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.

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

Yes / Establishing the age at which prehistoric individuals move away from their childhood residential location holds crucial information about the socio dynamics and mobility patterns in ancient societies. We present a novel combination of strontium isotope analyses performed on the over 3000 year old “Skrydstrup Woman” from Denmark, for whom we compiled a highly detailed month-scale model of her migration timeline. When combined with physical anthropological analyses this timeline can be related to the chronological age at which the residential location changed. We conducted a series of high-resolution strontium isotope analyses of hard and soft human tissues and combined these with anthropological investigations including CT-scanning and 3D visualizations. The Skrydstrup Woman lived during a pan-European period characterized by technical innovation and great social transformations stimulated by long-distance connections; consequently she represents an important part of both Danish and European prehistory. Our multidisciplinary study involves complementary biochemical, biomolecular and microscopy analyses of her scalp hair. Our results reveal that the Skrydstrup Woman was between 17–18 years old when she died, and that she moved from her place of origin -outside present day Denmark- to the Skrydstrup area in Denmark 47 to 42 months before she died. Hence, she was between 13 to 14 years old when she migrated to and resided in the area around Skrydstrup for the rest of her life. From an archaeological standpoint, this one-time and one-way movement of an elite female during the possible “age of marriageability” might suggest that she migrated with the aim of establishing an alliance between chiefdoms. Consequently, this detailed multidisciplinary investigation provides a novel tool to reconstruct high resolution chronology of individual mobility with the perspective of studying complex patterns of social and economic interaction in prehistory. / Carlsberg Foundation through the project entitled "Tales of Bronze Age Women" CF-15 0878 to KMF (http://www. carlsbergfondet.dk/en).

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

Recent years have seen a huge increase in the amount of data collected from multiple sources: mobile phones are ubiquitous, social networks are widely used, cities are more and more connected and the mobility of people and goods has risen to a global scale. The Big Data Era has opened the doors to new kinds of studies that were unthinkable with previous qualitative methods: human behavior can now be analyzed with a fine-grained resolution, patterns of mobility and behavior can be extracted from the incredible amount of data collected every day. Modern large cities are becoming more and more interconnected and this phenomenon leads to an increasing communication and activities’ synchronization. Due to the amount of data available or for anonymization reasons, it is often necessary to aggregate data spatially and temporally. A natural representation of clustered mobility data is the temporal network representation. In this thesis we focus on these two aspects of spatial distance in human mobility: (i) we study the synchronization of 76 Italian cities, using mobile phone data, showing that both distance between cities and city size determine the synchronization in communication rhythms. Moreover, we show that the effect of the distance in synchronization decreases when the size of the city increases; (ii) we investigate how clustering continuous spatio-temporal data affects spatio-temporal network measures for real-life and synthetic datasets and analyze how spatio-temporal networks’ measures vary at different aggregation levels.

Humans have fled environmental degradation for many millennia. Due partially to climate change, environments across the world have often degraded to the point that they can no longer securely sustain livelihoods. Entire communities and households have been displaced by extreme, rapid or creeping disasters; during their flight, they have left footprints across the globe that merit tracing. Sometimes this mobility is forced and at other times it is purely voluntary; for both, the mobility has roots in a changing environment. The footprint of environmentally influenced mobility (EIM) was traced through a series of three independent but related studies. The first study gained foundational perspective through an exploration of connections between climate drivers and natural and human impacts of climate change. This inquiry sought to answer the question, "How important is human mobility in the greater scheme of changing environments and changing climate?" Human mobility was one among 15 different climate drivers and impacts studied; the connections between all of them were examined to enable a quantitative comparison of system susceptibility, driving force, tight coupling and complexity. While degradation was the most complex of all natural elements, mobility surfaced as the human system element exerting the greatest forcing on other elements within the coupled system. The next study focused only on human mobility to explore how scholarly literature portrayed the two possible directions of the link between mobility and degrading environments--with a particular focus on urbanization as one manifestation of the phenomenon. Type A links, in which human mobility triggers environmental degradation, are portrayed in the literature as often as Type B links, in which degrading environments trigger human mobility. Surprisingly, science has not lent support to urbanization being a result of environmental change; plausible reasons for this are discussed. The final study canvassed expert opinion to examine why no scientific, humanitarian or governmental entity has succeeded in providing systematic support (e.g.., policy and interventions) to populations enduring environmentally influenced mobility. Four very different discourses emerged: Determined Humanists, Benevolent Pragmatists, Cynical Protectionists and Critical Realists. The complexity these discourses manifest help explain the inaction--a stalemate between actors--while confirming the inappropriateness of one-sided terminology and linear quantifications of environmentally influenced mobility. The results of these three studies demonstrate that human mobility has unequivocally destructive force that can trigger non-linear effects, potentially casting the coupled system into an unprecedented state; that the visible lack of scholarly exploration of environmentally influenced urbanization (EIU) can be partially explained by high system complexity and disciplinary research; and most important, that despite diametrically opposed viewpoints, experts unanimously agree that human mobility has strong connections to environmental change. Together, the results merge to confirm a "well-founded fear" on the part of those who dwell in degrading environments, and to highlight a pressing need to offer solutions both to those who remain in such environments as well as a name and protected status to those who flee them.