Relevant bibliographies by topics / Data environmental analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data environmental analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Data environmental analysis"

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Kulla, L., and R. Marušák. "Environmental risk assessment based on semi-quantitative analysis of forest management data." Journal of Forest Science 57, No. 3 (March 21, 2011): 89–95. http://dx.doi.org/10.17221/35/2010-jfs.

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The paper deals with environmental risk assessment in prevailingly unnatural spruce (Picea abies [L.] Karst.) forests in three regions with different patterns of forest damage in the Slovak part of the West Carpathians. Logistic regression was used to estimate the effect of 7 site-related, 5 stand-related and 2 anthropogenic factors on the probability that critical forest damage will occur. The results show that regression models can describe cause-effect relationships in regions with different regimes of forest decline. Stand age, proportion of spruce, and distance from the focus of biotic agent activity predicted decline in two regions with generally lower elevation in northern Slovakia (Kysuce and Orava). In a mountain region (Low Tatras), the importance of factors contributing to the static stability of trees and position towards dangerous winds increased significantly. The quality of the derived models and prospects for their usefulness in risk assessment are discussed.
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Rozental, O. M., and L. N. Alexandrovskaya. "Data Analysis in Environmental Management." Ecology and Industry of Russia 22, no. 3 (April 4, 2018): 56–59. http://dx.doi.org/10.18412/1816-0395-2018-3-56-59.

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Lesser, Virginia M., and Wayne R. Ott. "Environmental Statistics and Data Analysis." American Statistician 49, no. 4 (November 1995): 398. http://dx.doi.org/10.2307/2684587.

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Rathbun, Stephen L., and Wayne Ott. "Environmental Statistics and Data Analysis." Journal of the American Statistical Association 93, no. 441 (March 1998): 402. http://dx.doi.org/10.2307/2669643.

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Hamilton, Martin A. "Environmental Statistics and Data Analysis." Technometrics 38, no. 3 (August 1996): 292–93. http://dx.doi.org/10.1080/00401706.1996.10484518.

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O'Brien, Robert. "Environmental Statistics and Data Analysis." Journal of Quality Technology 30, no. 1 (January 1998): 105–6. http://dx.doi.org/10.1080/00224065.1998.11979827.

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Sayers, William T. "Environmental statistics and data analysis." Atmospheric Environment 30, no. 20 (October 1996): 3551. http://dx.doi.org/10.1016/1352-2310(96)00053-2.

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G., Kaziyeva, Esekeeva M., Sagnayevа S., Sembina G., and Abzhanova A. "Environmental Monitoring Data Analysis OLAP Toolplatform." BULLETIN of L.N. Gumilyov Eurasian National University. Technical Science and Technology Series 131, no. 2 (2020): 66–77. http://dx.doi.org/10.32523/2616-68-36-2020-131-2-66-77.

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Yuan, Lina, Huajun Chen, and Jing Gong. "An analysis of environmental data transmission." IOP Conference Series: Earth and Environmental Science 64 (May 2017): 012058. http://dx.doi.org/10.1088/1755-1315/64/1/012058.

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Esterby, S. R. "Trend analysis methods for environmental data." Environmetrics 4, no. 4 (December 1993): 459–81. http://dx.doi.org/10.1002/env.3170040407.

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Dissertations / Theses on the topic "Data environmental analysis"

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Li, Yang. "The spatial data quality analysis in the environmental modelling." Thesis, University of East London, 2001. http://roar.uel.ac.uk/1305/.

