Extracting Touristic Information from Online Image Collections

In this paper, we present a Geographical Information Retrieval system, which aims to automatically extract and analyze touristic information from photos of online image collections (in our case of study Flickr). Our system collect all the photos, and the related information, that are associated to a specific city. We then use Google Maps service to geolocate the retrieved photos, and finally we analyze geo-referenced data to obtain our goals: 1) determining and locating the most interesting places of the city, i.e. the most visited locations, and 2) reconstructing touristic routes of the users visiting the city. Information is filtered by using a set of constraints, which we apply to select only the users that reasonably are tourists visiting the city. Tests were performed on an Italian city, Palermo, that is rich in artistic and touristic attractions, but preliminary tests showed that our technique could successfully be applied to any city in the world with a reasonable number of touristic landmarks

Describing and Understanding Neighborhood Characteristics through Online Social Media

Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e., tags) that specifically characterizes a region (e.g., neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledgeComment: Accepted in WWW 2015, 2015, Florence, Ital

Preference mining techniques for customer behavior analysis

The thesis has studied a number of critical problems in data mining for customer behavior analysis and has proposed novel techniques for better modeling of the customers’ decision making process, more efficient analysis of their travel behavior, and more effective identification of their emerging preference

REAL TIME ASSISTANCE IN PHOTOGRAPHY USING SOCIAL MEDIA

Ph.DDOCTOR OF PHILOSOPH

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

European Handbook of Crowdsourced Geographic Information

This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research

European Handbook of Crowdsourced Geographic Information

"This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research.

Exploratory Browsing

In recent years the digital media has influenced many areas of our life. The transition from analogue to digital has substantially changed our ways of dealing with media collections. Today‟s interfaces for managing digital media mainly offer fixed linear models corresponding to the underlying technical concepts (folders, events, albums, etc.), or the metaphors borrowed from the analogue counterparts (e.g., stacks, film rolls). However, people‟s mental interpretations of their media collections often go beyond the scope of linear scan. Besides explicit search with specific goals, current interfaces can not sufficiently support the explorative and often non-linear behavior. This dissertation presents an exploration of interface design to enhance the browsing experience with media collections. The main outcome of this thesis is a new model of Exploratory Browsing to guide the design of interfaces to support the full range of browsing activities, especially the Exploratory Browsing. We define Exploratory Browsing as the behavior when the user is uncertain about her or his targets and needs to discover areas of interest (exploratory), in which she or he can explore in detail and possibly find some acceptable items (browsing). According to the browsing objectives, we group browsing activities into three categories: Search Browsing, General Purpose Browsing and Serendipitous Browsing. In the context of this thesis, Exploratory Browsing refers to the latter two browsing activities, which goes beyond explicit search with specific objectives. We systematically explore the design space of interfaces to support the Exploratory Browsing experience. Applying the methodology of User-Centered Design, we develop eight prototypes, covering two main usage contexts of browsing with personal collections and in online communities. The main studied media types are photographs and music. The main contribution of this thesis lies in deepening the understanding of how people‟s exploratory behavior has an impact on the interface design. This thesis contributes to the field of interface design for media collections in several aspects. With the goal to inform the interface design to support the Exploratory Browsing experience with media collections, we present a model of Exploratory Browsing, covering the full range of exploratory activities around media collections. We investigate this model in different usage contexts and develop eight prototypes. The substantial implications gathered during the development and evaluation of these prototypes inform the further refinement of our model: We uncover the underlying transitional relations between browsing activities and discover several stimulators to encourage a fluid and effective activity transition. Based on this model, we propose a catalogue of general interface characteristics, and employ this catalogue as criteria to analyze the effectiveness of our prototypes. We also present several general suggestions for designing interfaces for media collections

Assessing the perceived environment through crowdsourced spatial photo content for application to the fields of landscape and urban planning

