abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Geological Sciences 2018

abstract: 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. / Dissertation/Thesis / Ph.D. Geography 2012

abstract: 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. / Dissertation/Thesis / Ph.D. Geography 2013

abstract: 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. / Dissertation/Thesis / Ph.D. Geography 2013

abstract: 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. / Dissertation/Thesis / Ph.D. Geography 2013

Este trabalho apresenta o desenvolvimento de um instrumento de apoio à gestão territorial, denominado Banco de Dados Geográficos – BDG, constituído de uma base de dados georreferenciadas, de um sistema de gerenciamento de banco de dados, de um sistema de informação geográfica – SIG e de um simulador de redes neurais artificiais – SRNA. O roteiro metodológico adotado permitiu a transposição do Detalhamento da Metodologia para Execução do Zoneamento Ecológico-Econômico pelos Estados da Amazônia Legal para um modelo conceitual materializado no BDG, que serviu de suporte para a criação de uma base de dados geográficos, na qual utilizou-se os conceitos de geo-campos e geo-objetos para modelagem das entidades geográficas definidas. Através deste ambiente computacional foram realizados procedimentos de correção e refinamento dos dados do meio físico e sócio-econômicos, de interpretação de imagens de satélite e análises e combinações dos dados, que permitiram definir unidades básicas de informação do território, a partir das quais foram geradas as sínteses referentes à potencialidade social e econômica, à sustentabilidade do ambiente, aos subsídios para ordenação do território, incluindo orientações à gestão do território na área de estudo localizada no sudoeste do estado de Rondônia. Sobre os dados do meio físico, foram utilizadas duas técnicas de análise geográfica: álgebra de mapas e rede neural artificial, que produziram cenários relativos à vulnerabilidade natural à erosão. A análise das matrizes de erros obtidas da tabulação cruzada entre os cenários, revelou uma boa exatidão global (acima de 90%) entre os cenários obtidos através da modelagem via álgebra de mapas e via rede neural artificial e, uma exatidão global regular (em torno de 60%), quando foram comparados os cenários obtidos via álgebra de mapas e via rede neural artificial com o cenário obtido através de procedimentos manuais. / This work presents the development of a tool to support the land management called Geographical Data Base (GDB) formed by a georrefered data base, a data base management system (DBMS), a geographic information system (GIS) and an artificial neural net simulator (ANNS). The methodological approach allowed the conceptual modelling of the methodology of the ZEE (Ecological-Economic Zoning) institutional program within GDB, using both field and object concepts, in which the geographic entities were modelled. Using this computacional framework both natural and socio-economic data were corrected and improved, and also procedures of satellite image interpretation using image processing techniques, of analysis and data manipulation using GIS tools, were accomplished. These procedures allowed to define basic units of mapping and to get the following synthesis for the study area located in State of Rondonia: social potenciality, environmental vulnerability, environmental sustentability, land management maps, and guidelines about land management. With the abiotic and biotic data, two different geographical inference methods were used to produce the environmental vulnerability map: a) the common Map Algebra approach and b) an Artificial Neural Network approach – as a technique to deal with the non-linearities involved in inferencial processes. Error matrices were computed from cross tabulation among different scenaries obtained from those inference methods. A good global accuracy (over 90%) was obtained when ANN and Map Algebra scenaries were compared. Medium global accuracies (around 60%) were obtained when ANN and Map Algebra were compared with scenaries obtained by manual procedures.

Submitted by Reginaldo Soares de Freitas (reginaldo.freitas@ufv.br) on 2018-06-06T18:51:26Z No. of bitstreams: 1 texto completo.pdf: 10760567 bytes, checksum: e11aa8ab3d3f98f215de81a9ce967fcc (MD5) / Approved for entry into archive by Reginaldo Soares de Freitas (reginaldo.freitas@ufv.br) on 2018-06-06T18:51:49Z (GMT) No. of bitstreams: 1 texto completo.pdf: 10760567 bytes, checksum: e11aa8ab3d3f98f215de81a9ce967fcc (MD5) / Approved for entry into archive by Reginaldo Soares de Freitas (reginaldo.freitas@ufv.br) on 2018-06-06T18:52:00Z (GMT) No. of bitstreams: 1 texto completo.pdf: 10760567 bytes, checksum: e11aa8ab3d3f98f215de81a9ce967fcc (MD5) / Made available in DSpace on 2018-06-06T18:52:08Z (GMT). No. of bitstreams: 1 texto completo.pdf: 10760567 bytes, checksum: e11aa8ab3d3f98f215de81a9ce967fcc (MD5) Previous issue date: 2017-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This thesis presents an exact parallel algorithm for computing the intersection be- tween two 3D triangular meshes, as used in CAD/CAM (Computer Aided De- sign/Computer Aided Manufacturing), CFD (Computational Fluid Dynamics), GIS (Geographical Information Science) and additive manufacturing (also known as 3D Printing). Geometric software packages occasionally fail to compute the correct result because of the algorithm implementation complexity (that usually needs to handle several special cases) and of precision problems caused by floating point arithmetic. A failure in an intersection computation algorithm may propagate to any software using the algorithm as a subroutine. As datasets get bigger (and the chances of failure in an inexact algorithm increase), exact algorithms become even more important. While other methods for exactly intersecting meshes exist, their performance makes them non-suitable for applications where the fast processing of big geometric models is important (such as interactive CAD systems). The key to obtain robustness and performance is a combination of 5 separate techniques: • Multiple precision rational numbers, to exactly represent the coordinates of the objects and completely eliminate roundoff errors during the computations. • Simulation of Simplicity, a symbolic perturbation technique, to ensure that all geometric degeneracies (special cases) are properly handled. • Simple data representations and local information, to simplify the correct pro- cessing of the data and make the algorithm more parallelizable. • A uniform grid, to efficiently index the data, and accelerate some of the steps of the algorithm such as testing pairs of triangles for intersection or locating points in the mesh. / Só foram apresentados título e resumo em inglês.

<p> Geospatial thinking and reasoning skills (GSTR) are currently not routinely integrated into public health curriculum for undergraduate students in institutions of higher education.&nbsp;&nbsp;However, integrating GSTR skills into curriculum has been shown to increase spatial thinking skills which leads to better cognitive thinking and problem solving skills.&nbsp; An <i>Examining Vector Borne Disease Transmission</i> (EVBDT) curriculum unit was developed using the geospatial curriculum approach to investigate malaria, dengue fever and zika disease patterns and spread in relation to the environment and to promote GSTR.&nbsp;&nbsp;The purpose of this design based research study was to understand public health content learning and GSTR skill acquisition with undergraduate learners through use of the geospatial curriculum approach. The undergraduate students who participated in this study (n = 95) were enrolled in public health content classes at two separate institutions.&nbsp;&nbsp;Data was collected for this study using a classroom observation instrument, pre-test and post-test measures for the Spatial Habits of the Mind (SHOM) survey, a pre-test, post-test 1 and delayed post-test 2 EVBDT assessment that included public health content and GSTR skill items, as well as a post implementation survey to understand students&rsquo; perceptions of GIS use in the curriculum.&nbsp;&nbsp;Findings demonstrated significant mean differences showing growth in public health content learning and GSTR skills. Three GSTR subscales&mdash;inferences, relationships, and reasoning&mdash;resulted in significant gains.&nbsp;&nbsp;Additionally, results revealed complete adherence to the design principles of the geospatial curriculum approach during implementation. The findings provide support that Web GIS with appropriate curriculum design can engage students and impact both learning outcomes and geospatial thinking and reasoning skills in public health education.</p>

No description available.

abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Geography 2020