GIS datasets often contain much more data than you need. There are several methods available to reduce or extract data from larger, more complex datasets.
Feature-based extraction
Selecting features
In ArcMap, Selection > Select By Attributes and Selection > Select By Location tools let you interactively select features and view the highlighted selection as part of a feature layer. Their geoprocessing tool counterparts are Select Layer By Attribute and Select Layer By Location. The Make Feature Layer (and the related Make Query Table) geoprocessing tool creates a layer that lets you do calculations and selections. You can build SQL queries into the feature layer to select particular features or rows from the source data. These tools can be found in the Layers and Table Views toolset of the Data Management toolbox.
Learn more about working with layers and table views in geoprocessing
The Select tool allows you to use a SQL query to make a new feature class of features selected from an existing feature class. The Table Select tool creates a new table using a SQL query on an existing table. These two tools operate on a feature class or table and create a new feature class or table.
Clipping features
You can also extract data by clipping or splitting. Both of these methods overlay your original feature class and another feature class to create new output feature classes. The Clip tool creates one new feature class that contains only the parts of the original features that fall within the polygons in the clip feature class. The Split tool creates a new feature class for each polygon with a unique value in the split feature class; these feature classes each contain only the features from the original feature class that fall within the polygons.
Dissolving features
Another approach for extracting information from more complex data is to dissolve or eliminate features. The Dissolve tool combines polygons that share a value into larger polygons. This is particularly useful when you have data that is divided into numerous finely detailed categories and you need more aggregated data. For example, you might use Dissolve to recombine smaller watershed polygons into larger drainage basins, or parcels into blocks. The Eliminate tool combines selected polygons, often splinter polygons that are smaller than a given size (usually, splinter polygons are caused by overlay of slightly discrepant datasets), with adjacent polygons to remove spurious polygons from the dataset. Dissolving and eliminating features can be used to extract features that share particular attributes and combine them into larger features with less diversity. The Aggregate Polygons tool combines clusters of small polygons into larger polygons. This is designed for cartographic generalization.
Raster-based extraction
Raster data extraction tools include tools that simplify complex or noisy data and tools that create a spatial subset or sample of a raster.
In the first category are tools like Aggregate, Boundary Clean, Expand, Majority Filter, Nibble, Region Group, Shrink, and Thin.
In the second category are tools in the Extraction toolset, which provide a variety of tools for subsetting rasters by shapes and attributes, as well as converting the raster to a set of points with the Extract Values To Points tool and the Sample tool. Other tools include Resample, which aggregates cells into larger cells, and Clip, which performs a rectangular cookie-cut of a raster.