Modeling Visit Potential of Geographic Locations Based on Mobility Data

Every day people interact with the environment by passing or visiting geographic locations. Information about such entity-location interactions can be used in a number of applications and its value has been recognized by companies and public institutions. However, although the necessary tracking technologies such as GPS, GSM or RFID have long found their way into everyday life, the practical usage of visit information is still limited. Besides economic and ethical reasons for the restricted usage of entity-location interactions there are also two very basic problems. First, no formal definition of entity-location interaction quantities exists. Second, at the current state of technology, no tracking technology guarantees complete observations, and the treatment of missing data in mobility applications has been neglected in trajectory data mining so far. This thesis therefore focuses on the definition and estimation of quantities about the visiting behavior between mobile entities and geographic locations from incomplete mobility data. In a first step we provide an application-independent language to evaluate entity-location interactions. Based on a uniform notation, we define a family of quantities called visit potential, which contains the most basic interaction quantities and can be extended on need. By identifying the common background of all quantities we are able to analyze relationships between different quantities and to infer consistency requirements between related parameterizations of the quantities. We demonstrate the general applicability of visit potential using two real-world applications for which we give a precise definition of the employed entity-location interaction quantities in terms of visit potential. Second, this thesis provides the first systematic analysis of methods for the handling of missing data in mobility mining. We select a set of promising methods that take different approaches to handling missing data and test their robustness with respect to different scenarios. Our analyses consider different mechanisms and intensities of missing data under artificial censoring as well as varying visit intensities. We hereby analyze not only the applicability of the selected methods but also provide a systematic approach for parameterization and testing that can also be applied to the analysis of other mobility data sets. Our experiments show that only two of the tested methods supply unbiased estimates of visit potential quantities and are applicable to the domain. In addition, both methods supply unbiased estimates only of a single quantity. Therefore, it will be a future challenge to design methods for the entire collection of visit potential quantities. The topic of this thesis is motivated by applied research at the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS for business applications in outdoor advertisement. We will use the outdoor advertisement scenario throughout this thesis for demonstration and experimentation.Modellierung von Besuchsgrößen geographischer Orte anhand von Mobilitätsdaten Täglich interagieren Menschen mit ihrer Umgebung, indem sie sich im geografischen Raum bewegen oder gezielt geografische Orte aufsuchen. Informationen über derartige Besuche sind sehr wertvoll und können in einer Reihe von Anwendungen eingesetzt werden. Üblicherweise werden dazu die Bewegungen von Personen mit Hilfe von GPS, GSM oder RFID Technologien verfolgt. Durch eine räumliche Verschneidung der Trajektorien mit der Positionsangabe eines bestimmten Ortes können dann die Besuche extrahiert werden. Allerdings ist derzeitig die Verwendung von Besuchsinformationen in der Praxis begrenzt. Dies hat, neben ökonomischen und ethischen Gründen, vor allem zwei grundlegende Ursachen. Erstens existiert keine formelle Definition von Größen, um Besuchsinformationen einheitlich auszuwerten. Zweitens können aktuelle Technologien keine vollständige Erfassung von Bewegungsinformationen garantieren. Das bedeutet, dass die Basisdaten zur Auswertung von Besuchsinformationen grundsätzlich Lücken enthalten. Für eine fehlerfreie Auswertung der Daten müssen diese Lücken adäquat behandelt werden. Allerdings wurde dieses Thema in der bisherigen Data Mining Literatur zur Auswertung von Bewegungsdaten vernachlässigt. Daher widmet sich diese Dissertation der Definition von Größen zur Auswertung von Besuchsinformationen sowie dem Schätzen dieser Größen aus unvollständigen Bewegungsdaten. Im ersten Teil der Dissertation wird eine anwendungsunabhängige Beschreibungssprache formuliert, um Besuchsinformationen auszuwerten. Auf Basis einer einheitlichen Notation wird eine Familie von Größen namens visit potential definiert, die grundlegende Besuchsgrößen enthält und offen für Erweiterungen ist. Die gemeinsame Basis aller Besuchsgrößen erlaubt weiterhin, Beziehungen zwischen verschiedenen Größen zu analysieren sowie Konsistenzanforderungen zwischen ähnlichen Parametrisierungen der Größen abzuleiten. Abschließend zeigt die Arbeit die generelle Anwendbarkeit der definierten Besuchsgrößen in zwei realen Anwendungen, für die eine präzise Definition der eingesetzten Statistiken mit Hilfe der Besuchsgrößen gegeben wird. Der zweite Teil der Dissertation enthält die erste systematische Methodenanalyse für die Handhabung von unvollständigen Bewegungsdaten. Hierfür werden vier vielversprechende Methoden aus unterschiedlichen Bereichen zur Behandlung von fehlenden Daten ausgewählt und auf ihre Robustheit unter verschiedenen Annahmen getestet. Mit Hilfe einer künstlichen Zensur werden verschiedene Mechanismen und Grade von fehlenden Daten untersucht. Außerdem wird die Robustheit der Methoden für verschieden hohe Besuchsniveaus betrachtet. Die durchgeführten Experimente geben dabei nicht nur Auskunft über die Anwendbarkeit der getesteten Methoden, sondern stellen auch ein systematisches Vorgehen für das Testen und Parametrisieren weiterer Methoden zur Verfügung. Die Ergebnisse der Experimente belegen, dass nur zwei der vier ausgewählten Methoden für die Schätzung von Besuchsgrößen geeignet sind. Beide Methoden liefern jedoch nur für jeweils eine Besuchsgröße erwartungstreue Schätzwerte. Daher besteht eine zukünftige Herausforderung darin, Schätzmethoden für die Gesamtheit an Besuchsgrößen zu entwickeln. Diese Arbeit ist durch anwendungsorientierte Forschung am Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS im Bereich der Außenwerbung motiviert. Das Außenwerbeszenario sowie die darüber zur Verfügung gestellten Anwendungsdaten werden durchgängig zur Demonstration und für die Experimente in der Arbeit eingesetzt

