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Dissertations / Theses on the topic 'Geospatial data Environment'
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Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.
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Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.
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Wilson, R. "Quantifying Himalayan glacier change from the 1960s to early 2000s, using corona, glims and aster geospatial data." Thesis, University of Salford, 2015. http://usir.salford.ac.uk/35932/.
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Since reaching their LIAMs, Himalayan glaciers have generally undergone a period of retreat, evident from large moraines left at former ice limits. Currently, however, detailed assessments of Himalayan glacier fluctuations over the past century are limited and fail to compare spatially or temporally to records available in Central Europe, North America and Scandinavia. Consequently, the variability and magnitude of glacial change across the Himalayas, which is a key indicator of climatic change in this region, is yet to be fully understood. Against a background of poor data availability, Corona imagery and historic GLIMS glacier outlines now offer an opportunity to assess glacier extent for regions of the Himalayas pre-1980. Corona imagery, acquired by a US space-borne reconnaissance mission operational from 1960 to 1970, represents a particularly unique dataset offering high resolution imagery (~1.8 m) with stereo-scopic capabilities. Utilising Corona imagery, there is an opportunity to produce detailed maps of Himalayan glacier extent and extract ice surface elevation estimations, in some instances, for the first time. Despite having been de-classified in 1995, the use of Corona data in the Himalayas has been neglected, mainly because of orthorectification challenges related to its unique geometric distortions. Hence, there remains a need to develop a low cost and easily replicable method of accurately orthorectifying Corona imagery enabling its use as a large-scale glacier mapping tool in the Himalayas. In response to this need, Corona images are orthorectified in this study through the use of: (1) a non-metric photogrammetry approach; and (2) horizontal and vertical reference data acquired from ortho-ASTER imagery and the freely available ASTER GDEM. By comparing glacier measurements derived from Corona imagery, GLIMS data and more contemporary ASTER data, changes in glacier area, length and in some instances volume, between the 1960/70s and early 2000s, were quantified for glaciers selected within four study areas located in Uttarakhand, India and Central Nepal. Importantly, this cross-regional glacier change dataset both complements and enhances current Himalayan records. Most notably, results indicate that glaciers selected in the Bhagirathi and Pindar/Kali basins, Uttarakhand, reduced in area by a relatively small 7.97±0.29% and 7.54±0.26%, respectively. Contrastingly, glaciers selected in the more easterly located Seti and Trisula basins reduced in area by 29.78±0.2% and 50.55±0.08%, respectively. Comparisons of Corona DEM (derived from Corona stereo-pairs) and ASTER Global DEM elevations at the terminus regions of four glaciers revealed extensive surface lowering, ranging from 87±27 m to 142±27 m. For Corona processing, the methods applied were shown to orthorectify Corona images to an accuracy that allows comparable glacier outlines to be delineated, further demonstrating the mapping potential of this dataset. However, for Corona DEM extraction, the use of ASTER spatial control data was shown to be inadequate and the presence of large vertical errors in the DEMs generated hindered the measurement of glacier volume change. For this purpose, it is therefore recommended that the methods developed are tested with the use of very high resolution spatial control data.
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Abdul, Majeed Zainal. "From geospatial data capture to the delivery of GIS-ready information : improved management within a GIS environment." Thesis, University of Newcastle Upon Tyne, 2006. http://hdl.handle.net/10443/680.
