Submitted by Noeme Timbo (noeme.timbo@metodista.br) on 2017-01-20T16:20:11Z No. of bitstreams: 1 SergioSantos.pdf: 3735779 bytes, checksum: fadd14df08fd5683ee6cfabaf104427c (MD5) / Made available in DSpace on 2017-01-20T16:20:11Z (GMT). No. of bitstreams: 1 SergioSantos.pdf: 3735779 bytes, checksum: fadd14df08fd5683ee6cfabaf104427c (MD5) Previous issue date: 2016-11-30 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The research starts from the Portuguese philosopher Manuel Sérgio work, investigating the implications and resonances in educational horizons from the comprehension of “ser-motrício”. The basics research questions are: How to experience, understand and interpret the human being in his motrician nature? What is his true way of being in action, overcoming the lonely physical look, considering the experience of life, language and human complexity? Which outspreads, resonances and educational implications may arise by these comprehensions and interpretations? Understanding this projection, we studied the “ser-motrício” in its ontological roots subsidized by the life experience, interpretation and human appreciation. In the educational dimension, it was considered that the “ser-motrício”, who feels, thinks, apprehends, incorporates, wishes, interacts, wonders, expresses and potencializes all of this condition in the connection of his experience of life with multiple languages, has received a comprehensive and more influenced treatment by the paradigm of simplicity and reductionism that, somehow, resonate in fragmented educational practices. To overcome this reductionism, we defend the creative praxis as authentic dimension of “ser-motrício”, in a project called "Appreciation of Human Motricity," a constructive / A pesquisa parte da obra do filósofo português Manuel Sérgio, investigando as implicações e as ressonâncias nos horizontes educativos a partir da compreensão do ser-motrício. As questões basilares da pesquisa são: Como vivenciar, compreender e interpretar o ser humano em sua natureza motrícia? Qual seu modo autêntico de ser em ação, superando o olhar do físico tão-só, considerando a experiência da vida, a linguagem e a complexidade humana? Quais desdobramentos, ressonâncias e implicações educativas podem surgir dessas compreensões e interpretações? Para compreender essa projeção, estudamos o ser-motrício em suas raízes ontológicas subsidiados pela vivência, interpretação e apreciação humana. Na dimensão educativa, considerou-se que o ser-motrício que sente, pensa, apreende, incorpora, deseja, interage, imagina, expressa e potencializa toda essa condição no entrelaçamento da experiência vivida com as múltiplas linguagens, tem recebido um tratamento compreensivo mais influenciado pelo paradigma da simplicidade e do reducionismo que, de certo modo, ressoam em práticas educativas fragmentadas. Para superar esse reducionismo defendemos a práxis criadora como dimensão autêntica do ser-motrício, num projeto denominado “Apreciação da Motricidade Humana”, um caminho formador de realização e da vida em plenitude.

abstract: The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people's daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of "curse of choices", resulting in the difficulty for a user to make a satisfied decision on "where to go" in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing. Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects. Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

The rapid advancement of information and communication technologies (ICT) has dramatically changed the way people conduct daily activities. One of the reasons for such advances is the pervasiveness of location-aware devices, and people’s ability to publish and receive information about their surrounding environment. The organization, integration, and analysis of these crowdsensed geographic information is an important task for GIScience research, especially for better understanding place characteristics as well as human activities and movement dynamics in different spaces. In this dissertation research, a semantic modeling and analytic framework based on semantic web technologies is designed to handle information related with human space-time activities (e.g., information about human activities, movement, and surrounding places) for a smart space. Domain ontology for space-time activities and places that captures the essential entities in a spatial domain, and the relationships among them. Based on the developed domain ontology, a Resource Description Framework (RDF) data model is proposed that integrates spatial, temporal and semantic dimensions of space-time activities and places. Three different types of scheduled space-time activities (SXTF, SFTX, SXTX) and their potential spatiotemporal interactions are formalized with OWL and SWRL rules. Using a university campus as an example spatial domain, a RDF knowledgebase is created that integrates scheduled course activities and tweet activities in the campus area. Human movement dynamics for the campus area is analyzed from spatial, temporal, and people’s perspectives using semantic query approach. The ontological knowledge in RDF knowledgebase is further fused with place affordance knowledge learned through training deep learning model on place review data. The integration of place affordance knowledge with people’s intended activities allows the semantic analytic framework to make more personalized location recommendations for people’s daily activities.

