Modelling of Personalised Landmarks Inaugural-Dissertation for the degree of Doctor of Natural Sciences (Dr. rer. nat.) in the Faculty of Applied Computer Science University of Augsburg by Eva Nuhn 2019 Evaluation committee: Prof. Dr. Sabine Timpf Prof. Dr. Kai-Florian Richter Prof. Dr. Jukka M. Krisp Date of Defence: October 28th, 2019 Abstract Numerous studies claim that personal dimensions - such as personal interests or prior spatial knowledge - influence the identification of landmarks for pedestrians. We assume that the collection of personal data is the highest effort for the identification of personalised landmarks. Therefore, we need to make sure that the data collection effort is justified relative to the benefits that can be accrued through the provision of personalised landmarks. In this thesis we determine which personal dimensions play a role for the identifi- cation of personalised landmarks and focus on prior spatial knowledge and personal interests. On the basis of these dimensions we personalise existing mathematical models. From the amount of possible models available, we limit our investigations on four of them: the weighted sum model, the weighted product model, a decision flow chart, and a decision tree model. We train and implement the personalised models and use them to identify landmarks selected by participants of a survey. The results of the models are evaluated and compared with statistical methods. In addition, we train conventional, non-personalised models and use them also to identify the landmarks selected by the participants. We compare the results of both models, personalised and conventional, to see if there are advantages of personalisation. The comparison shows that although the personalised models respond sensitively to personal dimensions, a personalised model does not identify significantly more landmarks selected by survey participants than a conventional model. This means that the collecting of personal data is unlikely to justify the effort. Therefore, it is most likely sufficient to focus on existing conventional, non-personalised models and to concentrate on their use in applied pedestrian wayfinding applications. Landmark Identification, Personalisation, Spatial Knowledge, Personal Interests Zusammenfassung Zahlreiche Studien behaupten, dass persönliche Dimensionen - wie persönliches Interesse oder vorheriges räumliches Wissen - die Identifikation von Landmarken für Fußgänger beeinflussen. Wir gehen davon aus, dass der größte Aufwand zur Identifikation personalisierter Landmarken bei der Erhebung personenbezogener Daten entsteht. Deshalb muss sichergestellt werden, dass der Aufwand für die Datenerhebung im Vergleich zu den Vorteilen, welche die Bereitstellung personalisierter Landmarken mit sich bringt, gerechtfertigt ist. In dieser Arbeit ermitteln wir, welche persönlichen Dimensionen eine Rolle bei der Identifikation personalisierter Landmarken spielen und konzentrieren uns auf vorheriges räumliches Wissen und persönliche Interessen. Auf Basis der Dimensionen personalisieren wir bestehende mathematische Modelle. Aus der Fülle möglicher Modelle beschränken wir unsere Untersuchungen auf vier ausgewählte: das gewichtete Summenmodell, das gewichtete Produktmodell, ein Entscheidungsflussdiagramm und ein Entscheidungsbaummodell. Wir trainieren und implementieren die personalisierten Modelle und verwenden sie, um die von den Teilnehmern einer Studie ausgewählten Landmarken zu identifizieren. Die Ergebnisse der Modelle werden mit statistischen Methoden ausgewertet und verglichen. Des Weiteren trainieren wir konventionelle, nicht personalisierte Modelle und verwenden sie ebenfalls zur Identifikation der von Teilnehmern ausgewählten Landmarken. Wir vergleichen die Ergebnisse beider Modelle, personalisiert und konventionell, um festzustellen, ob die Personalisierung Vorteile bietet. Der Vergleich zeigt, dass obwohl die personalisierten Modelle sensitiv auf per- sönliche Dimensionen reagieren, ein personalisiertes Modell nicht signifikant mehr von den Studienteilnehmern ausgewählte Landmarken als ein konventionelles Modell identifiziert. Dies bedeutet, dass der Aufwand für die Erhebung personalisierter Daten vermutlich nicht gerechtfertigt ist. Daher ist es höchstwahrscheinlich ausreichend, sich auf vorhandene konventionelle, nicht personalisierte Modelle und auf deren Verwendung in Wegfindungsanwendungen für Fußgänger zu konzentrieren. Landmarkenidentifikation, Personalisierung, Räumliches Wissen, Persönliche Inter- essen Acknowledgments I would like to acknowledge the help and support of many people. First of all, I am grateful to Prof. Dr. Sabine Timpf for giving me the opportunity to come back to University, for encouraging me, and for helping me to develop what has become this thesis. In