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This thesis presents the research undertaken to investigate how geospatial data handling techniques and technology can be potentially used to enhance the existing management of entire survey datasets from their captured stage to a GIS-ready state and the delivery of this to the user. Discovery of the current systems for managing survey data and information in the Survey and Mapping Department Malaysia (JUPEM) has been presented. In addition, the surveying practice and processes carried out have been examined, especially the different type of data and information existed within the raw data capture right through to the production of GIS-ready information. The current GIS technology and techniques for managing geospatial data have been inspected to gain an in-depth understanding of them. Geospatial object as an approach to model reality of the world has been discovered and used to model the raw, processed, the GIS-ready information. To implement the management, a prototype Database Management System (DBMS) has been implemented, and a trial data population and processing steps have been carried out. An enhancement of the managemenot f the datasetsf rom geospatiald ata capturet o the GIS-ready infori-nation has beend emonstratedT. o deliver online the final product, demonstrationo f available methods were illustrated, and then contrasted. A range of datasets around Malaysian context were used in the research. The investigation revealed that raw, processed and GIS-ready information can be successfully modelled as object in an object-relational spatial database. Using inherent GIS tools, survey datasets management and processing steps within the same system are evidently achieved in a prototype implemented DBMS. An improved management showing the capability of 'drill-down search' and 'two-way traceability' to access and search spatial and non-spatial information in the system is effectively illustrated. Demonstration of the vendor specific and open source technology for the GIS-ready information delivery leads to the comparison between them. The thesis concludes by recognising that a management for raw captured data, processed set of data and GIS-ready information, and the delivery of this, within GIS environment is possible. The inherent GIS tools and DBMS have presented a single-view system for geospatial data management providing superior interfaces that are easy to learn and use, and users are able to specify and perform the desired tasks efficiently. Delivery of data has some constraints that need to be considered before embarking into either vendor specific application or open source technology. In JUPEM, time and cost can be reduced by applying and implementing the suggested GIS application for cadastral and topographic surveys right up to the creation of GIS-ready information, as detailed in the thesis. The research also finds that the in-depth understanding and experience, practically and theoretically, of all aspects of current GIS technologies and techniques gained through this research has achieved an overarching inspiration: equalisation of a high level of awareness and ability of staff in handling GIS project development within currently developing countries with those in the developed countries, and within the national survey and mapping department with those of other government departments and commercial GIS contractors.
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Wodajo, Bikila Teklu. "Geospatial analysis of spaceborne remote sensing data for assessing disaster impacts and modeling surface runoff in the built-environment /." Full text available from ProQuest UM Digital Dissertations, 2009. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=0&did=1798970281&SrchMode=1&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1269359779&clientId=22256.
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Thesis (Ph.D.)--University of Mississippi, 2009.Typescript. Vita. "May 2009." Committee director: Waheed Uddin Includes bibliographical references (leaves 329-344). Also available online via ProQuest to authorized users.
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Rathnayaka, Mudiyanselage Udara Madushantha Somarathna. "Data quality analysis in a GIS environment of OpenStreetMap geodatabase for Sri Lanka." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
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The purpose of the present study is to analyze the data quality of OpenStreetMap geodatabase in a GIS environment; the case of study is a region of Sri Lanka. OpenStreetMap (OSM) is one of the most well-known crowd-sourced products, providing a global map base thanks to the mapping activity carried out by volunteers all around the world.
As the quality of collected information remains a significant concern for the geospatial information community and in geospatial data management, a qualitative and quantitative assessment of OSM data is of great importance, due to the large diffusion and adoption of this kind of volunteered geospatial information (VGI).
This study concerns the OSM dataset currently available for the Mawanella area in Sri Lanka and has been performed in an open-source Geographic Information System (GIS) environment, QGIS. OSM vector files are the raw materials for the analysis.
The evaluation has been realized considering the main quality attributes to be maintained in a mapping product, either based on intrinsic properties and on the relationship with official databases available for the same area.
The results of the study suggest that the current quality of OSM maps in the study area is fairly good, but completeness is poor and must be improved.
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Brazenor, Clare. "The spatial dimensions of Native Title." Connect to thesis, 2000. http://eprints.unimelb.edu.au/archive/00001050.
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Shimazaki, Hiroto. "Application-oriented approaches of geospatial data analysis : case studies on global environmental problems." 京都大学 (Kyoto University), 2009. http://hdl.handle.net/2433/126501.
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Kyoto University (京都大学)0048
新制・課程博士
博士(工学)
甲第14926号
工博第3153号
新制||工||1473(附属図書館)
27364
UT51-2009-M840
京都大学大学院工学研究科都市環境工学専攻
(主査)教授 田村 正行, 准教授 立川 康人, 准教授 須﨑 純一
学位規則第4条第1項該当
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Santana, Abel. "Geospatial sensor integration| Design of an automated collection, management and visualization system for water quality data." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10007418.