Pedestrian Mobility Mining with Movement Patterns

In street-based mobility mining, pedestrian volume estimation receives increasing attention, as it provides important applications such as billboard evaluation, attraction ranking and emergency support systems. In practice, empirical measurements are sparse due to budget limitations and constrained mounting options. Therefore, estimation of pedestrian quantity is required to perform pedestrian mobility analysis at unobserved locations. Accurate pedestrian mobility analysis is difficult to achieve due to the non-random path selection of individual pedestrians (resulting from motivated movement behaviour), causing the pedestrian volumes to distribute non-uniformly among the traffic network. Existing approaches (pedestrian simulations and data mining methods) are hard to adjust to sensor measurements or require more expensive input data (e.g. high fidelity floor plans or total number of pedestrians in the site) and are thus unfeasible. In order to achieve a mobility model that encodes pedestrian volumes accurately, we propose two methods under the regression framework which overcome the limitations of existing methods. Namely, these two methods incorporate not just topological information and episodic sensor readings, but also prior knowledge on movement preferences and movement patterns. The first one is based on Least Squares Regression (LSR). The advantage of this method is the easy inclusion of route choice heuristics and robustness towards contradicting measurements. The second method is Gaussian Process Regression (GPR). The advantages of this method are the possibilities to include expert knowledge on pedestrian movement and to estimate the uncertainty in predicting the unknown frequencies. Furthermore the kernel matrix of the pedestrian frequencies returned by the method supports sensor placement decisions. Major benefits of the regression approach are (1) seamless integration of expert data and (2) simple reproduction of sensor measurements. Further advantages are (3) invariance of the results against traffic network homeomorphism and (4) the computational complexity depends not on the number of modeled pedestrians but on the traffic network complexity. We compare our novel approaches to state-of-the-art pedestrian simulation (Generalized Centrifugal Force Model) as well as existing Data Mining methods for traffic volume estimation (Spatial k-Nearest Neighbour) and commonly used graph kernels for the Gaussian Process Regression (Squared Exponential, Regularized Laplacian and Diffusion Kernel) in terms of prediction performance (measured with mean absolute error). Our methods showed significantly lower error rates. Since pattern knowledge is not easy to obtain, we present algorithms for pattern acquisition and analysis from Episodic Movement Data. The proposed analysis of Episodic Movement Data involve spatio-temporal aggregation of visits and flows, cluster analyses and dependency models. For pedestrian mobility data collection we further developed and successfully applied the recently evolved Bluetooth tracking technology. The introduced methods are combined to a system for pedestrian mobility analysis which comprises three layers. The Sensor Layer (1) monitors geo-coded sensor recordings on people’s presence and hands this episodic movement data in as input to the next layer. By use of standardized Open Geographic Consortium (OGC) compliant interfaces for data collection, we support seamless integration of various sensor technologies depending on the application requirements. The Query Layer (2) interacts with the user, who could ask for analyses within a given region and a certain time interval. Results are returned to the user in OGC conform Geography Markup Language (GML) format. The user query triggers the (3) Analysis Layer which utilizes the mobility model for pedestrian volume estimation. The proposed approach is promising for location performance evaluation and attractor identification. Thus, it was successfully applied to numerous industrial applications: Zurich central train station, the zoo of Duisburg (Germany) and a football stadium (Stade des Costières Nîmes, France)