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The spatial data quality analysis is essential in environmental modelling for efficiently addressing the environmental change. As the complexity of data sets and the modelling capability of computer systems increase, the need to address the quality of both data and models is increasingly important. Integration with environmental modelling, the spatial data quality analysis and the geocomputation paradigm have been three important areas of GIS research. In this research they are brought together in the context of coastal oil spill modelling. The research covers the issues of measurement, modelling and management of spatial data quality. Coupling GIS and environmental modelling, the systematic solution is developed for coastal oil spill modelling which is representative of complex environmental models. The procedures of geospatial data quality analysis were implemented not only with existing GIS funLionality but also with various Geocomputation techniques. Spatial data quality analyses of inputs and model performances, which include sensitivity analyses, error propagation analyses and fitness-for-use analyses, were carried out for the coastal oil spill modelling. The results show that in coastal oil spill modelling, a better understanding and improvement of spatial data quality can be achieved through such analyses. The examples illustrate both the diversity of techniques and tools required when investigating spatial data quality issues in environmental modelling. The evidence of feasibility and practicality are also provided for these flexible analysis approaches. An overall methodology is developed at each stage of a project; with particular emphasis at inception to ensure adequate data quality on which to construct the models. Furthermore, the coupling strategy of GIS and environmental modelling is revised to include a geo-data quality analysis (GQA) engine. With growing availability of proprietary and public domain software suitable for spatial data quality analysis, GQA engines will be formed with the evolution of such software into tightly-coupled collection of tools external to GIS. The GQA engine would itself be tightly-coupled with GIS and environmental models to form a modelling framework.
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Yang, Bin, and 杨彬. "A novel framework for binning environmental genomic fragments." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45789344.

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Huang, Yunshui Charles. "A prototype of data analysis visualization tool." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/12125.

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Valtersson, Einar. "Comparison of data analysis methods KMSProTF and MsDEMPCA using Magnetotelluric data." Thesis, Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80195.

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In this work two ways of processing controlled-source magnetotelluric (MT) data were tried and compared against each other. The aim was to evaluate the differences between a multivariate-based processing method to a bivariate processing method. The software KMSProTF represents conventional processing using bivariate and robust statistics. MsDEMPCA utilizes a multi-variate Criss-Cross regression scheme to improve the condition of the data-matrix before robustly decomposing it into principal components. Data from the FENICS-19 survey in northern Scandinavia was processed to transfer functions (TF) using the respective method. The TFs were visually interpreted in KMSProTF. There were no significant differences found between the methods. In addition a calibration between instruments was carried out, which caused an exclusion of parts of the data-set.
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Adu-Prah, Samuel. "GEOGRAPHIC DATA MINING AND GEOVISUALIZATION FOR UNDERSTANDING ENVIRONMENTAL AND PUBLIC HEALTH DATA." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/657.