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Water resources have become an increasingly important topic in the world of resource management. Researchers are constantly looking for ways to make the process of data collection, analyzation and visualization more efficient. The current methods involve a multi-step process that is cumbersome and require a lot of man-hours from start to finish. The cost of water quality sensors is also an issue for researchers. Many sensors on the market today range from the $1,000 to $10,000 range making it difficult for small research firms and non profits to conduct water quality monitoring projects. This project attempts to construct a water quality-monitoring platform that will automate the process of collecting, analyzing and visualization water quality data. Special attention will be given to the data visualization aspect of the process with an emphasis on web mapping applications.
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Hanley, John P. "A New Evolutionary Algorithm For Mining Noisy, Epistatic, Geospatial Survey Data Associated With Chagas Disease." ScholarWorks @ UVM, 2017. http://scholarworks.uvm.edu/graddis/727.
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The scientific community is just beginning to understand some of the profound affects that feature interactions and heterogeneity have on natural systems. Despite the belief that these nonlinear and heterogeneous interactions exist across numerous real-world systems (e.g., from the development of personalized drug therapies to market predictions of consumer behaviors), the tools for analysis have not kept pace. This research was motivated by the desire to mine data from large socioeconomic surveys aimed at identifying the drivers of household infestation by a Triatomine insect that transmits the life-threatening Chagas disease. To decrease the risk of transmission, our colleagues at the laboratory of applied entomology and parasitology have implemented mitigation strategies (known as Ecohealth interventions); however, limited resources necessitate the search for better risk models. Mining these complex Chagas survey data for potential predictive features is challenging due to imbalanced class outcomes, missing data, heterogeneity, and the non-independence of some features.
We develop an evolutionary algorithm (EA) to identify feature interactions in "Big Datasets" with desired categorical outcomes (e.g., disease or infestation). The method is non-parametric and uses the hypergeometric PMF as a fitness function to tackle challenges associated with using p-values in Big Data (e.g., p-values decrease inversely with the size of the dataset). To demonstrate the EA effectiveness, we first test the algorithm on three benchmark datasets. These include two classic Boolean classifier problems: (1) the "majority-on" problem and (2) the multiplexer problem, as well as (3) a simulated single nucleotide polymorphism (SNP) disease dataset. Next, we apply the EA to real-world Chagas Disease survey data and successfully archived numerous high-order feature interactions associated with infestation that would not have been discovered using traditional statistics. These feature interactions are also explored using network analysis. The spatial autocorrelation of the genetic data (SNPs of Triatoma dimidiata) was captured using geostatistics. Specifically, a modified semivariogram analysis was performed to characterize the SNP data and help elucidate the movement of the vector within two villages. For both villages, the SNP information showed strong spatial autocorrelation albeit with different geostatistical characteristics (sills, ranges, and nuggets). These metrics were leveraged to create risk maps that suggest the more forested village had a sylvatic source of infestation, while the other village had a domestic/peridomestic source. This initial exploration into using Big Data to analyze disease risk shows that novel and modified existing statistical tools can improve the assessment of risk on a fine-scale.
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Ruby, Caitlin A. "Application of Coastal and Marine Ecological Classification Standard (CMECS) to Remotely Operated Vehicle (Rov) Video Data for Enhanced Geospatial Analysis of Deep Sea Environments." Thesis, Mississippi State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10268275.
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The Coastal and Marine Ecological Classification Standard (CMECS) provides a comprehensive framework of common terminology for organizing physical, chemical, biological, and geological information about marine ecosystems. Federally endorsed as a dynamic content standard, all federally funded data must be compliant by 2018; however, applying CMECS to deep sea datasets and underwater video have not been extensively examined. The presented research demonstrates the extent to which CMECS can be applied to deep sea benthic habitats, assesses the feasibility of applying CMECS to remotely operated vehicle (ROV) video data in near-real-time, and establishes best practices for mapping environmental aspects and observed deep sea habitats as viewed by the ROV’s forward-facing camera. All data were collected during 2014 in the Northern Gulf of Mexico by the National Oceanic and Atmospheric Administration’s (NOAA) ROV Deep Discoverer and ship Okeanos Explorer.