Mobilitätsforschung zur Reichweitenbestimmung in der Deutschen und Schweizer Außenwerbung – Neue Wege mit GPS

Die bestmögliche Positionierung einer Plakatstelle ist seit jeher ein wichtiger Bestandteil in der Außenwerbung. Aus plausiblen Gründen werden Plakatstandorte so gewählt, dass sie häufig von Menschen passiert werden. Während jedoch die Lokalität und das Umfeld bei der Wahl von Plakatstandorten schon immer ein entscheidendes Kriterium gewesen ist, spielen die räumlichen Zusammenhänge bei der Leistungsbewertung und damit bei der Preisbildung von Plakatkampagnen erst in den vergangenen Jahren eine wichtige Rolle. Ziel der Leistungsbewertung ist es festzustellen, wer, wie oft und woher eine Person an einer Plakatkampagne vorbei gekommen ist. Dabei ist die räumliche Verteilung von Plakatstellen von entscheidender Bedeutung. Neue, GPS-basierte Messmethoden der Mobilitätserfassung erlauben die räumlich differenzierte Ausweisung von Leistungswerten für beliebig zusammengestellte Kampagnen sowie soziodemographische und räumlich ausgewählte Zielgruppen. Damit unterscheidet sich dieser GPS-Ansatz von den klassisch eingesetzten Methoden, die bisher versucht haben, über Befragungen von Testpersonen die Mobilität zu rekonstruieren, und nur Durchschnittswerte für Kampagnen anbieten konnten. So wurde z.B. für eine Stadt wie Köln nicht unterschieden, ob es sich bei einer Plakatkampagne um eine stark über die Stadt gestreute oder stark konzentrierte Kampagne handelt. Diese Dissertation widmet sich einer Gesamtschau über neuartige GPS-Verfahren in der Deutschen und Schweizer Außenwerbung und deren Anwendung in der Praxis. Sie ist ein erstmaliger Versuch, eine systematisierte Übersicht über die aktuellen Forschungsergebnisse in der Außenwerbung bzw. Mobilitätsforschung zu erstellen. Die bestehenden Publikationen zur Außenwerbeforschung (Pasquier 1997, Engel und Hofsäss 2003) stammen noch aus der Zeit vor der Mobilitätserfassung mit GPS sowie dem Einsatz von Geographischen Informationssystemen und sind somit veraltet. Zudem betrachten diese Publikationen keine geographischen Aspekte, die mit dem neuen Ansatz in den Fokus der Leistungsbewertung rücken und eine Erfolgsgeschichte für die Geographie bzw. das Geomarketing darstellen. Zur Systematisierung dieser Arbeit zählt, geeignete Lösungswege im Umgang mit zeitlichen und räumlichen Datenlücken zu diskutieren, zu erproben, sowie Validitäts- und Robustheitsanalysen durchzuführen. Es werden geeignete Tests definiert, die z.B. Selektionseffekte in der Rekrutierung von Probanden offen legen. Es wird die Problematik behandelt, wie Leistungswerte auf eine Neuerhebung der Mobilität reagieren und wo die Grenzen einer Leistungswertbestimmung liegen. Dabei sind die Ausgangslage und die Anforderungen in der Schweiz und in Deutschland unterschiedlich. So ist in Deutschland die GPS Stichprobengröße viel geringer als in der Schweiz. Dies hat direkte Konsequenzen auf die Modellierungsschritte und das Ergebnis. Die verwendeten Methoden sind weit über das Gebiet der Außenwerbung und Mediaplanung hinaus für die Modellierung von Mobilität von Interesse

