Matching Items (36)

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Implementing rapid assessment of the trail environments of arid regions: indicator development and implementation scenarios

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

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.

Contributors

Agent

Created

Date Created
  • 2013

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Remote Sensing and Modeling of Stressed Aquifer Systems and the Associated Hazards

Description

Aquifers host the largest accessible freshwater resource in the world. However, groundwater reserves are declining in many places. Often coincident with drought, high extraction rates and inadequate replenishment result in

Aquifers host the largest accessible freshwater resource in the world. However, groundwater reserves are declining in many places. Often coincident with drought, high extraction rates and inadequate replenishment result in groundwater overdraft and permanent land subsidence. Land subsidence is the cause of aquifer storage capacity reduction, altered topographic gradients which can exacerbate floods, and differential displacement that can lead to earth fissures and infrastructure damage. Improving understanding of the sources and mechanisms driving aquifer deformation is important for resource management planning and hazard mitigation.

Poroelastic theory describes the coupling of differential stress, strain, and pore pressure, which are modulated by material properties. To model these relationships, displacement time series are estimated via satellite interferometry and hydraulic head levels from observation wells provide an in-situ dataset. In combination, the deconstruction and isolation of selected time-frequency components allow for estimating aquifer parameters, including the elastic and inelastic storage coefficients, compaction time constants, and vertical hydraulic conductivity. Together these parameters describe the storage response of an aquifer system to changes in hydraulic head and surface elevation. Understanding aquifer parameters is useful for the ongoing management of groundwater resources.

Case studies in Phoenix and Tucson, Arizona, focus on land subsidence from groundwater withdrawal as well as distinct responses to artificial recharge efforts. In Christchurch, New Zealand, possible changes to aquifer properties due to earthquakes are investigated. In Houston, Texas, flood severity during Hurricane Harvey is linked to subsidence, which modifies base flood elevations and topographic gradients.

Contributors

Agent

Created

Date Created
  • 2018

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Evaluation of Geodesign as a Planning Framework for American Indian Communities in the Southwest United States

Description

The overarching aim of this dissertation is to evaluate Geodesign as a planning approach for American Indian communities in the American Southwest. There has been a call amongst indigenous planners

The overarching aim of this dissertation is to evaluate Geodesign as a planning approach for American Indian communities in the American Southwest. There has been a call amongst indigenous planners for a planning approach that prioritizes indigenous and community values and traditions while incorporating Western planning techniques. Case studies from communities in the Navajo Nation and the Tohono O’odham Nation are used to evaluate Geodesign because they possess sovereign powers of self-government within their reservation boundaries and have historical and technical barriers that have limited land use planning efforts. This research aimed to increase the knowledge base of indigenous planning, participatory Geographic information systems (GIS), resiliency, and Geodesign in three ways. First, the research examines how Geodesign can incorporate indigenous values within a community-based land use plan. Results showed overwhelmingly that indigenous participants felt that the resulting plan reflected their traditions and values, that the community voice was heard, and that Geodesign would be a recommended planning approach for other indigenous communities. Second, the research examined the degree in which Geodesign could incorporate local knowledge in planning and build resiliency against natural hazards such as flooding. Participants identified local hazards, actively engaged in developing strategies to mitigate flood risk, and utilized spatial assessments to plan for a more flood resilient region. Finally, the research examined the role of the planner in conducting Geodesign planning efforts and how Geodesign can empower marginalized communities to engage in the planning process using Arnstein’s ladder as an evaluation tool. Results demonstrated that outside professional planners, scientists, and geospatial analysts needed to assume the role of a facilitator, decision making resource, and a capacity builder over traditional roles of being the plan maker. This research also showed that Geodesign came much closer to meeting American Indian community expectations for public participation in decision making than previous planning efforts. This research demonstrated that Geodesign planning approaches could be utilized by American Indian communities to assume control of the planning process according to local values, traditions, and culture while meeting rigorous Western planning standards.

