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The dynamics of urban water use are characterized by spatial and temporal variability that is influenced by associated factors at different scales. Thus it is important to capture the relationship between urban water use and its determinants in a spatio-temporal framework in order to enhance understanding and management of urban

The dynamics of urban water use are characterized by spatial and temporal variability that is influenced by associated factors at different scales. Thus it is important to capture the relationship between urban water use and its determinants in a spatio-temporal framework in order to enhance understanding and management of urban water demand. This dissertation aims to contribute to understanding the spatio-temporal relationships between single-family residential (SFR) water use and its determinants in a desert city. The dissertation has three distinct papers to support this goal. In the first paper, I demonstrate that aggregated scale data can be reliably used to study the relationship between SFR water use and its determinants without leading to significant ecological fallacy. The usability of aggregated scale data facilitates scientific inquiry about SFR water use with more available aggregated scale data. The second paper advances understanding of the relationship between SFR water use and its associated factors by accounting for the spatial and temporal dependence in a panel data setting. The third paper of this dissertation studies the historical contingency, spatial heterogeneity, and spatial connectivity in the relationship of SFR water use and its determinants by comparing three different regression models. This dissertation demonstrates the importance and necessity of incorporating spatio-temporal components, such as scale, dependence, and heterogeneity, into SFR water use research. Spatial statistical models should be used to understand the effects of associated factors on water use and test the effectiveness of certain management policies since spatial effects probably will significantly influence the estimates if only non-spatial statistical models are used. Urban water demand management should pay attention to the spatial heterogeneity in predicting the future water demand to achieve more accurate estimates, and spatial statistical models provide a promising method to do this job.
ContributorsOuyang, Yun (Author) / Wentz, Elizabeth (Thesis advisor) / Ruddell, Benjamin (Thesis advisor) / Harlan, Sharon (Committee member) / Janssen, Marcus (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Sustainable urbanism offers a set of best practice planning and design prescriptions intended to reverse the negative environmental consequences of urban sprawl, which dominates new urban development in the United States. Master planned developments implementing sustainable urbanism are proliferating globally, garnering accolades within the planning community and skepticism among social

Sustainable urbanism offers a set of best practice planning and design prescriptions intended to reverse the negative environmental consequences of urban sprawl, which dominates new urban development in the United States. Master planned developments implementing sustainable urbanism are proliferating globally, garnering accolades within the planning community and skepticism among social scientists. Despite attention from supporters and critics alike, little is known about the actual environmental performance of sustainable urbanism. This dissertation addresses the reasons for this paucity of evidence and the capacity of sustainable urbanism to deliver the espoused environmental outcomes through alternative urban design and the conventional master planning framework for development through three manuscripts. The first manuscript considers the reasons why geography, which would appear to be a natural empirical home for research on sustainable urbanism, has yet to accumulate evidence that links design alternatives to environmental outcomes or to explain the social processes that mediate those outcomes. It argues that geography has failed to develop a coherent subfield based on nature-city interactions and suggests interdisciplinary bridging concepts to invigorate greater interaction between the urban and nature-society geographic subfields. The subsequent chapters deploy these bridging concepts to empirically examine case-studies in sustainable urbanism. The second manuscript utilizes fine scale spatial data to quantify differences in ecosystem services delivery across three urban designs in two phases of Civano, a sustainable urbanism planned development in Tucson, Arizona, and an adjacent, typical suburban development comparison community. The third manuscript considers the extent to which conventional master planning processes are fundamentally at odds with urban environmental sustainability through interviews with stakeholders involved in three planned developments: Civano (Tucson, Arizona), Mueller (Austin, Texas), and Prairie Crossing (Grayslake, Illinois). Findings from the three manuscripts reveal deep challenges in conceptualizing an empirical area of inquiry on sustainable urbanism, measuring the outcomes of urban design alternatives, and innovating planning practice within the constraints of existing institutions that facilitate conventional development. Despite these challenges, synthesizing the insights of geography and cognate fields holds promise in building an empirical body of knowledge that complements pioneering efforts of planners to innovate urban planning practice through the sustainable urbanism alternative.
ContributorsTurner, Victoria (Author) / Gober, Patricia (Thesis advisor) / Eakin, Hallie (Committee member) / Kinzig, Ann (Committee member) / Talen, Emily (Committee member) / Arizona State University (Publisher)
Created2013
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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

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
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Description
As part of the effort to streamline management efforts in protected areas worldwide and assist accountability reporting, new techniques to help guide conservation goals and monitor progress are needed. Rapid assessment is recognized as a field-level data collection technique, but each rapid assessment index is limited to only the ecoregion

