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- Genre: Academic theses
Description
The Southwestern Willow Flycatcher (Empidonax traillii extimus) has been studied for over two decades and listed as endangered for most of that time. Though the flycatcher has been granted protected status since 1995, critical habitat designation for the flycatcher has not shared the same history. Critical habitat designation is essential for achieving the long-term goals defined in the flycatcher recovery plan where emphasis is on both the protection of this species and "the habitats supporting these flycatchers [that] must be protected from threats and loss" (U.S. Fish and Wildlife Service 2002). I used a long-term data set of habitat characteristics collected at three study areas along the Lower Colorado River to develop a method for quantifying habitat quality for flycatcher. The data set contained flycatcher nest observations (use) and habitat availability (random location) from 2003-2010 that I statistically analyzed for flycatcher selection preferences. Using both Pearson's Chi-square test and SPSS Principal Component Analysis (PCA) I determined that flycatchers were selecting 30 habitat traits significantly different among an initial list of 127 habitat characteristics. Using PCA, I calculated a weighted value of influence for each significant trait per study area and used those values to develop a habitat classification system to build predictive models for flycatcher habitat quality. I used ArcGIS® Model Builder to develop three habitat suitability models for each of the habitat types occurring in western riparian systems, native, mixed exotic and exotic dominated that are frequented by breeding flycatchers. I designed a fourth model, Topock Marsh, to test model accuracy on habitat quality for flycatchers using reserved accuracy assessment points of previous nest locations. The results of the fourth model accurately predicted a decline in habitat at Topock Marsh that was confirmed by SWCA survey reports released in 2011 and 2012 documenting a significant decline in flycatcher productivity in the Topock Marsh study area.
ContributorsChenevert-Steffler, Ann (Author) / Miller, William (Thesis advisor) / Bateman, Heather (Committee member) / Alford, Eddie (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation investigates the long-term consequences of human land-use practices in general, and in early agricultural villages in specific. This pioneering case study investigates the "collapse" of the Early (Pre-Pottery) Neolithic lifeway, which was a major transformational event marked by significant changes in settlement patterns, material culture, and social markers. To move beyond traditional narratives of cultural collapse, I employ a Complex Adaptive Systems approach to this research, and combine agent-based computer simulations of Neolithic land-use with dynamic and spatially-explicit GIS-based environmental models to conduct experiments into long-term trajectories of different potential Neolithic socio-environmental systems. My analysis outlines how the Early Neolithic "collapse" was likely instigated by a non-linear sequence of events, and that it would have been impossible for Neolithic peoples to recognize the long-term outcome of their actions. The experiment-based simulation approach shows that, starting from the same initial conditions, complex combinations of feedback amplification, stochasticity, responses to internal and external stimuli, and the accumulation of incremental changes to the socio-natural landscape, can lead to widely divergent outcomes over time. Thus, rather than being an inevitable consequence of specific Neolithic land-use choices, the "catastrophic" transformation at the end of the Early Neolithic was an emergent property of the Early Neolithic socio-natural system itself, and thus likely not an easily predictable event. In this way, my work uses the technique of simulation modeling to connect CAS theory with the archaeological and geoarchaeological record to help better understand the causes and consequences of socio-ecological transformation at a regional scale. The research is broadly applicable to other archaeological cases of resilience and collapse, and is truly interdisciplinary in that it draws on fields such as geomorphology, computer science, and agronomy in addition to archaeology.
