Matching Items (8)
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- All Subjects: Geography
- All Subjects: spatial statistics
- Genre: Academic theses
- Creators: Boone, Christopher
Description
This dissertation explores vulnerability to extreme heat hazards in the Maricopa County, Arizona metropolitan region. By engaging an interdisciplinary approach, I uncover the epidemiological, historical-geographical, and mitigation dimensions of human vulnerability to extreme heat in a rapidly urbanizing region characterized by an intense urban heat island and summertime heat waves. I first frame the overall research within global climate change and hazards vulnerability research literature, and then present three case studies. I conclude with a synthesis of the findings and lessons learned from my interdisciplinary approach using an urban political ecology framework. In the first case study I construct and map a predictive index of sensitivity to heat health risks for neighborhoods, compare predicted neighborhood sensitivity to heat-related hospitalization rates, and estimate relative risk of hospitalizations for neighborhoods. In the second case study, I unpack the history and geography of land use/land cover change, urban development and marginalization of minorities that created the metropolitan region's urban heat island and consequently, the present conditions of extreme heat exposure and vulnerability in the urban core. The third study uses computational microclimate modeling to evaluate the potential of a vegetation-based intervention for mitigating extreme heat in an urban core neighborhood. Several findings relevant to extreme heat vulnerability emerge from the case studies. First, two main socio-demographic groups are found to be at higher risk for heat illness: low-income minorities in sparsely-vegetated neighborhoods in the urban core, and the elderly and socially-isolated in the expansive suburban fringe of Maricopa County. The second case study reveals that current conditions of heat exposure in the region's urban heat island are the legacy of historical marginalization of minorities and large-scale land-use/land cover transformations of natural desert land covers into heat-retaining urban surfaces of the built environment. Third, summertime air temperature reductions in the range 0.9-1.9 °C and of up to 8.4 °C in surface temperatures in the urban core can be achieved through desert-adapted canopied vegetation, suggesting that, at the microscale, the urban heat island can be mitigated by creating vegetated park cool islands. A synthesis of the three case studies using the urban political ecology framework argues that climate changed-induced heat hazards in cities must be problematized within the socio-ecological transformations that produce and reproduce urban landscapes of risk. The interdisciplinary approach to heat hazards in this dissertation advances understanding of the social and ecological drivers of extreme heat by drawing on multiple theories and methods from sociology, urban and Marxist geography, microclimatology, spatial epidemiology, environmental history, political economy and urban political ecology.
ContributorsDeclet-Barreto, Juan (Author) / Harlan, Sharon L (Thesis advisor) / Bolin, Bob (Thesis advisor) / Hirt, Paul (Committee member) / Boone, Christopher (Committee member) / Arizona State University (Publisher)
Created2013
Description
The lack of substantive, multi-dimensional perspectives on civic space planning and design has undermined the potential role of these valuable social and ecological amenities in advancing urban sustainability goals. Responding to these deficiencies, this dissertation utilized mixed quantitative and qualitative methods and synthesized multiple social and natural science perspectives to inform the development of progressive civic space planning and design, theory, and public policy aimed at improving the social, economic, and environmental health of cities. Using Phoenix, Arizona as a case study, the analysis was tailored to arid cities, yet the products and findings are flexible enough to be geographically customized to the social, environmental, built, and public policy goals of other urbanized regions. Organized into three articles, the first paper applies geospatial and statistical methods to analyze and classify urban parks in Phoenix based on multiple social, ecological, and built criteria, including landuse-land cover, `greenness,' and site amenities, as well as the socio- economic and built characteristics of park neighborhoods. The second article uses spatial empirical analysis to rezone the City of Phoenix following transect form-based code. The current park system was then assessed within this framework and recommendations are presented to inform the planning and design of civic spaces sensitive to their social and built context. The final paper culminates in the development of a planning tool and site design guidelines for civic space planning and design across the urban-to-natural gradient augmented with multiple ecosystem service considerations and tailored to desert cities.