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Within the theoretical framework of this study it is recognized that a very large amount of real-world facts and geospatial data are collected and stored. Decision makers cannot consider all the available disparate raw facts and data. Problem-specific variables, including complex geographic identifiers have to be selected from this data and be validated. The problems associated with environmental- and public-health data are that (1) geospatial components of the data are not considered in analysis and decision making process, (2) meaningful geospatial patterns and clusters are often overlooked, and (3) public health practitioners find it difficult to comprehend geospatial data. Inspired by the advent of geographic data mining and geovisualization in public and environmental health, the goal of this study is to unveil the spatiotemporal dynamics in the prevalence of overweight and obesity in United States youths at regional and local levels over a twelve-year study period. Specific objectives of this dissertation are to (1) apply regionalization algorithms effective for the identification of meaningful clusters that are in spatial uniformity to youth overweight and obesity, and (2) use Geographic Information System (GIS), spatial analysis techniques, and statistical methods to explore the data sets for health outcomes, and (3) explore geovisualization techniques to transform discovered patterns in the data sets for recognition, flexible interaction and improve interpretation. To achieve the goal and the specific objectives of this dissertation, we used data sets from the National Longitudinal Survey of Youth 1997 (NLSY'97) early release (1997-2004), NLSY'97 current release (2005 - 2008), census 2000 data and yearly population estimates from 2001 to 2008, and synthetic data sets. The NLSY97 Cohort database range varied from 6,923 to 8,565 individuals during the period. At the beginning of the cohort study the age of individuals participating in this study was between 12 and 17 years, and in 2008, they were between 24 and 28 years. For the data mining tool, we applied the Regionalization with Dynamically Constrained Agglomerative clustering and Partitioning (REDCAP) algorithms to identify hierarchical regions based on measures of weight metrics of the U.S. youths. The applied algorithms are the single linkage clustering (SLK), average linkage clustering (ALK), complete linkage clustering (CLK), and the Ward's method. Moreover, we used GIS, spatial analysis techniques, and statistical methods to analyze the spatial varying association of overweight and obesity prevalence in the youth and to geographically visualize the results. The methods used included the ordinary least square (OLS) model, the spatial generalized linear mixed model (GLMM), Kulldorff's Scan space-time analysis, and the spatial interpolation techniques (inverse distance weighting). The three main findings for this study are: first, among the four algorithms ALK, Ward and CLK identified regions effectively than SLK which performed very poorly. The ALK provided more promising regions than the rest of the algorithms by producing spatial uniformity effectively related to the weight variable (body mass index). The regionalization algorithm-ALK provided new insights about overweight and obesity, by detecting new spatial clusters with over 30% prevalence. New meaningful clusters were detected in 15 counties, including Yazoo, Holmes, Lincoln, and Attala, in Mississippi; Wise, Delta, Hunt, Liberty, and Hardin in Texas; St Charles, St James, and Calcasieu in Louisiana; Choctaw, Sumter, and Tuscaloosa in Alabama. Demographically, these counties have race/ethnic composition of about 75% White, 11.6% Black and 13.4% others. Second, results from this study indicated that there is an upward trend in the prevalence of overweight and obesity in United States youths both in males and in females. Male youth obesity increased from 10.3% (95% CI=9.0, 11.0) in 1999 to 27.0% (95% CI=26.0, 28.0) in 2008. Likewise, female obesity increased from 9.6% (95% CI=8.0, 11.0) in 1999 to 28.9% (95% CI=27.0, 30.0) during the same period. Youth obesity prevalence was higher among females than among males. Aging is a substantial factor that has statistically highly significant association (p < 0.001) with prevalence of overweight and obesity. Third, significant cluster years for high rates were detected in 2003-2008 (relative risk 1.92, 3.4 annual prevalence cases per 100000, p < 0.0001) and that of low rates in 1997-2002 (relative risk 0.39, annual prevalence cases per 100000, p < 0.0001). Three meaningful spatiotemporal clusters of obesity (p < 0.0001) were detected in counties located within the South, Lower North Eastern, and North Central regions. Counties identified as consistently experiencing high prevalence of obesity and with the potential of becoming an obesogenic environment in the future are Copiah, Holmes, and Hinds in Mississippi; Harris and Chamber, Texas; Oklahoma and McCain, Oklahoma; Jefferson, Louisiana; and Chicot and Jefferson, Arkansas. Surprisingly, there were mixed trends in youth obesity prevalence patterns in rural and urban areas. Finally, from a public health perspective, this research have shown that in-depth knowledge of whether and in what respect certain areas have worse health outcomes can be helpful in designing effective community interventions to promote healthy living. Furthermore, specific information obtained from this dissertation can help guide geographically-targeted programs, policies, and preventive initiatives for overweight and obesity prevalence in the United States.
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Autret, Arnaud. "Modular neural networks for analysis of flow cytometry data." Thesis, University of South Wales, 2003. https://pure.southwales.ac.uk/en/studentthesis/modular-neural-networks-for-analysis-of-flow-cytometry-data(49f3349b-e86a-4bfb-a689-c853323b6f2d).html.