As part of the effort to streamline management efforts in protected areas worldwide and assist accountability reporting, new techniques to help guide conservation goals and monitor progress are needed. Rapid assessment is recognized as a field-level data collection technique, but each rapid assessment index is limited to only the ecoregion for which it is designed. This dissertation contributes to the existing bodies of conservation monitoring and tourism management literature in four ways: (i.) Indicators are developed for rapid assessment in arid and semi-arid regions, and the processes by which new indicators should be developed is explained; (ii.) Interpolation of surveyed data is explored as a step in the analysis process of a dataset collected through rapid assessment; (iii.) Viewshed is used to explore differences in impacts at two study sites and its underutilization in this context of conservation management is explored; and (iv.) A crowdsourcing tool to distribute the effort of monitoring trail areas is developed and deployed, and the results are used to explore this data collection's usefulness as a management tool.
ContributorsGutbrod, Elyssa (Author) / Dorn, Ronald I. (Thesis advisor) / Cerveny, Niccole (Committee member) / Whitley, David (Committee member) / Wentz, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Species distribution modeling is used to study changes in biodiversity and species range shifts, two currently well-known manifestations of climate change. The focus of this study is to explore how distributions of suitable habitat might shift under climate change for shrub communities within the Santa Monica Mountains National Recreation Area

Species distribution modeling is used to study changes in biodiversity and species range shifts, two currently well-known manifestations of climate change. The focus of this study is to explore how distributions of suitable habitat might shift under climate change for shrub communities within the Santa Monica Mountains National Recreation Area (SMMNRA), through a comparison of community level to individual species level distribution modeling. Species level modeling is more commonly utilized, in part because community level modeling requires detailed community composition data that are not always available. However, community level modeling may better detect patterns in biodiversity. To examine the projected impact on suitable habitat in the study area, I used the MaxEnt modeling algorithm to create and evaluate species distribution models with presence only data for two future climate models at community and individual species levels. I contrasted the outcomes as a method to describe uncertainty in projected models. To derive a range of sensitivity outcomes I extracted probability frequency distributions for suitable habitat from raster grids for communities modeled directly as species groups and contrasted those with communities assembled from intersected individual species models. The intersected species models were more sensitive to climate change relative to the grouped community models. Suitable habitat in SMMNRA's bounds was projected to decline from about 30-90% for the intersected models and about 20-80% for the grouped models from its current state. Models generally captured floristic distinction between community types as drought tolerance. Overall the impact on drought tolerant communities, growing in hotter, drier habitat such as Coastal Sage Scrub, was predicted to be less than on communities growing in cooler, moister more interior habitat, such as some chaparral types. Of the two future climate change models, the wetter model projected less impact for most communities. These results help define risk exposure for communities and species in this conservation area and could be used by managers to focus vegetation monitoring tasks to detect early response to climate change. Increasingly hot and dry conditions could motivate opportunistic restoration projects for Coastal Sage Scrub, a threatened vegetation type in Southern California.
ContributorsJames, Jennifer (Author) / Franklin, Janet (Thesis advisor) / Rey, Sergio (Committee member) / Wentz, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2014
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Description
The border policies of the United States and Mexico that have evolved over the previous decades have pushed illegal immigration and drug smuggling to remote and often public lands. Valuable natural resources and tourist sites suffer an inordinate level of environmental impacts as a result of activities, from new roads

The border policies of the United States and Mexico that have evolved over the previous decades have pushed illegal immigration and drug smuggling to remote and often public lands. Valuable natural resources and tourist sites suffer an inordinate level of environmental impacts as a result of activities, from new roads and trash to cut fence lines and abandoned vehicles. Public land managers struggle to characterize impacts and plan for effective landscape level rehabilitation projects that are the most cost effective and environmentally beneficial for a region given resource limitations. A decision support tool is developed to facilitate public land management: Borderlands Environmental Rehabilitation Spatial Decision Support System (BERSDSS). The utility of the system is demonstrated using a case study of the Sonoran Desert National Monument, Arizona.
ContributorsFisher, Sharisse (Author) / Murray, Alan T. (Thesis advisor) / Wentz, Elizabeth (Committee member) / Rey, Sergio (Committee member) / Arizona State University (Publisher)
Created2013
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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

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
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Description

Choropleth maps are a common form of online cartographic visualization. They reveal patterns in spatial distributions of a variable by associating colors with data values measured at areal units. Although this capability of pattern revelation has popularized the use of choropleth maps, existing methods for their online delivery are limited

