<p>Research interests in the studies of complex systems have been booming in many disciplines for the last decade. As the nature of geographic environment is a complex system, researches in this field are anticipated. In particular, the urban street networks in the Geographic Information System (GIS) as complex networks are brought forth for the thesis study. Meanwhile, identifying the scale-free property, which is represented as the power law distribution from a mathematical perspective, is a hot topic in the studies of complex systems. Many previous studies estimated the power law distributions with graphic method, which used linear regression method to identify the exponent value and estimate the quality that the power law fits to the empirical data. However, such strategy is considered to cause inaccurate results and lead to biased judgments. Whereas, the Maximum Likelihood Estimation (MLE) and the Goodness of fit test based on Kolmogorov-Smironv (KS) statistics will provide more solid and trustable results for the estimations. Therefore, this thesis addresses these updated methods exploring the topological patterns of urban street networks from an analytical perspective, which is estimating the power law distributions for the connectivity degree and length of the urban streets. Simultaneously, this thesis explores the street networks from a visual perspective as well. The visual perspective adopts the large network visualization tool (LaNet-vi), which is developed based on the k-core decomposition algorithm, to analyze the cores of the urban street networks. By retrieving the spatial information of the networks from GIS, it actually enables us to see how the urban street networks decomposed topologically and spatially. In particular, the 40 US urban street networks are reformed as natural street networks by using three "natural street" models.</p><p>The results from analytical perspective show that the 80/20 principle still exists for both the street connectivity degree and length qualitatively, which means around 20% natural streets in each network have a connectivity degree or length value above the average level, while the 80% ones are below the average. Moreover, the quantitative analysis revealed the fact that most of the distributions from the street connectivity degree or length of the 40 natural street networks follow a power law distribution with an exponential cut-off. Some of the rest cases are verified to have power law distributions and some extreme cases are still unclear for identifying which distribution form to fit. The comparisons are made to the power law statement from previous study which used the linear regression method. Moreover, the visual perspective not only provides us the chance to see the inner structures about the hierarchies and cores of the natural street networks topologically and spatially, but also serves as a reflection for the analytical perspective. Such relationships are discussed and the possibility of combining these two aspects are pointed out. In addition, the future work is also proposed for making better studies in this field.</p>

This dissertation focuses on the spatial aspects of oil and natural gas production to investigate the extent and effects of inefficient and unnecessary spatial competition. Because oil and natural gas are migratory, operators can cause hydrocarbon resources to flow from a neighboring property onto his or her own through rapid extraction. This problem is compounded when productive leases are comparatively small, as is the case in Texas. Following an introduction and literature review, the third chapter takes advantage of a natural experiment to demonstrate how spillovers in production limit total cumulative recovery, and how the assignment of secure property rights can enhance economic outcomes. The chapter examines production from wells in Oklahoma and Texas near the panhandle border. While wells on either side of this line have similar geologies and so should be similarly productive they are exposed to different treatments: Oklahoma has a much higher rate of unitization (a contractual scheme where competing owners hire a common operator and share profits), whereas the unitization rate in Texas is lower. Using regression discontinuity design, I find that Oklahoma wells are produced more slowly early on, and that this results in greater cumulative recovery over the course of a well’s life (150% more relative to Texas). These results are robust after controlling for reservoir specific effects, and across parametric, semi-parametric and nonparametric specifications. xiiThe fourth chapter quantifies the degree to which competing owners interfere with each other’s production through spatial spillovers. I use a spatial econometric model that controls for spatial autocorrelation and spatial dependence and can therefore identify the spillovers in production. Additionally, by comparing leases owned by competing producers to leases owned by a common producer, I show empirically how securing property rights through common ownership can alleviate the externality in production. A priori, one would expect that when a common producer owns adjacent leases, the producer has the incentive to fully account for how spillovers in production affect neighboring wells. Conversely, when adjacent landowners are in competition to extract the resource, they will not account for the damage rapid production causes at neighboring wells. After controlling for secondary injection I find that this is indeed the case for Slaughter field of West Texas. The fifth chapter investigates the statistical properties of oil and natural gas production. I find striking evidence that both oil and natural gas production are power-law distributed with the exponent approximately equal to one. This distribution might arise from disequilibrium in production and exploration. Highlighting this distribution is important because it has potential consequences for the political economy of regulation as well as for resource management. For example, because the most productive wells lie in the far-right tail of the distribution, regulation geared to prevent a Deepwater Horizon scale spill need fall on a vanishingly small percent of wells. The distribution also has consequences for management because a company profitability depends disproportionately on how it manages its most productive wells. The sixth chapter provides a short conclusion.