Engagement With Air Quality Information: Stated Versus Revealed Preferences

Air pollution has a significant impact on health but is often invisible to the naked eye. Real-time air quality information can help people take action to protect their health. However, little is known on how to most effectively frame air quality information to promote public health. We conducted a field experiment to study people’s engagement with real-time air quality information provided through a smartphone application (app). We tested 12 different messaging strategies on both intent to engage with air quality information (through a survey), and actual engagement with air quality information tracked through the app in response to the messaging strategies. Our results, based on 835 survey respondents and 2,740 app users, show that intent to engage and actual engagement differ. Overall, users’ demographics were the most important predictor of engagement with messages. This research demonstrates the significance of testing messaging strategies through field experiments rather than through surveys, and the importance of targeted messages

Exploring the relationship between prevalence of overweight and obesity in 10-11 year olds and the outdoor physical environment in North East England

PhD ThesisChildhood overweight and obesity have been at the forefront of the public health agenda for a decade. Within this time a paradigm shift within medical and social sciences has altered the focus on personal determinants of obesity towards environmental and societal level influences. The neighbourhood environment is implicated in health, encompassing all aspects of the energy balance equation (i.e. physical activity (PA), diet and weight). Relatively little is known about neighbourhood-level health associations in young people, particularly within the UK. At the heart of this thesis is the Children’s Neighbourhood Environment Study (CNES) which aimed to identify physical environment correlates and mediating factors of PA and dietary intake behaviours and resultant weight outcomes in young people (10–11 years) within the North East of England. In response to persistent recommendations in obesogenic environment literature CNES applied a cross-disciplinary mixed-methods approach to research. This comprised: focus groups, participant-reported PA and dietary behaviours, participant and parent reported neighbourhood enviorment perceptions, objective (utilizing a GIS-based approach) and subjective neighbourhood environment measurement and appraisal. Youth PA showed statistically significant positive association with park and green space access, total street length and total road length but inverse association with mixed land use; associations with other neighbourhood features did not reach statistical significance. Dietary intake showed no statistically significant association with the neighbourhood environment. Elevated weight status showed statistically significant positive association with mixed land use and the absence of cycling facilities; associations with other neighbourhood features did not reach statistical significance. Page ii of 416 CNES adopted a robust and comprehensive cross-disciplinary approach, the first study of its kind in the UK. It implicates the neighbourhood environment in enabling and disabling PA behaviours and weight outcomes in young people. CNES has successfully identified strategic areas to target public health intervention and inform urban planning to facilitate health

A randomised controlled trial and cost-effectiveness evaluation of 'booster' interventions to sustain increases in physical activity in middle-aged adults in deprived urban neighbourhoods

Background: More evidence is needed on the potential role of 'booster' interventions in the maintenance of increases in physical activity levels after a brief intervention in relatively sedentary populations. Objectives: To determine whether objectively measured physical activity, 6 months after a brief intervention, is increased in those receiving physical activity 'booster' consultations delivered in a motivational interviewing (MI) style, either face to face or by telephone. Design: Three-arm, parallel-group, pragmatic, superiority randomised controlled trial with nested qualitative research fidelity and geographical information systems and health economic substudies. Treatment allocation was carried out using a web-based simple randomisation procedure with equal allocation probabilities. Principal investigators and study statisticians were blinded to treatment allocation until after the final analysis only. Setting: Deprived areas of Sheffield, UK. Participants: Previously sedentary people, aged 40-64 years, living in deprived areas of Sheffield, UK, who had increased their physical activity levels after receiving a brief intervention. Interventions: Participants were randomised to the control group (no further intervention) or to two sessions of MI, either face to face ('full booster') or by telephone ('mini booster'). Sessions were delivered 1 and 2 months post-randomisation. Main outcome measures: The primary outcome was total energy expenditure (TEE) per day in kcal from 7-day accelerometry, measured using an Actiheart device (CamNtech Ltd, Cambridge, UK). Independent evaluation of practitioner competence was carried out using the Motivational Interviewing Treatment Integrity assessment. An estimate of the per-participant intervention costs, resource use data collected by questionnaire and health-related quality of life data were analysed to produce a range of economic models from a short-term NHS perspective. An additional series of models were developed that used TEE values to estimate the long-term cost-effectiveness. Results: In total, 282 people were randomised (control = 96; mini booster = 92, full booster = 94) of whom 160 had a minimum of 4 out of 7 days' accelerometry data at 3 months (control = 61, mini booster = 47, full booster = 52). The mean difference in TEE per day between baseline and 3 months favoured the control arm over the combined booster arm but this was not statistically significant (-39 kcal, 95% confidence interval -173 to 95, p = 0.57). The autonomy-enabled MI communication style was generally acceptable, although some participants wanted a more paternalistic approach and most expressed enthusiasm for monitoring and feedback components of the intervention and research. Full boosters were more popular than mini boosters. Practitioners achieved and maintained a consistent level of MI competence. Walking distance to the nearest municipal green space or leisure facilities was not associated with physical activity levels. Two alternative modelling approaches both suggested that neither intervention was likely to be cost-effective. Conclusions: Although some individuals do find a community-based, brief MI 'booster' intervention supportive, the low levels of recruitment and retention and the lack of impact on objectively measured physical activity levels in those with adequate outcome data suggest that it is unlikely to represent a clinically effective or cost-effective intervention for the maintenance of recently acquired physical activity increases in deprived middle-aged urban populations. Future research with middle-aged and relatively deprived populations should explore interventions to promote physical activity that require less proactive engagement from individuals, including environmental interventions