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In predicting environmental hazards or estimating the impact of human activities on the marine ecosystem, scientists have multiplied the need for sample analysis. The classical microscopic approach is time consuming and wastes the talent and intellectual abilities of trained specialists. Therefore, scientists developed an automated optical tool, called a Flow Cytometer (FC), to analyse samples quickly and in large quantities. The flow cytometer has successfully been applied to real phytoplankton studies. However, analysis of the data extracted from samples is still required. Artificial Neural Networks (ANNs) are one of the tools applied to FC data analysis. Despite several successful applications, ANNs have not been widely adopted by the marine biologist community, as they can not possible to change the number of species in the classification problem without retraining of the full system from scratch. Training is time consuming and requires expertise in ANNs. Moreover, most ANN paradigms cannot cope effectively with unknown data, such as data coming from new phytoplankton species or from species outside the scope of the studies. This project developed a new ANN technique based on a modular architecture that removes the need for retraining and allows unknowns to be detected and rejected. Furthermore, the Support Vector Machine architecture is applied in this domain for the first time and compared against another ANN paradigm called Radial Basis Function Networks. The results show that the modular architecture is able to effectively deal with new data which can be incorporated into the ANN architecture without fully retraining the system.
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Wood, Duncan Andrew. "Analysis of passive microwave data for large area environmental monitoring." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq24519.pdf.

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Welle, Paul. "Remotely Sensed Data for High Resolution Agro-Environmental Policy Analysis." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1012.

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Policy analyses of agricultural and environmental systems are often limited due to data constraints. Measurement campaigns can be costly, especially when the area of interest includes oceans, forests, agricultural regions or other dispersed spatial domains. Satellite based remote sensing offers a way to increase the spatial and temporal resolution of policy analysis concerning these systems. However, there are key limitations to the implementation of satellite data. Uncertainty in data derived from remote-sensing can be significant, and traditional methods of policy analysis for managing uncertainty on large datasets can be computationally expensive. Moreover, while satellite data can increasingly offer estimates of some parameters such as weather or crop use, other information regarding demographic or economic data is unlikely to be estimated using these techniques. Managing these challenges in practical policy analysis remains a challenge. In this dissertation, I conduct five case studies which rely heavily on data sourced from orbital sensors. First, I assess the magnitude of climate and anthropogenic stress on coral reef ecosystems. Second, I conduct an impact assessment of soil salinity on California agriculture. Third, I measure the propensity of growers to adapt their cropping practices to soil salinization in agriculture. Fourth, I analyze whether small-scale desalination units could be applied on farms in California in order mitigate the effects of drought and salinization as well as prevent agricultural drainage from entering vulnerable ecosystems. And fifth, I assess the feasibility of satellite-based remote sensing for salinity measurement at global scale. Through these case studies, I confront both the challenges and benefits associated with implementing satellite based-remote sensing for improved policy analysis.
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Alexander, Lauren P. "Cell phone location data for travel behavior analysis." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99592.

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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 111-118).
Mobile phone technology generates vast amounts of data at low costs all over the world. This rich data provides digital traces when and where individuals travel, improving our ability to understand, model, and predict human mobility. Especially in this era of rapid urbanization, mobile phone data presents exciting new opportunities to plan transportation infrastructure and services that meet the mobility needs and challenges associated with increasing travel demand. But to realize these benefits, methods must be developed to utilize and integrate this data into existing urban and transportation modeling frameworks. In this thesis, we draw on techniques from the transportation engineering and urban computing communities to estimate travel demand and infrastructure usage. The methods we present utilize call detail records (CDRs) from mobile phones in conjunction with geospatial data, census records, and surveys, to generate representative origin-destination matrices, route trips through road networks, and evaluate traffic congestion. Moreover, we implement these algorithms in a flexible, modular, and computationally efficient software system. This platform provides an end-to-end solution that integrates raw, massive data to generate estimates of travel demand and infrastructure performance in any city, and produces interactive visualizations to effectively communicate these results. Finally, we demonstrate an application of these data and methods to evaluate the impact of ride-sharing on urban traffic. Using these approaches, we generate travel demand estimates analogous to many of the outputs of conventional travel demand models, demonstrating the potential of mobile phone data as a low cost option for transportation planning. We hope this work will serve as unified and comprehensive guide to integrating new big data resources into transportation modeling practices.
by Lauren P. Alexander.
S.M. in Transportation
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Shyr, Feng-Yeu. "Combining laboratory and field data in rail fatigue analysis." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/28024.