ContributorsIbes, Dorothy (Author) / Talen, Emily (Thesis advisor) / Boone, Christopher (Committee member) / Crewe, Katherine (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study explores the potential risks associated with the 65 U.S.-based commercial nuclear power plants (NPPs) and the distribution of those risks among the populations of both their respective host communities and of the communities located in outlying areas. First, I examine the relevant environmental justice issues. I start by examining the racial/ethnic composition of the host community populations, as well as the disparities in socio-economic status that exist, if any, between the host communities and communities located in outlying areas. Second, I estimate the statistical associations that exist, if any, between a population's distance from a NPP and several independent variables. I conduct multivariate ordinary least square (OLS) regression analyses and spatial autocorrelation regression (SAR) analyses at the national, regional and individual-NPP levels. Third, I construct a NPP potential risk index (NPP PRI) that defines four discrete risk categories--namely, very high risk, high risk, moderate risk, and low risk. The NPP PRI allows me then to estimate the demographic characteristics of the populations exposed to each so-defined level of risk. Fourth, using the Palo Verde NPP as the subject, I simulate a scenario in which a NPP experiences a core-damage accident. I use the RASCAL 4.3 software to simulate the path of dispersion of the resultant radioactive plume, and to investigate the statistical associations that exist, if any, between the dispersed radioactive plume and the demographic characteristics of the populations located within the plume's footprint. This study utilizes distributive justice theories to understand the distribution of the potential risks associated with NPPs, many of which are unpredictable, irreversible and inescapable. I employ an approach that takes into account multiple stakeholders in order to provide avenues for all parties to express concerns, and to ensure the relevance and actionability of any resulting policy recommendations.
ContributorsKyne, Dean (Author) / Bolin, Bob (Thesis advisor) / Boone, Christopher (Committee member) / Pijawka, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
The manner in which land and water are used and managed is a major influencing factor of global environmental change. Globally, modifications to the landscape have drastically transformed social and ecological communities. Land and water management practices also influences people's vulnerability to hazards. Other interrelated factors are compounding problems of environmental change as a result of land and water use changes. Such factors include climate change, sea level rise, the frequency and severity of hurricanes, and increased populations in coastal regions. The implication of global climate change for small islands and small island communities is especially troublesome. Socially, small islands have a limited resource base, deal with varying degrees of insularity, generally have little political power, and have limited economic opportunities. The physical attributes of small islands also increase their vulnerability to global climate change, including limited land area, limited fresh water supplies, and greater distances to resources. The focus of this research project is to document place-specific - and in this case island-specific - human-environmental interactions from a political ecology perspective as a means to address local concerns and possible consequences of global environmental change. The place in which these interactions are examined is the barrier island and village of Ocracoke, North Carolina. I focus on the specific historical-geography of land and water management on Ocracoke as a means to examine relationships between local human-environmental interactions and environmental change.
ContributorsPompeii, Brian J (Author) / Bolin, Bob (Thesis advisor) / Boone, Christopher (Committee member) / Lukinbeal, Christopher (Committee member) / Arizona State University (Publisher)
Created2014
Description
Our dependence on fossil fuels is driving anthropogenic climate change. Solar energy is the most abundant and cleanest alternative to fossil fuels, but its practicability is influenced by a complex interplay of factors (policy, geospatial, and market) and scales (global, national, urban). This thesis provides a holistic evaluation of these factors and scales with the goal of improving our understanding of the mechanisms and challenges of transitioning to solar energy.
This analysis used geospatial, demographic, policy, legislative record, environmental, and industry data, plus a series of semi-structured, in-person interviews. Methods included geostatistical calculation, statistical linear regression and multivariate modeling, and qualitative inductive analysis. The results reveal valuable insights at each scale, but moreover a gestalt model across the factors and scales draws out a larger pattern at play of the transmutational weighting and increasing complexity of interplay as the level of analysis cascades down through the three geographic scales.
This analysis used geospatial, demographic, policy, legislative record, environmental, and industry data, plus a series of semi-structured, in-person interviews. Methods included geostatistical calculation, statistical linear regression and multivariate modeling, and qualitative inductive analysis. The results reveal valuable insights at each scale, but moreover a gestalt model across the factors and scales draws out a larger pattern at play of the transmutational weighting and increasing complexity of interplay as the level of analysis cascades down through the three geographic scales.