Data Science, Data Visualization, and Digital Twins

Real-time, web-based, and interactive visualisations are proven to be outstanding methodologies and tools in numerous fields when knowledge in sophisticated data science and visualisation techniques is available. The rationale for this is because modern data science analytical approaches like machine/deep learning or artificial intelligence, as well as digital twinning, promise to give data insights, enable informed decision-making, and facilitate rich interactions among stakeholders.The benefits of data visualisation, data science, and digital twinning technologies motivate this book, which exhibits and presents numerous developed and advanced data science and visualisation approaches. Chapters cover such topics as deep learning techniques, web and dashboard-based visualisations during the COVID pandemic, 3D modelling of trees for mobile communications, digital twinning in the mining industry, data science libraries, and potential areas of future data science development

Drones and the Creative Industry

This open access, interdisciplinary book presents innovative strategies in the use of civil drones in the cultural and creative industry. Specially aimed at small and medium-sized enterprises (SMEs), the book offers valuable insights from the fields of marketing, engineering, arts and management. With contributions from experts representing varied interests throughout the creative industry, including academic researchers, software developers and engineers, it analyzes the needs of the creative industry when using civil drones both outdoors and indoors. The book also provides timely recommendations to the industry, as well as guidance for academics and policymakers

Empirical Evaluation of Route-Based Landscape Experiences

This thesis explores a method of visual analysis that aims to create a more in-depth understanding of how individuals see and visually perceive their environment. Here we explore a geospatial tool, called Visual Magnitude, to assess road-based experiences. We aimed to provide evidence of a relationship between the tool and scenic rating preferences from a survey. The content of this thesis is split between two articles. The first article, contained in Chapter 2, focuses on optimizing the selection of viewpoints along route-based envrionments. In this study we ask the question is there an optimal sampling rate of viewpoints along a route that can increase efficency in running a visual magnitude analysis and still represent accurately represent the envrionment. We found that for visually sensitive areas, a 30-meter sampling distance produced optimal results. For other landscapes a 50-meter sampling distance poduced resonable results in both sampling points and retained raster area. The second article, contained in Chapter 3, is an applied visual magnitude study where we use the optimal sampling distance of 30-meters to extract visual magnitude values for 15 different envrionments. These values are then compared to scenic rating values that we collected though a survey where participants saw videos of the same 15 envrionments and rated their scenic quailty. By doing this we were able to provide emperical evidence that the visual magnitude tool can be a way to predict best visual experiences within Utah. With the results from these studies we can make suggestions to professionals on how they can better use this GIS tool. These suggestions include sampling distances for multiple envrionments and the potential for this tool to be used as a poxy when attempting to interpret how landscapes observers feel about them. This additional infromation will help planners in understanding and making decisions more informed planning decisions along roadways and surrounding areas that have the highest potential impact on observers. By using this tool planners can assess where those areas are and the amount of impact that positive or negitive planning decisions will have on observers