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Xu, Haowen. "Data-driven framework for forecasting sedimentation at culverts." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6892.
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The increasing intensity and frequency of precipitation in recent decades, combined with the human interventions in watersheds, has drastically altered the natural regimes of water and sediment transport in watersheds over the whole contiguous United States. Sediment-transport related concerns include the sustainability of aquatic biology, the stability of the river morphology, and the security and vulnerability of various riverine structures. For the present context, the concerns are related to the acceleration of upland erosion (sediment production) and in-stream sediment-transport processes that eventually lead to sediment accumulation at culverts (structures that pass streams under roadways). This nuisance has become widespread in many transportation agencies in the United States, as it has a direct bearing on maintaining normal culvert operations during extreme flows when these waterway crossings are essential for the communities they serve. Despite the prevalence of culvert sedimentation, current specifications for culvert design do not typically consider aspects of sediment transport and deposition.
The overall study objective is to systematically identify the likelihood of culvert sedimentation as a function of stream and culvert geometry, along with landscape characteristics (process drivers of culvert sedimentation) in the culvert drainage area. The ideal approach for predicting sedimentation is to track sediment sources dislocated from the watershed, their overland movement, and their delivery into the streams using physical-based modeling. However, there are considerable knowledge gaps in addressing the sedimentation at culverts as an end-to-end process, especially in connecting the upland with in-stream processes and simulating the sediment deposition at culverts in non-uniform, unsteady flows, while also taking into account the vegetation growth in culverts’ vicinity. It is, therefore, no surprise that existing research, textbooks, and guidelines do not typically provide adequate information on sediment control at culverts.
This dissertation presents a generalizable data-driven framework that integrates various machine-learning and visual analytics techniques with GIS in a web-based geospatial platform to explore the complex environmental processes of culvert sedimentation. The framework offers systematic procedures for (1) classifying the culvert sedimentation degree using a time-series of aerial images; (2) identifying key process-drivers from a variety of environmental and culvert structural characteristics through feature selections and interactive visual interfaces; (3) supporting human interactions to perceive empirical relationships between drivers and the culvert sedimentation degree through multivariate Geovisualization and Self-Organizing Map (SOM); and (4) forecasting culvert sedimentation potential across Iowa using machine learning algorithms. Developed using modular design and atop national datasets, the framework is generalizable and extendable, and therefore can be applied to address similar river management issues, such as habitat deterioration and water pollution, at the Contiguous US scale.
The platform developed through this Ph.D. study offers a web-based problem-solving environment for a) managing inventory and retrieving culvert structural information; b) integrating diverse culvert-related datasets (e.g., culvert inventory, hydrological and land use data, and observations on the degree of sedimentation in the vicinity of culverts) in a digital repository; c) supporting culvert field inspections and real-time data collection through mobile devices; and d) hosting the data-driven framework for exploring culvert sedimentation drivers and forecasting culvert sedimentation potential across Iowa. Insights provided through the data-driven framework can be applied to support decisions for culvert management and sedimentation mitigation, as well as to provide suggestions on parameter selections for the design of these structures.
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Jackson, Etta Delores. "The Role of Geospatial Information and Effective Partnerships in the Implementation of the International Agenda for Sustainable Development." Antioch University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=antioch1594291234482502.
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Pinger, Andrew James. "Application of GIS and Spatial Analysis of Golden Eagle Fatalities Caused by Wind Turbines at the Altamont Pass Wind Resource." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/197.