Choropleth maps are a common form of online cartographic visualization. They reveal patterns in spatial distributions of a variable by associating colors with data values measured at areal units. Although this capability of pattern revelation has popularized the use of choropleth maps, existing methods for their online delivery are limited in supporting dynamic map generation from large areal data. This limitation has become increasingly problematic in online choropleth mapping as access to small area statistics, such as high-resolution census data and real-time aggregates of geospatial data streams, has never been easier due to advances in geospatial web technologies. The current literature shows that the challenge of large areal data can be mitigated through tiled maps where pre-processed map data are hierarchically partitioned into tiny rectangular images or map chunks for efficient data transmission. Various approaches have emerged lately to enable this tile-based choropleth mapping, yet little empirical evidence exists on their ability to handle spatial data with large numbers of areal units, thus complicating technical decision making in the development of online choropleth mapping applications. To fill this knowledge gap, this dissertation study conducts a scalability evaluation of three tile-based methods discussed in the literature: raster, scalable vector graphics (SVG), and HTML5 Canvas. For the evaluation, the study develops two test applications, generates map tiles from five different boundaries of the United States, and measures the response times of the applications under multiple test operations. While specific to the experimental setups of the study, the evaluation results show that the raster method scales better across various types of user interaction than the other methods. Empirical evidence also points to the superior scalability of Canvas to SVG in dynamic rendering of vector tiles, but not necessarily for partial updates of the tiles. These findings indicate that the raster method is better suited for dynamic choropleth rendering from large areal data, while Canvas would be more suitable than SVG when such rendering frequently involves complete updates of vector shapes.

ContributorsHwang, Myunghwa (Author) / Anselin, Luc (Thesis advisor) / Rey, Sergio J. (Committee member) / Wentz, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2013
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Description
It has been identified in the literature that there exists a "spatial mismatch" between geographical concentrations of lower-income or minority people who have relatively lower rates of car ownership, lower skills or educational attainment and who mainly rely on public transit for their travel, and low-skilled jobs for which they

It has been identified in the literature that there exists a "spatial mismatch" between geographical concentrations of lower-income or minority people who have relatively lower rates of car ownership, lower skills or educational attainment and who mainly rely on public transit for their travel, and low-skilled jobs for which they more easily qualify. Given this situation, various types of transportation projects have been constructed to improve public transit services and, alongside other goals, improve the connection between low-skilled workers and jobs. As indicators of performance, measures of job accessibility are commonly used in to gauge how such improvements have facilitated job access. Following this approach, this study investigates the impact of the Phoenix Metro Light Rail on job accessibility for the transit users, by calculating job accessibility before and after the opening of the system. Moreover, it also investigates the demographic profile of those who have benefited from improvements in job accessibility----both by income and by ethnicity. Job accessibility is measured using the cumulative opportunity approach which quantifies the job accessibility within different travel time limits, such as 30 and 45 minutes. ArcGIS is used for data processing and results visualization. Results show that the Phoenix light rail has improved job accessibility of the traffic analysis zones that are along the light rail line and Hispanic and lower-income groups have benefited more than their counterparts.
ContributorsLiu, Liyuan (Author) / Golub, Aaron (Thesis advisor) / Wentz, Elizabeth (Committee member) / Kuby, Michael (Committee member) / Arizona State University (Publisher)
Created2014
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Description

This dissertation advances spatial decision support system development theory by using a geodesign approach to evaluate design alternatives for such systems, including the impacts of the spatial model, technical spatial data, and user interface tools. These components are evaluated with a case study spatial decision support system for watershed management

This dissertation advances spatial decision support system development theory by using a geodesign approach to evaluate design alternatives for such systems, including the impacts of the spatial model, technical spatial data, and user interface tools. These components are evaluated with a case study spatial decision support system for watershed management in the Niantic River watershed in Connecticut, USA. In addition to this case study, this dissertation provides a broader perspective on applying the approach to spatial decision support systems in general. The spatial model presented is validated, the impacts of the model are considered. The technical spatial data are evaluated using a new method developed to quantify data fitness for use in a spatial decision support system. Finally, the tools of the user interface are assessed by applying a conceptual framework and evaluating the resulting tools via user survey.

ContributorsShimizu, Melinda (Author) / Wentz, Elizabeth (Thesis advisor) / Kirkwood, Craig W. (Committee member) / Gold, Arthur J. (Committee member) / Pahle, Robert (Committee member) / Arizona State University (Publisher)
Created2014