A statistical approach for studying urban human dynamics

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThis doctoral dissertation proposed several statistical approaches to analyse urban dynamics with aiming to provide tools for decision making processes and urban studies. It assumed that human activity and human mobility compose urban dynamics. Initially, it studied geolocated social media data and considered them as a proxy for where and when people carry out what it is defined as the human activity. It employed techniques associated with generalised linear models, functional data analysis, hierarchical clustering, and epidemic data, to explain the spatio-temporal distribution of the places where people interact with their social networks. Afterwards, to understand the mobility in urban environments, data coming from an underground railway system were used. The information was considered repeated daily measurements to capture the regularity of human behaviour. By implementing methods from functional principal components data analysis and hierarchical clustering, it was possible to describe the system and identify human mobility patterns

Contextual contact tracing based spatio enhanced compartment modelling & spatial risk assessment

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe current situation of COVID-19 appears as a paradigm shift that seems to have farreaching impacts on the way humans will now continue with their daily routine. The overall scenario highlights the paramount importance of infectious disease surveillance, which necessitates immediate monitoring for effective preparedness and efficient response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. This thesis research investigates both requirements in a hybrid approach by the implementation of disease outbreak modelling and exploring its induced dynamic spatial risk in the form of Risk Assessment, along with its real-time integration back into the disease model. The study implements human mobility based contact tracing in the form of an event-based stochastic SIR model as a baseline and further modifies the existing setup to be inclusive of the spatial risk. This modification of each individual-level contact’s intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results suggest that the Spatio-SIR model tends to perform more meaningful events concerned with the Susceptible population rather than events to the Infected or Quarantined. With an example of a real-world scenario of induced spatial high-risk, it is highlighted that the new Spatio-SIR model can empower the analyst with a capability to explore disease dynamics from an additional perspective. The study concludes that even if this domain is hindered due to lack of data availability, the investigation process related to it should keep on exploring methods to effectively understand the disease dynamics

SimpliFly: A Methodology for Simplification and Thematic Enhancement of Trajectories.

Movement data sets collected using today's advanced tracking devices consist of complex trajectories in terms of length, shape, and number of recorded positions. Multiple additional attributes characterizing the movement and its environment are often also included making the level of complexity even higher. Simplification of trajectories can improve the visibility of relevant information by reducing less relevant details while maintaining important movement patterns. We propose a systematic stepwise methodology for simplifying and thematically enhancing trajectories in order to support their visual analysis. The methodology is applied iteratively and is composed of: (a) a simplification step applied to reduce the morphological complexity of the trajectories, (b) a thematic enhancement step which aims at accentuating patterns of movement, and (c) the representation and interactive exploration of the results in order to make interpretations of the findings and further refinement to the simplification and enhancement process. We illustrate our methodology through an analysis example of two different types of tracks, aircraft and pedestrian movement

Data science applications to connected vehicles: Key barriers to overcome

The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.JRC.C.6-Economics of Climate Change, Energy and Transpor

Understanding functional urban centrality: spatio-functional interaction and its socio-economic impact in central Shanghai

A deeper understanding of the structural characteristics of urban settings is a prerequisite to evaluating the effects of urban design and planning proposals more efficiently. This thesis aims at shaping a new, comprehensive approach to uncover the structure of cities through the investigation of a diachronic spatio-functional process and the socio-economic impacts of such a process. It proposes a spatial network-based framework, in which individual street segments, indexed by space syntax centrality measures, are utilised to develop a series of more complex urban function connectivity measures by an analysis of the spatial network and land-use patterns in tandem. The specific application of this approach in Central Shanghai is conducted with a threefold focus: firstly, to trace the evolutionary interdependence between the spatial grids and the land-use distribution; secondly, to explain the varying economic value of the spatio-functional relationship in the housing market; and thirdly, to capture the impact of the spatiol-functional interaction on the variation of co-presence. The outputs confirm that the centrality structures of the spatial network and the land-use distribution affect each other over time; however, certain degrees of inconsistency are observed, suggesting a distinct complementary relationship between these two systems, which is further validated by the improvement of the proposed model’s predictability of urban performance. The findings verify the hypothesis that urban spatio-functional synergy is a strong determinant of the formation of urban function regions, the delineation of housing submarkets, and the discrepancy of the spatial co-presence in the city. These results demonstrate that urban performance is directly affected by the way the spatial and functional structures of the city interact. Such findings support the proposition that understanding the complexities of the spatio-functional interaction in a morphological analysis can enhance the efficiency of urban design and planning interventions, which aim to improve socioeconomic conditions in cities

Bicycle Sharing Systems: Fast and Slow Urban Mobility Dynamics

In cities all around the world, new forms of urban micromobility have observed rapid and wide-scale adoption due to their benefits as a shared mode that are environmentally friendly, convenient and accessible. Bicycle sharing systems are the most established among these modes, facilitating complete end-to-end journeys as well as forming a solution for the first/last mile issue that public transportation users face in getting to and from transit stations. They mark the beginnings of a gradual transition towards a more sustainable transportation model that include greater use of shared and active modes. As such, understanding the way in which these systems are used is essential in order to improve their management and efficiency. Given the lack of operator published data, this thesis aims to explore the utility of open bicycle sharing system data standards that are intended for real-time dissemination of bicycle locations in uncovering novel insights into their activity dynamics over varying temporal and geographical scales. The thesis starts by exploring bicycle sharing systems at a global-scale, uncovering their long-term growth and evolution through the development of data cleaning and metric creation heuristics that also form the foundations of the most comprehensive classification of systems. Having established the values of these metrics in conducting comparisons at scale, the thesis then analyses the medium-term impacts of mobility interventions in the context of the COVID-19 pandemic, employing spatio-temporal and network analysis methods that highlight their adaptability and resilience. Finally, the thesis closes with the analysis of granular spatial and temporal dynamics within a dockless system in London that enable the identification of the variations in journey locations throughout different times of the day. In each of these cases, the research highlights the indispensable value of open data and the important role that bicycle sharing systems play in urban mobility