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Agent

Created

Date Created
  • 2020

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Developing a cohesive space-time information framework for analyzing movement trajectories in real and simulated environments

Description

In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and

In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across a wide range of fields include urban and transportation planning, Location-Based Services, and logistics. This research is designed to contribute to the existing state-of-the-art in tracking and modeling mobile objects, specifically targeting three challenges in investigating spatio-temporal patterns and processes; 1) a lack of space-time analysis tools; 2) a lack of studies about empirical data analysis and context awareness of mobile objects; and 3) a lack of studies about how to evaluate and test agent-based models of complex mobile phenomena. Three studies are proposed to investigate these challenges; the first study develops an integrated data analysis toolkit for exploration of spatio-temporal patterns and processes of mobile objects; the second study investigates two movement behaviors, 1) theoretical random walks and 2) human movements in urban space collected by GPS; and, the third study contributes to the research challenge of evaluating the form and fit of Agent-Based Models of human movement in urban space. The main contribution of this work is the conceptualization and implementation of a Geographic Knowledge Discovery approach for extracting high-level knowledge from low-level datasets about mobile objects. This allows better understanding of space-time patterns and processes of mobile objects by revealing their complex movement behaviors, interactions, and collective behaviors. In detail, this research proposes a novel analytical framework that integrates time geography, trajectory data mining, and 3D volume visualization. In addition, a toolkit that utilizes the framework is developed and used for investigating theoretical and empirical datasets about mobile objects. The results showed that the framework and the toolkit demonstrate a great capability to identify and visualize clusters of various movement behaviors in space and time.

Contributors

Agent

Created

Date Created
  • 2011

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Spatiotemporal data mining, analysis, and visualization of human activity data

Description

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and related metadata of their daily activities from the website OpenStreetMap.org; and finally (3) preschool children's daily activities and interactions tagged with time and geographical location were collected with a novel TabletPC-based behavioral coding system. The proposed methodology is applied to these data to (1) automatically recommend optimal multi-day and multi-stay travel itineraries for travelers based on discovered attractions from geo-tagged photos, (2) automatically detect movement types of unknown moving objects from GPS trajectories, and (3) explore dynamic social and socio-spatial patterns of preschool children's behavior from both geographic and social perspectives.

Contributors

Agent

Created

Date Created
  • 2012

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Statistical evaluation and GIS model development to predict and classify habitat quality for the endangered southwestern willow flycatcher

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

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.

Contributors

Agent

Created

Date Created
  • 2013

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A taxonomy of parallel vector spatial analysis algorithms

Description

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

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.

Contributors

Agent

Created

Date Created
  • 2015

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Characterization of landslide geometry and movement near Black Canyon City, Arizona

Description

I investigate the Black Canyon City landslide (BCC landslide), a prominent deep-seated landslide located northeast of Black Canyon City, Arizona. Although the landslide does not appear to pose a significant

I investigate the Black Canyon City landslide (BCC landslide), a prominent deep-seated landslide located northeast of Black Canyon City, Arizona. Although the landslide does not appear to pose a significant hazard to structures, its prominent features and high topographic relief make it an excellent site to study the geologic setting under which such features develop. This study has the potential to contribute toward understanding the landscape evolution in similar geologic and topographic settings, and for characterizing the underlying structural processes of this deep-seated feature. We use field and remotely-based surface geology and geomorphological mapping to characterize the landslide geometry and its surface displacement. We use the Structure from Motion (SfM) method to generate a 0.2 m resolution digital elevation model and rectified ortho-photo imagery from unmanned aerial vehicle (UAV) - and balloon-based images and used them as the base map for our mapping. The ~0.6 km2 landslide is easily identified through remotely-sensed imagery and in the field because of the prominent east-west trending fractures defining its upper extensional portion. The landslide displaces a series of Early and Middle Miocene volcanic and sedimentary rocks. The main head scarp is ~600 m long and oriented E-W with some NW-SE oriented minor scarps. Numerous fractures varying from millimeters to meters in opening were identified throughout the landslide body (mostly with longitudinal orientation). The occurrence of a distinctive layer of dark reddish basalt presents a key displaced marker to estimate the long-term deformation of the slide mass. Using this marker, the total vertical displacement is estimated to be ~70 m, with maximum movement of ~95 m to the SE. This study indicates that the landslide motion is translational with a slight rotational character. We estimate the rate of the slide motion by resurvey of monuments on and off the slide, and examination of disturbed vegetation located along the fractures. The analysis indicates a slow integrated average landslide velocity of 10-60 mm/yr. The slide motion is probably driven during annual wet periods when increased saturation of the slide mass weakens the basal slip surface and the overall mass of the slide is increased. Results from our study suggest that the slide is stable and does not pose significant hazard for the surrounding area given no extreme changes in the environmental condition. Although the landslide is categorized as very slow (according to Cruden and Varnes, 1996), monitoring the landslide is still necessary.

