What is the process of combining data from different sources for analysis called?

The process of combining data from different sources for analysis is known as data integration. This process involves merging various datasets to create a unified view that can provide more comprehensive insights than individual datasets alone. Data integration is crucial in geospatial analysis as it allows for the synthesis of information from various sources, which may include geographic data, demographic data, and environmental data, thereby enabling more robust analyses.

In geospatial contexts, data integration often involves aligning the datasets based on common attributes or spatial references. This can facilitate a better understanding of relationships and trends that are not apparent when examining isolated datasets. Techniques for data integration might include spatial joining, merging tables, or using specialized software tools designed to handle complex datasets.

The other processes mentioned, such as data selection, data transformation, and data aggregation, serve different purposes within the data lifecycle. Data selection involves choosing specific datasets relevant to an analysis, data transformation refers to changing the data's format or structure, and data aggregation involves summarizing or consolidating data into a more compact form. While all these processes are important in data handling, data integration specifically focuses on the holistic combination of diverse datasets for overarching analytical goals.