One of the main problems in statistical climatology is to construct a parsimonious model of natural climate variability. Such a model serves for instance as a null hypothesis for detection of human induced climate changes and of periodic climate signals. Fitting thismodel to various climatic time series also helps to infer the origins of underlying temporal variability and to cross validate it between different data sets. We consider the use of a spectral power-law model in this role for the surface temperature, for the free atmospheric air temperature of the troposphere and stratosphere, and for the total ozone. First, we lay down a methodological foundation for our work. We compare two variants of five different power-law fitting methods by means of Monte-Carlo simulations and their application to observed air temperature. Then using the best two methods we fit the power-law model to several observational products and climate model simulations. We make use of specialized atmospheric general circulation model simulations and of the simulations of the Coupled Model Intercomparison Project 3 (CMIP3). The specialized simulations allow us to explain the power-law exponent spatial distribution and to account for discrepancies in scaling behaviour between different observational products. We find that most of the pre-industrial control and 20th century model simulations capture many aspects of the observed horizontal and vertical distribution of the power-law exponents. At the surface, regions with robust power-law exponents—the North Atlantic, the North Pacific, and the Southern Ocean — coincide with regions with strong inter-decadal variability. In the free atmosphere, the large power-law exponents are detected on annual to decadal time scales in the tropical and subtropical troposphere and stratosphere. The spectral steepness in the former is explained by its strong coupling to the surface and in the latter by its sensitivity to volcanic aerosols. However power-law behaviour in the tropics and in the free atmosphere saturates on multi-decadal timescales. We propose a novel diagnostic to evaluate the relative goodness-of-fit of the autoregressive model of the first order (AR1) and the power-law model. The collective behaviour of CMIP3 simulations appears to fall between the two statistical models. Our results suggest that the power-law model should serve as an upper bound and the AR1 model should serve as a lower bound for climate persistence on monthly to decadal time scales. On the applied side we find that the presence of power-law like natural variability increases the uncertainty on the long-term total ozone trend in the Northern Hemisphere high latitudes attributable to anthropogenic chlorine by about a factor of 1.5, and lengthens the expected time to detect ozone recovery by a similar amount.

One of the main problems in statistical climatology is to construct a parsimonious model of natural climate variability. Such a model serves for instance as a null hypothesis for detection of human induced climate changes and of periodic climate signals. Fitting thismodel to various climatic time series also helps to infer the origins of underlying temporal variability and to cross validate it between different data sets. We consider the use of a spectral power-law model in this role for the surface temperature, for the free atmospheric air temperature of the troposphere and stratosphere, and for the total ozone. First, we lay down a methodological foundation for our work. We compare two variants of five different power-law fitting methods by means of Monte-Carlo simulations and their application to observed air temperature. Then using the best two methods we fit the power-law model to several observational products and climate model simulations. We make use of specialized atmospheric general circulation model simulations and of the simulations of the Coupled Model Intercomparison Project 3 (CMIP3). The specialized simulations allow us to explain the power-law exponent spatial distribution and to account for discrepancies in scaling behaviour between different observational products. We find that most of the pre-industrial control and 20th century model simulations capture many aspects of the observed horizontal and vertical distribution of the power-law exponents. At the surface, regions with robust power-law exponents—the North Atlantic, the North Pacific, and the Southern Ocean — coincide with regions with strong inter-decadal variability. In the free atmosphere, the large power-law exponents are detected on annual to decadal time scales in the tropical and subtropical troposphere and stratosphere. The spectral steepness in the former is explained by its strong coupling to the surface and in the latter by its sensitivity to volcanic aerosols. However power-law behaviour in the tropics and in the free atmosphere saturates on multi-decadal timescales. We propose a novel diagnostic to evaluate the relative goodness-of-fit of the autoregressive model of the first order (AR1) and the power-law model. The collective behaviour of CMIP3 simulations appears to fall between the two statistical models. Our results suggest that the power-law model should serve as an upper bound and the AR1 model should serve as a lower bound for climate persistence on monthly to decadal time scales. On the applied side we find that the presence of power-law like natural variability increases the uncertainty on the long-term total ozone trend in the Northern Hemisphere high latitudes attributable to anthropogenic chlorine by about a factor of 1.5, and lengthens the expected time to detect ozone recovery by a similar amount.