ContributorsUllah, Isaac (Author) / Barton, C. Michael (Thesis advisor) / Banning, Edward B. (Committee member) / Clark, Geoffrey (Committee member) / Arrowsmith, J. Ramon (Committee member) / Arizona State University (Publisher)
Created2013
Description
The objective of this dissertation is to empirically analyze the results of the retail location decision making process and how chain networks evolve given their value platform. It employs one of the largest cross-sectional databases of retailers ever assembled, including 50 US retail chains and over 70,000 store locations. Three closely related articles, which develop new theory explaining location deployment and behaviors of retailers, are presented. The first article, "Regionalism in US Retailing," presents a comprehensive spatial analysis of the domestic patterns of retailers. Geographic Information Systems (GIS) and statistics examine the degree to which the chains are deployed regionally versus nationally. Regional bias is found to be associated with store counts, small market deployment, and the location of the founding store, but not the age of the chain. Chains that started in smaller markets deploy more stores in other small markets and vice versa for chains that started in larger markets. The second article, "The Location Types of US Retailers," is an inductive analysis of the types of locations chosen by the retailers. Retail locations are classified into types using cluster analysis on situational and trade area data at the geographical scale of the individual stores. A total of twelve distinct location types were identified. A second cluster analysis groups together the chains with the most similar location profiles. Retailers within the same retail business often chose similar types of locations and were placed in the same clusters. Retailers generally restrict their deployment to one of three overall strategies including metropolitan, large retail areas, or market size variety. The third article, "Modeling Retail Chain Expansion and Maturity through Wave Analysis: Theory and Application to Walmart and Target," presents a theory of retail chain expansion and maturity whereby retailers expand in waves with alternating periods of faster and slower growth. Walmart diffused gradually from Arkansas and Target grew from the coasts inward. They were similar, however, in that after expanding into an area they reached a point of saturation and opened fewer stores, then moved on to other areas, only to revisit the earlier areas for new stores.
ContributorsJoseph, Lawrence (Author) / Kuby, Michael (Thesis advisor) / Matthews, Richard (Committee member) / Ó Huallacháin, Breandán (Committee member) / Kumar, Ajith (Committee member) / Arizona State University (Publisher)
Created2013
Description
Facility location models are usually employed to assist decision processes in urban and regional planning. The focus of this research is extensions of a classic location problem, the Weber problem, to address continuously distributed demand as well as multiple facilities. Addressing continuous demand and multi-facilities represents major challenges. Given advances in geographic information systems (GIS), computational science and associated technologies, spatial optimization provides a possibility for improved problem solution. Essential here is how to represent facilities and demand in geographic space. In one respect, spatial abstraction as discrete points is generally assumed as it simplifies model formulation and reduces computational complexity. However, errors in derived solutions are likely not negligible, especially when demand varies continuously across a region. In another respect, although mathematical functions describing continuous distributions can be employed, such theoretical surfaces are generally approximated in practice using finite spatial samples due to a lack of complete information. To this end, the dissertation first investigates the implications of continuous surface approximation and explicitly shows errors in solutions obtained from fitted demand surfaces through empirical applications. The dissertation then presents a method to improve spatial representation of continuous demand. This is based on infill asymptotic theory, which indicates that errors in fitted surfaces tend to zero as the number of sample points increases to infinity. The implication for facility location modeling is that a solution to the discrete problem with greater demand point density will approach the theoretical optimum for the continuous counterpart. Therefore, in this research discrete points are used to represent continuous demand to explore this theoretical convergence, which is less restrictive and less problem altering compared to existing alternatives. The proposed continuous representation method is further extended to develop heuristics to solve the continuous Weber and multi-Weber problems, where one or more facilities can be sited anywhere in continuous space to best serve continuously distributed demand. Two spatial optimization approaches are proposed for the two extensions of the Weber problem, respectively. The special characteristics of those approaches are that they integrate optimization techniques and GIS functionality. Empirical results highlight the advantages of the developed approaches and the importance of solution integration within GIS.