The importance of modelling the spreading of infectious diseases as part of a public health strategy has been highlighted by the ongoing coronavirus pandemic. This includes identifying the geographical areas or travel routes most likely to contribute to the spreading of an outbreak. These areas and routes can then be monitored as part of an early warning system, be part of intervention strategies, e.g. lockdowns, aiming to mitigate the spreading of the disease or be a focus of vaccination campaigns.  This thesis focus on developing a network-based infection model between the municipalities of Sweden in order to identify the areas most likely to contribute to an epidemic. First, a human mobility model is constructed based on the well-known radiation model. Then a network-based SEIR compartmental model is employed to simulate epidemic outbreaks with various parameters. Finally, the adoption of the influence maximization problem known in network science to identify the municipalities having the largest impact on the spreading of infectious diseases.  The resulting super-spreading municipalities point towards confirmation of the known fact that central highly populated regions in highly populated areas carry a greater risk than their neighbours initially. However, once these areas are targeted, the other resulting nodes show a greater variety in geographical location than expected. Furthermore, a correlation can be seen between increased infections time and greater variety, although more empirical data is required to support this claim.   For further evaluation of the model, the mobility network was studied due to its central role in creating data for the model parameters. Commuting data in the Gothenburg region were compared to the estimations, showing an overall good accuracy with major deviations in few cases.

Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2020 / Malaria is a mosquito borne disease, a major cause of human morbidity and mortality in most of the developing countries in Africa. South Africa is one of the countries with high risk of malaria transmission, with many cases reported in Mpumalanga and Limpopo provinces. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors (rainfall, temperature, normalised difference vegetation index, and elevation) on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation-Markov chain Monte Carlo process and maximum likelihood, respectively, were utilised in the comparison process. Bayesian methods appeared to be better than the classical method in analysing malaria incidence in the Limpopo province of South Africa. The classical framework identified rainfall and temperature during the night to be the significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts of Limpopo province. However, the Bayesian method identified rainfall, normalised difference vegetation index, elevation, temperature during the day and temperature during the night to be the significant predictors of malaria incidence in Mopani, Sekhukhune, Vhembe and Waterberg districts of Limpopo province. Both methods also affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo province. Future research may involve studies on the methods to select the best prior distributions. / National Research Foundation (NRF)

This dissertation presents contributions to the fields of mobility modeling, wireless sensor networks (WSNs) with mobile sinks, and opportunistic communication in theme parks. The two main directions of our contributions are human mobility models and strategies for the mobile sink positioning and communication in wireless networks. The first direction of the dissertation is related to human mobility modeling. Modeling the movement of human subjects is important to improve the performance of wireless networks with human participants and the validation of such networks through simulations. The movements in areas such as theme parks follow specific patterns that are not taken into consideration by the general purpose mobility models. We develop two types of mobility models of theme park visitors. The first model represents the typical movement of visitors as they are visiting various attractions and landmarks of the park. The second model represents the movement of the visitors as they aim to evacuate the park after a natural or man-made disaster. The second direction focuses on the movement patterns of mobile sinks and their communication in responding to various events and incidents within the theme park. When an event occurs, the system needs to determine which mobile sink will respond to the event and its trajectory. The overall objective is to optimize the event coverage by minimizing the time needed for the chosen mobile sink to reach the incident area. We extend this work by considering the positioning problem of mobile sinks and preservation of the connected topology. We propose a new variant of p-center problem for optimal placement and communication of the mobile sinks. We provide a solution to this problem through collaborative event coverage of the WSNs with mobile sinks. Finally, we develop a network model with opportunistic communication for tracking the evacuation of theme park visitors during disasters. This model involves people with smartphones that store and carry messages. The mobile sinks are responsible for communicating with the smartphones and reaching out to the regions of the emergent events.

