Adjusting the Level of Aggregation to Purpose

Aggregation is the combination of many components into one. One important role of aggregation is to extract information from data. The performance of an economy cannot be determined accurately from a few businesses, nor can the state of biodiversity be determined from the presence or absence of a single species. The aggregation of many components (transactions in an economy, species in an ecoregion) is needed to produce meaningful information.

Another role of aggregation is to produce information in a way that enhances communication. When indicators are combined into aggregates, they can provide a better picture of the entire system by concentrating on key relationships between subsystems and between major components and facilitate analysis of critical strengths and weaknesses. No information is lost if the constituent indicators, the underlying data, and the algorithms are there to be queried. However, the value of such queries depends on the technical capabilities of users. Without such capabilities, users might interpret aggregation as a loss in transparency.

Three main types of aggregates can be identified:

Aggregated indicators. These include summations of accounts constructed from raw data measured in the same unit, such as the System of National Accounts (money), material accounts (weight), and energy accounts (energy). The data are aggregated by simple addition, with no need for weighting. Examples are the gross domestic product (GDP), Total Material Requirement, and Total Energy Requirement. Reliability is affected by completeness of data coverage and the organizational consistency of the accounting framework. Synthetic indicators. These are summations of data not derived from accounts. They combine the large number of measurements (or estimates) necessary to produce indicators of phenomena comprising many variables and rendered in a common unit, such as human health and longevity, species diversity, and freedom and security. Examples are health-adjusted life expectancy at birth (years of life minus years lost to disease and injury) and the Biodiversity Intactness Index (numbers of native species minus estimated numbers lost as a result of land use activities). Indices. These are combinations of lower-level indicators. When indicators measure the same class of components and are in a common unit (e.g., a city's air quality index), aggregation is straightforward. It is more complex when many different components are measured in unlike units, as in the Human Development Index, the Well-being Index, the Environmental Sustain-ability Index, and the indices produced via the Dashboard of Sustain-ability and Compass of Sustainability. All of these indices convert indi cator measurements to a performance score by applying standardized statistical normalization methods. They differ in how this is done and in the rigor of the procedures used to combine different components.

Aggregation requires measurements in the same unit. Transparency and reliability are affected by the method of converting base data to a common unit and by the procedure for combining (normalized) base data from different components. Indices are more prone to distortion because they combine unlike components. But aggregated and synthetic indicators are not immune either.

Simple base data may also aggregate information. In some cases this is desirable, as in measurement of water quality at the mouth (or downstream frontier point) of a river, which provides a summation of the water quality of the basin. In other cases it is undesirable, as when an average value masks major variations in performance within the spatial unit concerned.

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