Assessing information on aspects of identification, perception, emotion, and social interaction with respect to the environment is of particular importance to the fields of natural resource management. Our ability to visualize this type of information has rapidly improved with the proliferation of social media sites throughout the Internet in recent years. While many methods to extract information on human behavior from crowdsourced geodata already exist, this work focuses on visualizing landscape perception for application to the fields of landscape and urban planning. Visualization of people’s perceptual responses to landscape is demonstrated with crowdsourced photo geodata from Flickr, a popular photo sharing community. A basic, general method to map, visualize and evaluate perception and perceptual values is proposed. The approach utilizes common tools for spatial knowledge discovery and builds on existing research, but is specifically designed for implementation within the context of landscape perception analysis and particularly suited as a base for further evaluation in multiple scenarios. To demonstrate the process in application, three novel types of visualizations are presented: the mapping of lines of sight in Yosemite Valley, the assessment of landscape change in the area surrounding the High Line in Manhattan, and individual location analysis for Coit Tower in San Francisco. The results suggest that analyzing crowdsourced data may contribute to a more balanced assessment of the perceived landscape, which provides a basis for a better integration of public values into planning processes.:Contents 3 1 Introduction 7 1.1 Motivation 7 1.2 Literature review and conceptual scope 9 1.3 Terminology 11 1.4 Related research 12 1.5 Objectives 14 1.6 Methodology 16 1.7 Formal conventions 21 I. Part I: Conceptual framework 23 1.1 Visual perception 23 1.2 Theory and practice in landscape perception assessment 27 1.2.1 Expert valuation versus participation 27 1.2.2 Photography-based landscape perception assessment 32 1.2.2.1. Photo-based surveys 32 1.2.2.2. Photo-based Internet surveys 35 1.2.2.3. Photo-interviewing and participant photography 37 1.2.3 Conclusions 40 1.3 Conceptual approach 42 1.3.1 A framing theory: Distributed cognition 42 1.3.2 Description of the approach 46 1.3.3 Choosing the right data source 48 1.3.3.1. Availability of crowdsourced and georeferenced photo data 48 1.3.3.2. Suitability for analyzing human behavior and perception 51 1.3.4 Relations between data and the phenomenon under observation 55 1.3.4.1. Photo taking and landscape perception 55 1.3.4.2. User motivation in the context of photo sharing in communities 61 1.3.4.3. Describing and tagging photos: Forms of attributing meaning 66 1.3.5 Considerations for measuring and weighting data 70 1.3.6 Conclusions 77 II. Part II: Application example – Flickr photo analysis and evaluation of results 80 2.1 Software architecture 80 2.2 Materials and methods 86 2.2.1 Data retrieval, initial data structure and overall quantification 86 2.2.2 Global data bias 89 2.2.3 Basic techniques for filtering and classifying data 94 2.2.3.1. Where: photo locations 94 2.2.3.2. Who: user origin 96 2.2.3.3. When: time of photo taking 102 2.2.3.4. What: tag frequency 108   2.2.4 Methods for aggregating data 113 2.2.4.1. Clustering of photo locations 113 2.2.4.2. Clustering of tag locations 115 2.3 Application to planning: techniques for visualizing data 118 2.3.1 Introduction 118 2.3.2 Tag maps 121 2.3.2.1. Description of technique 121 2.3.2.2. Results: San Francisco and Berkeley waterfront 126 2.3.2.3. Results: Berkeley downtown and university campus 129 2.3.2.4. Results: Dresden and the Elbe Valley 132 2.3.2.5. Results: Greater Toronto Area and City of Toronto 136 2.3.2.6. Results: Baden-Württemberg 143 2.3.2.7. Summary 156 2.3.3 Temporal comparison for assessing landscape change 158 2.3.3.1. Description of technique 158 2.3.3.2. Results: The High Line, NY 159 2.3.3.3. Summary 160 2.3.4 Determining lines of sight and important visual connections 161 2.3.4.1. Description of technique 161 2.3.4.2. Results: Yosemite Valley 162 2.3.4.3. Results: Golden Gate and Bay Bridge 167 2.3.4.4. Results: CN Tower, Toronto 168 2.3.4.5. Summary 170 2.3.5 Individual