resorts

Optimal ski route selection is a challenge based on a multitude of factors, such as the steepness, compass direction, or crowdedness. The personal preferences of every skier towards these factors require individual adaptations, which aggravate this task. Current approaches within this domain do not combine automated routing capabilities with user preferences, missing out on the possibility of integrating domain knowledge in the analysis process. We introduce SkiVis, a visual analytics application to interactively explore ski slopes and provide routing recommendations based on user preferences. In collaboration with ski guides and enthusiasts, we elicited requirements and guidelines for such an application and propose different workflows depending on the skiers' familiarity with the resort. In a case study on the resort of Ski Arlberg, we illustrate how to leverage volunteered geographic information to enable a numerical comparison between slopes. We evaluated our approach through a pair-analytics study and demonstrate how it supports skiers in discovering relevant and preference-based ski routes. Besides the tasks investigated in the study, we derive additional use cases from the interviews that showcase the further potential of SkiVis, and contribute directions for further research opportunities.Comment: 11 pages, 10 figure

Route Packing: Geospatially-Accurate Visualization of Route Networks

We present route packing}, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes. The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical colors, and decorates them with glyphs to show their directions. Furthermore, nodes representing sources and sinks use glyphs to indicate whether routes stop at the node or merely pass through it. We conducted a crowd-sourced user study investigating route tracing performance with road networks visualized using our route packing technique. Our findings highlight the visual parameters under which the technique yields optimal performance

Visualizing and Interacting with Geospatial Networks:A Survey and Design Space

This paper surveys visualization and interaction techniques for geospatial networks from a total of 95 papers. Geospatial networks are graphs where nodes and links can be associated with geographic locations. Examples can include social networks, trade and migration, as well as traffic and transport networks. Visualizing geospatial networks poses numerous challenges around the integration of both network and geographical information as well as additional information such as node and link attributes, time, and uncertainty. Our overview analyzes existing techniques along four dimensions: i) the representation of geographical information, ii) the representation of network information, iii) the visual integration of both, and iv) the use of interaction. These four dimensions allow us to discuss techniques with respect to the trade-offs they make between showing information across all these dimensions and how they solve the problem of showing as much information as necessary while maintaining readability of the visualization. https://geonetworks.github.io.Comment: To be published in the Computer Graphics Forum (CGF) journa

A Survey on Transit Map Layout – from Design, Machine, and Human Perspectives

Transit maps are designed to present information for using public transportation systems, such as urban railways. Creating a transit map is a time‐consuming process, which requires iterative information selection, layout design, and usability validation, and thus maps cannot easily be customised or updated frequently. To improve this, scientists investigate fully‐ or semi‐automatic techniques in order to produce high quality transit maps using computers and further examine their corresponding usability. Nonetheless, the quality gap between manually‐drawn maps and machine‐generated maps is still large. To elaborate the current research status, this state‐of‐the‐art report provides an overview of the transit map generation process, primarily from Design, Machine, and Human perspectives. A systematic categorisation is introduced to describe the design pipeline, and an extensive analysis of perspectives is conducted to support the proposed taxonomy. We conclude this survey with a discussion on the current research status, open challenges, and future directions

Automatic creation of schematic maps : a case study of the railway network at the Swedish Transport Administration