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Books on the topic "Data environmental analysis"

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Dormann, Carsten. Environmental Data Analysis. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2.

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Menke, William. Environmental data analysis with MatLab. Burlington: Elsevier, 2012.

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Environmental statistics and data analysis. Boca Raton: Lewis Publishers, 1995.

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Hewitt, C. N., ed. Methods of Environmental Data Analysis. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2920-6.

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Hewitt, C. N., ed. Methods of Environmental Data Analysis. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-010-9512-9.

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Analyzing environmental data. Hoboken, NJ: Wiley, 2004.

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Practical environmental statistics and data analysis. Glendale, AZ: ILM Publications, 2011.

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Nondetects and data analysis: Statistics for censored environmental data. Hoboken, N.J: Wiley-Interscience, 2005.

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Emetere, Moses Eterigho, and Esther Titilayo Akinlabi. Introduction to Environmental Data Analysis and Modeling. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36207-2.

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Smith, Roy Keith. Interpretation of organic data. Amsterdam, NY: Genium Pub. Corp., 2000.

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Book chapters on the topic "Data environmental analysis"

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Harmancioglu, N. B., S. D. Ozkul, and O. Fistikoglu. "Data Analysis." In Environmental Data Management, 141–96. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-015-9056-3_7.

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Dormann, Carsten. "Samples, Random Variables—Histograms, Density Distribution." In Environmental Data Analysis, 1–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_1.

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Dormann, Carsten. "Regression in R—Part II." In Environmental Data Analysis, 129–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_10.

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Dormann, Carsten. "The Linear Model: t-test and ANOVA." In Environmental Data Analysis, 147–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_11.

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Dormann, Carsten. "The Linear Model: t-test and ANOVA in R." In Environmental Data Analysis, 163–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_12.

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Dormann, Carsten. "Hypotheses and Tests." In Environmental Data Analysis, 177–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_13.

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Dormann, Carsten. "Experimental Design." In Environmental Data Analysis, 185–205. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_14.

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Dormann, Carsten. "Multiple Regression: Regression with Multiple Predictors." In Environmental Data Analysis, 207–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_15.

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Dormann, Carsten. "Multiple Regression in R." In Environmental Data Analysis, 227–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_16.

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Dormann, Carsten. "Outlook." In Environmental Data Analysis, 257–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_17.

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Conference papers on the topic "Data environmental analysis"

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"ANNOTATING UniProt METAGENOMIC AND ENVIRONMENTAL SEQUENCES IN UniMES." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003350803670368.

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"PREDICTED RELATIVE METABOLOMIC TURNOVER - Predicting Changes in the Environmental Metabolome from the Metagenome." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003314803370345.

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Pawlowicz, Richard M., Mitra Fattahipour, Alex Richardson, Trevor Rowe, and Roelof Versteeg. "Web Based Environmental Data Analysis." In GeoCongress 2006. Reston, VA: American Society of Civil Engineers, 2006. http://dx.doi.org/10.1061/40803(187)152.

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Lisowski, Przemysław, Adam Piórkowski, and Andrzej Lesniak. "Tools for the Storage and Analysis of Spatial Big Data." In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.216.

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Storing large amounts of spatial data in GIS systems is problematic. This problem is growing due to ever- increasing data production from a variety of data sources. The phenomenon of collecting huge amounts of data is called Big Data. Existing solutions are capable of processing and storing large volumes of spatial data. These solutions also show new approaches to data processing. Conventional techniques work with ordinary data but are not suitable for large datasets. Their efficient action is possible only when connected to distributed file systems and algorithms able to reduce tasks. This review focuses on the characteristics of large spatial data and discusses opportunities offered by spatial big data systems. The work also draws attention to the problems of indexing and access to data, and proposed solutions in this area.
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Tryggvason, Bjami V. "Analysis of Space Shuttle Acceleration Data." In International Conference On Environmental Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1994. http://dx.doi.org/10.4271/941417.

