We've learned that GIS is a combination of spatial and non-spatial data and we understand that non-spatial data is data about a spatial location. Within the GIS, we store non-spatial data in tables. These data tables can be viewed in ArcGIS and can actually be a few different kinds of files such as Microsoft Excel tables, Microsoft Access databases, Comma Separated Value (CSV) text files, and other kinds of text files which present as row and column based data.
Within the GIS, each spatial file has a non-spatial data table associated with it that has been given the special name of attribute table, or the data table which contains the values which could be attributed to a specific feature. Every spatial file has an attribute table, and when they are associated with a vector file, they are always visible for us to examine and perform calculations with the data. When the attribute table is associated with raster files, the attribute table may or may not be directly accessible for viewing, but understand that even if you can't open it, that doesn’t mean it doesn't exist.
Attribute tables, as we will learn in Chapter Five, contain a few mandatory and software created fields, or the columns of the table which run up and down, such as which geometry type the vector file holds and what the area of each polygon represented in the file is, but the rest of the fields are optional. These optional fields contain all the values that describe a specific feature and are stored right inside the attribute table. However, there are lots and lots of times when we need to take an external data table and join it with the vector file (as we will also learn all about in Chapter Five). These external data tables can house literally decades of information about some spatial features. If you're thinking that sounds like a lot of data, it is. It is a lot of data to store with just one spatial file, which is why we have external data tables.
3.5.2: Recognizing Data Tables in ArcGIS Pro
Much like vector and raster file icons, data tables have specific icons as well. Most data tables share just a few different icons while Microsoft Excel files have a special icon. ArcGIS even sees column and row presented text files (like CSVs) as table icons (other text files have a specific icon to recognize them as text files). The file icon for data tables which are not directly associated with spatial data Data that deals with location, such as lists of addresses, the footprint of a building, the boundaries of cities and counties, etc. in ArcGIS looks much like a small data table, while Microsoft Excel files (those with a .xls or .xlsx file extension) have a similar looking table. We do not have an icon for attribute tables in ArcGIS since we know that the advantage of looking at spatial data Data that deals with location, such as lists of addresses, the footprint of a building, the boundaries of cities and counties, etc. in the GIS Geographic Information Systems the software used to create, store, and manage spatial data Data that deals with location, such as lists of addresses, the footprint of a building, the boundaries of cities and counties, etc. , analyze spatial problems, and display the data in cartographic layouts Geographic Information Sciences is the 3 - 8 individual files are seen as one single file.
| Figure 3.18: Data Table in ArcGIS Software | ||
|---|---|---|
 |   |  |
| Non-table based text files | Two example of a non-Excel data table icon | Microsoft Excel data tables |
3.5.3: Data Types in Data Tables
Within data tables, there are three main types of data: nominal, ordinal, and interval/ratio. These classifications determine how data can be analyzed, visualized, and used in GIS Geographic Information Systems the software used to create, store, and manage spatial data Data that deals with location, such as lists of addresses, the footprint of a building, the boundaries of cities and counties, etc. , analyze spatial problems, and display the data in cartographic layouts Geographic Information Sciences applications. Understanding the differences between these data types is essential when performing spatial analysis, labeling features, or symbolizing maps.
Nominal Data
Nominal data (think "noun"-inal data) provides a descriptive labels about data, usually the name or description of (okay, that’s an adjective but "adjective"-inal wasn’t as clever) a feature. Examples of nominal data are: Colorado, blue, pine trees. Nominal data is simply names or attributes assigned to objects.
Examples of nominal data include:
- Place names (e.g., "Colorado," "New York")
- Colors (e.g., "blue," "green")
- Tree species (e.g., "pine," "oak")
Nominal data cannot be ordered or measured numerically in a meaningful way—it is purely categorical.
Ordinal
Ordinal data (think "order"-inal) includes values that are ranked or ordered in a meaningful sequence, but the differences between values are not necessarily equal or measurable. Unlike nominal data, ordinal data establishes a hierarchy or relative positioning, but it does not assume a consistent scale.
For example, if we rank soil erosion risk on a scale from 1 to 10:
- A feature with a value of 5 has less erosion than a feature with a value of 10,
- But a value of 10 is not necessarily twice as much erosion as a value of 5—the scale could be exponential or irregular.
Other examples of ordinal data include:
- Survey responses (e.g., "strongly agree," "agree," "neutral," "disagree")
- Economic classes (e.g., "low income," "middle income," "high income")
- Risk levels (e.g., "low," "medium," "high")
Ordinal values establish a ranking, but we cannot assume an equal difference between levels.
Interval/Ratio Data
Interval/Ratio data provides the most precise and quantitative measurement, where both rank, order, and absolute differences between values are meaningful. Unlike ordinal data, interval/ratio data follows a linear scale, meaning the difference between values is consistent and measurable.
Examples of interval/ratio data include:
- Height (e.g., 5 feet, 6 feet, 10 feet)
- Weight (e.g., 100 lbs, 150 lbs, 200 lbs)
- Depth (e.g., 10 meters, 20 meters, 30 meters)
- Temperature (e.g., 35°F vs. 70°F → 70°F is twice as warm as 35°F)
With ordinal data, "high," "medium," and "low" describe relative positions without an exact scale. However, interval/ratio data provides an absolute difference—a person who is 6 feet tall is exactly twice as tall as a child who is 3 feet tall.
Understanding these distinctions is important for GIS Geographic Information Systems the software used to create, store, and manage spatial data Data that deals with location, such as lists of addresses, the footprint of a building, the boundaries of cities and counties, etc. , analyze spatial problems, and display the data in cartographic layouts Geographic Information Sciences data analysis, classification, and symbolization, as different types of data require different processing methods and visualization techniques.