Private cars are responsible for 15% of carbon emissions in the European Union. Ride hailing services like taxis could serve the door-to-door mobility demand of private car users with fewer overall vehicles. If the service combines multiple user trips, it might even reduce the distance driven compared to private cars which becomes ecologically sustainable. Such ride sharing services are particularly sustainable when many users share one vehicle. But connecting the trips of all users yields many small detours. These detours reduce if some users walk a short distance to a neighboring stop. When multiple stops are combined, vehicles drive to fewer stops. Such stop pooling promises to make ride sharing even more sustainable. Some ride sharing services already integrate short user walks into their system. But the effects of stop pooling on ride sharing systems are yet to be understood. Methods from theoretical physics like mean-field theory and agent-based modeling enable a systemic analysis of complex ride sharing systems. This thesis demonstrates that ride sharing may be more sustainable when users accept short walks. With stop pooling, users wait shorter for vehicles and drive shorter because of more direct vehicle routes. In consequence, the user travel time decreases on average despite additional walk time at constant fleet size. Put differently, stop pooling allows to reduce the fleet size at constant travel time. This also reduces the distance driven by all vehicles that is proportional to the fleet size when sufficient users share one vehicle. This result is robust in a data-driven model using taxi trip data from Manhattan (New York City, USA) with fluctuating demand over the day. At constant fleet size the travel time fluctuates with the demand and might deviate a lot from the expected average travel time. Such unreliable travel times might deter users from ride sharing. However, stop pooling reduces the travel time, the more the higher the travel time without walking. Consequently, stop pooling also reduces the fluctuations in the travel time. This effect is particularly large when adapting the maximum allowed walk distance to the current demand. In adaptive stop pooling users walk further at higher demand. Then, the travel time in ride sharing is more reliable when users accept short walks. All in all, this thesis contributes to the fundamental understanding of the collective dynamics of ride sharing and the effect of stop pooling at a systemic level while also explaining underlying mechanisms. The results suggest that ride sharing providers and users benefit from integrating adaptive stop pooling into the service. Based on the results, a framework can be established that roughly adjusts fleet size to demand to ensure that the ride sharing service operates sustainably. Even if this fleet size remains constant throughout the day, adaptive stop pooling keeps the travel time reliable.:1. Introduction 1 1.1. Private Cars are Unsustainable . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2. Potentially More Sustainable Ride Sharing Faces Detours . . . . . . . . . . . . . 2 1.3. Less Detours in Ride Sharing with Walking to Pooled Stops . . . . . . . . . . . . 4 1.4. Physics Methods Help Understanding Ride Sharing . . . . . . . . . . . . . . . . . 5 1.5. Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Fundamentals - A Physics Perspective on Ride Sharing 7 2.1. State of Research on Ride Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1. Ride Sharing Systems are Complex . . . . . . . . . . . . . . . . . . . . . . 8 2.1.2. Measuring Efficiency and Sustainability of Ride Sharing Services . . . . . 8 2.1.3. Ride Sharing might be More Sustainable when Users Accept Short Walks 10 2.1.4. Data-Driven Analysis Yields more Detailed Results . . . . . . . . . . . . . 11 2.1.5. Open Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2. Theoretical Physics Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.1. What is a Complex System? . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.2. Mean-Field Theory Simplifies Complex Systems . . . . . . . . . . . . . . 13 2.2.3. Model Complex Systems Based on Agents, not on Equations . . . . . . . 14 2.2.4. Methods from Statistical Physics to Evaluate Multi-Agent Simulations . . 14 2.2.5. Model Street Networks Using Graph Theory . . . . . . . . . . . . . . . . 20 3. Model for Ride Sharing with Walking to Pooled Stops 25 3.1. Ride Sharing Combines Trips with Similar Directions . . . . . . . . . . . . . . . . 25 3.2. Stop Pooling with Dynamic Stop Locations Maintains Flexibility . . . . . . . . . 26 3.3. Simple Algorithm Assigns Users by Reducing Bus Detour . . . . . . . . . . . . . 