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3.1. Database structure
The database was constructed using Microsoft Access software which offers a number of advantages. It is an extremely powerful relational database tool with excellent application development. In addition, it is both widely used by other organisations, such as EUROSTAT, and is able to support a large number of other database formats. Access, therefore, offers the potential for a great degree of compatibility with other databases that are already established.
The database is designed to minimise duplication of data and retain flexibility to allow interrogation for a wide range of queries. Figure 3.1 outlines the major component data tables held within the database. Four tables hold the information relating to monitoring required for each directive, these are labelled sampling, core, frequency and analysis. The information contained in these is as follows:
In the four main tables described above data have been entered as faithfully as possible to that given in the directives (English versions). This reproduction of directive requirements has associated problems, small differences in the words used between one directive and another will mean that the database is incapable of accurately resolving commonalties. To solve these problems the database holds several look-up tables which attempt to standardise aspects of the data contained. A clear illustration of this comes from the determinand look-up table.
Determinand described by directive | Standardised term for determinand |
Phenol | Phenol |
Phenols | Phenol |
Phenolic compounds | Phenol |
In the above example phenol has been described by different directives in slightly different ways, the introduction of a standardised term allows the database to readily assess commonality. By careful consideration look-up tables can be constructed to assess commonality at any level. For example, the determinand look-up table has been further extended by grouping determinands into broad categories.
Determinand described by directive | Standardised term for determinand | Broad determinand type |
Total ammonium | Ammonia | Nutrient |
Non-ionised ammonia | Ammonia | Nutrient |
Ammonia | Ammonia | Nutrient |
Nitrites | Nitrite | Nutrient |
Nitrite | Nitrite | Nutrient |
This allows comparison between directives in terms of the types of determinand that are required for monitoring.
Figure 3.1 Schematic representation of the major elements of the database structure
Information from the directives was categorised in the database in terms of:
1. Water type (using the seven categories stated in the directives):
2. Matrix:
3. Determinand category (quantity and quality):
4. Sampling
5. Analysis
6. Reporting
An identical database was constructed for the international agreements. This allowed comparison of directives and international agreements at any level of detail either, within the databases, or through other packages such as Microsoft Excel.
3.2. Data analysis
Much of the analysis of the database has been undertaken using graphical-descriptions of the relationships between the monitoring requirements in the various directives. This has the aim of reducing the complexity of the multivariate information to a low-dimensional picture of how the directives may interrelate. The two techniques exploited here are hierarchical clustering and non-parametric multi-dimensional scaling (MDS). Each of these methods start explicitly from a triangular matrix of similarity coefficients computed between every pair of directives. This coefficient is a simple algebraic measure of how close the directives are, for example if two directives required monitoring of exactly the same determinands they would be 100% similar at this level.
Following the calculation of the similarity matrix, the results can be represented by a dendogram, linking the samples in hierarchical groups on the basis of the similarity between each cluster, or using MDS which attempts to place directives on a map. Both of these methods give a visual representation of "closeness" of the monitoring requirements of any combination of directives, Figure 3.2 outlines the stages involved the multivariate analysis.
Figure 3.2 Stages in multivariate analysis (adapted from Clarke and Warwick 1994)
For references, please go to https://www.eea.europa.eu/publications/92-9167-003-4/page005.html or scan the QR code.
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