There are many different ways of analyzing geospatial data. Sometimes you may need to pre-process the data before using it in an analysis, or prepare it so that it is in the expected form for a specific tool or method. Sometimes you'll need to use several processing tools in a specific sequence to get the desired output. The possibilities are endless, but below are some common methods that you might want to consider.
Some data is only available as a table. For example, there might be a table of health statistics for each county in New York state. To view this data on a map, we can often use a "table join" to join the table to an existing layer of county boundaries.
Sometimes the dataset you find contains more than what you need. Creating a subset of the data, with just the part you need, can speed up subsequent processing, and can help prevent your map from being too cluttered with unnecessary features. There are several ways to create a subset. Examples for ArcMap are given below, but similar functions exist in other programs.
For example, if you have a dataset of wells of many types, but are only interested in the gas wells, go to the Selection menu > Select By Attributes and set up a WHERE condition like:
"well_type" = 'GD'
In this syntax, the double quotes are used around an attribute (field or column) name, and single quotes are used around a literal value.
Once the selection is made, save the selection to a new file (right-cilck the layer name > Data > Export data...)
Sometimes it is useful to aggregate data to larger units. For example, instead of working with individual well points, we could aggregate that data by county, town, Census block group, or some other level for which we have boundary polygons. By calculating a count of how many wells are in each county, we might decide to focus a project on just a handful of counties rather than the entire state.
If we want to generate a raster dataset of well density across the map, we could use various density tools.
It is often useful to analyze features in relation to their proximity to other features. For example, we might want to know how far each school is from the nearest gas well (using the Generate Near Table tool). Or we might want to count how many wells are within 1km of each school (by generating a Buffer around each school, and then counting the number of well points within each buffer polygon, as described above).
Other proximity-based tools are described in the Overview of the Proximity toolset.