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- All Subjects: Geography
- All Subjects: spatial statistics
- Genre: Academic theses
- Creators: Georgescu, Matei
Numerical simulations using a state-of-the-science regional climate model are utilized to address a trio of scientifically relevant questions with wide global applicability. The importance of an accurate representation of land use and land cover is first demonstrated through comparison of numerical simulations against observations. Second, the simulated effect of anthropogenic heating is quantified. Lastly, numerical simulations are performed using pre-historic scenarios of land use and land cover to examine and quantify the impact of Mexico City's urban expansion and changes in surface water features on its regional climate.
Embedded within the regression framework, local models can estimate conditioned relationships between observed spatial phenomena and hypothesized explanatory variables and help infer the intangible spatial processes that contribute to the observed spatial patterns. Rather than investigating averaged characteristics corresponding to processes over space as global models do, these models estimate a surface of spatially varying parameters with a value for each location. Additionally, some models such as variants within the Geographically Weighted Regression (GWR) framework, also estimate a parameter to represent the spatial scale across which the processes vary representing the inherent heterogeneity of the estimated surfaces. Since different processes tend to operate at unique spatial scales, some extensions to local models such as Multiscale GWR (MGWR) estimate unique scales of association for each predictor in a model and generate significantly more information on the nature of geographic processes than their predecessors. However, developments within the realm of local models are fairly nascent and hence an understanding around their correct application as well as recognizing their true potential in exploring fundamental spatial science issues is under-developed. The techniques within these frameworks are also currently limited thus restricting the kinds of data that can be analyzed using these models. Therefore the goal of this dissertation is to advance techniques within local multiscale modeling specifically by coining new diagnostics, exploring their novel application in understanding long-standing issues concerning spatial scale and by expanding the tool base to allow their use in wider empirical applications. This goal is realized through three distinct research objectives over four chapters, followed by a discussion on the future of the developments within local multiscale modeling. A correct understanding of the capability and promise of local multiscale models and expanding the fields where they can be employed will not only enhance geographical research by strengthening the intuition of the nature of geographic processes, but will also exemplify the importance and need for using such tools bringing quantitative spatial science to the fore.
The second paper considers marginal agricultural land use for bioenergy crop cultivation to meet future liquid fuels demand from cellulosic biofuels sustainably and profitably. At a wholesale fuel price of $4 gallons-of-gasoline-equivalent, 30 to 90.7 billion gallons of cellulosic biofuels can be supplied by converting 22 to 79.3 million hectares of marginal lands in the Eastern United States (U.S.). Displacing marginal croplands (9.4-13.7 million hectares) reduces stress on water resources by preserving soil moisture. This displacement is comparable to existing land use for first-generation biofuels, limiting food supply impacts. Coupled modeling reveals positive hydroclimate feedback on bioenergy crop yields that moderates the land footprint.
The third paper examines the sustainability implications of expanding use of marginal lands for corn cultivation in the Western Corn Belt, a commercially important and environmentally sensitive U.S. region. Corn cultivation on lower quality lands, which tend to overlap with marginal agricultural lands, is shown to be nearly three times more sensitive to changes in crop prices. Therefore, corn cultivation disproportionately expanded into these lands following price spikes.
Underutilized spaces can contribute towards sustainability at small and large scales in a complementary fashion. While supplying fresh produce locally and delivering other benefits in terms of energy use and public health, UA can also reduce pressures on croplands and complement non-urban food production. This complementarity can help diversify agricultural land use for meeting other goals, like supplying biofuels. However, understanding the role of market forces and economic linkages is critical to anticipate any unintended consequences due to such re-organization of land use.
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.
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.
The first paper of this dissertation found that divergent design intentions lead to unique trajectories for LSA, the urban heat island effect, and bird community at two urban riparian sites in the Phoenix metropolitan area. The second paper examined institutional shifts that occurred during Cuba’s “special period in time of peace” and found that the resulting land tenure changes both modified and maintained the LSA of the country, changing cropland but preserving forest land. The third paper found that globalized forces may be contributing to the homogenizing urban form of large, populous cities in China, India, and the United States—especially for the ten largest cities in each country—with implications for surface urban heat island intensity. Expanding knowledge on social drivers of land system and environmental change provides insights on designing landscapes that optimize for a range of social and ecological trade-offs.