ContributorsYao, Jing (Author) / Murray, Alan T. (Thesis advisor) / Mirchandani, Pitu B. (Committee member) / Kuby, Michael J (Committee member) / Arizona State University (Publisher)
Created2012
Description
Decision makers contend with uncertainty when working through complex decision problems. Yet uncertainty visualization, and tools for working with uncertainty in GIS, are not widely used or requested in decision support. This dissertation suggests a disjoint exists between practice and research that stems from differences in how visualization researchers conceptualize uncertainty and how decision makers frame uncertainty. To bridge this gap between practice and research, this dissertation explores uncertainty visualization as a means for reframing uncertainty in geographic information systems for use in policy decision support through three connected topics. Initially, this research explores visualizing the relationship between uncertainty and policy outcomes as a means for incorporating policymakers' decision frames when visualizing uncertainty. Outcome spaces are presented as a method to represent the effect of uncertainty on policy outcomes. This method of uncertainty visualization acts as an uncertainty map, representing all possible outcomes for specific policy decisions. This conceptual model incorporates two variables, but implicit uncertainty can be extended to multivariate representations. Subsequently, this work presented a new conceptualization of uncertainty, termed explicit and implicit, that integrates decision makers' framing of uncertainty into uncertainty visualization. Explicit uncertainty is seen as being separate from the policy outcomes, being described or displayed separately from the underlying data. In contrast, implicit uncertainty links uncertainty to decision outcomes, and while understood, it is not displayed separately from the data. The distinction between explicit and implicit is illustrated through several examples of uncertainty visualization founded in decision science theory. Lastly, the final topic assesses outcome spaces for communicating uncertainty though a human subject study. This study evaluates the effectiveness of the implicit uncertainty visualization method for communicating uncertainty for policy decision support. The results suggest that implicit uncertainty visualization successfully communicates uncertainty in results, even though uncertainty is not explicitly shown. Participants also found the implicit visualization effective for evaluating policy outcomes. Interestingly, participants also found the explicit uncertainty visualization to be effective for evaluating the policy outcomes, results that conflict with prior research.
ContributorsDeitrick, Stephanie (Author) / Wentz, Elizabeth (Thesis advisor) / Goodchild, Michael (Committee member) / Edsall, Robert (Committee member) / Gober, Patricia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Extreme hot-weather events have become life-threatening natural phenomena in many cities around the world, and the health impacts of excessive heat are expected to increase with climate change (Huang et al. 2011; Knowlton et al. 2007; Meehl and Tebaldi 2004; Patz 2005). Heat waves will likely have the worst health impacts in urban areas, where large numbers of vulnerable people reside and where local-scale urban heat island effects (UHI) retard and reduce nighttime cooling. This dissertation presents three empirical case studies that were conducted to advance our understanding of human vulnerability to heat in coupled human-natural systems. Using vulnerability theory as a framework, I analyzed how various social and environmental components of a system interact to exacerbate or mitigate heat impacts on human health, with the goal of contributing to the conceptualization of human vulnerability to heat. The studies: 1) compared the relationship between temperature and health outcomes in Chicago and Phoenix; 2) compared a map derived from a theoretical generic index of vulnerability to heat with a map derived from actual heat-related hospitalizations in Phoenix; and 3) used geospatial information on health data at two areal units to identify the hot spots for two heat health outcomes in Phoenix. The results show a 10-degree Celsius difference in the threshold temperatures at which heat-stress calls in Phoenix and Chicago are likely to increase drastically, and that Chicago is likely to be more sensitive to climate change than Phoenix. I also found that heat-vulnerability indices are sensitive to scale, measurement, and context, and that cities will need to incorporate place-based factors to increase the usefulness of vulnerability indices and mapping to decision making. Finally, I found that identification of geographical hot-spot of heat-related illness depends on the type of data used, scale of measurement, and normalization procedures. I recommend using multiple datasets and different approaches to spatial analysis to overcome this limitation and help decision makers develop effective intervention strategies.