location analysis 171 2.3.5.1. Description of technique 171 2.3.5.2. Results: Coit Tower, San Francisco 171 2.3.5.3. Results: CN Tower, Toronto 172 2.3.5.4. Summary 173 2.4 Quality and accuracy of results 175 2.4.1 Methodology 175 2.4.2 Accuracy of data 175 2.4.3 Validity and reliability of visualizations 178 2.4.3.1. Reliability 178 2.4.3.2. Validity 180 2.5 Implementation example: the London View Framework 181 2.5.1 Description 181 2.5.2 Evaluation methodology 183 2.5.3 Analysis 184 2.5.3.1. Landmarks 184 2.5.3.2. Views 192 2.5.4 Summary 199 III. Discussion 203 3.1 Application of the framework from a wider perspective 203 3.2 Significance of results 204 3.3 Further research 205   3.4 Discussion of workshop results and further feedback 206 3.4.1 Workshops at University of Waterloo and University of Toronto, Canada 206 3.4.2 Workshop at University of Technology Dresden, Germany 209 3.4.3 Feedback from presentations, discussions, exhibitions: second thoughts 210 IV. Conclusions 212 V. References 213 5.1 Literature 213 5.2 List of web references 228 5.3 List of figures 230 5.4 List of tables 234 5.5 List of maps 235 5.6 List of appendices 236 VI. Appendices 237  Als Wahrnehmung wird der Bewusstseinsprozess des subjektiven Verstehens der Umwelt bezeichnet. Grundlage für diesen Prozess ist die Gewinnung von Informationen über die Sinne, also aus visuellen, olfaktorischen, akustischen und anderen Reizen. Die Wahrnehmung ist aber auch wesentlich durch interne Prozesse beeinflusst. Das menschliche Gehirn ist fortlaufend damit beschäftigt, sowohl bewusst als auch unbewusst Sinneswahrnehmungen mit Erinnerungen abzugleichen, zu vereinfachen, zu assoziieren, vorherzusagen oder zu vergleichen. Aus diesem Grund ist es schwierig, die Wahrnehmung von Orten und Landschaften in Planungsprozessen zu berücksichtigen. Jedoch wird genau dies von der Europäischen Landschaftskonvention gefordert, die Landschaft als einen bestimmten Bereich definiert, so wie er von Besuchern und Einwohnern wahrgenommen wird (“as a zone or area as perceived by local people or visitors”, ELC Art. 1, Abs. 38). Während viele Fortschritte und Erkenntnisse, zum Beispiel aus den Kognitionswissenschaften, heute helfen, die Wahrnehmung einzelner Menschen zu verstehen, konnte die Stadt- und Landschaftsplanung kaum profitieren. Es fehlt an Kenntnissen über das Zusammenwirken der Wahrnehmung vieler Menschen. Schon Stadtplaner Kevin Lynch beschäftigte dieses gemeinsame, kollektive ‚Bild‘ der menschlichen Umwelt ("generalized mental picture", Lynch, 1960, p. 4). Seitdem wurden kaum nennenswerte Fortschritte bei der Erfassung der allgemeinen, öffentlichen Wahrnehmung von Stadt- und Landschaft erzielt. Dies war Anlass und Motivation für die vorliegende Arbeit. Eine bisher in der Planung ungenutzte Informationsquelle für die Erfassung der Wahrnehmung vieler Menschen bietet sich in Form von crowdsourced Daten (auch ‚Big Data‘), also großen Mengen an Daten die von vielen Menschen im Internet zusammengetragen werden. Im Vergleich zu konventionellen Daten, zum Beispiel solchen die durch Experten erhoben werden und durch öffentliche Träger zur Verfügung stehen, eröffnet sich durch crowdsourced Daten eine bisher nicht verfügbare Quelle für Informationen, um die komplexen Zusammenhänge zwischen Raum, Identität und subjektiver Wahrnehmung zu verstehen. Dabei enthalten crowdsourced Daten lediglich Spuren menschlicher Entscheidungen. Aufgrund der Menge ist es aber möglich, wesentliche Informationen über die Wahrnehmung derer, die diese Daten zusammengetragen haben, zu gewinnen. Dies ermöglicht es Planern zu verstehen, wie Menschen ihre unmittelbare Umgebung wahrnehmen und mit ihr interagieren. Darüber hinaus wird es immer wichtiger, die Ansichten Vieler in Planungsprozessen zu berücksichtigen (Lynam, De Jong, Sheil, Kusumanto, & Evans, 2007; Brody, 2004). Der Wunsch nach öffentlicher Beteiligung sowie die Anzahl an beteiligten Stakeholdern nehmen dabei konstant zu. Durch das Nutzen dieser neuen Informationsquelle bietet sich eine Alternative zu herkömmlichen Ansätzen wie Umfragen, die genutzt werden um beispielsweise Meinungen, Positionen, Werte, Normen oder Vorlieben von bestimmten sozialen Gruppen zu messen. Indem es crowdsourced Daten erleichtern, solch soziokulturelle