28 3.3.1. Standard Ride Sharing Algorithm . . . . . . . . . . . . . . . . . . . . . . 28 3.3.2. Stop Pooling Algorithm at Similar Speed . . . . . . . . . . . . . . . . . . 29 3.4. Basic Setting in Continuous Space . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4.1. Uniform Request Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4.2. Heterogeneous Request Distribution . . . . . . . . . . . . . . . . . . . . . 32 3.5. Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.5.1. Relative Distance Driven Measures Ecological Sustainability . . . . . . . . 33 3.5.2. Measure Service Quality by Average User Travel Time . . . . . . . . . . . 34 3.5.3. Further Observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.4. Bisection Method to Find Minimal Travel Time with Small Effort . . . . 36 3.6. Model Extensions Yield More Detailed Results . . . . . . . . . . . . . . . . . . . 37 3.6.1. Fine-Grained Street Network Enables Short Walk Distances . . . . . . . . 38 iii Contents 3.6.2. Data-Driven Demand is Heterogeneous . . . . . . . . . . . . . . . . . . . . 39 3.6.3. Explicit Stop Times Ensure Penalty For Each Stop . . . . . . . . . . . . . 41 3.6.4. Imbalanced Demand Requires Rebalancing of Buses . . . . . . . . . . . . 42 3.6.5. More Detailed Assignment Algorithm Uses Constraints . . . . . . . . . . 43 4. Quantifying Sustainability of Ride Sharing 45 4.1. Two Mechanisms Influence Ride Sharing Sustainability . . . . . . . . . . . . . . . 46 4.1.1. Pickup Detours Increase Distance Driven . . . . . . . . . . . . . . . . . . 46 4.1.2. Trip Overlap Reduces Distance Driven . . . . . . . . . . . . . . . . . . . . 47 4.2. Distance Driven Reduces with Bus Occupancy . . . . . . . . . . . . . . . . . . . 48 4.3. Ride Sharing is more Sustainable than Private Cars for Sufficient Load . . . . . . 50 4.4. Result is Robust for more Complex Models . . . . . . . . . . . . . . . . . . . . . 52 4.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5. Ride Sharing Sustainability with Stop Pooling 55 5.1. Ride Sharing Trades Sustainability for Travel Time . . . . . . . . . . . . . . . . . 57 5.2. Stop Pooling is more Sustainable at Same Travel Time . . . . . . . . . . . . . . . 58 5.2.1. Roughly Constant Distance Driven Despite Saved Stops . . . . . . . . . . 58 5.2.2. Stop Pooling Reduces Travel Time . . . . . . . . . . . . . . . . . . . . . . 59 5.2.3. Stop Pooling Breaks The Trade-off Between Sustainability And Travel Time 60 5.3. Higher Stop Pooling Effect for High Loads . . . . . . . . . . . . . . . . . . . . . . 61 5.3.1. Stop Pooling Limits Growth of Best Travel Time . . . . . . . . . . . . . . 62 5.3.2. Stop Pooling Breaks Trade-off for Sufficient Load . . . . . . . . . . . . . . 63 5.4. Robust Effect for Simple Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 5.5. Robust Effect with More Detailed Model . . . . . . . . . . . . . . . . . . . . . . . 66 5.5.1. Load Quantifies Stop Pooling Sustainability . . . . . . . . . . . . . . . . . 67 5.5.2. Already 1.2 Minutes Walk Time might Save 1 Minute Travel Time . . . . 68 5.5.3. Robust Result for Different Parameters . . . . . . . . . . . . . . . . . . . 69 5.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6. Ride Sharing Reliability with Stop Pooling 71 6.1. Unreliable Standard Ride Sharing with Fluctuating Demand . . . . . . . . . . . . 72 6.2. More Reliable Stop Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.3. Robust Effect of Stop Pooling with Limited User Delay . . . . . . . . . . . . . . 77 6.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.5. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 7. Discussion 81 7.1. Results and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.1. When is Ride Sharing More Sustainable than Private Cars? . . . . . . . . 81 7.1.2. How Does Stop Pooling Influence Sustainability of Ride Sharing? . . . . . 82 7.1.3. How Does Stop Pooling Influence Reliability of Ride Sharing? . . . . . . . 82 7.2. Limitations of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.2.1. Simple Algorithms for Ride Sharing and Stop Pooling . . . . . . . . . . . 82 7.2.2. Integrate Adaptive Stop Pooling into Virtual Bus Stops . . . . . . . . . . 83 7.2.3. Distance Driven as Estimator for Ecological Sustainability . . . . . . . . . 83 7.2.4. Deviations from Load Prediction . . . . . . . . . . . . . . . . . . . . . . . 84 7.2.5. Mean-Field Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.2.6. Further Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 A. Appendix 87 A.1. Manhattan Street Network Resembles Grid . . . . . . . . . . . . . . . . . . . . . 