ContributorsChuang, Wen-Ching (Author) / Gober, Patricia (Thesis advisor) / Boone, Christopher (Committee member) / Guhathakurta, Subhrajit (Committee member) / Ruddell, Darren (Committee member) / Arizona State University (Publisher)
Created2013
Description
The shortest path between two locations is important for spatial analysis, location modeling, and wayfinding tasks. Depending on permissible movement and availability of data, the shortest path is either derived from a pre-defined transportation network or constructed in continuous space. However, continuous space movement adds substantial complexity to identifying the shortest path as the influence of obstacles has to be considered to avoid errors and biases in a derived path. This obstacle-avoiding shortest path in continuous space has been referred to as Euclidean shortest path (ESP), and attracted the attention of many researchers. It has been proven that constructing a graph is an effective approach to limit infinite search options associated with continuous space, reducing the problem to a finite set of potential paths. To date, various methods have been developed for ESP derivation. However, their computational efficiency is limited due to fundamental limitations in graph construction. In this research, a novel algorithm is developed for efficient identification of a graph guaranteed to contain the ESP. This new approach is referred to as the convexpath algorithm, and exploits spatial knowledge and GIS functionality to efficiently construct a graph. The convexpath algorithm utilizes the notion of a convex hull to simultaneously identify relevant obstacles and construct the graph. Additionally, a spatial filtering technique based on intermediate shortest path is enhances intelligent identification of relevant obstacles. Empirical applications show that the convexpath algorithm is able to construct a graph and derive the ESP with significantly improved efficiency compared to visibility and local visibility graph approaches. Furthermore, to boost the performance of convexpath in big data environments, a parallelization approach is proposed and applied to exploit computationally intensive spatial operations of convexpath. Multicore CPU parallelization demonstrates noticeable efficiency gain over the sequential convexpath. Finally, spatial representation and approximation issues associated with raster-based approximation of the ESP are assessed. This dissertation provides a comprehensive treatment of the ESP, and details an important approach for deriving an optimal ESP in real time.
ContributorsHong, Insu (Author) / Murray, Alan T. (Thesis advisor) / Kuby, Micheal (Committee member) / Rey, Sergio (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation creates models of past potential vegetation in the Southern Levant during most of the Holocene, from the beginnings of farming through the rise of urbanized civilization (12 to 2.5 ka BP). The time scale encompasses the rise and collapse of the earliest agrarian civilizations in this region. The archaeological record suggests that increases in social complexity were linked to climatic episodes (e.g., favorable climatic conditions coincide with intervals of prosperity or marked social development such as the Neolithic Revolution ca. 11.5 ka BP, the Secondary Products Revolution ca. 6 ka BP, and the Middle Bronze Age ca. 4 ka BP). The opposite can be said about periods of climatic deterioration, when settled villages were abandoned as the inhabitants returned to nomadic or semi nomadic lifestyles (e.g., abandonment of the largest Neolithic farming towns after 8 ka BP and collapse of Bronze Age towns and cities after 3.5 ka BP during the Late Bronze Age). This study develops chronologically refined models of past vegetation from 12 to 2.5 ka BP, at 500 year intervals, using GIS, remote sensing and statistical modeling tools (MAXENT) that derive from species distribution modeling. Plants are sensitive to alterations in their environment and respond accordingly. Because of this, they are valuable indicators of landscape change. An extensive database of historical and field gathered observations was created. Using this database as well as environmental variables that include temperature and precipitation surfaces for the whole study period (also at 500 year intervals), the potential vegetation of the region was modeled. Through this means, a continuous chronology of potential vegetation of the Southern Levantwas built. The produced paleo-vegetation models generally agree with the proxy records. They indicate a gradual decline of forests and expansion of steppe and desert throughout the Holocene, interrupted briefly during the Mid Holocene (ca. 4 ka BP, Middle Bronze Age). They also suggest that during the Early Holocene, forest areas were extensive, spreading into the Northern Negev. The two remaining forested areas in the Northern and Southern Plateau Region in Jordan were also connected during this time. The models also show general agreement with the major cultural developments, with forested areas either expanding or remaining stable during prosperous periods (e.g., Pre Pottery Neolithic and Middle Bronze Age), and significantly contracting during moments of instability (e.g., Late Bronze Age).
ContributorsSoto-Berelov, Mariela (Author) / Fall, Patricia L. (Thesis advisor) / Myint, Soe (Committee member) / Turner, Billie L (Committee member) / Falconer, Steven (Committee member) / Arizona State University (Publisher)
Created2011
Description
Understanding the resilience of water management systems is critical for the continued existence and growth of communities today, in urban and rural contexts alike. In recent years, many studies have evaluated long-term human-environmental interactions related to water management across the world, highlighting both resilient systems and those that eventually succumb to their vulnerabilities. To understand the multitude of factors impacting resilience, scholars often use the concept of adaptive capacity. Adaptive capacity is the ability of actors in a system to make adaptations in anticipation of and in response to change to minimize potential negative impacts.