This dissertation focuses on the spatial aspects of oil and natural gas production to investigate the extent and effects of inefficient and unnecessary spatial competition. Because oil and natural gas are migratory, operators can cause hydrocarbon resources to flow from a neighboring property onto his or her own through rapid extraction. This problem is compounded when productive leases are comparatively small, as is the case in Texas. Following an introduction and literature review, the third chapter takes advantage of a natural experiment to demonstrate how spillovers in production limit total cumulative recovery, and how the assignment of secure property rights can enhance economic outcomes. The chapter examines production from wells in Oklahoma and Texas near the panhandle border. While wells on either side of this line have similar geologies and so should be similarly productive they are exposed to different treatments: Oklahoma has a much higher rate of unitization (a contractual scheme where competing owners hire a common operator and share profits), whereas the unitization rate in Texas is lower. Using regression discontinuity design, I find that Oklahoma wells are produced more slowly early on, and that this results in greater cumulative recovery over the course of a well’s life (150% more relative to Texas). These results are robust after controlling for reservoir specific effects, and across parametric, semi-parametric and nonparametric specifications. xiiThe fourth chapter quantifies the degree to which competing owners interfere with each other’s production through spatial spillovers. I use a spatial econometric model that controls for spatial autocorrelation and spatial dependence and can therefore identify the spillovers in production. Additionally, by comparing leases owned by competing producers to leases owned by a common producer, I show empirically how securing property rights through common ownership can alleviate the externality in production. A priori, one would expect that when a common producer owns adjacent leases, the producer has the incentive to fully account for how spillovers in production affect neighboring wells. Conversely, when adjacent landowners are in competition to extract the resource, they will not account for the damage rapid production causes at neighboring wells. After controlling for secondary injection I find that this is indeed the case for Slaughter field of West Texas. The fifth chapter investigates the statistical properties of oil and natural gas production. I find striking evidence that both oil and natural gas production are power-law distributed with the exponent approximately equal to one. This distribution might arise from disequilibrium in production and exploration. Highlighting this distribution is important because it has potential consequences for the political economy of regulation as well as for resource management. For example, because the most productive wells lie in the far-right tail of the distribution, regulation geared to prevent a Deepwater Horizon scale spill need fall on a vanishingly small percent of wells. The distribution also has consequences for management because a company profitability depends disproportionately on how it manages its most productive wells. The sixth chapter provides a short conclusion.

