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- All Subjects: Geography
- All Subjects: spatial statistics
- Genre: Academic theses
- Creators: Hondula, David M.
Many coastal cities around the world are becoming increasingly vulnerable to natural disasters, particularly flooding driven by tropical storm and hurricane storm surge – typically the most destructive feature of these storms, generating significant economic damage and loss of life. This increase in vulnerability is driven by the interactions between a wide number of complex social and climatic factors, including population growth, irresponsible urban development, a decrease in essential service provision, sea level rise, and changing storm regimes. These issues are exacerbated by the short-term strategic planning that dominates political action and economic decision-making, resulting in many vulnerable coastal communities being particularly unprepared for large, infrequent storm surge events. This lack of preparedness manifests in several ways, but one of the most visible is the lack of comprehensive evacuation and rescue operation plans for use after major storm surge flooding occurs. Typical evacuation or rescue plans are built using a model of a region’s intact road network. While useful for pre-disaster purposes, the immediate aftermath of large floods sees enormous swaths of a given region’s road system flooded, rendering most of these plans largely useless. Post-storm evacuation and rescue requires large amounts of atypical travel through a region (i.e., across non-road surfaces). Traditional road network models (such as those that are used to generate evacuation routes) are unable to conceptualize this type of transportation, and so are of limited utility during post-disaster scenarios. To solve these problems, this dissertation introduces an alternative network conceptualization that preserves important on-network information but also accounts for the possibility of off-network travel during a disaster. Providing this in situ context is necessary to adequately model transportation through a post-storm landscape, one in which evacuees and rescuers are regularly departing from roads and one in which many roads are completely interdicted by flooding. This modeling approach is used to automatically generate routes through a flooded coastal urban area, as well as to identify potentially critical road segments in advance of an actual storm. These tools may help both emergency managers better prepare for large storms, and urban planners in their efforts to mitigate flood damage.
Urban climate conditions are the physical manifestation of formal and informal social forces of design, policy, and urban management. The urban design community (e.g. planners, architects, urban designers, landscape architects, engineers) impacts urban development through influential built projects and design discourse. Their decisions create urban landscapes that impact physiological and mental health for people that live in and around them. Therefore, to understand possible opportunities for decision-making to support healthier urban environments and communities, this dissertation examines the role of neighborhood design on the thermal environment and the effect the thermal environment has on mental health. In situ data collection and numerical modeling are used to assess current and proposed urban design configurations in the Edison Eastlake public housing community in central Phoenix for their efficacy in cooling the thermal environment. A distributed lagged non-linear model is used to investigate the relative risk of hospitalization for schizophrenia in Maricopa County based on atmospheric conditions. The dissertation incorporates both an assessment of design strategies for the cooling of the thermal environment and an analysis of the existing thermal environment’s relationship with mental health. By reframing the urban design of neighborhoods through the lens of urban climate, this research reinforces the importance of incorporating the community into the planning process and highlights some unintended outcomes of prioritizing the thermal environment in urban design.
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.
Interdisciplinary research has highlighted how social-ecological dynamics drive the structure and function of the urban landscape across multiple scales. Land management decisions operate across various levels, from individuals in their backyard to local municipalities and broader political-economic forces. These decisions then scale up and down across the landscape to influence ecological functioning, such as the provisioning of biodiversity. Likewise, people are influenced by, and respond to, their environment. However, there is a lack of integrated research, especially research that considers the spatial and temporal complexities of social-ecological dynamics, to fully understand how people influence ecosystems or how the resulting landscape in turn influences human decision making, attitudes, and well-being.
My dissertation connects these interdisciplinary themes to examine three questions linked by their investigation of the interactions between people and biodiversity: (1) How do the social and spatial patterns within an arid city affect people’s attitudes about their regional desert environment? (2) How are novel communities in cities assembled given the social-ecological dynamics that influence the processes that structure ecological communities? (3) How can we reposition bird species traits into a conservation framework that explains the complexity of the interactions between people and urban bird communities? I found that social-ecological dynamics between people, the environment, and biodiversity are tightly interwoven in urban ecosystems. The regional desert environment shapes people’s attitudes along spatial and social configurations, which holds implications for yard management decisions. Multi-scalar management decisions then influence biodiversity throughout cities, which shifts public perceptions of urban nature. Overall, my research acts as a bridge between social and ecological sciences to theoretically and empirically integrate research focused on biodiversity conservation in complex, social-ecological systems. My goal as a scholar is to understand the balance between social and ecological implications of landscape change to support human well-being and promote biodiversity conservation.
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.
Exertional heat stroke continues to be one of the leading causes of illness and death in sport in the United States, with an athlete’s experienced microclimate varying by venue design and location. A limited number of studies have attempted to determine the relationship between observed wet bulb globe temperature (WBGT) and WBGT derived from regional weather station data. Moreover, only one study has quantified the relationship between regionally modeled and on-site measured WBGT over different athletic surfaces (natural grass, rubber track, and concrete tennis court). The current research expands on previous studies to examine how different athletic surfaces influence the thermal environment in the Phoenix Metropolitan Area using a combination of fieldwork, modeling, and statistical analysis. Meteorological data were collected from 0700–1900hr across 6 days in June and 5 days in August 2019 in Tempe, Arizona at various Sun Devil Athletics facilities. This research also explored the influence of surface temperatures on WBGT and the changes projected under a future warmer climate. Results indicate that based on American College of Sports Medicine guidelines practice would not be cancelled in June (WBGT≥32.3°C); however, in August, ~33% of practice time was lost across multiple surfaces. The second-tier recommendations (WBGT≥30.1°C) to limit intense exercise were reached an average of 7 hours each day for all surfaces in August. Further, WBGT was calculated using data from four Arizona Meteorological Network (AZMET) weather stations to provide regional WBGT values for comparison. The on-site (field/court) WBGT values were consistently higher than regional values and significantly different (p<0.05). Thus, using regionally-modeled WBGT data to guide activity or clothing modification for heat safety may lead to misclassification and unsafe conditions. Surface temperature measurements indicate a maximum temperature (170°F) occurring around solar noon, yet WBGT reached its highest level mid-afternoon and on the artificial turf surface (2–5PM). Climate projections show that WBGT values are expected to rise, further restricting the amount of practice and games than can take place outdoors during the afternoon. The findings from this study can be used to inform athletic trainers and coaches about the thermal environment through WBGT values on-field.