Idag vill man effektivisera användningen av geografiska informationssystem (GIS), vilket uppmuntrar järnvägsförvaltningarna att centralisera sina data. Därför hämtar de både geografiska och schematiska framställningar från samma databas. Men, en svårighet som möter Trafikverket är att olika representationer (geografiska och schematiska) måste uppdateras separat, vilket innebär större kostnader och kan innebära inkonsekvens i riktigheten av dessa två representationer. Således, denna studie syftar till att undersöka hur ArcGIS Schematics automatiskt kan generera den schematiska kartan från en geografisk databas som är lämplig för järnvägsapplikationer. Dessutom utvärdera den om de data som finns i den aktuella databasen som tillhandahålls av Trafikverket kan användas för att skapa schematiska kartor i ArcGIS Schematics. Resultaten visar att ArcGIS Schematics automatiskt kan skapa och uppdatera schematiska kartor över järnvägsnäten. Detta gör framställningen av schematiska kartor mycket lättare för Trafikverket, medan de för närvarande skapar de schematiska kartorna manuellt. Dessutom visar utvärderingen att av de data som används av Trafikverket för närvarande kan användas av ArcGIS Schematics, men vissa förbättringar måste göras på deras datamodell. En ny egenskap måste adderas till datamodellen, som definierar typer av olika delar av spår. Därför måste definieras om tåget är på en genom väg och åker direkt eller över från en divergerande väg som används för att ändra rutten och ansluta olika vägar. Dessutom, kontrollera topologiska kartor för att förbättra riktigheten i data har varit viktigt. Studien innehåller också en jämförelse med hur järnvägsoperatörer i Frankrike och Holland jobbar med schematiska kartor.Today, efficient use of Geographical Information Systems (GIS) encourages the railways administrations to centralize their data. Therefore, they extract both geographical and schematic representations from the same database. But, one difficulty which is encountered by the Swedish Transport Administration (Trafikverket), is that different representations (geographical and schematic) have to be updated separately which imply larger costs and there is also a rise of inconsistency between the two representations. Thus, this research aims at examining ArcGIS Schematics extension for automatically generating and updating the schematic map from a geographical database that is suitable for railway applications. In addition, it wants to evaluate the data in the current database provided by Trafikverket for creating the schematic maps as a suitable input for ArcGIS Schematics. The results show that ArcGIS Schematics extension can automatically create and update the schematic map for railway networks, and it makes the situations much easier for Trafikverket while currently they create the schematic maps manually. In addition, the evaluation of data that Trafikverket uses currently shows that ArcGIS Schematics extension is matched with their data, but some improvements must be done on their data model. It means that a definition for defining the types of different parts of tracks and switch legs regarding as main parts or excluded parts must be added to their data model. Moreover, controlling the topological maps regarding the accuracy of data has been important

Understanding and Telling Stories across Online and Real-world Cultural and Historical Artefacts

Storytelling is a natural way for humans to make sense of their world. Narratives structure experience into expected forms that improve understanding of relationships between discrete objects and events. This is the rationale behind museum curation, which organises objects in the physical museum space to reveal how they are related. This thesis explores how to support people to tell and experience narratives across multiple objects. For the online world, a model of curatorial inquiry is introduced which is designed to support a historical inquiry from online sources. This model extends existing inquiry models and is inspired by museum practice in which curators organize objects into museum narratives. For the physical world, a model is introduced that describes navigation through both the physical and conceptual neighbourhood of a set of objects. It is designed to support tourist activities across a non-portable set of cultural objects, such as statues, buildings, or landscape features. Key findings, based on both participant studies and analysis of data from Foursquare, is that while people are keen to understand stories that link places in a physical space, they prefer to navigate using physical, rather than conceptual proximity, and to visit places that are popular. This is counter to many mobile tour guides that focus on prompting navigation to similar places. The proposal of this thesis is therefore to develop applications that support tourists in understanding both what is physically nearby and conceptually nearby. This would allow them to use physical proximity - or any preferred alternative – to select where to go next, whilst supporting them to make links between the places they visit. In this way tourists would be provided with enough information about the relationships of places within a physical neighbourhood that they can start to understand and create their own stories about them

Revisiting Urban Dynamics through Social Urban Data:

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities? To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.   After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources. A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics. The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities

Revisiting Urban Dynamics through Social Urban Data

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities?  To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.  After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources.  A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics.  The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities

Moving beyond sequential design: Reflections on a rich multi-channel approach to data visualization

We reflect on a four-year engagement with transport authorities and others involving a large dataset describing the use of a public bicycle-sharing scheme. We describe the role visualization of these data played in fostering engagement with policy makers, transport operators, the transport research community, the museum and gallery sector and the general public. We identify each of these as ‘channels’ – evolving relationships between producers and consumers of visualization – where traditional roles of the visualization expert and domain expert are blurred. In each case, we identify the different design decisions that were required to support each of these channels and the role played by the visualization process. Using chauffeured interaction with a flexible visual analytics system we demonstrate how insight was gained by policy makers into gendered spatio-temporal cycle behaviors, how this led to further insight into workplace commuting activity, group cycling behavior and explanations for street navigation choice. We demonstrate how this supported, and was supported by, the seemingly unrelated development of narrative-driven visualization via TEDx, of the creation and the setting of an art installation and the curating of digital and physical artefacts. We assert that existing models of visualization design, of tool/technique development and of insight generation do not adequately capture the richness of parallel engagement via these multiple channels of communication. We argue that developing multiple channels in parallel opens up opportunities for visualization design and analysis by building trust and authority and supporting creativity. This rich, non-sequential approach to visualization design is likely to foster serendipity, deepen insight and increase impact