Research interests in the studies of complex systems have been booming in many disciplines for the last decade. As the nature of geographic environment is a complex system, researches in this field are anticipated. In particular, the urban street networks in the Geographic Information System (GIS) as complex networks are brought forth for the thesis study. Meanwhile, identifying the scale-free property, which is represented as the power law distribution from a mathematical perspective, is a hot topic in the studies of complex systems. Many previous studies estimated the power law distributions with graphic method, which used linear regression method to identify the exponent value and estimate the quality that the power law fits to the empirical data. However, such strategy is considered to cause inaccurate results and lead to biased judgments. Whereas, the Maximum Likelihood Estimation (MLE) and the Goodness of fit test based on Kolmogorov-Smironv (KS) statistics will provide more solid and trustable results for the estimations. Therefore, this thesis addresses these updated methods exploring the topological patterns of urban street networks from an analytical perspective, which is estimating the power law distributions for the connectivity degree and length of the urban streets. Simultaneously, this thesis explores the street networks from a visual perspective as well. The visual perspective adopts the large network visualization tool (LaNet-vi), which is developed based on the k-core decomposition algorithm, to analyze the cores of the urban street networks. By retrieving the spatial information of the networks from GIS, it actually enables us to see how the urban street networks decomposed topologically and spatially. In particular, the 40 US urban street networks are reformed as natural street networks by using three "natural street" models. The results from analytical perspective show that the 80/20 principle still exists for both the street connectivity degree and length qualitatively, which means around 20% natural streets in each network have a connectivity degree or length value above the average level, while the 80% ones are below the average. Moreover, the quantitative analysis revealed the fact that most of the distributions from the street connectivity degree or length of the 40 natural street networks follow a power law distribution with an exponential cut-off. Some of the rest cases are verified to have power law distributions and some extreme cases are still unclear for identifying which distribution form to fit. The comparisons are made to the power law statement from previous study which used the linear regression method. Moreover, the visual perspective not only provides us the chance to see the inner structures about the hierarchies and cores of the natural street networks topologically and spatially, but also serves as a reflection for the analytical perspective. Such relationships are discussed and the possibility of combining these two aspects are pointed out. In addition, the future work is also proposed for making better studies in this field.

The first-order properties like degree distribution of nodes and the clustering co-efficient have been the prime focus of research in the study of structural properties of networks. The presence of a power law in the degree distribution of nodes has been considered as an important structural characteristic of social and information networks. Higher-order structural properties such as edge embeddedness may also play a more important role in many on-line social networks but have not been studied before. In this research, we study the distribution of higher-order structural properties of a network, such as edge embeddedness, in complex network models and on-line social networks. We empirically study the embeddedness distribution of a variety of network models and theoretically prove that a recently-proposed network model, the random $k$-tree, has a power-law embedded distribution. We conduct extensive experiments on the embeddedness distribution in real-world networks and provide evidence on the correlation between embeddedeness and communication patterns among the members in an on-line social network. / Graduate

We examine the boundary-layer flow of generalised Newtonian fluids. A specific member of this class of non-Newtonian fluids, namely the Ostwald-de Waele or power-law fluid, is studied in some detail. We show, through the numerical solution of the governing equations, that this empirical model of fluids encountered in physical and industrial situations is of limited benefit when considering the boundary-layer flow of such a fluid. We then develop and employ a Carreau viscosity model in the same context and show that the numerical marching scheme has better convergence behaviour than was the case for power-law fluids. We also investigate the boundary-layer flow of a Newtonian fluid over a thin film of non-Newtonian fluid, described by a Carreau fluid model, by focusing specifically on similarity-type solutions. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1456589 / Thesis (Ph.D.) - University of Adelaide, School of Mathematical Sciences, 2009

Thesis (Ph.D.)--Brandeis University, 2010. / "UMI no: 3390523." MICROFILM COPY ALSO AVAILABLE IN THE UNIVERSITY ARCHIVES. Includes bibliographical references.

Landslides present a geomorphological hazard in alpine regions, threatening life, infrastructure and property. Presented in this thesis is the development of a new Regional Landslide Inventory (RI) for the European Alps. The new inventory is used to investigate links between landslide size and frequency in the European Alps and weather and climatic controls. Temperatures in the European Alps have risen by 2 C since the end of the Little Ice Age (LIA); a trend which is set to continue. Previous research has shown that past landslide clusters are centred around periods of signi ficant climate change, thus understanding how this translates to the current warming trend is important both for communities living in the European Alps and for the insurance industry. The RI compiled here, provides a substantial temporal and spatial picture of landsliding in the Alps; with particular focus on the Swiss and French Alps. The temporal distribution and estimates of completeness were tested through the use of segmented models, scaling relationships and area-frequency distributions; the post-1970 portion of the database is considered most complete, although underestimating the frequency of medium-sized landslides. Analysis of the RI in the context of synoptic weather types demonstrates that high precipitation over the European Alps is consistent with higher landslide frequencies. Whilst analysis with climate data show that annual landslide frequencies are correlated with changes in precipitation and temperature across the European Alps; accounting for up to 35% of the seasonal variation in landslide frequency.