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Surveys have shown that several hundred billion weather forecasts are obtained by the United States public each year, and that weather news is one of the most consumed topics in the media. This indicates that the forecast provides information that is significant to the public, and that the public utilizes

Surveys have shown that several hundred billion weather forecasts are obtained by the United States public each year, and that weather news is one of the most consumed topics in the media. This indicates that the forecast provides information that is significant to the public, and that the public utilizes details associated with it to inform aspects of their life. Phoenix, Arizona is a dry, desert region that experiences a monsoon season and extreme heat. How then, does the weather forecast influence the way Phoenix residents make decisions? This paper aims to draw connections between the weather forecast, decision making, and people who live in a desert environment. To do this, a ten-minute survey was deployed through Amazon Mechanical Turk (MTurk) in which 379 respondents were targeted. The survey asks 45 multiple choice and ranking questions categorized into four sections: obtainment of the forecast, forecast variables of interest, informed decision making based on unique weather variables, and demographics. This research illuminates how residents in the Phoenix metropolitan area use the local weather forecast for decision-making on daily activities, and the main meteorological factors that drive those decisions.

ContributorsMarturano, Julia (Author) / Middel, Ariane (Thesis director) / Schneider, Florian (Committee member) / School of Geographical Sciences and Urban Planning (Contributor, Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Slum development and growth is quite popular in developing countries. Many studies have been done on what social and economic factors are the drivers in establishment of informal settlements at a single cross-section of time, however limited work has been done in studying their spatial growth patterns over time. This

Slum development and growth is quite popular in developing countries. Many studies have been done on what social and economic factors are the drivers in establishment of informal settlements at a single cross-section of time, however limited work has been done in studying their spatial growth patterns over time. This study attempts to study a sample of 30 informal settlements that exist in the National Capital Territory of India over a period of 40 years and identify relationships between the spatial growth rates and relevant factors identified in previous socio-economic studies of slums using advanced statistical methods. One of the key contributions of this paper is indicating the usefulness of satellite imagery or remote sensing data in spatial-longitudinal studies. This research utilizes readily available LANDSAT images to recognize the decadal spatial growth from 1970 to 2000, and also in extension, calculate the BI (transformed NDVI) as a proxy for the intensity of development for the settlements. A series of regression models were run after processing the data, and the levels of significance were then studied and compared to see which relationships indicated the highest levels of significance. It was observed that the change in BI had a higher strength of relationships with the change in independent variables than the settlement area growth. Also, logarithmic and cubic models showed the highest R-Square values than any other tested models.
ContributorsPrakash, Mihir (Author) / Guhathakurta, Subhrajit (Thesis advisor) / Myint, Soe W. (Committee member) / Aggarwal, Rimjhim (Committee member) / Arizona State University (Publisher)
Created2011
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In recent years environmental life-cycle assessments (LCA) have been increasingly used to support planning and development of sustainable infrastructure. This study demonstrates the application of LCA to estimate embedded energy use and greenhouse gas (GHG) emissions related to materials manufacturing and construction processes for low and high density single-family neighborhoods

In recent years environmental life-cycle assessments (LCA) have been increasingly used to support planning and development of sustainable infrastructure. This study demonstrates the application of LCA to estimate embedded energy use and greenhouse gas (GHG) emissions related to materials manufacturing and construction processes for low and high density single-family neighborhoods typically found in the Southwest. The LCA analysis presented in this study includes the assessment of more than 8,500 single family detached units, and 130 miles of related roadway infrastructure. The study estimates embedded and GHG emissions as a function of building size (1,500 - 3000 square feet), number of stories (1 or 2), and exterior wall material composition (stucco, brick, block, wood), roof material composition (clay tile, cement tile, asphalt shingles, built up), and as a function of roadway typology per mile (asphalt local residential roads, collectors, arterials). While a hybrid economic input-out life-cycle assessment is applied to estimate the energy and GHG emissions impacts of the residential units, the PaLATE tool is applied to determine the environmental effects of pavements and roads. The results indicate that low density single family neighborhoods are 2 - 2.5 X more energy and GHG intensive, per residential dwelling (unit) built, than high density residential neighborhoods. This relationship holds regardless of whether the functional unit is per acre or per capita. The results also indicate that a typical low density neighborhood (less than 2 dwellings per acre) requires 78 percent more energy and resource in roadway infrastructure per residential unit than a traditional small lot high density (more than 6 dwelling per acre). Also, this study shows that new master planned communities tend to be more energy intensive than traditional non master planned residential developments.
ContributorsFrijia, Stephane (Author) / Guhathakurta, Subhrajit (Committee member) / Williams, Eric D. (Committee member) / Pijawka, David K (Committee member) / Arizona State University (Publisher)
Created2011
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This doctoral dissertation research aims to develop a comprehensive definition of urban open spaces and to determine the extent of environmental, social and economic impacts of open spaces on cities and the people living there. The approach I take to define urban open space is to apply fuzzy set theory