Contributors

Agent

Created

Date Created
  • 2016

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Knowledge-driven methods for geographic information extraction in the biomedical domain

Description

Accounting for over a third of all emerging and re-emerging infections, viruses represent a major public health threat, which researchers and epidemiologists across the world have been attempting to contain

Accounting for over a third of all emerging and re-emerging infections, viruses represent a major public health threat, which researchers and epidemiologists across the world have been attempting to contain for decades. Recently, genomics-based surveillance of viruses through methods such as virus phylogeography has grown into a popular tool for infectious disease monitoring. When conducting such surveillance studies, researchers need to manually retrieve geographic metadata denoting the location of infected host (LOIH) of viruses from public sequence databases such as GenBank and any publication related to their study. The large volume of semi-structured and unstructured information that must be reviewed for this task, along with the ambiguity of geographic locations, make it especially challenging. Prior work has demonstrated that the majority of GenBank records lack sufficient geographic granularity concerning the LOIH of viruses. As a result, reviewing full-text publications is often necessary for conducting in-depth analysis of virus migration, which can be a very time-consuming process. Moreover, integrating geographic metadata pertaining to the LOIH of viruses from different sources, including different fields in GenBank records as well as full-text publications, and normalizing the integrated metadata to unique identifiers for subsequent analysis, are also challenging tasks, often requiring expert domain knowledge. Therefore, automated information extraction (IE) methods could help significantly accelerate this process, positively impacting public health research. However, very few research studies have attempted the use of IE methods in this domain.

This work explores the use of novel knowledge-driven geographic IE heuristics for extracting, integrating, and normalizing the LOIH of viruses based on information available in GenBank and related publications; when evaluated on manually annotated test sets, the methods were found to have a high accuracy and shown to be adequate for addressing this challenging problem. It also presents GeoBoost, a pioneering software system for georeferencing GenBank records, as well as a large-scale database containing over two million virus GenBank records georeferenced using the algorithms introduced here. The methods, database and software developed here could help support diverse public health domains focusing on sequence-informed virus surveillance, thereby enhancing existing platforms for controlling and containing disease outbreaks.

Contributors

Agent

Created

Date Created
  • 2019

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Improving Urban Cooling in the Semi-arid Phoenix Metropolis: Land System Science, Landscape Ecology and Urban Climatology Approaches

Description

The global increase in urbanization has raised questions about urban sustainability to which multiple research communities have entered. Those communities addressing interest in the urban heat island (UHI) effect and

The global increase in urbanization has raised questions about urban sustainability to which multiple research communities have entered. Those communities addressing interest in the urban heat island (UHI) effect and extreme temperatures include land system science, urban/landscape ecology, and urban climatology. General investigations of UHI have focused primarily on land surface and canopy layer air temperatures. The surface temperature is of prime importance to UHI studies because of its central rule in the surface energy balance, direct effects on air temperature, and outdoor thermal comfort. Focusing on the diurnal surface temperature variations in Phoenix, Arizona, especially on the cool (green space) island effect and the surface heat island effect, the dissertation develops three research papers that improve the integration among the abovementioned sub-fields. Specifically, these papers involve: (1) the quantification and modeling of the diurnal cooling benefits of green space; (2) the optimization of green space locations to reduce the surface heat island effect in daytime and nighttime; and, (3) an evaluation of the effects of vertical urban forms on land surface temperature using Google Street View. These works demonstrate that the pattern of new green spaces in central Phoenix could be optimized such that 96% of the maximum daytime and nighttime cooling benefits would be achieved, and that Google Street View data offers an alternative to other data, providing the vertical dimensions of land-cover for addressing surface temperature impacts, increasing the model accuracy over the use of horizontal land-cover data alone. Taken together, the dissertation points the way towards the integration of research directions to better understand the consequences of detailed land conditions on temperatures in urban areas, providing insights for urban designs to alleviate these extremes.

Contributors

Agent

Created

Date Created
  • 2018