In this three-paper dissertation, I evaluate the adaptive capacity of the water management systems of two medieval Khmer cities, located in present-day Cambodia, over the course of centuries. Angkor was the capital of the Khmer Empire for over 600 years (9 th -15 th centuries CE), except for one brief period when the capital was relocated to Koh Ker (921 – 944 CE). These cities both have massive water management systems that provide a comparative context for studying resilience; while Angkor thrived for hundreds of years, Koh Ker was occupied as the capital of the empire for a relatively short period. In the first paper, I trace the chronological and spatial development of two types of settlement patterns (epicenters and lower-density temple-reservoir settlement units) at Angkor in relation to state-sponsored hydraulic infrastructure. In the second and third papers, I conduct a diachronic analysis using empirical data for the adaptive capacity of the water management systems at both cities. The results suggest that adaptive capacity is useful for identifying causal factors in the resilience and failures of systems over the long term. The case studies also demonstrate the importance and warn of the danger of large centralized water management features.
In this three-paper dissertation, I evaluate the adaptive capacity of the water management systems of two medieval Khmer cities, located in present-day Cambodia, over the course of centuries. Angkor was the capital of the Khmer Empire for over 600 years (9 th -15 th centuries CE), except for one brief period when the capital was relocated to Koh Ker (921 – 944 CE). These cities both have massive water management systems that provide a comparative context for studying resilience; while Angkor thrived for hundreds of years, Koh Ker was occupied as the capital of the empire for a relatively short period. In the first paper, I trace the chronological and spatial development of two types of settlement patterns (epicenters and lower-density temple-reservoir settlement units) at Angkor in relation to state-sponsored hydraulic infrastructure. In the second and third papers, I conduct a diachronic analysis using empirical data for the adaptive capacity of the water management systems at both cities. The results suggest that adaptive capacity is useful for identifying causal factors in the resilience and failures of systems over the long term. The case studies also demonstrate the importance and warn of the danger of large centralized water management features.
ContributorsKlassen, Sarah E (Author) / Nelson, Ben (Thesis advisor) / Redman, Charles (Thesis advisor) / Evans, Damian (Committee member) / Smith, Mike E (Committee member) / Barton, Michael C (Committee member) / Arizona State University (Publisher)
Created2018
Description
The Western Continental United States has a rapidly changing and complex ecosystem that provides valuable resources to a large portion of the nation. Changes in social and environmental factors have been observed to be significantly correlated to usable ground and surface water levels. The assessment of water level changes and their influences on a semi-national level is needed to support planning and decision making for water resource management at local levels. Although many studies have been done in Ground and Surface Water (GSW) trend analysis, very few have attempted determine correlations with other factors. The number of studies done on correlation factors at a semi-national scale and near decadal temporal scale is even fewer. In this study, freshwater resources in GSW changes from 2004 to 2017 were quantified and used to determine if and how environmental and social variables are related to GSW changes using publicly available remotely sensed and census data. Results indicate that mean annual changes of GSW of the study period are significantly correlated with LULC changes related to deforestation, urbanization, environmental trends, as well as social variables. Further analysis indicates a strong correlation in the rate of change of GSW to LULC changes related to deforestation, environmental trends, as well as social variables. GSW slope trend analysis also reveals a negative trend in California, New Mexico, Arizona, and Nevada. Whereas a positive GSW trend is evident in the northeast part of the study area. GSW trends were found to be somewhat consistent in the states of Utah, Idaho, and Colorado, implying that there was no GSW changes over time in these states.
ContributorsReynolds, Ryan (Author) / Myint, Soe (Thesis advisor) / Werth, Susanna (Committee member) / Brazel, Anthony (Committee member) / Arizona State University (Publisher)
Created2018