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Organizing physical flow data: from input-output tables to data warehouses
Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Data on flows of materials and substances through the economy and the environment are collected by many different organizations and play a key role in the science of industrial ecology. In this thesis, a framework is suggested for structuring and organizing such data. First, the investigation focuses on the quantities of primary interest in material flow studies and how they can be stored and organized in a data warehouse. This process is shown to provide easy access to data, well-structured data management, a basis for knowledge discovery, and effective analysis of collected data. Secondly, a theoretical framework is proposed for handling and structuring multidimensional flow data, and for facilitating mathematics-assisted modeling in industrial ecology. In particular, it is shown how mathematical operations can be used to merge and compare flow data originating from different studies. Finally, it is illustrated how bootstrap analysis, Bayesian models and balancing procedures can be employed to systematize the quality and uncertainty assessment of physical flow data. Together, these three different aspects of handling physical flow data constitute a new framework that offers better knowledge, quality, and consistency of the data used in industrial ecology.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet , 2005. , 42 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 5
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-30083Local ID: 15549ISBN: 91-85297-55-0 (print)OAI: oai:DiVA.org:liu-30083DiVA: diva2:250904
Public defence
2005-09-30, Glashuset, B-huset, Campus Valla, Linköping, 10:15 (Swedish)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-12-17
List of papers
1. Data cubes and matrix formulae for convenient handling of physical flow data
Open this publication in new window or tab >>Data cubes and matrix formulae for convenient handling of physical flow data
2006 (English)In: Journal of Industrial Ecology, ISSN 1088-1980, E-ISSN 1530-9290, Vol. 10, no 1-2, 43-60 p.Article in journal (Refereed) Published
Abstract [en]

If the technosphere and the biosphere are divided into cells, the presence and turnover of a substance in a study area can be summarized in a vector of stocks and a matrix of flows between different pairs of cells. Likewise the stocks and flows of several substances or materials in one or more time periods can be summarized in multidimensional data cubes. In this article, we provide a theoretical framework for handling physical flow data, and we demonstrate how a set of matrix operations can facilitate exploratory analysis and quality assessment of such data regardless of the number of substances, materials, and time periods considered. In particular, we show how matrices and cubes of flow data can be recalculated when the collection of cells is modified by joining cells, and also what information is required to recalculate flows when cells are split. Furthermore, we illustrate how and under what circumstances substance-flow data originating from different studies with different collections of cells can be compared or merged. The generic character of the given formulae facilitates the development of software for physical flow data.

Keyword
Aggregation, Disaggregation, Matrix operations, Multidimensional data arrays, Substance flows, Theoretical framework
National Category
Social Sciences
Identifiers
urn:nbn:se:liu:diva-50041 (URN)10.1162/108819806775545439 (DOI)
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-12
2. Data warehousing for effective analysis of substance flows through the economy and the environment
Open this publication in new window or tab >>Data warehousing for effective analysis of substance flows through the economy and the environment
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Data on flows of materials and substances through the economy and the environment are collected by a large number of organizations and subsequently stored in different systems and formats. We advocate that data warehousing can increase the value of existing data. To create a seed for such a process, we analyze the informational requirements for effective assessment of material and substance flow data originating from disparate sources. In particular, we outline how flow data can be stored and analyzed along with information about the investigated systems. A list of entities that need to be created and stored in a relational database is established, and an entity relationship (ER) diagram is employed to describe how the entities are related to each other. We also outline how uncertainty measures of estimated flows can be stored as metadata in the data warehouse to support quality assessment of the results of physical flow studies. Moreover, we illustrate possible links to official statistics and other auxiliary data sources. Data cube representations connected with the proposed database can facilitate statistical analysis of collected data.

National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-86476 (URN)
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2012-12-17
3. Uncertainty bounds for metal levels in waste wood
Open this publication in new window or tab >>Uncertainty bounds for metal levels in waste wood
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Estimates of the mean levels of various metals in wastes are normally presented without any measures of uncertainty, and, if such measures are given, they are almost always based on subjective judgments. Here, we show that adequate statistical techniques exist for calculating uncertainty bounds. If data are sparse, Bayesian statistical methods can be employed to combine expert knowledge with the information provided by actually measuring metal concentrations in wastes. If larger datasets are available, a viable alternative is to use bias-corrected bootstrap intervals to compute uncertainty bounds. To facilitate selection of a method, we illustrate how sensitive the Bayesian inference is to the prior knowledge that is incorporated into the analysis, and how outliers influence the obtained uncertainty bounds. Inasmuch as levels of metals in solid wastes can vary substantially and the underlying probability distributions are often highly skewed, we examine the performance of lognormal models.

Keyword
uncertainty, Bayesian inference, bootstrap, lognormal, metals, waste wood
National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-86477 (URN)
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2012-12-17
4. A multi-step balancing procedure for quality assessment of substance flow data
Open this publication in new window or tab >>A multi-step balancing procedure for quality assessment of substance flow data
2005 (English)Report (Other academic)
Abstract [en]

All estimates of substance flows are more or less uncertain, which implies that the collected data can violate mass balance constraints that should be valid. In this article, we introduce multi-stage balancing algorithms that can accommodate prior information about mass balance constraints and uncertainty of the collected data. In particular, we formulate the balancing task as an optimization problem for a given set of prior information. If it is suspected that some flows have been overlooked, the balancing is achieved by minimizing the total increase in flows that is required to satisfy the given mass balance constraints. If the major problem consists of errors or uncertainty in the raw data, the sum of squares of all adjustments needed is minimized. We present a software prototype in which the balancing is integrated with a variety of tools for quality assessment of collected data, and use data from a previously published study of nitrogen flows in Sweden to illustrate the steps involved in the proposed algorithms.

Series
LiTH-MAT-R, ISSN 0348-2960 ; 1
Keyword
substance flows, mass balance, matrix operations, optimization
National Category
Natural Sciences
Identifiers
urn:nbn:se:liu:diva-86478 (URN)
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2012-12-17

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