87 A.2. Computation Details of Bisection Method . . . . . . . . . . . . . . . . . . . . . . 88 A.3. Average Pickup Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 A.4. Robustness of Ride Sharing Sustainability . . . . . . . . . . . . . . . . . . . . . . 90 A.5. Stop Pooling Saves Stops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 A.6. Stop Pooling Effectively Reduces Load . . . . . . . . . . . . . . . . . . . . . . . . 92 A.7. Example Breaking of Trade-off in Simple Model . . . . . . . . . . . . . . . . . . . 93 A.8. Transition in Best Walk Limit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 A.9. Maximal Trade-off Shift Increases with Load . . . . . . . . . . . . . . . . . . . . 95 A.10.Rebalancing Buses is more Important with Constraint . . . . . . . . . . . . . . . 97 A.11.Breaking of Trade-off in Complex Model . . . . . . . . . . . . . . . . . . . . . . . 98 A.12.More Stop Pooling at Destinations and High Demand . . . . . . . . . . . . . . . 99 A.13.Roughly Constant Wait and Drive Time in Adaptive Stop Pooling . . . . . . . . 100 A.14.Influence of Capacity Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 A.15.Walk Time of Rejected Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Bibliography 101 Acknowledgment 116 Statement of Contributions 118

Human mobility is a complex phenomenon emerging from the nexus between social, demographic, economic, political and environmental systems. In this thesis we develop novel mathematical models for the study of complex systems, to improve our understanding of mobility patterns and enhance our ability to predict local and global flows for real-world applications.The first and second chapters introduce the concept of human mobility from the point of view of complex systems science, showing the relation between human movements and their predominant drivers. In the second chapter in particular, we will illustrate the state of the art and a summary of our scientific contributions. The rest of the thesis is divided into three parts: structure, causes and effects.The third chapter is about the structure of a complex system: it represents our methodological contribution to Network Science, and in particular to the problem of network reconstruction and topological analysis. We propose a novel methodological framework for the definition of the topological descriptors of a complex network, when the underlying structure is uncertain. The most used topological descriptors are redefined – even at the level of a single node – as probability distributions, thus eluding the reconstruction phase. With this work we have provided a new approach to study the topological characteristics of complex networks from a probabilistic perspective. The forth chapter deals with the effects of human mobility: it represents our scientific contribution to the debate about the COVID-19 pandemic and its consequences. We present a complex-causal analysis to investigate the relationship between environmental conditions and human activity, considered as the components of a complex socio-environmental system. In particular, we derive the network of relations between different flavors of human mobility data and other social and environmental variables. Moreover, we studied the effects of the restrictions imposed on human mobility – and human activities in general – on the environmental system. Our results highlight a statistically significant qualitative improvement in the environmental variable of interest, but this improvement was not caused solely by the restrictions due to COVID-19 pandemic, such as the lockdown.The fifth and sixth chapters deal with the modelling of causes of human mobility: the former is a concise chapter that illustrate the phenomenon of human displacements caused by environmental disasters. Specifically, we analysed data from different sources to understand the factors involved in shaping mobility patterns after tropical cyclones. The latter presents the Feature-Enriched Radiation Model (FERM), our generalization of the Radiation Model which is a state-of-the-art mathematical model for human mobility. While the original Radiation Model considers only the population as a proxy for mobility drivers, the FERM can handle any type of exogenous information that is used to define the attractiveness of different geographical locations. The model exploits this information to divert the mobility flows towards the most attractive locations, balancing the role of the population distribution. The mobility patterns at different scales can be reshaped, following the exogenous drivers encoded in the features, without neglecting the global configuration of the system.

