abstract: Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggregation, and the epsilon band. The fifth method used conventional crisp classification. I applied these methods to a case study map and simulated data in two sets: a test set with misregistration error, and a control set with equivalent uniform random error. For all five methods, I used raw accuracy or the kappa statistic to measure similarity. Rough-set epsilon bands report the most similarity increase in test maps relative to control data. Conversely, the fuzzy inference model reports a decrease in test map similarity. / Dissertation/Thesis / M.A. Geography 2010

abstract: 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. / Dissertation/Thesis / M.U.E.P. Environmental Design and Planning 2011

abstract: ABSTRACT The elephant tree, Bursera microphylla, is at the northern limit of its range in central Arizona. This species is sensitive to frost damage thus limiting its occurrence in more northern areas of the southwest. Marginal populations of B. microphylla are found in mountain ranges of Central Arizona and are known to occur in the rugged mountain range system of the South Mountain Municipal Park (SMMP). Little is known of the distribution of this species within the park and details relevant to the health of both individual plants and the population such as diameter and number of trunks, height, and presence of damage have not been examined. This study was designed, in part, to test the hypothesis that favorable microhabitats at SMMP are created by particular combinations of abiotic features including aspect, slope, elevation and solar radiation. Data on abiotic factors, as well as specific individual plant locations and characteristics were obtained for 100 individuals. Temperature data was collected in vertical transects at different altitudinal levels. Some of these data were used in spatial analyses to generate a habitat suitability model using GIS software. Furthermore, collected data was analyzed using Matlab© software to identify potential trends in the variation of morphological traits. In addition, for comparative purposes similar information at one hundred computer-generated randomly chosen points throughout SMMP was obtained. The GIS spatial analyses indicated that aspect, slope, elevation, and relative solar radiance are strongly associated as major climatic components of the microhabitat of B. microphylla. Temperature data demonstrated that there are significant differences in ambient temperature among different altitudinal gradients with middle elevations being more favorable. Furthermore, analyses performed using Matlab© to explore trends of elevation as a factor indicated that multiple trunk plants are more commonly found at higher elevations than single trunk plants, there is a positive correlation of trunk diameter with elevation, and that canopy volume has a negative correlation with respect to elevation. It was concluded that microhabitats where B. microphylla occurs at the northern limit of its range require a particular combination of abiotic features that can be easily altered by climatic changes. / Dissertation/Thesis / M.S. Applied Biological Sciences 2011

<p> The University of Nevada, Reno is expected to exceed student population projections, stressing the capacity of campus facilities in 2018. With student growth outpacing capacity, questions arise related to campus planning, use of space, and sustainability. The University is planning to increase the size of campus by approximately 9 acres by expanding into the Gateway Precinct, providing land for the University to build additional residential, classroom, research, and office space. A planning approach based on two analyses&mdash;on campus residential and on campus academic and research space&mdash;modeled building densities to determine if the Gateway Precinct and identified main campus development sites will provide enough space to accommodate a student head count of 30,000. </p><p> ArcMap, a Geographic Information System (GIS), assisted in modeling land cover of two building categories, campus residential, and non-residential academic and research. Existing and planned campus buildings exhibiting high, medium, or low densities were selected to be represented as building prototypes. The selected buildings include four campus residence halls and five academic and research buildings, producing a total of nine building prototypes. The parcels in the Gateway Precinct, University District, and identified on campus development sites were individually analyzed in ArcMap. All areas where development could occur were termed development sites, each sites size was calculated in square feet. Understanding the base capacity of each site and the projected gross square feet required to support a growing campus permitted the modeling of high, medium, and low density build-out scenarios. </p><p> The on campus residential analysis showed that even the densest building prototype was unable to meet projected campus residential demands without requiring additional land outside of the University&rsquo;s main campus and the Gateway Precinct. Results show if strictly building high density structures, the Gateway Precinct and identified main campus sites provide ample land for projected academic and research space demands. However, when adhering to medium and low density building standards, additional land outside of the Gateway Precinct and the University&rsquo;s main campus will be required to sustain future demands. High density growth is recommended for the Gateway Precinct, limiting outward expansion and retaining a compact campus core, allowing University sustainability goals to be achieved.</p><p>

xi, 54 p. : ill. (some col.) / On the Middle Fork John Day River (MFJD), a low gradient, meandering river in eastern Oregon, restoration includes engineered log structures intended to increase in-stream complexity and habitat diversity. Effects of log structures on riverbed topography can be captured through repeat topographic surveys, digital elevation model (DEM) of differencing (DoD), and aerial imagery. This study evaluates the (1) potential for remote sensing analysis, (2) effect of survey point density on DEMs, and (3) application of DoDs, in monitoring riverbed changes in the MFJD. An average point spacing and density finer than 0.50m and 1.25pts/m<super>2</super> captures riverbed complexities. Although elevation changes were expected to be minimal, DoDs revealed -0.9 to 0.5m elevation changes associated with log structure designs. Incorporating numerical thresholds into future monitoring survey methods will improve the modeling of MFJD riverbed surfaces. Monitoring riverbed changes through DoDs can inform improvements to future restoration design and the effectiveness of log structures. / Committee in charge: Patricia McDowell, Chairperson; Andrew Marcus, Member

abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Geography 2015

abstract: Food deserts are defined as regions with low average income, low accessibility to grocery stores, and high adverse health outcomes. Food deserts have thus become an important area of public health research, and many actions are being taken across the country to "solve" the variety of problems food deserts represent. Despite the many solutions promoted to improve food security, healthy food access, and health outcomes among individuals living in food desert areas, not all activities have been critically assessed for their potential for sustained impact. Further, little research has been conducted in the state of Arizona regarding food-related ‘assets’ available to employ in solutions to food desert problems. This analysis gives a glimpse into the complex nature of food deserts, which are impacted by a variety of factors, from economics to public policy to culture. It further provides a current assessment of available assets for potential use in ameliorating the negative impacts of food deserts on Arizona citizens. A graphical asset mapping analysis offers specific consideration of farmers markets and food hubs to possibly aid food deserts in the state. / Dissertation/Thesis / Masters Thesis Biology 2015

abstract: Nearly 25 years ago, parallel computing techniques were first applied to vector spatial analysis methods. This initial research was driven by the desire to reduce computing times in order to support scaling to larger problem sets. Since this initial work, rapid technological advancement has driven the availability of High Performance Computing (HPC) resources, in the form of multi-core desktop computers, distributed geographic information processing systems, e.g. computational grids, and single site HPC clusters. In step with increases in computational resources, significant advancement in the capabilities to capture and store large quantities of spatially enabled data have been realized. A key component to utilizing vast data quantities in HPC environments, scalable algorithms, have failed to keep pace. The National Science Foundation has identified the lack of scalable algorithms in codified frameworks as an essential research product. Fulfillment of this goal is challenging given the lack of a codified theoretical framework mapping atomic numeric operations from the spatial analysis stack to parallel programming paradigms, the diversity in vernacular utilized by research groups, the propensity for implementations to tightly couple to under- lying hardware, and the general difficulty in realizing scalable parallel algorithms. This dissertation develops a taxonomy of parallel vector spatial analysis algorithms with classification being defined by root mathematical operation and communication pattern, a computational dwarf. Six computational dwarfs are identified, three being drawn directly from an existing parallel computing taxonomy and three being created to capture characteristics unique to spatial analysis algorithms. The taxonomy provides a high-level classification decoupled from low-level implementation details such as hardware, communication protocols, implementation language, decomposition method, or file input and output. By taking a high-level approach implementation specifics are broadly proposed, breadth of coverage is achieved, and extensibility is ensured. The taxonomy is both informed and informed by five case studies im- plemented across multiple, divergent hardware environments. A major contribution of this dissertation is a theoretical framework to support the future development of concrete parallel vector spatial analysis frameworks through the identification of computational dwarfs and, by extension, successful implementation strategies. / Dissertation/Thesis / Doctoral Dissertation Geography 2015

abstract: Urban growth, from regional sprawl to global urbanization, is the most rapid, drastic, and irreversible form of human modification to the natural environment. Extensive land cover modifications during urban growth have altered the local energy balance, causing the city warmer than its surrounding rural environment, a phenomenon known as an urban heat island (UHI). How are the seasonal and diurnal surface temperatures related to the land surface characteristics, and what land cover types and/or patterns are desirable for ameliorating climate in a fast growing desert city? This dissertation scrutinizes these questions and seeks to address them using a combination of satellite remote sensing, geographical information science, and spatial statistical modeling techniques. This dissertation includes two main parts. The first part proposes to employ the continuous, pixel-based landscape gradient models in comparison to the discrete, patch-based mosaic models and evaluates model efficiency in two empirical contexts: urban landscape pattern mapping and land cover dynamics monitoring. The second part formalizes a novel statistical model called spatially filtered ridge regression (SFRR) that ensures accurate and stable statistical estimation despite the existence of multicollinearity and the inherent spatial effect. Results highlight the strong potential of local indicators of spatial dependence in landscape pattern mapping across various geographical scales. This is based on evidence from a sequence of exploratory comparative analyses and a time series study of land cover dynamics over Phoenix, AZ. The newly proposed SFRR method is capable of producing reliable estimates when analyzing statistical relationships involving geographic data and highly correlated predictor variables. An empirical application of the SFRR over Phoenix suggests that urban cooling can be achieved not only by altering the land cover abundance, but also by optimizing the spatial arrangements of urban land cover features. Considering the limited water supply, rapid urban expansion, and the continuously warming climate, judicious design and planning of urban land cover features is of increasing importance for conserving resources and enhancing quality of life. / Dissertation/Thesis / Doctoral Dissertation Geography 2016

abstract: As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of observations, being produced in real-time or near real-time, and including a diverse variety of information. In particular, spatial interaction (SI) data - a collection of human interactions across a set of origins and destination locations - present unique challenges for distilling big data into insight. Therefore, this dissertation identifies some of the potential and pitfalls associated with new sources of big SI data. It also evaluates methods for modeling SI to investigate the relationships that drive SI processes in order to focus on human behavior rather than data description. A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models. Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior. / Dissertation/Thesis / Doctoral Dissertation Geography 2017