Werte zu bestimmen, können die Ergebnisse vor allem bei der schwierigen Gewichtung gegensätzlicher Interessen und Ansichten helfen. Es wird die Ansicht geteilt, dass die Nutzung von crowdsourced Daten, indem Einschätzungen von Experten ergänzt werden, letztendlich zu einer faireren, ausgeglichenen Berücksichtigung der Allgemeinheit in Entscheidungsprozessen führen kann (Erickson, 2011, p.1). Eine große Anzahl an Methoden ist bereits verfügbar, um aus dieser Datenquelle wichtige landschaftsbezogene Informationen auszulesen. Beispiele sind die Bewertung der Attraktivität von Landschaften, die Bestimmung der Bedeutung von Sehenswürdigkeiten oder Wahrzeichen, oder die Einschätzung von Reisevorlieben von Nutzergruppen. Viele der bisherigen Methoden wurden jedoch als ungenügend empfunden, um die speziellen Bedürfnisse und das breite Spektrum an Fragestellungen zur Landschaftswahrnehmung in Stadt- und Landschaftsplanung zu berücksichtigen. Das Ziel der vorliegenden Arbeit ist es, praxisrelevantes Wissen zu vermitteln, welches es Planern erlaubt, selbstständig Daten zu erforschen, zu visualisieren und zu interpretieren. Der Schlüssel für eine erfolgreiche Umsetzung wird dabei in der Synthese von Wissen aus drei Kategorien gesehen, theoretische Grundlagen (1), technisches Wissen zur Datenverarbeitung (2) sowie Kenntnisse zur grafischen Visualisierungen (3). Die theoretischen Grundlagen werden im ersten Teil der Arbeit (Part I) präsentiert. In diesem Teil werden zunächst Schwachpunkte aktueller Verfahren diskutiert, um anschließend einen neuen, konzeptionell-technischen Ansatz vorzuschlagen der gezielt auf die Ergänzung bereits vorhandener Methoden zielt. Im zweiten Teil der Arbeit (Part II) wird anhand eines Datenbeispiels die Anwendung des Ansatzes exemplarisch demonstriert. Fragestellungen die angesprochen werden reichen von der Datenabfrage, Verarbeitung, Analyse, Visualisierung, bis zur Interpretation von Grafiken in Planungsprozessen. Als Basis dient dabei ein Datenset mit 147 Millionen georeferenzierte Foto-Daten und 882 Millionen Tags der Fotoaustauschplatform Flickr, welches in den Jahren 2007 bis 2015 von 1,3 Millionen Nutzern zusammengetragen wurde. Anhand dieser Daten wird die Entwicklung neuer Visualisierungstechniken exemplarisch vorgestellt. Beispiele umfassen Spatio-temporal Tag Clouds, eine experimentelle Technik zur Generierung von wahrnehmungsgewichteten Karten, die Visualisierung von wahrgenommenem Landschaftswandel, das Abbilden von wahrnehmungsgewichteten Sichtlinien, sowie die Auswertung von individueller Wahrnehmung von und an bestimmten Orten. Die Anwendung dieser Techniken wird anhand verschiedener Testregionen in den USA, Kanada und Deutschland für alle Maßstabsebenen geprüft und diskutiert. Dies umfasst beispielsweise die Erfassung und Bewertung von Sichtlinien und visuellen Bezügen in Yosemite Valley, das Monitoring von wahrgenommenen Veränderungen im Bereich der High Line in New York, die Auswertung von individueller Wahrnehmung für Coit Tower in San Francisco, oder die Beurteilung von regional wahrgenommenen identitätsstiftenden Landschaftswerten für Baden-Württemberg und die Greater Toronto Area (GTA). Anschließend werden Ansätze vorgestellt, um die Qualität und Validität von Visualisierungen einzuschätzen. Abschließend wird anhand eines konkreten Planungsbeispiels, des London View Management Frameworks (LVMF), eine spezifische Implementation des Ansatzes und der Visualisierungen kurz aufgezeigt und diskutiert. Mit der Arbeit wird vor allem das breite Potential betont, welches die Nutzung von crowdsourced Daten für die Bewertung von Landschaftswahrnehmung in Stadt- und Landschaftsplanung bereithält. Insbesondere crowdsourced Fotodaten werden als wichtige zusätzliche Informationsquelle gesehen, da sie eine bisher nicht verfügbare Perspektive auf die allgemeine, öffentliche Wahrnehmung der Umwelt ermöglichen. Während der breiteren Anwendung noch einige Grenzen gesetzt sind, können die vorgestellten experimentellen Methoden und Techniken schon wichtige Aufschlüsse über eine ganze Reihe von wahrgenommenen Landschaftswerten geben. Auf konzeptioneller Ebene stellt die Arbeit eine erste Grundlage für weitere Forschung dar. Bevor jedoch eine breite Anwendung in der Praxis möglich ist, müssen