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The Altamont Pass Wind Resource Area (Altamont) near Livermore, California is the oldest and largest wind farm in the United States. It is known as a location of high avian mortality, especially for diurnal raptors such as the Golden Eagle (Aquila chrysaetos). Using the avian monitoring data collected at Altamont for over thirteen years (1998-2003, 2005- 2011), records were analyzed of 134 golden eagle deaths caused by wind turbine collisions. All wind turbines present during the same temporal range were characterized according to turbine variables, and geographic placement characteristics. Values of turbines that killed golden eagles were compared to values of turbnes that did not. It was discovered that turbines that have killed golden eagles (kill turbines) share characteristics that are significantly different from those that have not. Kill turbines are more often situated on lattice structure towers, have larger rotor blade-swept areas, placed in less dense turbine arrays, are further away from the next nearest turbine and are less often placed on top of ridgelines compared to nonkill turbines. Finally, kill turbines are more often situated at the end of a turbine row than are nonkill turbines. The differences between kill and nonkill turbine model, hill slope, tower height, generating capacity, array diversity, row count of turbines and placement in a hill saddle were found to be not significant. These findings support in part, earlier turbine studies at Altamont, but do not concur with all previous findings. The methods used in this study can be applied to any bird species at Altamont and at any wind resource area throughout the world. As the wind industry continues to grow, techniques used in studies such as this are an important tool that can be used to direct wildlife conservation policies.
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Archer, Jana E. "Spatiotemporal Analyses of Recycled Water Production." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3223.
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Increased demands on water supplies caused by population expansion, saltwater intrusion, and drought have led to water shortages which may be addressed by use of recycled water as recycled water products. Study I investigated recycled water production in Florida and California during 2009 to detect gaps in distribution and identify areas for expansion. Gaps were detected along the panhandle and Miami, Florida, as well as the northern and southwestern regions in California. Study II examined gaps in distribution, identified temporal change, and located areas for expansion for Florida in 2009 and 2015. Production increased in the northern and southern regions of Florida but decreased in Southwest Florida. Recycled water is an essential component water management a broader adoption of recycled water will increase water conservation in water-stressed coastal communities by allocating recycled water for purposes that once used potable freshwater.
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Teng, Yueh-chuan, and 鄧岳荃. "Map Interface Content Interoperability in Geospatial SOA Environment with Open Geographic Data." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/82459092928047031340.
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碩士國立成功大學
測量及空間資訊學系碩博士班
95
Full accessibility and correct use of distributed geospatial resource are two of the critical issues to recent GIS developments. With the innovated progress of recent geospatial SOA, open geospatial data format and web service have largely removed data acquisition obstacles. How to develop a middleware environment to effectively integrate heterogeneous geospatial resource, take advantages of the chaining capability of geospatial web services and develop built-in professional geospatial knowledge have emerged as our future challenges. Map interface operations in middleware environment were chosen as the major topic of this research. Besides taking full advantages of the accessibility of heterogeneous geographic data via web service, we expect to further improve the map interface display and application via built-in cartographic knowledge in middleware environment. To achieve better interoperability of heterogeneous data, a general-purpose data description framework based on the fundamental characteristics of geographic data is proposed. Complying with the ISO/TC211 19100 series international standards, the description framework enables all distributed geospatial features to automatically carry common and necessary description information. The middleware can therefore interpret the acquired data content in a standardized way and ensure the correct use of map operations. Served as a common description framework, it can be applied to any application domains and can be expanded whenever necessary. We further established a primitive geospatial SOA following various OGC standards(WMS、WFS、WCTS、OpenLS and Catalogue Service) that allows the middleware to collect and process required data via loose-coupling of web service. Based on the proposed description framework and built-in cartographic knowledge, the developed middleware can meet the demands of the correct display and operation of heterogeneous data in map interface, as well as avoid the possible wrong data use of na��ve users. It is clear that middleware will play a dominant role to bridge the gaps of users and data providers in the future GIS environment. Though we only focus on the common characteristic of geographic data in this research, the proposed fundamental middleware environment has sufficient flexibility to further improve the integration of heterogeneous data by including additional domain-specific knowledge.
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RongGu, You, and 顧又榮. "An efficient geospatial data management system for LiDAR in a distributed environment." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/13515359377281907375.
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Fischer, Payton Nicole. "Modeling Suitable Habitat for the Common Mudpuppy (Necturus maculosus maculosus) Utilizing Regional Data and Environmental DNA." Thesis, 2020. http://hdl.handle.net/1805/22884.