particular I want to thank her because she was always ready to discuss problems with me and to find solutions. I am grateful to Prof. Dr. Kai-Florian Richter for reviewing my thesis and for giving me valuable comments at conferences and workshops. I thank Prof. Dr. Jukka Krisp for reviewing my thesis and for giving me valuable suggestions during numerous GIG-meetings. I also thank Prof. Dr. Ulrike Ohl for examining my thesis. I am thankful to all current and former colleagues in our Geoinformatics Group for their support: Maja, with whom I shared my office and who was always ready for discussion and advice. Christian, who supported me with the data acquisition for the survey. Linfang and Jean, with whom I shared the VisLab when I just started at the University in Augsburg. Andreas and Johannes, who were always open for questions and discussions. I would like to thank the two students of my project seminar who helped to collect the data for the survey, to acquire participants, and to interview them. And, of course, I thank all the participants who were willing to complete the survey. I am grateful to my former math teacher, who accepted me at high school, and who unfortunately passed away one month after my defence. Although he never knew, I wouldn’t be here today without him. A very special gratitude goes out to my former colleagues from the UniBW - the place where the idea of writing a PhD thesis was born. Especially I want to thank Manu, Admire, Agnes, Stephan, Iris, and Pietzi and, of course, Prof. Reinhardt. I thank my family and friends. My parents for always supporting and encouraging me to go my own way. My mother who helped to finish this thesis by improving the English wordings. My parents in law who were there when I was away for conferences. My friends for the great time we have together. My biggest thanks go to my two wonderful girls. They developed very creative ways to keep themselves entertained while I was working on this thesis instead of spending time with them. Last but not least I want to thank my husband Matthias who never lost patience with me. This made it possible for me to meet deadlines and finally to finish this thesis that otherwise would have been impossible. In a system that automatically generates route directions, not including landmarks is a violation of cognitive ergonomics. Richter & Klippel (2007, p. 459) Contents 1 Introduction 1 1.1 HypothesisandGoals .......................... 3 1.2 Approach ................................. 4 1.3 ScopeandMethods............................ 7 1.4 Expected Result and Contributions . 9 1.5 ThesisOutline .............................. 10 2 Related Work 13 2.1 Landmarks in Human Wayfinding . 13 2.1.1 Cognitive Aspects of Human Wayfinding . 13 2.1.2 Definition and Characterisation of Landmarks . 18 2.2 Modelling of Landmarks for Route Directions . 24 2.2.1 Landmark Identification . 24 2.2.2 Landmark Integration . 27 2.3 Towards Personalised Landmarks . 30 2.4 Implications for Modelling Personalised Landmarks . 33 3 Mathematical Models and Analysis Methods 37 3.1 Mathematicalmodels........................... 38 3.1.1 ModelsbasedonTheory..................... 38 3.1.2 Machine Learning Model . 41 3.2 Model Training, Validating, and Testing . 43 3.2.1 Traditional Machine Learning Approach . 44 3.2.2 ’Training’ of Models based on Theory . 47 3.2.3 Division in Training and Test Set . 48 3.3 Sensitivity Analysis of the Models . 50 3.4 Comparison of Model Results . 51 3.5 StudySetup................................ 53 i 4 Landmark Identification Models 55 4.1 Dimensions of Landmark Identification Models . 55 4.1.1 Landmark Dimensions . 55 4.1.2 Personal Dimensions . 59 4.2 Salience Measures for the Dimensions . 66 4.2.1 Landmark Dimensions . 66 4.2.2 Personal Dimensions . 70 4.2.3 SalienceVector .......................... 72 4.3 Overallsalience.............................. 72 4.3.1 Conventional Weighted Sum Model (CwSm) . 72 4.3.2 Personalised Weighted Sum Model (PwSm) . 73 4.3.3 Conventional Weighted Product Model (CwPm) . 74 4.3.4 Personalised Weighted Product Model (PwPm) . 74 4.3.5 Conventional Decision Flow Chart (CdFc) . 74 4.3.6 Personalised Decision Flow Chart (PdFc) . 75 4.3.7 Conventional Decision Tree Model (CdTm) and Personalised Decision Tree Model (PdTm) . 78 5 Data Collection and Preparation 81 5.1 Data Collection for the Dimensions . 81 5.1.1 Landmark Dimensions . 81 5.1.2 Personal Dimensions . 87 5.2 Calculating Salience for the Dimensions . 94 5.2.1 Landmark Dimensions . 94 5.2.2 Personal Dimensions . 94 5.2.3 InputDatafortheModels. 95 5.3 Data Division in Training and Test Set . 96 5.4 Overall Salience according to Conventional Models . 97 5.4.1 Conventional Weighted Sum Model . 98 5.4.2 Conventional Weighted Product Model . 98 5.4.3 Conventional Decision