entscheidende Fragen gelöst werden, beispielsweise zum Copyright, zur Definition von ethischen Standards innerhalb der Profession, sowie zum Schutz der Privatsphäre Beteiligter. Längerfristig wird nicht nur die Nutzung der Daten als wichtig angesehen, sondern auch die Erschließung der essentiellen Möglichkeiten dieser Entwicklung zur besseren Kommunikation mit Auftraggebern, Beteiligten und der Öffentlichkeit in Planungs- und Entscheidungsprozessen.:Contents 3 1 Introduction 7 1.1 Motivation 7 1.2 Literature review and conceptual scope 9 1.3 Terminology 11 1.4 Related research 12 1.5 Objectives 14 1.6 Methodology 16 1.7 Formal conventions 21 I. Part I: Conceptual framework 23 1.1 Visual perception 23 1.2 Theory and practice in landscape perception assessment 27 1.2.1 Expert valuation versus participation 27 1.2.2 Photography-based landscape perception assessment 32 1.2.2.1. Photo-based surveys 32 1.2.2.2. Photo-based Internet surveys 35 1.2.2.3. Photo-interviewing and participant photography 37 1.2.3 Conclusions 40 1.3 Conceptual approach 42 1.3.1 A framing theory: Distributed cognition 42 1.3.2 Description of the approach 46 1.3.3 Choosing the right data source 48 1.3.3.1. Availability of crowdsourced and georeferenced photo data 48 1.3.3.2. Suitability for analyzing human behavior and perception 51 1.3.4 Relations between data and the phenomenon under observation 55 1.3.4.1. Photo taking and landscape perception 55 1.3.4.2. User motivation in the context of photo sharing in communities 61 1.3.4.3. Describing and tagging photos: Forms of attributing meaning 66 1.3.5 Considerations for measuring and weighting data 70 1.3.6 Conclusions 77 II. Part II: Application example – Flickr photo analysis and evaluation of results 80 2.1 Software architecture 80 2.2 Materials and methods 86 2.2.1 Data retrieval, initial data structure and overall quantification 86 2.2.2 Global data bias 89 2.2.3 Basic techniques for filtering and classifying data 94 2.2.3.1. Where: photo locations 94 2.2.3.2. Who: user origin 96 2.2.3.3. When: time of photo taking 102 2.2.3.4. What: tag frequency 108   2.2.4 Methods for aggregating data 113 2.2.4.1. Clustering of photo locations 113 2.2.4.2. Clustering of tag locations 115 2.3 Application to planning: techniques for visualizing data 118 2.3.1 Introduction 118 2.3.2 Tag maps 121 2.3.2.1. Description of technique 121 2.3.2.2. Results: San Francisco and Berkeley waterfront 126 2.3.2.3. Results: Berkeley downtown and university campus 129 2.3.2.4. Results: Dresden and the Elbe Valley 132 2.3.2.5. Results: Greater Toronto Area and City of Toronto 136 2.3.2.6. Results: Baden-Württemberg 143 2.3.2.7. Summary 156 2.3.3 Temporal comparison for assessing landscape change 158 2.3.3.1. Description of technique 158 2.3.3.2. Results: The High Line, NY 159 2.3.3.3. Summary 160 2.3.4 Determining lines of sight and important visual connections 161 2.3.4.1. Description of technique 161 2.3.4.2. Results: Yosemite Valley 162 2.3.4.3. Results: Golden Gate and Bay Bridge 167 2.3.4.4. Results: CN Tower, Toronto 168 2.3.4.5. Summary 170 2.3.5 Individual location analysis 171 2.3.5.1. Description of technique 171 2.3.5.2. Results: Coit Tower, San Francisco 171 2.3.5.3. Results: CN Tower, Toronto 172 2.3.5.4. Summary 173 2.4 Quality and accuracy of results 175 2.4.1 Methodology 175 2.4.2 Accuracy of data 175 2.4.3 Validity and reliability of visualizations 178 2.4.3.1. Reliability 178 2.4.3.2. Validity 180 2.5 Implementation example: the London View Framework 181 2.5.1 Description 181 2.5.2 Evaluation methodology 183 2.5.3 Analysis 184 2.5.3.1. Landmarks 184 2.5.3.2. Views 192 2.5.4 Summary 199 III. Discussion 203 3.1 Application of the framework from a wider perspective 203 3.2 Significance of results 204 3.3 Further research 205   3.4 Discussion of workshop results and further feedback 206 3.4.1 Workshops at University of Waterloo and University of Toronto, Canada 206 3.4.2 Workshop at University of Technology Dresden, Germany 209 3.4.3 Feedback from presentations, discussions, exhibitions: second thoughts 210 IV. Conclusions 212 V. References 213 5.1 Literature 213 5.2 List of web references 228 5.3 List of figures 230 5.4 List of tables 234 5.5 List of maps 235 5.6 List of appendices 236 VI. Appendices 237