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Indiana University-Purdue University Indianapolis (IUPUI)The distribution of the Common Mudpuppy (Necturus maculosus maculosus) is widespread but greatly understood. It is assumed that mudpuppy populations are declining due to poor habitat quality. However, there is not enough data to support this claim. The distribution of the mudpuppy is throughout the entire state, but only 43 of the 92 counties in Indiana have records. This project utilized habitat suitability modeling, focused on Indiana, to gain a better understanding of their distribution within the state. Data from Ohio and the Salamander Mussel (Simpsonais ambigua) were included to bolster the dataset. Environmental DNA was included to validate the model. Variables used in this analysis were Strahler Stream Order, distance to forest, percent agriculture, and tree canopy cover. Results showed that stream orders 4 to 6, a shorter distance to forest, less agriculture, and 30 to 40% of tree canopy cover was what contributed to suitable habitat. Stream order was the variable that contributed to the model the most. The areas of suitable habitat found were the HUC08 sub-watersheds in the northeastern and southwestern corners of the state. These areas included 19 counties were there were no previous records of mudpuppies. Environmental DNA showed that the negative samples were not found in suitable habitat. Further supporting the predicted area of suitable habitat. It is recommended that conservation efforts focus on the northeastern and southwestern regions. Interpreting this data to align with the regions set by the Indiana State Wildlife Action Plan shows that conservation should focus in the Great Lakes, Interior Plateau, and Valley and Hills area. It is recommended that more environmental data be conducted and that proactive conservation measures are implemented.
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McNamee, Sara Helen. "Geospatial Data Accessibility in Web 2.0 Environments." 2011. http://trace.tennessee.edu/utk_gradthes/898.
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Geographically referenced data is becoming a robust source of information because the use of place-based relevance searching is being employed as a popular form of information access and dispersal. To address this trend, the researcher conducted a study on the usability of the USA National Phenology Network (http://www.usanpn.org/), engaging 6 volunteer participants structured usability test of the USANPN mapping application. The participants were asked to complete two tasks, and data was collected both during (in the form of a think aloud exercise) and after the test (in the form of an exit interview). From the data collected, the researcher aimed to identify common and serious usability issues using both quantitative usability metrics and the qualitative think aloud and interview data. This study was primarily directed at assessing the usability of a geospatial Web 2.0 application and identifying common user problems. The researcher concluded that the search functionality and general navigation options were the most pressing usability issues associated with using the USA National Phenology Website to contribute geospatial data.
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Sanchis, Huertas Ana. "Providing energy efficiency location-based strategies for buildings using linked open data." Master's thesis, 2012. http://hdl.handle.net/10362/8315.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Climate change is a main concern for humanity from the ending of 20th century. To improve and take care of our environment, a set of measures has been developed to monitor, manage, reduce consumption and raise efficiency of buildings, including the integration of renewable energies and the implementation of passive measures like the improvement of the building envelope. Complex methodologies are used in order to achieve these objectives. Using different tools and data translating is needed, and the loss of accuracy from the detailed input information is most of the times unavoidable. Moreover, including these measures in the development of a project have become a try and error process involving building characteristics, location data and energy efficiency measures. The raising of new technologies, capable of dealing with location-based data and semantics to relate and structure information in a machine readable way, may allow us to provide a set of technical measures to improve energy efficiency in an accessible, open, understandable and easy way from a few data about location and building characteristics. This work tries to define a model and its necessary and sufficient set of data. Its application will provide customized strategies acting as pre-feasibility constraints to help buildings achieve their energy efficiency objectives from its very conception. The model intends to be useful for non-expert users who want to know about their energy savings possibilities, and for professionals willing to get a sustainable starting point for their projects.
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Sullivan, Colleen M. (Colleen Marie). "Assessment of the potential for conflict between existing ocean space use and renewable energy development off the coast of Oregon." Thesis, 2012. http://hdl.handle.net/1957/30172.
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Oregon's ocean waters are a potential source of wind, wave, and tidal energy; of interest to renewable energy entrepreneurs and to the U.S. government as it seeks to bolster energy security. In order to install technology to capture this energy, however, it may be necessary to mitigate conflict with existing ocean space users. The objective of this research was to construct a conflict analysis model in a GIS to answer the following research questions: (1) Within the study area off the coast of Oregon, where are stakeholders currently using ocean space and how many uses overlap? (2) To what extent might existing ocean space use present potential for conflict with renewable energy development? (3) How do various types of uncertainty affect analysis results? (4) What are the implications of these findings for ecosystem based management of the ocean?
