7,074 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Spatial adaptive settlement systems in archaeology. Modelling long-term settlement formation from spatial micro interactions

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    Despite research history spanning more than a century, settlement patterns still hold a promise to contribute to the theories of large-scale processes in human history. Mostly they have been presented as passive imprints of past human activities and spatial interactions they shape have not been studied as the driving force of historical processes. While archaeological knowledge has been used to construct geographical theories of evolution of settlement there still exist gaps in this knowledge. Currently no theoretical framework has been adopted to explore them as spatial systems emerging from micro-choices of small population units. The goal of this thesis is to propose a conceptual model of adaptive settlement systems based on complex adaptive systems framework. The model frames settlement system formation processes as an adaptive system containing spatial features, information flows, decision making population units (agents) and forming cross scale feedback loops between location choices of individuals and space modified by their aggregated choices. The goal of the model is to find new ways of interpretation of archaeological locational data as well as closer theoretical integration of micro-level choices and meso-level settlement structures. The thesis is divided into five chapters, the first chapter is dedicated to conceptualisation of the general model based on existing literature and shows that settlement systems are inherently complex adaptive systems and therefore require tools of complexity science for causal explanations. The following chapters explore both empirical and theoretical simulated settlement patterns based dedicated to studying selected information flows and feedbacks in the context of the whole system. Second and third chapters explore the case study of the Stone Age settlement in Estonia comparing residential location choice principles of different periods. In chapter 2 the relation between environmental conditions and residential choice is explored statistically. The results confirm that the relation is significant but varies between different archaeological phenomena. In the third chapter hunter-fisher-gatherer and early agrarian Corded Ware settlement systems were compared spatially using inductive models. The results indicated a large difference in their perception of landscape regarding suitability for habitation. It led to conclusions that early agrarian land use significantly extended land use potential and provided a competitive spatial benefit. In addition to spatial differences, model performance was compared and the difference was discussed in the context of proposed adaptive settlement system model. Last two chapters present theoretical agent-based simulation experiments intended to study effects discussed in relation to environmental model performance and environmental determinism in general. In the fourth chapter the central place foragingmodel was embedded in the proposed model and resource depletion, as an environmental modification mechanism, was explored. The study excluded the possibility that mobility itself would lead to modelling effects discussed in the previous chapter. The purpose of the last chapter is the disentanglement of the complex relations between social versus human-environment interactions. The study exposed non-linear spatial effects expected population density can have on the system and the general robustness of environmental inductive models in archaeology to randomness and social effect. The model indicates that social interactions between individuals lead to formation of a group agency which is determined by the environment even if individual cognitions consider the environment insignificant. It also indicates that spatial configuration of the environment has a certain influence towards population clustering therefore providing a potential pathway to population aggregation. Those empirical and theoretical results showed the new insights provided by the complex adaptive systems framework. Some of the results, including the explanation of empirical results, required the conceptual model to provide a framework of interpretation

    Spatial epidemiology of a highly transmissible disease in urban neighbourhoods: Using COVID-19 outbreaks in Toronto as a case study

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    The emergence of infectious diseases in an urban area involves a complex interaction between the socioecological processes in the neighbourhood and urbanization. As a result, such an urban environment can be the incubator of new epidemics and spread diseases more rapidly in densely populated areas than elsewhere. Most recently, the Coronavirus-19 (COVID-19) pandemic has brought unprecedented challenges around the world. Toronto, the capital city of Ontario, Canada, has been severely impacted by COVID-19. Understanding the spatiotemporal patterns and the key drivers of such patterns is imperative for designing and implementing an effective public health program to control the spread of the pandemic. This dissertation was designed to contribute to the global research effort on the COVID-19 pandemic by conducting spatial epidemiological studies to enhance our understanding of the disease's epidemiology in a spatial context to guide enhancing the public health strategies in controlling the disease. Comprised of three original research manuscripts, this dissertation focuses on the spatial epidemiology of COVID-19 at a neighbourhood scale in Toronto. Each manuscript makes scientific contributions and enhances our knowledge of how interactions between different socioecological processes in the neighbourhood and urbanization can influence spatial spread and patterns of COVID-19 in Toronto with the application of novel and advanced methodological approaches. The findings of the outcomes of the analyses are intended to contribute to the public health policy that informs neighbourhood-based disease intervention initiatives by the public health authorities, local government, and policymakers. The first manuscript analyzes the globally and locally variable socioeconomic drivers of COVID-19 incidence and examines how these relationships vary across different neighbourhoods. In the global model, lower levels of education and the percentage of immigrants were found to have a positive association with increased risk for COVID-19. This study provides the methodological framework for identifying the local variations in the association between risk for COVID-19 and socioeconomic factors in an urban environment by applying a local multiscale geographically weighted regression (MGWR) modelling approach. The MGWR model is an improvement over the methods used in earlier studies of COVID-19 in identifying local variations of COVID-19 by incorporating a correction factor for the multiple testing problem in the geographically weighted regression models. The second manuscript quantifies the associations between COVID-19 cases and urban socioeconomic and land surface temperature (LST) at the neighbourhood scale in Toronto. Four spatiotemporal Bayesian hierarchical models with spatial, temporal, and varying space-time interaction terms are compared. The results of this study identified the seasonal trends of COVID-19 risk, where the spatiotemporal trends show increasing, decreasing, or stable patterns, and identified area-specific spatial risk for targeted interventions. Educational level and high land surface temperature are shown to have a positive association with the risk for COVID-19. In this study, high spatial and temporal resolution satellite images were used to extract LST, and atmospheric corrections methods were applied to these images by adopting a land surface emissivity (LSE) model, which provided a high estimation accuracy. The methodological approach of this work will help researchers understand how to acquire long time-series data of LST at a spatial scale from satellite images, develop methodological approaches for atmospheric correction and create the environmental data with a high estimation accuracy to fit into modelling disease. Applying to policy, the findings of this study can inform the design and implementation of urban planning strategies and programs to control disease risks. The third manuscript developed a novel approach for visualization of the spread of infectious disease outbreaks by incorporating neighbourhood networks and the time-series data of the disease at the neighbourhood level. The findings of the model provide an understanding of the direction and magnitude of spatial risk for the outbreak and guide for the importance of early intervention in order to stop the spread of the outbreak. The manuscript also identified hotspots using incidence rate and disease persistence, the findings of which may inform public health planners to develop priority-based intervention plans in a resource constraint situation