Fostering urban resilience and creating agility to disaster response is an urgent task faced by cities worldwide in the context of climate change and increasing frequencies of natural disasters. Understanding and tracking the dynamic process of resilience to disasters is the first step to operationalize the concept of urban resilience. In this dissertation, I present four related but evolutionary perspectives to investigate the impact of natural disasters on interactive human-environment systems as well as the dynamic process of resilience, including human mobility, spatial networks, and coupled mobility and sentiment perspectives. In the first, human-mobility perspective, I examine the nuanced impact of a severe winter storm on human mobility patterns and the relationship between perturbed mobility during the storm and recurrent mobility under normal circumstances. In the second, where I adopt a spatial network perspective, I investigate the dynamic process of resilience over time by analyzing networked human-spatial systems using an ecology-inspired approach. The third perspective involves sentiment as an additional factor to human mobility to understand urban dynamics during an earthquake. In this perspective, I explore the relation between disaster magnitude and a population's collective sentiment, as well as temporal correlations between sentiment and mobility. Each of the three empirical studies employs a quantitative, empirical research methodology and uses voluntarily reported geo-referenced data collected through a Twitter Streaming API. After multiple investigations on diverse types of natural disaster (e.g. severe winter storm, flooding, hurricane, and earthquake), I develop a Detecting Urban Emergencies Technique (DUET), as the fourth part of my dissertation, for identifying and tracking general types of emergencies in a short period without prior definitions of emergent topics. Research findings from the three empirical studies and the proposed DUET detection technique introduce a new lens and approach for understanding population dynamics and achieving urban resilience. This dissertation contributes to a more complete understanding of urban resilience to disasters with crowdsourced data, and enables more effective urban informatics in the face of extreme events. / PHD / Cities worldwide are facing the challenges of climate change and increasing frequencies of natural disasters. The first step of enhancing disaster responses in urban areas is to operationalize the concept of urban resilience by understanding the impact of disasters on urban systems at both spatial and temporal scales. In this dissertation, urban systems are characterized by individuals’ movements, networks of spatial units, and population’s sentiment, which also form three different but evolutionary perspectives to investigate the impact over time. In the first, human-movement perspective, I examine the nuanced impact of a severe winter storm on individuals’ movement patterns and the relationship between individuals’ most frequented locations (e.g. home or working places) under normal circumstances and their visited locations during the winter storm. In the second, where I adopt a spatial network perspective, I investigate the temporal process of resilience by analyzing networked human-spatial systems pre-, during, and post-disaster using an ecology-inspired approach. The third perspective involves sentiment, which is a measurement of people’s emotion and attitude, as an additional factor to human movement to understand the impact of an earthquake on the urban system. In this perspective, I explore the relation between an earthquake’s magnitude and a population’s collective sentiment, as well as how sentiment and movement changed over time. These three empirical studies use a quantitative research methodology with geotagged tweets, which are collected from a Twitter Streaming API. After many investigations on different types of natural disaster, I develop a Detecting Urban Emergencies Technique (DUET) for identifying and tracking general types of emergencies in a short period. The empirical findings and the proposed DUET detection technique introduce a bottom-up perspective for understanding disasters’ impact and enhancing urban resilience. This dissertation contributes to a more complete understanding of disaster resilience in urban areas with crowdsourced data, and enables more open and effective disaster communication.

Yes / Ancient human mobility at the individual level is conventionally studied by the diverse application of suitable techniques (e.g. aDNA, radiogenic strontium isotopes, as well as oxygen and lead isotopes) to either hard and/or soft tissues. However, the limited preservation of coexisting hard and soft human tissues hampers the possibilities of investigating high-resolution diachronic mobility periods in the life of a single individual. Here, we present the results of a multidisciplinary study of an exceptionally well preserved circa 3.400-year old Danish Bronze Age female find, known as the Egtved Girl. We applied biomolecular, biochemical and geochemical analyses to reconstruct her mobility and diet. We demonstrate that she originated from a place outside present day Denmark (the island of Bornholm excluded), and that she travelled back and forth over large distances during the final months of her life, while consuming a terrestrial diet with intervals of reduced protein intake. We also provide evidence that all her garments were made of non-locally produced wool. Our study advocates the huge potential of combining biomolecular and biogeochemical provenance tracer analyses to hard and soft tissues of a single ancient individual for the reconstruction of high-resolution human mobility. / The Danish National Research Foundation; The Carlsberg Foundation, L'Oreal Denmark-UNESCO; The ERC agreement no. 269442