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Filice, Monica, Pierantonio Luca, and Alfonso Nastro. "Intelligent Data Analysis in Environmental Sampling." In 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications. IEEE, 2005. http://dx.doi.org/10.1109/idaacs.2005.283056.

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Reczynski, Witold, Malgorzata Jakubowska, Boguslaw Bas, Ewa Niewiara, Władysław W. Kubiak, Theodore E. Simos, and George Maroulis. "Chemometric Tools in Environmental Data Analysis." In COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING: Theory and Computation: Old Problems and New Challenges. Lectures Presented at the International Conference on Computational Methods in Science and Engineering 2007 (ICCMSE 2007): VOLUME 1. AIP, 2007. http://dx.doi.org/10.1063/1.2836231.

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Perelman, Anri Y., Alexander D. Yegorov, and T. B. Kaziakhmedov. "Urban and industrial aerosol data analysis and lidar measurements." In Environmental Sensing III, edited by Jean-Pierre Wolf. SPIE, 1997. http://dx.doi.org/10.1117/12.275127.

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Gonçalves, A. Manuela, Marco Costa, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Using Udometric Network Data to Estimate an Environmental Covariate." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637929.

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Gupta, Bindu, Kaushal Paneri, Gunjan Sehgal, Karamjit Singh, Geetika Sharma, and Gautam Shroff. "Visual Statistical Analysis of Environmental Sensor Data." In 2017 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2017. http://dx.doi.org/10.1109/vast.2017.8585515.

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Reports on the topic "Data environmental analysis"

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Soloviev, Alexander V., Robert H. Weisberg, and Mark E. Luther. SFOMC Task I: Oceanographic and Environmental Measurements (Environmental Array and Data Analysis, Year 4). Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada626855.

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Schell, Donna J. Environmental Compliance Assessment Data: Analysis of Data Generated by the Army's ECAS Program. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada393101.

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Shyr, L. J., H. Herrera, and R. Haaker. The role of data analysis in sampling design of environmental monitoring. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/584917.

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Haney, Tom, and Jill Lundell. Historical Data Analysis Supporting the Data Quality Objectives for the INL Site Environmental Soil Monitoring Program. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1769951.

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Andreu, Anne, William Crolley, and Bernard Paresol. Analysis of inventory data derived fuel characteristics and fire behavior under various environmental conditions. Office of Scientific and Technical Information (OSTI), February 2013. http://dx.doi.org/10.2172/1087111.

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Persily, Andrew, and Josh Gorfain. Analysis of ventilation data from the U.S. Environmental Protection Agency building assessment survey and evaluation (BASE) study. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7145.

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Persily, Andrew, and Josh Gorfain. Analysis of ventilation data from the U.S. Environmental Protection Agency Building Assessment Survey and Evaluation (BASE) study. Gaithersburg, MD: National Institute of Standards and Technology, 2008. http://dx.doi.org/10.6028/nist.ir.7145r.

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Coley, R. F., H. I. Avci, and L. J. Habegger. Data collection and analysis in support of the US Department of Energy Environmental Restoration and Waste Management Programmatic Environmental Impact Statement waste management alternatives. Office of Scientific and Technical Information (OSTI), March 1994. http://dx.doi.org/10.2172/10132466.

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Palermo, Michael R., and Robert M. Engler. Environmental Effects of Dredging. Interim Guidance for Predicting Quality of Effluent Discharged from Confined Dredged Material Disposal Areas--Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 1985. http://dx.doi.org/10.21236/ada292980.

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Frey, H. Christopher, and David S. Rhodes. Quantitative Analysis of Variability and Uncertainty in Environmental Data and Models. Volume 1. Theory and Methodology Based Upon Bootstrap Simulation. Office of Scientific and Technical Information (OSTI), April 1999. http://dx.doi.org/10.2172/1178941.

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