    Forest planning utilizing high spatial resolution data

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    This thesis presents planning approaches adapted for high spatial resolution data from remote sensing and evaluate whether such approaches can enhance the provision of ecosystem services from forests. The presented methods are compared with conventional, stand-level methods. The main focus lies on the planning concept of dynamic treatment units (DTU), where treatments in small units for modelling ecosystem processes and forest management are clustered spatiotemporally to form treatment units realistic in practical forestry. The methodological foundation of the thesis is mainly airborne laser scanning data (raster cells 12.5x12.5 m2), different optimization methods and the forest decision support system Heureka. Paper I demonstrates a mixed-integer programming model for DTU planning, and the results highlight the economic advances of clustering harvests. Paper II and III presents an addition to a DTU heuristic from the literature and further evaluates its performance. Results show that direct modelling of fixed costs for harvest operations can improve plans and that DTU planning enhances the economic outcome of forestry. The higher spatial resolution of data in the DTU approach enables the planning model to assign management with higher precision than if stand-based planning is applied. Paper IV evaluates whether this phenomenon is also valid for ecological values. Here, an approach adapted for cell-level data is compared to a schematic approach, dealing with stand-level data, for the purpose of allocating retention patches. The evaluation of economic and ecological values indicate that high spatial resolution data and an adapted planning approach increased the ecological values, while differences in economy were small. In conclusion, the studies in this thesis demonstrate how forest planning can utilize high spatial resolution data from remote sensing, and the results suggest that there is a potential to increase the overall provision of ecosystem services if such methods are applied

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials

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    Cellular solids and micro-lattices are a class of lightweight architected materials that have been established for their unique mechanical, thermal, and acoustic properties. It has been shown that by tuning material architecture, a combination of topology and solid(s) distribution, one can design new material systems, also known as metamaterials, with superior performance compared to conventional monolithic solids. Despite the continuously growing complexity of synthesized microstructures, mainly enabled by developments in additive manufacturing, correlating their morphological characteristics to the resulting material properties has not advanced equally. This work aims to develop a systematic data-driven framework that is capable of identifying all key microstructural characteristics and evaluating their effect on a target material property. The framework relies on integrating virtual structure generation and quantification algorithms with interpretable surrogate models. The effectiveness of the proposed approach is demonstrated by analyzing the effective stiffness of a broad class of two-dimensional (2D) cellular metamaterials with varying topological disorder. The results reveal the complex manner in which well-known stiffness contributors, including nodal connectivity, cooperate with often-overlooked microstructural features such as strut orientation, to determine macroscopic material behavior. We further re-examine Maxwell's criteria regarding the rigidity of frame structures, as they pertain to the effective stiffness of cellular solids and showcase microstructures that violate them. This framework can be used for structure-property correlation in different classes of metamaterials as well as the discovery of novel architectures with tailored combinations of material properties

    A conceptual framework for developing dashboards for big mobility data

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    Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets

    Beyond the Grid: The Micropolitics of Off-Grid Energy in Qandu-Qandu, South Africa

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    This is the final version. Available on open access from Wiley via the DOI in this recordIn this paper, we argue using smart technologies beyond the grid disrupts the access and use of existing energy sources, with profound impacts on everyday social life. We show how off-grid smart energy solutions constitute their own politics when considering existing conceptualisations of urban infrastructures in geography and the social sciences. To expose its politics, or “micropolitics”, we consider how tensions occur at the interface between infrastructures, where there are additions and modifications. We draw on an empirical example of Qandu-Qandu, an informal settlement in South Africa, to highlight how the placement, technical capabilities, and flexible financing options associated with off-grid solar energy create micropolitics with profound implications for everyday life. To conclude, we reflect on the value of using disruptions for understanding and enhancing equity in off-grid settings, contributing to the broader sustainability transitions narrative, and its “liveliness”
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