Spatial analysis encompasses everything we do with our geospatial data, from framing our research question to presenting our final results. The spatial analysis process involves using analytical techniques to examine our geospatial data and answer questions by highlighting or creating new information.
Due to the flexibility of GIS, spatial analysis can constitute one simple task or a series of complex tasks. A simple spatial analysis process might consist solely of visualizing data on a map for users to interpret, and a complex spatial analysis process can incorporate multiple datasets, spatial statistics, and Python scripts. The graphic on the right represents the spatial analysis process in general terms.
When working with geospatial data, it is important to have a fairly specific idea of what you want your final product to look like. Understanding your desired result makes it easier to think about how you should manipulate your data and what spatial analyses you should conduct.
Below are some questions to consider when outlining the spatial analysis process for your project:
Spatial analysis is an iterative process. It is best to refine your research question as much as possible before you start manipulating your data, but keep in mind that your questions may change after exploring the available data and learning about possible analyses and visualizations. It is important to prepare for changes to your original plan, because as you gain GIS knowledge and insight you will want to adjust your spatial analysis accordingly.
Definition from the Esri GIS Dictionary:
A GIS operation used to manipulate GIS data. A typical geoprocessing operation takes an input dataset, performs an operation on that dataset, and returns the result of the operation as an output dataset. Common geoprocessing operations include geographic feature overlay, feature selection and analysis, topology processing, raster processing, and data conversion. Geoprocessing allows for definition, management, and analysis of information used to form decisions.
Geoprocessing provides us with the tools and framework to manage and manipulate our geospatial data. The major distinction between geoprocessing and spatial analysis is that geoprocessing encompasses the tools that we use to analyze our data, and spatial analysis includes a wider process of acquiring, analyzing (usually incorporating geoprocessing), and presenting geospatial data. For example, we can use the "table join" geoprocessing tool in ArcGIS to combine two tables and produce a new, joined table. This table join may be just one of many geoprocessing tasks we undertake in our broader spatial analysis, which incorporates our acquisition and visualization of that data.