ContributorsHerche, Wesley (Author) / Melnick, Rob (Thesis advisor) / Boone, Christopher (Committee member) / Pasqualetti, Martin J (Committee member) / Arizona State University (Publisher)
Created2017
Description
Decades of research confirms that urban green spaces in the form of parks, gardens, and urban forests provide numerous environmental and social services including microclimate regulation, noise reduction, rainwater drainage, stress amelioration, etc. In post-industrial megacities of the twenty-first century, densely populated, violent and heavily polluted such as Mexico City, having access to safe and well-maintained green public space is in all respects necessary for people to maintain or improve their quality of life. However, according to recent reports by the Mexican Ministry of Environment, green public spaces in Mexico City are insufficient and unevenly distributed across the sixteen boroughs of the Mexican Distrito Federal. If it is known that parks are essential urban amenities, why are green public spaces in Mexico City scarce and so unevenly distributed? As a suite of theoretical frameworks, Urban Political Ecology (UPE) has been used to study uneven urban development and its resulting unequal socio-ecological relations. UPE explores the complex relationship between environmental change, socio-economic urban characteristics and political processes. This research includes a detailed analysis of the distributive justice of green public space (who gets what and why) based on socio-spatial data sets provided by the Environment and Land Management Agency for the Federal District. Moreover, this work went beyond spatial data depicting available green space (m2/habitant) and explored the relation between green space distribution and other socio-demographic attributes, i.e. gender, socio-economic status, education and age that according to environmental justice theory, are usually correlated to an specific (biased) distribution of environmental burdens and amenities. Moreover, using archival resources complemented with qualitative data generated through in-depth interviews with key actors involved in the creation, planning, construction and management of green public spaces, this research explored the significant role of public and private institutions in the development of Mexico City's parks and green publics spaces, with a special focus on the effects of neoliberal capitalism as the current urban political economy in the city.
ContributorsFernandez Alvarez, Rafael (Author) / Bolin, Bob (Thesis advisor) / Boone, Christopher (Committee member) / Lara-Valencia, Francisco (Committee member) / Arizona State University (Publisher)
Created2015
Description
Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were proposed to overcome the challenges in practice. There are three major parts in the dissertation.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
ContributorsLu, Xuetao (Author) / McCulloch, Robert (Thesis advisor) / Hahn, Paul (Committee member) / Lan, Shiwei (Committee member) / Zhou, Shuang (Committee member) / Saul, Steven (Committee member) / Arizona State University (Publisher)
Created2020
Description
Geographically Weighted Regression (GWR) has been broadly used in various fields to
model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter which controls the amount of distance-decay in weighting neighboring data around each location. The single bandwidth in GWR assumes that processes (relationships between the response variable and the predictor variables) all operate at the same scale. However, this posits a limitation in modeling potentially multi-scale processes which are more often seen in the real world. For example, the measured ambient temperature of a location is affected by the built environment, regional weather and global warming, all of which operate at different scales. A recent advancement to GWR termed Multiscale GWR (MGWR) removes the single bandwidth assumption and allows the bandwidths for each covariate to vary. This results in each parameter surface being allowed to have a different degree of spatial variation, reflecting variation across covariate-specific processes. In this way, MGWR has the capability to differentiate local, regional and global processes by using varying bandwidths for covariates. Additionally, bandwidths in MGWR become explicit indicators of the scale at various processes operate. The proposed dissertation covers three perspectives centering on MGWR: Computation; Inference; and Application. The first component focuses on addressing computational issues in MGWR to allow MGWR models to be calibrated more efficiently and to be applied on large datasets. The second component aims to statistically differentiate the spatial scales at which different processes operate by quantifying the uncertainty associated with each bandwidth obtained from MGWR. In the third component, an empirical study will be conducted to model the changing relationships between county-level socio-economic factors and voter preferences in the 2008-2016 United States presidential elections using MGWR.
model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter which controls the amount of distance-decay in weighting neighboring data around each location. The single bandwidth in GWR assumes that processes (relationships between the response variable and the predictor variables) all operate at the same scale. However, this posits a limitation in modeling potentially multi-scale processes which are more often seen in the real world. For example, the measured ambient temperature of a location is affected by the built environment, regional weather and global warming, all of which operate at different scales. A recent advancement to GWR termed Multiscale GWR (MGWR) removes the single bandwidth assumption and allows the bandwidths for each covariate to vary. This results in each parameter surface being allowed to have a different degree of spatial variation, reflecting variation across covariate-specific processes. In this way, MGWR has the capability to differentiate local, regional and global processes by using varying bandwidths for covariates. Additionally, bandwidths in MGWR become explicit indicators of the scale at various processes operate. The proposed dissertation covers three perspectives centering on MGWR: Computation; Inference; and Application. The first component focuses on addressing computational issues in MGWR to allow MGWR models to be calibrated more efficiently and to be applied on large datasets. The second component aims to statistically differentiate the spatial scales at which different processes operate by quantifying the uncertainty associated with each bandwidth obtained from MGWR. In the third component, an empirical study will be conducted to model the changing relationships between county-level socio-economic factors and voter preferences in the 2008-2016 United States presidential elections using MGWR.
ContributorsLi, Ziqi (Author) / Fotheringham, A. Stewart (Thesis advisor) / Goodchild, Michael F. (Committee member) / Li, Wenwen (Committee member) / Arizona State University (Publisher)
Created2020