All available spatial information on ocean space usage by commercial fishing, commercial non-fishing, recreational, Native American, and scientific communities was gathered. Stakeholder outreach with these communities was used to vet the collected data and allow each to contribute knowledge not previously available through GIS data clearinghouses maintained by government or interest groups. The resulting data were used as inputs to a conflict visualization model written in Python and imported to an ArcGIS tool. Results showed extensive coverage and overlap of existing ocean space uses; specifically that 99.7% of the 1-nm² grid cells of the study area are occupied by at least 6 different categories of ocean space use. The six uses with the greatest coverage were: Fishing – Trolling, Habitat, Military, Fishing - Closure Areas, Protected, and Marine Transportation - Low Intensity. An uncertainty analysis was also completed to illustrate the margin for error and therefore the necessity of appropriate stakeholder outreach during the renewable energy siting process, as opposed to relying only on a GIS.
Ranking of each category by its potential for conflict with renewable energy development demonstrated which areas of the ocean may be particularly contentious. Because rankings are subjective, a tool was created to allow users to input their own rankings. For the purpose of this report, default rankings were assigned to each as justified by the literature. Results under these assumptions showed that space use and potential for conflict were highest between the coast and approximately 30 nm at sea. This is likely because certain space use is limited by depth (e.g., recreational use); there is increased shipping density as vessels approach and depart major ports; and increased fuel costs associated with traveling further from shore.
Two potential applications of model results were demonstrated. First, comparison with existing wave energy permit sites highlighted relative potential for conflict among the sites and the input data detailed the specific uses present. Second, comparison with areas determined most suitable for development by the wave energy industry illustrated that areas of high suitability often also had high rankings for potential for conflict. It appeared that the factors that determined development suitability were often the same factors that drew current ocean space users to those locations.
Current support at the state, regional and federal level under the National Ocean Policy for the use of marine spatial planning as a tool to implement ecosystem based management of the oceans requires that tools such as the one developed in this research are used, to ensure that all components of the marine ecosystem are considered prior to implementation of a management plan. The addition of renewable energy to the current social landscape of the ocean will reduce the resource base for many categories of ocean space use. Model results demonstrated that mitigation of conflict between development and existing space use is not merely a best practice supported by current policy, but a necessity. Results presented a visualization of the social landscape of the ocean that could help managers determine which stakeholders to engage during the initial stage of choosing a site for development.Graduation date: 2012
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Richards, Larissa Christine. "Chemometric analysis of full scan direct mass spectrometry data for the discrimination and source apportionment of atmospheric volatile organic compounds measured from a moving vehicle." Thesis, 2021. http://hdl.handle.net/1828/13333.
