To calculate the population density of a city layer using data from a demographics table, which association should be utilized?

To calculate the population density of a city layer using data from a demographics table, utilizing a join is the most suitable method. A join enables you to combine attributes from two datasets based on a common field, such as a unique identifier or geographic area. When you join the demographics table to the city layer, you can access information like population and area to directly compute the population density by dividing the total population by the land area.

A relational join is particularly effective for this type of analysis because it integrates the attributes of both the city layer and the demographics data into a single dataset, allowing straightforward calculations. Once the join is executed, each city will have access to population data, which can then be used to derive the density values efficiently.

While other methods such as relates, spatial joins, and relationship classes serve specific purposes for data management and spatial analysis, they are not as immediately appropriate for calculating derived attributes like population density. A relate allows for linking datasets without merging them physically, but this wouldn't facilitate direct calculations as efficiently as a join. A spatial join focuses on geographic relationships rather than attribute merging, making it less relevant for this particular calculation. A relationship class is a database construct that organizes relationships but does not directly facilitate the calculation needed without additional steps. Thus