By now, we've established that GIS is the combination of spatial and non-spatial data. Often, spatial datasets come with attribute tables that store useful information, but just as often, the data we need isn’t included. In these cases, we must bring in additional information through joins and relates—processes that allow us to connect external datasets to our spatial features based on shared attributes.
A join is used when we want to temporarily or permanently attach a new table to our attribute table, adding new fields that can be used just like any other attribute. For example, a city’s parcel dataset might contain only location and ID numbers, while property value information is stored in a separate database. By joining the two, we can analyze and visualize property values spatially.
A relate, on the other hand, is used when the relationship between datasets isn’t one-to-one. Instead of merging data directly into the attribute table, a relate establishes a link between tables so we can view related records when needed. This is useful when a single spatial feature has multiple associated records, such as a single river segment that has multiple water quality samples taken over time.
Once we have our data properly structured and linked, we often need a way to identify patterns within it. That’s where data classification comes into play. Classification is the process of organizing data into meaningful categories to highlight trends, group similar features, and make maps easier to interpret. Whether we’re classifying land use types, income levels, or risk zones, grouping data into logical categories allows us to extract insights and communicate information effectively.
To work efficiently with these tools, we need ways to search, filter, and extract relevant information from our datasets. SQL (Structured Query Language) allows us to query attribute tables with precision, pulling only the data that meets our criteria. But not all selections can be made through attributes alone. Selection by location enables us to identify features based on their spatial relationships, helping us analyze how different datasets interact within a geographic space.
Finally, GIS isn’t just about viewing data—it’s about working with it. Attribute tables allow us to modify existing values, create new fields, and even automate calculations using Python and SQL expressions. These tools give us the ability to enrich and refine our data, ensuring that we can extract the most meaningful insights from our spatial analyses.
Mastering these concepts—joins, relates, classification, SQL queries, and spatial selection—will allow you to organize, analyze, and manipulate GIS data more effectively. As we move through this chapter, we’ll break down each of these processes and explore how they help transform raw data into actionable geographic insights.