aggregeringer

Aggregeringer, or aggregation, is the process of combining multiple data points into a single summary value or representation. It is used to reduce detail and reveal broader patterns in data sets. The concept appears across statistics, data analysis, database querying, geographic information systems, finance, and web analytics. An aggregation may summarize numeric data with measures such as sum, count, mean (average), median, minimum, maximum, and standard deviation; it can also use weighted forms, geometric mean, or harmonic mean. Non-numeric aggregations include counting occurrences by category and computing proportions or modes.

Common dimensions for aggregations include time, location, and category. Temporal aggregations are performed on time-series data (for example, daily sales totals). Spatial aggregations summarize data over geographic areas (areal totals or averages). Categorical aggregations group data by a category and report frequencies or percentages. In practice, aggregations are implemented with group-by operations in databases, aggregation functions in spreadsheets, or windowed computations in streaming data.

Applications of aggregeringer span reporting, dashboards, data warehouses, and research to support decision making. They also pose potential drawbacks: aggregation can obscure variation and nuance, and the choice of aggregation method can influence results (for example, means are sensitive to outliers, while medians are more robust). Privacy considerations arise when aggregated data could still reveal sensitive information. To mitigate these issues, analysts may report multiple aggregations, use robust statistics, or apply privacy-preserving techniques.

See also: data aggregation, data summarization, roll-up, group by, window function, areal statistics.