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Anthropogenic emissions into the troposphere can impact air quality, leading to poorer health outcomes in the affected areas. Volatile organic compounds (VOCs) are a group of chemical compounds, including some which are toxic, that are precursors in the formation of ground-level ozone and secondary organic aerosols. VOCs have a variety of sources, and the distribution of atmospheric VOCs differs significantly over time and space. Historically, the large number of chemical species present at low concentrations (parts-per-trillion to parts-per-billion by volume) have made VOCs difficult to measure in ambient air. However, with improvements in analytical instrumentation, these measurements are becoming more common place. Direct mass spectrometry (MS), such as membrane introduction mass spectrometry (MIMS) and proton-transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) facilitate real-time, continuous measurements of VOCs in air, with full scan mass spectral data capturing changes in chemical composition with high temporal resolution. Operated on-road, mobilized direct MS has been used for quantitative mapping of VOCs at the neighborhood scale, but identifying VOC sources based on the observed mixture of molecules in the full scan MS dataset has yet to be explored. This dissertation describes the use of chemometric techniques to interrogate full scan MS data, and the progression from discriminating VOC samples of known chemical composition based on full scan MIMS data through to the apportionment of VOC sources measured continuously with a PTR-ToF-MS system operating in a moving vehicle. Lab‐constructed VOC samples of known chemical composition and concentration demonstrated the use of principal component analysis (PCA) to discriminate, and k-nearest neighbours to classify, samples based on normalized full scan MIMS data. Furthermore, multivariate curve resolution-alternating least squares (MCR-ALS) was used to resolve mixtures into molecular component contributions. PCA was also used to discriminate ‘real-world’ VOC mixtures (e.g., woodsmoke VOCs, headspace above aqueous hydrocarbon samples) of unknown chemical composition measured by MIMS. Using vehicle mounted MIMS and PTR-ToF-MS systems, full scan MS data of ambient atmospheric VOCs were collected and PCA was applied to the normalized full scan MS data. A supervised analysis performed PCA on samples collected near known VOC sources, while an unsupervised analysis using PCA followed by cluster analysis was used to identify groups in a continuous, time series PTR-ToF-MS dataset measured between Nanaimo and Crofton, British Columbia (BC). In both the supervised and unsupervised analysis, samples impacted by emissions from different sources (e.g., internal combustion engines, sawmills, composting facilities, pulp mills) were discriminated. With PCA, samples were discriminated based on differences in the observed full scan MS data, however real-world samples are often impacted by multiple VOC sources. MCR-weighted ALS (MCR-WALS) was applied to the continuous, time series PTR-ToF-MS data from three field campaigns on Vancouver Island, BC for source apportionment. Variable selection based on signal-to-noise ratios was used to reduce the mass list while retaining the observed m/z that capture changes in the mixture of VOCs measured, improving model results, and reducing computation time. Both point (e.g., anthropogenic hydrocarbon emissions, pulp mill emissions) and diffuse (e.g., VOCs from forest fire smoke) VOC sources were identified in the data, and were apportioned to determine their contributions to the measured samples. The data analyzed captured fine scale changes in the ambient VOCs present in the air, and geospatial maps of each individual source, and of the source apportionment were used to visualize the distribution of VOC sources across the sampling area. This work represents the first use of MCR-WALS to identify and apportion ambient VOC sources based on continuous PTR-ToF-MS data measured from a moving vehicle. The methods described can be applied to larger scale field campaigns for the source apportionment of VOCs across multiple days to capture diurnal and seasonal variations. Identifying spatial and temporal trends in the sources of VOCs at the regional scale can help to identify pollution ‘hot spots’ and inform evidence-based public policy for improving air quality.Graduate
2022-08-17
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Beerval, Ravichandra Kavya Urs. "Spatiotemporal analysis of extreme heat events in Indianapolis and Philadelphia for the years 2010 and 2011." Thesis, 2014. http://hdl.handle.net/1805/4083.
Full textAbstract:
Indiana University-Purdue University Indianapolis (IUPUI)Over the past two decades, northern parts of the United States have experienced extreme heat conditions. Some of the notable heat wave impacts have occurred in Chicago in 1995 with over 600 reported deaths and in Philadelphia in 1993 with over 180 reported deaths. The distribution of extreme heat events in Indianapolis has varied since the year 2000. The Urban Heat Island effect has caused the temperatures to rise unusually high during the summer months. Although the number of reported deaths in Indianapolis is smaller when compared to Chicago and Philadelphia, the heat wave in the year 2010 affected primarily the vulnerable population comprised of the elderly and the lower socio-economic groups. Studying the spatial distribution of high temperatures in the vulnerable areas helps determine not only the extent of the heat affected areas, but also to devise strategies and methods to plan, mitigate, and tackle extreme heat. In addition, examining spatial patterns of vulnerability can aid in development of a heat warning system to alert the populations at risk during extreme heat events. This study focuses on the qualitative and quantitative methods used to measure extreme heat events. Land surface temperatures obtained from the Landsat TM images provide useful means by which the spatial distribution of temperatures can be studied in relation to the temporal changes and socioeconomic vulnerability. The percentile method used, helps to determine the vulnerable areas and their extents. The maximum temperatures measured using LST conversion of the original digital number values of the Landsat TM images is reliable in terms of identifying the heat-affected regions.