This doctoral dissertation research aims to develop a comprehensive definition of urban open spaces and to determine the extent of environmental, social and economic impacts of open spaces on cities and the people living there. The approach I take to define urban open space is to apply fuzzy set theory to conceptualize the physical characteristics of open spaces. In addition, a 'W-green index' is developed to quantify the scope of greenness in urban open spaces. Finally, I characterize the environmental impact of open spaces' greenness on the surface temperature, explore the social benefits through observing recreation and relaxation, and identify the relationship between housing price and open space be creating a hedonic model on nearby housing to quantify the economic impact. Fuzzy open space mapping helps to investigate the landscape characteristics of existing-recognized open spaces as well as other areas that can serve as open spaces. Research findings indicated that two fuzzy open space values are effective to the variability in different land-use types and between arid and humid cities. W-Green index quantifies the greenness for various types of open spaces. Most parks in Tempe, Arizona are grass-dominant with higher W-Green index, while natural landscapes are shrub-dominant with lower index. W-Green index has the advantage to explain vegetation composition and structural characteristics in open spaces. The outputs of comprehensive analyses show that the different qualities and types of open spaces, including size, greenness, equipment (facility), and surrounding areas, have different patterns in the reduction of surface temperature and the number of physical activities. The variance in housing prices through the distance to park was, however, not clear in this research. This dissertation project provides better insight into how to describe, plan, and prioritize the functions and types of urban open spaces need for sustainable living. This project builds a comprehensive framework for analyzing urban open spaces in an arid city. This dissertation helps expand the view for urban environment and play a key role in establishing a strategy and finding decision-makings.

ContributorsKim, Won Kyung (Author) / Wentz, Elizabeth (Thesis advisor) / Myint, Soe W (Thesis advisor) / Brazel, Anthony (Committee member) / Guhathakurta, Subhrajit (Committee member) / Arizona State University (Publisher)
Created2011
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In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human

In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
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This study is an initial step in exploring how urban design typologies can help inform community asset research to broaden the definition of physical assets. Asset based community development research identifies specific types of physical assets such as streets, structures, housing or vacant lots. This research argues that a comprehensive

This study is an initial step in exploring how urban design typologies can help inform community asset research to broaden the definition of physical assets. Asset based community development research identifies specific types of physical assets such as streets, structures, housing or vacant lots. This research argues that a comprehensive look at physical assets is needed, taking into consideration urban typologies such as paths, landmarks, views and districts as well as the spatial relationships that influence their significance. Community asset literature and conditions specific to the Sunnyslope community in Phoenix, Arizona suggest that differences in ethnicity such as spatial segregation, and socio-economic status exist. However, the literature does not address how these differences in ethnicity might influence residents' perceptions of physical assets. This study explores the questions - How do perceptions of physical assets vary among women of different ethnicities? What, if any, are the reasons behind these ethnic differences in perception? The research applied a survey instrument with open-ended and close-ended questions, and a map to mark frequently used routes. Assets identified by recoding open-ended responses were statistically analyzed for frequencies. The most frequently mentioned assets were analyzed by GIS for spatial relationships. Women of White and Latino ethnicities frequently chose individual buildings and locations as physical assets over paths, views, districts and landmarks. White women identified urban typologies as physical assets. In contrast, Latino women identified no significant urban typologies as assets. The inclusion of urban typologies confirmed and expanded upon physical assets previously identified by other asset-based studies on the community of Sunnyslope. Notable differences in ethnicity were found in the perception of physical assets of economic significance, assets for use and assets of visual appeal. Besides ethnicity, age and proximity to assets also influenced asset perception of White and Latino women. Community organizations need to take into consideration the ethnic differences in perception of physical assets, in the context of culture, spatial segregation and differing family structures. The inclusion of urban typologies helped highlight the differences in ethnicities for physical assets of visual appeal, and the use of leisure and recreation facilities.
ContributorsThatte, Aparna (Author) / Ozel, Filiz (Thesis advisor) / Ahrentzen, Sherry (Thesis advisor) / Guhathakurta, Subhrajit (Committee member) / Arizona State University (Publisher)
Created2011
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Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models.

Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
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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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In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set

In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
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Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups

Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013