StreamingCube-Based Analytical Framework for Environmental Data Analysis

2019 ◽  
Author(s):  
Savong Bou ◽  
Hiroaki Shiokawa ◽  
Yasuhiro Hayase ◽  
Hiroyuki Kitagawa
Keyword(s):  
Data Analysis ◽  
Analytical Framework ◽  
Environmental Data

Regional Place-Systems Analysis Applied to Long-Range Transportation Planning

1996 ◽  
Vol 1518 (1) ◽  
pp. 19-21
Author(s):  
Leonard Voellinger ◽  
Claudia Oakes
Keyword(s):  
Long Range ◽  
Transportation Planning ◽  
Analytical Framework ◽  
Project Planning ◽  
Environmental Data ◽  
Data Types ◽  
Transportation Projects ◽  
Environmental Considerations ◽  
Environmental Element ◽  
Cultural Similarity

The Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) requires the integration of environmental considerations into transportation planning. Although previous legislation has required the consideration of environmental elements during project planning, ISTEA necessitates a different approach. During project-specific planning, each environmental element is researched to determine baseline conditions, and project plans are superimposed to determine potential impacts and the need for mitigative measures. This approach is appropriate for project-specific planning, but it presents only a snapshot of existing conditions because environmental data are changing constantly. The integration of environmental considerations into long-range plans requires a much broader focus. It must allow dynamic systems to change without affecting the plan's validity. A case study is presented of the Oklahoma statewide intermodal transportation plan, which uses recent geographic theory to integrate planning and human activity at varying scales. This theoretical framework is based on ecological and societal units of interaction called bioregions or place-systems. The environmental baseline and analysis for Oklahoma begin with the identification of place-systems in the state: areas of biophysical and cultural similarity and context. The delimitation of such regional place-systems is sufficiently generalized and flexible to accommodate many data types and sources, yet rigid enough to be useful for planning. Both quantitative data and descriptive information are included in an analytical framework suitable to relational data bases and geographic information systems applications. These are used to create a series of map and data overlays to project potential environmental impacts and constraints, as well as opportunities for developing future transportation projects. The methods used to delineate regional place-systems in Oklahoma and their subsequent use in environmental analyses and planning are described.

scholarly journals An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

2019 ◽  
Vol 9 (1S6) ◽  
pp. 52-61
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Energy Efficient ◽  
Residual Energy ◽  
Environmental Data ◽  
Energy Balancing ◽  
Control Centre

WSN consist of set of Sensing points which are responsible for collecting the detected information and then send the packets towards control centre which is responsible for processing of data. The applications of WSN include environmental data analysis, defence data collection and information. The survey of algorithms is done for the improvement of lifetime ratio. Four different algorithms namely Random, Random-CGT, EGT-Random and GTEB algorithms. The four algorithms are compared and then it is proved GTEB exhibits best behaviour with respect to energy consumed, number of non-holes, number of holes, Non-Hole to Hole ratio, residual energy, overhead and throughput.

scholarly journals An Ontology-based Visual Analytics for Apple Variety Testing

2021 ◽  
Author(s):  
Ekaterina Chuprikova ◽  
Abraham Mejia Aguilar ◽  
Roberto Monsorno
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Integration ◽  
Visual Analytics ◽  
Agricultural Sector ◽  
Environmental Data ◽  
Data Sources ◽  
Apple Variety ◽  
Testing Program ◽  
Variety Testing

<p>Increasing agricultural production challenges, such as climate change, environmental concerns, energy demands, and growing expectations from consumers triggered the necessity for innovation using data-driven approaches such as visual analytics. Although the visual analytics concept was introduced more than a decade ago, the latest developments in the data mining capacities made it possible to fully exploit the potential of this approach and gain insights into high complexity datasets (multi-source, multi-scale, and different stages). The current study focuses on developing prototypical visual analytics for an apple variety testing program in South Tyrol, Italy. Thus, the work aims (1) to establish a visual analytics interface enabled to integrate and harmonize information about apple variety testing and its interaction with climate by designing a semantic model; and (2) to create a single visual analytics user interface that can turn the data into knowledge for domain experts. </p><p>This study extends the visual analytics approach with a structural way of data organization (ontologies), data mining, and visualization techniques to retrieve knowledge from an extensive collection of apple variety testing program and environmental data. The prototype stands on three main components: ontology, data analysis, and data visualization. Ontologies provide a representation of expert knowledge and create standard concepts for data integration, opening the possibility to share the knowledge using a unified terminology and allowing for inference. Building upon relevant semantic models (e.g., agri-food experiment ontology, plant trait ontology, GeoSPARQL), we propose to extend them based on the apple variety testing and climate data. Data integration and harmonization through developing an ontology-based model provides a framework for integrating relevant concepts and relationships between them, data sources from different repositories, and defining a precise specification for the knowledge retrieval. Besides, as the variety testing is performed on different locations, the geospatial component can enrich the analysis with spatial properties. Furthermore, the visual narratives designed within this study will give a better-integrated view of data entities' relations and the meaningful patterns and clustering based on semantic concepts.</p><p>Therefore, the proposed approach is designed to improve decision-making about variety management through an interactive visual analytics system that can answer "what" and "why" about fruit-growing activities. Thus, the prototype has the potential to go beyond the traditional ways of organizing data by creating an advanced information system enabled to manage heterogeneous data sources and to provide a framework for more collaborative scientific data analysis. This study unites various interdisciplinary aspects and, in particular: Big Data analytics in the agricultural sector and visual methods; thus, the findings will contribute to the EU priority program in digital transformation in the European agricultural sector.</p><p>This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 894215.</p>

scholarly journals Application of an analytical framework for multivariate mediation analysis of environmental data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Max T. Aung ◽  
Yanyi Song ◽  
Kelly K. Ferguson ◽  
David E. Cantonwine ◽  
Lixia Zeng ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Confidence Interval ◽  
Dimension Reduction ◽  
Gestational Age ◽  
Mediation Analysis ◽  
Analytical Framework ◽  
Environmental Data ◽  
Environmental Toxicants ◽  
Bayesian Shrinkage ◽  
The Impact

Abstract Diverse toxicological mechanisms may mediate the impact of environmental toxicants (phthalates, phenols, polycyclic aromatic hydrocarbons, and metals) on pregnancy outcomes. In this study, we introduce an analytical framework for multivariate mediation analysis to identify mediation pathways (q = 61 mediators) in the relationship between environmental toxicants (p = 38 analytes) and gestational age at delivery. Our analytical framework includes: (1) conducting pairwise mediation for unique exposure-mediator combinations, (2) exposure dimension reduction by estimating environmental risk scores, and (3) multivariate mediator analysis using either Bayesian shrinkage mediation analysis, population value decomposition, or mediation pathway penalization. Dimension reduction demonstrates that a one-unit increase in phthalate risk score is associated with a total effect of 1.07 lower gestational age (in weeks) at delivery (95% confidence interval: 0.48–1.67) and eicosanoids from the cytochrome p450 pathway mediated 26% of this effect (95% confidence interval: 4–63%). Eicosanoid products derived from the cytochrome p450 pathway may be important mediators of phthalate toxicity.

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